Mistakes (Link Bibliography)

“Mistakes” links:

  1. https://www.lesswrong.com/posts/o7nRiBP9W8xR5E4v5/meta-analysis-of-writing-therapy

  2. https://www.lesswrong.com/posts/BcYBfG8KomcpcxkEg/crisis-of-faith

  3. https://www.amazon.com/Vitality-Energy-Spirit-Sourcebook-Shambhala/dp/1590306880/

  4. https://www.amazon.com/Being-Wrong-Adventures-Margin-Error/dp/0061176052/

  5. https://www.lesswrong.com/posts/kXAb5riiaJNrfR8v8/the-ritual

  6. https://www.amazon.com/Office-Yoga-Simple-Stretches-People/dp/0811826856/

  7. https://www.amazon.com/Neuropath-R-Scott-Bakker/dp/0765361574/

  8. http://bayes.wustl.edu/etj/articles/general.background.ps.gz

  9. https://www.amazon.com/Culture-Value-Ludwig-Wittgenstein/dp/0226904350/

  10. https://www.amazon.com/Russian-Silhouettes-Genna-Sosonko/dp/9056912933/

  11. Prediction-markets

  12. https://predictionbook.com/users/gwern

  13. http://nymag.com/scienceofus/2016/12/what-makes-kids-stop-believing-in-santa.html

  14. https://www.theatlantic.com/national/archive/2013/04/why-are-there-so-few-resurrected-corpses-in-the-united-states/274681/

  15. https://web.archive.org/web/20100423145709/http://www.firstthings.com/article/2010/04/believe-it-or-not

  16. https://www.amazon.com/Why-Am-Not-Christian-Conclusive/dp/1456588850/

  17. http://unqualified-reservations.blogspot.com/2008/01/how-i-stopped-believing-in-democracy.html

  18. 2003-korb.pdf: ⁠, Kevin Korb (2004-01-01; statistics  /​ ​​ ​bayes):

    Bayesian reasoning has been applied formally to statistical inference, machine learning and analysing scientific method. Here I apply it informally to more common forms of inference, namely natural language arguments. I analyse a variety of traditional fallacies, deductive, inductive and causal, and find more merit in them than is generally acknowledged. Bayesian principles provide a framework for understanding ordinary arguments which is well worth developing.

  19. https://tvtropes.org/pmwiki/pmwiki.php/Main/FlatEarthAtheist

  20. https://tvtropes.org/pmwiki/pmwiki.php/Main/HollywoodAtheist

  21. https://web.archive.org/web/20131013084016/http://prosblogion.ektopos.com/archives/2012/03/trust-in-testim.html

  22. 2006-harris.pdf: ⁠, Paul L. Harris, Melissa A. Koenig (2006-05-09; philosophy):

    Many adult beliefs are based on the testimony provided by other people rather than on firsthand observation. Children also learn from other people’s testimony. For example, they learn that mental processes depend on the brain, that the earth is spherical, and that hidden bodily organs constrain life and death. Such learning might indicate that other people’s testimony simply amplifies children’s access to empirical data. However, children’s understanding of God’s special powers and the afterlife shows that their acceptance of others’ testimony extends beyond the empirical domain. Thus, children appear to conceptualize unobservable scientific and religious entities similarly. Nevertheless, some children distinguish between the 2 domains, arguably because a different pattern of discourse surrounds scientific as compared to religious entities.

  23. http://isites.harvard.edu/fs/docs/icb.topic783896.files/Young%20childrens%20selective%20trust%20in%20informants.pdf

  24. ⁠, Mills, Candice M. Keil, Frank C (2005):

    Two experiments explored the development of cynicism by examining how children evaluate other people who make claims consistent or inconsistent with their self-interests. In Experiment 1, kindergartners, second graders, and fourth graders heard stories with ambiguous conclusions in which characters made statements that were aligned either with or against self-interest. Older children took into account the self-interests of characters in determining how much to believe them: They discounted statements aligned with self-interest, whereas they accepted statements going against self-interest. Experiment 2 examined children’s endorsement of three different explanations for potentially self-interested statements: lies, biases, and mistakes. Like adults, sixth graders endorsed lies and bias as plausible explanations for wrong statements aligned with self-interest; younger children did not endorse bias. Implications for the development of cynicism and children’s understanding of bias are discussed.

  25. http://www.iep.utm.edu/epi-luck/

  26. #the-occult

  27. https://philpapers.org/surveys/results.pl

  28. 2012-shenhav.pdf: ⁠, Amitai Shenhav, David G. Rand, Joshua D. Greene (2012; philosophy):

    Some have argued that belief in God is intuitive, a natural (by-)product of the human mind given its cognitive structure and social context. If this is true, the extent to which one believes in God may be influenced by one’s more general tendency to rely on intuition versus reflection. Three studies support this hypothesis, linking intuitive cognitive style to belief in God. Study 1 showed that individual differences in cognitive style predict belief in God. Participants completed the Cognitive Reflection Test (CRT; Frederick, 2005), which employs math problems that, although easily solvable, have intuitively compelling incorrect answers. Participants who gave more intuitive answers on the CRT reported stronger belief in God. This effect was not mediated by education level, income, political orientation, or other demographic variables. Study 2 showed that the correlation between CRT scores and belief in God also holds when cognitive ability (IQ) and aspects of personality were controlled. Moreover, both studies demonstrated that intuitive CRT responses predicted the degree to which individuals reported having strengthened their belief in God since childhood, but not their familial religiosity during childhood, suggesting a causal relationship between cognitive style and change in belief over time. Study 3 revealed such a causal relationship over the short term: Experimentally inducing a mindset that favors intuition over reflection increases self-reported belief in God.

    [Keywords: reasoning, religion, religiosity, reflection, atheism.]

  29. http://csjarchive.cogsci.rpi.edu/proceedings/2011/papers/0782/paper0782.pdf

  30. https://www.lesswrong.com/posts/RNSYDitY6688rP3Fb/atheism-and-the-autism-spectrum

  31. https://www.lesswrong.com/posts/T9akLQRr2rwy8tjM3/cognitive-style-tends-to-predict-religious-conviction

  32. https://www.lesswrong.com/posts/DuM5d7stfbm4KksM2/on-the-etiology-of-religious-belief

  33. https://www.amazon.com/When-God-Talks-Back-Understanding/dp/0307277275/

  34. https://plus.google.com/u/0/103530621949492999968/posts/8tQdy7kNfKE

  35. https://plus.google.com/u/0/103530621949492999968/posts/G6Q4Z6ANMRb

  36. https://plus.google.com/u/0/103530621949492999968/posts/dgdcF6Qmc9B

  37. https://plus.google.com/u/0/103530621949492999968/posts/eXhmZUpBmCm

  38. https://plus.google.com/u/0/103530621949492999968/posts/5AvbwfuNmPy

  39. https://plus.google.com/u/0/103530621949492999968/posts/dbFEadqScJH

  40. https://plus.google.com/u/0/103530621949492999968/posts/47YUHeASv9w

  41. https://plus.google.com/u/0/103530621949492999968/posts/AThvaCXCSp2

  42. https://plus.google.com/u/0/103530621949492999968/posts/1ZKVCxrSwTR

  43. https://plus.google.com/u/0/103530621949492999968/posts/SH2SqzCtVE2

  44. https://plus.google.com/u/0/103530621949492999968/posts/5f7z4KYwE6Z

  45. http://handleshaus.wordpress.com/2014/06/05/suppressing-tories/

  46. https://www.econlib.org/archives/2007/07/independence_da.html

  47. https://www.amazon.com/When-London-Was-Capital-America/dp/0300178131/

  48. http://takimag.com/article/declamations_of_independence_steve_sailer

  49. https://www.newyorker.com/magazine/2017/05/15/we-could-have-been-canada

  50. http://www.vox.com/2015/7/2/8884885/american-revolution-mistake

  51. http://eml.berkeley.edu/~webfac/cromer/e211_f12/LindertWilliamson.pdf

  52. http://www.marxists.org/reference/archive/smith-adam/works/wealth-of-nations/book04/ch02.htm

  53. 1965-thomas.pdf

  54. 1968-thomas.pdf

  55. http://employees.csbsju.edu/JOLSON/ECON315/Whaples2123771.pdf

  56. http://unqualified-reservations.blogspot.com/2009/01/gentle-introduction-to-unqualified_15.html

  57. http://unqualified-reservations.blogspot.com/2007/12/why-i-am-not-libertarian.html

  58. http://visualeconomics.creditloan.com/the-wealth-of-world-leaders/

  59. https://web.archive.org/web/20120422111624/http://www.thepowerindex.com.au/dictator-watch/robert-mugabe

  60. http://www.georgetowner.com/articles/2012/feb/07/wealth-presidents/

  61. http://persistentenlightenment.wordpress.com/2014/07/03/of-rights-and-witches-benthams-critique-of-the-declaration-of-independence/

  62. 2003-murray-humanaccomplishment.pdf: “Human Accomplishment”⁠, Charles Murray

  63. http://www.susanblackmore.co.uk/Articles/si87.html

  64. Culture-is-not-about-Esthetics

  65. index#fiction

  66. Nicotine

  67. Nicotine#performance

  68. Silk-Road

  69. https://www.amazon.com/The-Constitution-Liberty-Definitive-Collected/dp/0226315398/

  70. https://web.archive.org/web/20080905135023/http://www.fool.com/specials/2000/sp000322levin.htm

  71. http://www.thestreet.com/story/10006531/1/the-upshot-the-kids-in-the-hall-at-hq.html

  72. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3163136/table/tab1/

  73. https://www.ncbi.nlm.nih.gov/books/NBK44745/

  74. http://info-centre.jenage.de/assets/pdfs/library/vaupel_NATURE_2010.pdf

  75. ⁠, Christensen, Kaare Doblhammer, Gabriele Rau, Rol, Vaupel, James W (2009):

    If the pace of increase in life expectancy in developed countries over the past two centuries continues through the 21st century, most babies born since 2000 in France, Germany, Italy, the UK, the USA, Canada, Japan, and other countries with long life expectancies will celebrate their 100th birthdays. Although trends differ between countries, populations of nearly all such countries are ageing as a result of low fertility, low immigration, and long lives. A key question is: are increases in life expectancy accompanied by a concurrent postponement of functional limitations and disability? The answer is still open, but research suggests that ageing processes are modifiable and that people are living longer without severe disability. This finding, together with technological and medical development and redistribution of work, will be important for our chances to meet the challenges of ageing populations.

  76. ⁠, Fries, James F. Bruce, Bonnie Chakravarty, Eliza (2011):

    The Compression of Morbidity hypothesis-positing that the age of onset of chronic illness may be postponed more than the age at death and squeezing most of the morbidity in life into a shorter period with less lifetime disability-was introduced by our group in 1980. This paper is focused upon the evolution of the concept, the controversies and responses, the supportive multidisciplinary science, and the evolving lines of evidence that establish proof of concept. We summarize data from 20-year prospective longitudinal studies of lifestyle progression of disability, national population studies of trends in disability, and randomized of risk factor reduction with life-style-based “healthy aging” interventions. From the perspective of this influential and broadly cited paradigm, we review its current history, the development of a theoretical structure for healthy aging, and the challenges to develop coherent health policies directed at reduction in morbidity.

  77. https://www.lesswrong.com/posts/qNxPRh5jzrLorak6B/prediction-is-hard-especially-of-medicine

  78. http://www.kurzweilai.net/predictions.php

  79. https://web.archive.org/web/20111218234418/http://www.nationalreview.com/articles/print/278758

  80. http://www.hoover.org/publications/policy-review/article/5646

  81. plastination

  82. http://www.fhi.ox.ac.uk/reports/2008-3.pdf

  83. https://www.lesswrong.com/r/discussion/lw/dm5/why_could_you_be_optimistic_that_the_singularity/#comments

  84. https://www.bloomberg.com/news/2011-10-24/bias-blindness-and-how-we-truly-think-part-1-daniel-kahneman.html

  85. https://predictionbook.com/predictions/5915

  86. https://www.lesswrong.com/tag/aixi

  87. ⁠, Joel Veness, Kee Siong Ng, Marcus Hutter, William Uther, David Silver (2009-09-04):

    This paper introduces a principled approach for the design of a scalable general agent. Our approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it has been unclear whether the theory of AIXI could motivate the design of practical algorithms. We answer this hitherto open question in the affirmative, by providing the first computationally feasible approximation to the AIXI agent. To develop our approximation, we introduce a new Monte-Carlo Tree Search algorithm along with an agent-specific extension to the Context Tree Weighting algorithm. Empirically, we present a set of encouraging results on a variety of stochastic and partially observable domains. We conclude by proposing a number of directions for future research.

  88. http://www.vetta.org/2009/09/monte-carlo-aixi/

  89. https://www.lesswrong.com/posts/tjH8XPxAnr6JRbh7k/hard-takeoff

  90. https://www.lesswrong.com/posts/2MD3NMLBPCqPfnfre/cached-thoughts

  91. https://www.lesswrong.com/posts/5wMcKNAwB6X4mp9og/that-alien-message

  92. https://www.lesswrong.com/posts/KqhHhsBRzbf7eckTS/information-theory-and-foom

  93. http://www.aleph.se/andart/archives/2010/10/why_early_singularities_are_softer.html

  94. https://hplusmagazine.com/2011/07/08/future-technology-merger-or-trainwreck/

  95. 1994-falk

  96. https://www.lesswrong.com/posts/cPfZA6Bz9JEjPrC6B/an-inflection-point-for-probability-estimates-of-the-ai?commentId=gAydSjYhJmJ4ongX9

  97. http://economics.mit.edu/files/581.pdf

  98. http://econ-www.mit.edu/files/5554

  99. http://www.datascienceassn.org/sites/default/files/Economic%20Growth%20Given%20Machine%20Intelligence%202009%20Paper.pdf

  100. http://krugman.blogs.nytimes.com/2011/03/06/autor-autor/

  101. http://www.nber.org/papers/w16082

  102. http://www.federalreserve.gov/pubs/feds/2011/201141/index.html

  103. http://www.asymptosis.com/are-machines-replacing-humans-or-am-i-a-luddite.html

  104. https://blogs.wsj.com/economics/2011/09/19/only-advanced-degree-holders-see-wage-gains/

  105. https://www.amazon.com/Coming-Apart-State-America-1960-2010/dp/030745343X/

  106. http://thoughtbroadcast.com/2011/09/04/how-to-retire-at-age-27/

  107. http://thelastpsychiatrist.com/2011/09/how_to_be_mean_to_your_kids.html

  108. http://citeseerx.ist.psu.edu/viewdoc/download?doi=

  109. https://www.newyorker.com/magazine/2012/01/30/the-caging-of-america

  110. https://pdfs.semanticscholar.org/4351/d39c9602fec7f82ecbada1ceb225dd7bf88e.pdf

  111. https://web.archive.org/web/20141228172224/https://www.bloomberg.com/news/2011-03-31/why-unemployment-rose-so-much-dropped-so-fast-commentary-by-alan-krueger.html

  112. http://www.themoneyillusion.com/?p=16923

  113. http://www.themoneyillusion.com/?p=17076

  114. https://www.bloomberg.com/news/articles/2011-02-02/the-youth-unemployment-bomb

  115. http://hanson.gmu.edu/uploads.html

  116. https://www.lesswrong.com/posts/TQSb4wd6v5C3p6HX2/the-pascal-s-wager-fallacy-fallacy

  117. https://online.wsj.com/article/SB10001424053111903480904576512250915629460.html

  118. http://6thfloor.blogs.nytimes.com/2013/07/12/considering-the-horror-of-zuckerbergian-dystopias/

  119. https://www.nytimes.com/2013/03/31/opinion/sunday/friedman-need-a-job-invent-it.html

  120. http://www.tonywagner.com/

  121. https://slatestarcodex.com/2017/02/06/notes-from-the-asilomar-conference-on-beneficial-ai/

  122. http://public.econ.duke.edu/~psarcidi/prop209instfit.pdf

  123. http://www.nber.org/chapters/c10097.pdf

  124. http://citeseerx.ist.psu.edu/viewdoc/download?doi=

  125. ⁠, Bound, John Lovenheim, Michael F. Turner, Sarah (2010):

    Rising college enrollment over the last quarter century has not been met with a proportional increase in college completion. Comparing the high school classes of 1972 and 1992, we show declines in college completion rates have been most pronounced for men who first enroll in less selective public universities and community colleges. We decompose the decline into the components due to changes in preparedness of entering students and due to changes in collegiate characteristics, including type of institution and resources per student. While both factors play some role, the supply-side characteristics are most important in explaining changes in college completion. (JEL I23).

  126. http://www.psc.isr.umich.edu/pubs/pdf/rr10-698.pdf

  127. https://www.amazon.com/The-Race-between-Education-Technology/dp/0674035305/

  128. https://www.nytimes.com/2013/03/21/business/economy/as-men-lose-economic-ground-clues-in-the-family.html

  129. https://web.archive.org/web/20130121075633/http://thebrowser.com/interviews/daron-acemoglu-on-inequality?page=full

  130. https://www.econlib.org/archives/2012/03/the_myth_of_the_7.html

  131. https://www.econlib.org/archives/2012/03/goldin-katz_and.html

  132. http://www.econtalk.org/archives/2011/12/tabarrok_on_inn.html

  133. http://www.insidehighered.com/news/2014/01/22/see-how-liberal-arts-grads-really-fare-report-examines-long-term-data

  134. https://www.clevelandfed.org/research/commentary/2012/2012-13.cfm

  135. http://faculty.chicagobooth.edu/brent.neiman/research/KN.pdf

  136. http://ideas.repec.org/p/iza/izadps/dp2442.html

  137. https://www.econlib.org/archives/2014/02/why_the_college.html

  138. http://www.econlib.org/library/Ricardo/ricP7.html#Ch.31,%20On%20Machinery

  139. 2013-sachs.pdf

  140. http://www.igmchicago.org/igm-economic-experts-panel/poll-results?SurveyID=SV_0IAlhdDH2FoRDrm

  141. https://www.overcomingbias.com/2009/10/take-both-econ-tech-seriously.html

  142. https://pdfs.semanticscholar.org/f00d/e689ec0e93bc27d5e721cad99f32829d7ffb.pdf

  143. http://www.richmondfed.org/publications/research/economic_brief/2011/pdf/eb_11-09.pdf

  144. https://www.bls.gov/cps/duration.htm

  145. https://www.economist.com/blogs/babbage/2011/11/artificial-intelligence

  146. https://www.brookings.edu/blog/up-front/2011/09/09/a-decade-of-slack-labor-markets/

  147. http://www.econ.yale.edu/smith/econ116a/keynes1.pdf

  148. Prediction-markets#how-i-make-predictions

  149. http://wiki.lesswrong.com/wiki/Outside_view

  150. https://www.theatlantic.com/business/archive/2013/01/the-end-of-labor-how-to-protect-workers-from-the-rise-of-the-robots/267135/

  151. https://www.motherjones.com/media/2013/05/robots-artificial-intelligence-jobs-automation/

  152. http://www.forbes.com/sites/modeledbehavior/2013/05/13/inequality-in-the-robot-future/

  153. https://www.overcomingbias.com/2013/05/robot-econ-primer.html

  154. https://www.lesswrong.com/posts/ZiRKzx3yv7NyA5rjF/the-robots-ai-and-unemployment-anti-faq

  155. https://marginalrevolution.com/marginalrevolution/2013/08/eliezer-yudkowsky-asks-about-automation.html

  156. http://eml.berkeley.edu/~webfac/moretti/e251_s13/mishel.pdf

  157. http://www.epi.org/publication/technology-inequality-dont-blame-the-robots/

  158. http://www.nuff.ox.ac.uk/Users/Allen/engelspause.pdf

  159. http://ftalphaville.ft.com/2015/03/09/2120134/jobs-automation-engels-pause-and-the-limits-of-history/

  160. https://journals.plos.org/plosbiology/article/info:doi/10.1371/journal.pbio.1001071

  161. http://xkcd.com/356/

  162. http://xkcd.com/386/

  163. ⁠, Paul Graham (2009-02):

    As a rule, any mention of religion on an online forum degenerates into a religious argument. Why? Why does this happen with religion and not with Javascript or baking or other topics people talk about on forums?

    …I think what religion and politics have in common is that they become part of people’s identity, and people can never have a fruitful argument about something that’s part of their identity. By definition they’re partisan.

    Which topics engage people’s identity depends on the people, not the topic. For example, a discussion about a battle that included citizens of one or more of the countries involved would probably degenerate into a political argument. But a discussion today about a battle that took place in the Bronze Age probably wouldn’t. No one would know what side to be on. So it’s not politics that’s the source of the trouble, but identity. When people say a discussion has degenerated into a religious war, what they really mean is that it has started to be driven mostly by people’s identities. [1: When that happens, it tends to happen fast, like a core going critical. The threshold for participating goes down to zero, which brings in more people. And they tend to say incendiary things, which draw more and angrier counterarguments.]

    …More generally, you can have a fruitful discussion about a topic only if it doesn’t engage the identities of any of the participants. What makes politics and religion such minefields is that they engage so many people’s identities. But you could in principle have a useful conversation about them with some people. And there are other topics that might seem harmless, like the relative merits of Ford and Chevy pickup trucks, that you couldn’t safely talk about with others.

    Most people reading this will already be fairly tolerant. But there is a step beyond thinking of yourself as x but tolerating y: not even to consider yourself an x. The more labels you have for yourself, the dumber they make you.

  164. http://humanvarieties.org/2013/03/29/cryptic-admixture-mixed-race-siblings-social-outcomes/

  165. https://www.lesswrong.com/posts/cit3HYXehBsr4d36Q/open-thread-january-15-31-2012?commentId=rWkZ3TGRsL7DhSsNR

  166. 2013-rietveld.pdf: ⁠, Cornelius A. Rietveld, Sarah E. Medland, Jaime Derringer, Jian Yang, Tõnu Esko, Nicolas W. Martin, Harm-Jan Westra, Konstantin Shakhbazov, Abdel Abdellaoui, Arpana Agrawal, Eva Albrecht, Behrooz Z. Alizadeh, Najaf Amin, John Barnard, Sebastian E. Baumeister, Kelly S. Benke, Lawrence F. Bielak, Jeffrey A. Boatman, Patricia A. Boyle, Gail Davies, Christiaan de Leeuw, Niina Eklund, Daniel S. Evans, Rudolf Ferhmann, Krista Fischer, Christian Gieger, Håkon K. Gjessing, Sara Hägg, Jennifer R. Harris, Caroline Hayward, Christina Holzapfel, Carla A. Ibrahim-Verbaas, Erik Ingelsson, Bo Jacobsson, Peter K. Joshi, Astanand Jugessur, Marika Kaakinen, Stavroula Kanoni, Juha Karjalainen, Ivana Kolcic, Kati Kristiansson, Zoltán Kutalik, Jari Lahti, Sang H. Lee, Peng Lin, Penelope A. Lind, Yongmei Liu, Kurt Lohman, Marisa Loitfelder, George McMahon, Pedro Marques Vidal, Osorio Meirelles, Lili Milani, Ronny Myhre, Marja-Liisa Nuotio, Christopher J. Oldmeadow, Katja E. Petrovic, Wouter J. Peyrot, Ozren Polašek, Lydia Quaye, Eva Reinmaa, John P. Rice, Thais S. Rizzi, Helena Schmidt, Reinhold Schmidt, Albert V. Smith, Jennifer A. Smith, Toshiko Tanaka, Antonio Terracciano, Matthijs J. H. M. van der Loos, Veronique Vitart, Henry Völzke, Jürgen Wellmann, Lei Yu, Wei Zhao, Jüri Allik, John R. Attia, Stefania Bandinelli, François Bastardot, Jonathan Beauchamp, David A. Bennett, Klaus Berger, Laura J. Bierut, Dorret I. Boomsma, Ute Bültmann, Harry Campbell, Christopher F. Chabris, Lynn Cherkas, Mina K. Chung, Francesco Cucca, Mariza de Andrade, Philip L. De Jager, Jan-Emmanuel De Neve, Ian J. Deary, George V. Dedoussis, Panos Deloukas, Maria Dimitriou, Guðný Eiríksdóttir, Martin F. Elderson, Johan G. Eriksson, David M. Evans, Jessica D. Faul, Luigi Ferrucci, Melissa E. Garcia, Henrik Grönberg, Vilmundur Guðnason, Per Hall, Juliette M. Harris, Tamara B. Harris, Nicholas D. Hastie, Andrew C. Heath, Dena G. Hernandez, Wolfgang Hoffmann, Adriaan Hofman, Rolf Holle, Elizabeth G. Holliday, Jouke-Jan Hottenga, William G. Iacono, Thomas Illig, Marjo-Riitta Järvelin, Mika Kähönen, Jaakko Kaprio, Robert M. Kirkpatrick, Matthew Kowgier, Antti Latvala, Lenore J. Launer, Debbie A. Lawlor, Terho Lehtimäki, Jingmei Li, Paul Lichtenstein, Peter Lichtner, David C. Liewald, Pamela A. Madden, Patrik K. E. Magnusson, Tomi E. Mäkinen, Marco Masala, Matt McGue, Andres Metspalu, Andreas Mielck, Michael B. Miller, Grant W. Montgomery, Sutapa Mukherjee, Dale R. Nyholt, Ben A. Oostra, Lyle J. Palmer, Aarno Palotie, Brenda W. J. H. Penninx, Markus Perola, Patricia A. Peyser, Martin Preisig, Katri Räikkönen, Olli T. Raitakari, Anu Realo, Susan M. Ring, Samuli Ripatti, Fernando Rivadeneira, Igor Rudan, Aldo Rustichini, Veikko Salomaa, Antti-Pekka Sarin, David Schlessinger, Rodney J. Scott, Harold Snieder, Beate St Pourcain, John M. Starr, Jae Hoon Sul, Ida Surakka, Rauli Svento, Alexander Teumer, The LifeLines Cohort Study, Henning Tiemeier, Frank J. A. van Rooij, David R. Van Wagoner, Erkki Vartiainen, Jorma Viikari, Peter Vollenweider, Judith M. Vonk, Gérard Waeber, David R. Weir, H.-Erich Wichmann, Elisabeth Widen, Gonneke Willemsen, James F. Wilson, Alan F. Wright, Dalton Conley, George Davey-Smith, Lude Franke, Patrick J. F. Groenen, Albert Hofman, Magnus Johannesson, Sharon L. R. Kardia, Robert F. Krueger, David Laibson, Nicholas G. Martin, Michelle N. Meyer, Danielle Posthuma, A. Roy Thurik, Nicholas J. Timpson, André G. Uitterlinden, Cornelia M. van Duijn, Peter M. Visscher, Daniel J. Benjamin, David Cesarini, Philipp D. Koellinger (2013-06-21; iq):

    A (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide statistically-significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R2 ≈ 0.02%), approximately 1 month of schooling per allele. A linear from all measured SNPs accounts for ≈2% of the in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our estimates can power analyses in social-science genetics.

    [A landmark study in behavioral genetics and intelligence: the first well-powered GWAS to detect genetic variants for intelligence and education which replicate out of sample and are proven to be causal in a between-sibling study.]

  167. ⁠, Mary E. Ward, George McMahon, Beate St Pourcain, David M. Evans, Cornelius A. Rietveld, Daniel J. Benjamin, Philipp D. Koellinger, David Cesarini, SSGAC, George Davey Smith, Nicholas J. Timpson (2014-05-22):

    Genome-wide association study results have yielded evidence for the association of common genetic variants with crude measures of completed educational attainment in adults. Whilst informative, these results do not inform as to the mechanism of these effects or their presence at earlier ages and where educational performance is more routinely and more precisely assessed. Single nucleotide polymorphisms exhibiting genome-wide statistically-significant associations with adult educational attainment were combined to derive an unweighted allele score in 5,979 and 6,145 young participants from the Avon Longitudinal Study of Parents and Children with key stage 3 national curriculum test results (SATS results) available at age 13 to 14 years in English and mathematics respectively. Standardised (z-scored) results for English and mathematics showed an expected relationship with sex, with girls exhibiting an advantage over boys in English (0.433 SD (95% 0.395, 0.470), p <10−10) with more similar results (though in the opposite direction) in mathematics (0.042 SD (95%CI 0.004, 0.080), p = 0.030). Each additional adult educational attainment increasing allele was associated with 0.041 SD (95%CI 0.020, 0.063), p = 1.79×10−04 and 0.028 SD (95%CI 0.007, 0.050), p = 0.01 increases in standardised SATS score for English and mathematics respectively. Educational attainment is a complex multifactorial behavioural trait which has not had heritable contributions to it fully characterised. We were able to apply the results from a large study of adult educational attainment to a study of child exam performance marking events in the process of learning rather than realised adult end product. Our results support evidence for common, small genetic contributions to educational attainment, but also emphasise the likely lifecourse nature of this genetic effect. Results here also, by an alternative route, suggest that existing methods for child examination are able to recognise early life variation likely to be related to ultimate educational attainment.

  168. Zeo#value-of-information-voi

  169. DNB-FAQ#aging

  170. 2015-polderman.pdf: ⁠, Tinca J. C. Polderman, Beben Benyamin, Christiaan A. de Leeuw, Patrick F. Sullivan, Arjen van Bochoven, Peter M. Visscher, Danielle Posthuma (2015-05-18; genetics  /​ ​​ ​heritable):

    Despite a century of research on complex traits in humans, the relative importance and specific nature of the influences of genes and environment on human traits remain controversial. We report a of twin correlations and reported variance components for 17,804 traits from 2,748 publications including 14,558,903 partly dependent twin pairs, virtually all published twin studies of complex traits. Estimates of heritability cluster strongly within functional domains, and across all traits the reported heritability is 49%. For a majority (69%) of traits, the observed twin correlations are consistent with a simple and parsimonious model where twin resemblance is solely due to additive genetic variation. The data are inconsistent with substantial influences from shared environment or non-additive genetic variation. This study provides the most comprehensive analysis of the causes of individual differences in human traits thus far and will guide future gene-mapping efforts. All the results can be visualized using the MaTCH webtool.

  171. 2015-polderman-supplement-1.pdf

  172. 2015-polderman-supplement-2.xlsx

  173. ⁠, Tian Ge, Chia-Yen Chen, Benjamin M. Neale, Mert R. Sabuncu, Jordan W. Smoller (2016-08-18):

    Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. However, assessing the comparative heritability of multiple traits estimated in different cohorts may be misleading due to the population-specific nature of heritability. Here we report the SNP heritability for 551 complex traits derived from the large-scale, population-based ⁠, comprising both quantitative phenotypes and disease codes, and examine the moderating effect of three major demographic variables (age, sex and ) on the heritability estimates. Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in comparing and interpreting heritability.

  174. https://www.nytimes.com/2014/05/25/opinion/sunday/the-closest-of-strangers.html

  175. https://www.nature.com/articles/srep28496

  176. http://medicine.tums.ac.ir:803/Users/Javad_TavakoliBazzaz/Medical%20Genetics-2/Heritability%20in%20the%20genomics%20era.pdf

  177. http://www.genetics.org/content/202/2/377

  178. http://rstb.royalsocietypublishing.org/content/365/1537/73.full

  179. ⁠, Peter M. Visscher, Sarah E. Medland, Manuel A. R. Ferreira, Katherine I. Morley, Gu Zhu, Belinda K. Cornes, Grant W. Montgomery, Nicholas G. Martin (2006-02-06):

    The study of continuously varying, quantitative traits is important in evolutionary biology, agriculture, and medicine. Variation in such traits is attributable to many, possibly interacting, genes whose expression may be sensitive to the environment, which makes their dissection into underlying causative factors difficult. An important population parameter for quantitative traits is heritability, the proportion of total variance that is due to genetic factors. Response to artificial and and the degree of resemblance between relatives are all a function of this parameter. Following the classic paper by in 1918, the estimation of additive and dominance genetic variance and heritability in populations is based upon the expected proportion of genes shared between different types of relatives, and explicit, often controversial and untestable models of genetic and non-genetic causes of family resemblance. With genome-wide coverage of genetic markers it is now possible to estimate such parameters solely within families using the actual degree of identity-by-descent sharing between relatives. Using genome scans on 4,401 quasi-independent sib pairs of which 3,375 pairs had phenotypes, we estimated the heritability of height from empirical genome-wide identity-by-descent sharing, which varied from 0.374 to 0.617 (mean 0.498, standard deviation 0.036). The variance in identity-by-descent sharing per chromosome and per genome was consistent with theory. The estimate of the heritability for height was 0.80 with no evidence for non-genetic causes of sib resemblance, consistent with results from independent twin and family studies but using an entirely separate source of information. Our application shows that it is feasible to estimate genetic variance solely from within-family segregation and provides an independent validation of previously untestable assumptions. Given sufficient data, our new paradigm will allow the estimation of genetic variation for disease susceptibility and quantitative traits that is free from confounding with non-genetic factors and will allow partitioning of genetic variation into additive and non-additive components.


    Quantitative geneticists attempt to understand variation between individuals within a population for traits such as height in humans and the number of bristles in fruit flies. This has been traditionally done by partitioning the variation in underlying sources due to genetic and environmental factors, using the observed amount of variation between and within families. A problem with this approach is that one can never be sure that the estimates are correct, because nature and nurture can be confounded without one knowing it. The authors got around this problem by comparing the similarity between relatives as a function of the exact proportion of genes that they have in common, looking only within families. Using this approach, the authors estimated the amount of total variation for height in humans that is due to genetic factors from 3,375 sibling pairs. For each pair, the authors estimated the proportion of genes that they share from DNA markers. It was found that about 80% of the total variation can be explained by genetic factors, close to results that are obtained from classical studies. This study provides the first validation of an estimate of genetic variation by using a source of information that is free from nature–nurture assumptions.

  180. 2016-kendler.pdf

  181. 2012-vandongen.pdf: ⁠, Jenny van Dongen, P. Eline Slagboom, Harmen H. M. Draisma, Nicholas G. Martin, Dorret I. Boomsma (2012-07-31; genetics  /​ ​​ ​heritable):

    The classical twin study has been a powerful heuristic in biomedical, psychiatric and behavioural research for decades. Twin registries worldwide have collected biological material and longitudinal phenotypic data on tens of thousands of twins, providing a valuable resource for studying complex phenotypes and their underlying biology. In this Review, we consider the continuing value of twin studies in the current era of molecular genetic studies. We conclude that classical twin methods combined with novel technologies represent a powerful approach towards identifying and understanding the molecular pathways that underlie complex traits.

  182. ⁠, Noah Zaitlen, Peter Kraft, Nick Patterson, Bogdan Pasaniuc, Gaurav Bhatia, Samuela Pollack, Alkes L. Price (2013-04-06):

    Important knowledge about the determinants of complex human phenotypes can be obtained from the estimation of heritability, the fraction of phenotypic variation in a population that is determined by genetic factors. Here, we make use of extensive phenotype data in Iceland, long-range phased genotypes, and a population-wide genealogical database to examine the heritability of 11 quantitative and 12 dichotomous phenotypes in a sample of 38,167 individuals. Most previous estimates of heritability are derived from family-based approaches such as twin studies, which may be biased upwards by epistatic interactions or shared environment. Our estimates of heritability, based on both closely and distantly related pairs of individuals, are significantly lower than those from previous studies. We examine phenotypic correlations across a range of relationships, from siblings to first cousins, and find that the excess phenotypic correlation in these related individuals is predominantly due to shared environment as opposed to dominance or ⁠. We also develop a new method to jointly estimate narrow-sense heritability and the heritability explained by genotyped SNPs. Unlike existing methods, this approach permits the use of information from both closely and distantly related pairs of individuals, thereby reducing the variance of estimates of heritability explained by genotyped SNPs while preventing upward bias. Our results show that common SNPs explain a larger proportion of the heritability than previously thought, with SNPs present on Illumina 300K genotyping arrays explaining more than half of the heritability for the 23 phenotypes examined in this study. Much of the remaining heritability is likely to be due to rare alleles that are not captured by standard genotyping arrays.

    Author Summary:

    Phenotype is a function of a genome and its environment. Heritability is the fraction of variation in a phenotype determined by genetic factors in a population. Current methods to estimate heritability rely on the phenotypic correlations of closely related individuals and are potentially upwardly biased, due to the impact of epistasis and shared environment. We develop new methods to estimate heritability over both closely and distantly related individuals. By examining the phenotypic correlation among different types of related individuals such as siblings, half-siblings, and first cousins, we show that shared environment is the primary determinant of inflated estimates of heritability. For a large number of phenotypes, it is not known how much of the heritability is explained by SNPs included on current genotyping platforms. Existing methods to estimate this component of heritability are biased in the presence of related individuals. We develop a method that permits the inclusion of both closely and distantly related individuals when estimating heritability explained by genotyped SNPs and use it to make estimates for 23 medically relevant phenotypes. These estimates can be used to increase our understanding of the distribution and frequency of functionally relevant variants and thereby inform the design of future studies.

  183. ⁠, Fernanda Polubriaginof, Kayla Quinnies, Rami Vanguri, Alexandre Yahi, Mary Simmerling, Iuliana Ionita-Laza, Hojjat Salmasian, Suzanne Bakken, George Hripcsak, David Goldstein, Krzysztof Kiryluk, David K. Vawdrey, Nicholas P. Tatonetti (2016-07-28):

    Heritability is a fundamental characteristic of human disease essential to the development of a biological understanding of the causes of disease. Traditionally, heritability studies are a laborious process of patient recruitment and phenotype ascertainment. (EHR) passively capture a wide range and depth of clinically relevant data and represent a novel resource for studying heritability of many traits and conditions that are not typically accessible. In addition to a wealth of disease phenotypes, nearly every hospital collects and stores next-of-kin information on the emergency contact forms when a patient is admitted. Until now, these data have gone completely unused for research purposes. We introduce a novel algorithm to infer familial relationships using emergency contact information while maintaining privacy. Here we show that EHR data yield accurate estimates of heritability across all available phenotypes using millions familial relationships mined from emergency contact data at two large academic medical centers. Estimates of heritability were consistent between sites and with previously reported estimates. Inconsistencies were indicative of limitations and opportunities unique to EHR research. Critically, these analyses provide a novel validation of the utility of electronic health records in inferences about the biological basis of disease.

  184. 2015-yang.pdf: ⁠, Jian Yang, Andrew Bakshi, Zhihong Zhu, Gibran Hemani, Anna A. E Vinkhuyzen, Sang Hong Lee, Matthew R. Robinson, John R. B. Perry, Ilja M. Nolte, Jana V. van VlietOstaptchouk, Harold Snieder, The LifeLines Cohort Study, Tonu Esko, Lili Milani, Reedik Mgi, Andres Metspalu, Anders Hamsten, Patrik K. E Magnusson, Nancy L. Pedersen, Erik Ingelsson, Nicole Soranzo, Matthew C. Keller, Naomi R. Wray, Michael E. Goddard, Peter M. Visscher (2015-08-31; genetics  /​ ​​ ​heritable):

    We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome sequencing data that ~97% and ~68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all ~17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s.e. = 2.5%) of variance for (BMI), and we find evidence that height-associated and BMI-associated variants have been under natural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60–70% for height and 30–40% for BMI. Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.

  185. ⁠, Gaurav Bhatia, Alexander Gusev, Po-Ru Loh, Bjarni J. Vilhjálmsson, Stephan Ripke, Working Group of the Psychiatric Genomics Consortium, Shaun Purcell, Eli Stahl, Mark Daly, Teresa R. de Candia, Kenneth S. Kendler, Michael C. O’Donovan, Sang Hong Lee, Naomi R. Wray, Benjamin M. Neale, Matthew C. Keller, Noah A. Zaitlen, Bogdan Pasaniuc, Jian Yang, Alkes L. Price (2015-07-12):

    While genome-wide associations generally explain only a small proportion of the narrow-sense heritability of complex disease (h2), recent work has shown that more heritability is explained by all genotyped SNPs (hg2). However, much of the heritability is still missing (hg2 < h2). For example, for schizophrenia, h2 is estimated at 0.7-0.8 but hg2 is estimated at ~0.3. Efforts at increasing coverage through accurately imputed variants have yielded only small increases in the heritability explained, and poorly imputed variants can lead to assay artifacts for traits. We propose to estimate the heritability explained by a set of haplotype variants (haploSNPs) constructed directly from the study sample (hhap2). Our method constructs a set of haplotypes from phased genotypes by extending shared haplotypes subject to the 4-gamete test. In a large schizophrenia data set (PGC2-SCZ), haploSNPs with MAF > 0.1% explained substantially more phenotypic variance (hhap2 = 0.64 (S.E. 0.084)) than genotyped SNPs alone (hg2 = 0.32 (S.E. 0.029)). These estimates were based on cross-cohort comparisons, ensuring that cohort-specific assay artifacts did not contribute to our estimates. In a large multiple sclerosis data set (WTCCC2-MS), we observed an even larger difference between hhap2 and hg2, though data from other cohorts will be required to validate this result. Overall, our results suggest that haplotypes of common SNPs can explain a large fraction of missing heritability of complex disease, shedding light on genetic architecture and informing disease mapping strategies.

  186. https://jaymans.wordpress.com/obesity-facts/

  187. 2016-hwang.pdf

  188. 2016-lynch.pdf: “Heritability and causal reasoning”⁠, Kate E. Lynch

  189. ⁠, Goodrich, Julia K. Waters, Jillian L. Poole, Angela C. Sutter, Jessica L. Koren, Omry Blekhman, Ran Beaumont, Michelle Van Treuren, William Knight, Rob Bell, Jordana T. Spector, Timothy D. Clark, Andrew G. Ley, Ruth E (2014):

    Host genetics and the gut can both influence metabolic phenotypes. However, whether host genetic variation shapes the gut microbiome and interacts with it to affect host phenotype is unclear. Here, we compared microbiotas across >1,000 fecal samples obtained from the TwinsUK population, including 416 twin pairs. We identified many microbial taxa whose abundances were influenced by host genetics. The most heritable taxon, the family Christensenellaceae, formed a co-occurrence network with other heritable Bacteria and with methanogenic Archaea. Furthermore, Christensenellaceae and its partners were enriched in individuals with low body mass index (BMI). An obese-associated microbiome was amended with Christensenella minuta, a cultured member of the Christensenellaceae, and transplanted to germ-free mice. C. minuta amendment reduced weight gain and altered the microbiome of recipient mice. Our findings indicate that host genetics influence the composition of the human gut microbiome and can do so in ways that impact host metabolism.

  190. https://www.nature.com/articles/ncomms11174

  191. ⁠, Daphna Rothschild, Omer Weissbrod, Elad Barkan, Tal Korem, David Zeevi, Paul I. Costea, Anastasia Godneva, Iris Kalka, Noam Bar, Niv Zmora, Meirav Pevsner-Fischer, David Israeli, Noa Kosower, Gal Malka, Bat Chen Wolf, Tali Avnit-Sagi, Maya Lotan-Pompan, Adina Weinberger, Zamir Halpern, Shai Carmi, Eran Elinav, Eran Segal (2017-06-16):

    Human gut microbiome composition is shaped by multiple host intrinsic and extrinsic factors, but the relative contribution of host genetic compared to environmental factors remains elusive. Here, we genotyped a cohort of 696 healthy individuals from several distinct ancestral origins and a relatively common environment, and demonstrate that there is no statistically-significant association between microbiome composition and ethnicity, single nucleotide polymorphisms (SNPs), or overall genetic similarity, and that only 5 of 211 (2.4%) previously reported microbiome-SNP associations replicate in our cohort. In contrast, we find similarities in the microbiome composition of genetically unrelated individuals who share a household. We define the term biome-explainability as the variance of a host phenotype explained by the microbiome after accounting for the contribution of human genetics. Consistent with our finding that microbiome and host genetics are largely independent, we find significant biome-explainability levels of 16–33% for body mass index (BMI), fasting glucose, high-density lipoprotein (HDL) cholesterol, waist circumference, waist-hip ratio (WHR), and lactose consumption. We further show that several human phenotypes can be predicted substantially more accurately when adding microbiome data to host genetics data, and that the contribution of both data sources to prediction accuracy is largely additive. Overall, our results suggest that human microbiome composition is dominated by environmental factors rather than by host genetics.

  192. https://old.reddit.com/r/Microbiome/comments/6hojbc/environmental_factors_dominate_over_host_genetics/

  193. https://www.nature.com/articles/ncomms15361

  194. ⁠, Clare Bycroft, Colin Freeman, Desislava Petkova, Gavin Band, Lloyd T. Elliott, Kevin Sharp, Allan Motyer, Damjan Vukcevic, Olivier Delaneau, Jared O’Connell, Adrian Cortes, Samantha Welsh, Gil McVean, Stephen Leslie, Peter Donnelly, Jonathan Marchini (2017-07-20):

    The UK Biobank project is a large prospective cohort study of ~500,000 individuals from across the United Kingdom, aged between 40–69 at recruitment. A rich variety of phenotypic and health-related information is available on each participant, making the resource unprecedented in its size and scope. Here we describe the genome-wide genotype data (~805,000 markers) collected on all individuals in the cohort and its quality control procedures. Genotype data on this scale offers novel opportunities for assessing quality issues, although the wide range of ancestries of the individuals in the cohort also creates particular challenges. We also conducted a set of analyses that reveal properties of the genetic data—such as population structure and relatedness—that can be important for downstream analyses. In addition, we phased and imputed genotypes into the dataset, using computationally efficient methods combined with the Haplotype Reference Consortium (HRC) and UK10K haplotype resource. This increases the number of testable variants by over 100× to ~96 million variants. We also imputed classical allelic variation at 11 human leukocyte antigen (HLA) genes, and as a quality control check of this imputation, we replicate signals of known associations between HLA alleles and many common diseases. We describe tools that allow efficient genome-wide association studies (GWAS) of multiple traits and fast phenome-wide association studies (PheWAS), which work together with a new compressed file format that has been used to distribute the dataset. As a further check of the genotyped and imputed datasets, we performed a test-case genome-wide association scan on a well-studied human trait, standing height.

  195. ⁠, Oriol Canela-Xandri, Konrad Rawlik, Albert Tenesa (2017-08-16):

    Genome-wide association studies have revealed many loci contributing to the variation of complex traits, yet the majority of loci that contribute to the heritability of complex traits remain elusive. Large study populations with sufficient statistical power are required to detect the small effect sizes of the yet unidentified genetic variants. However, the analysis of huge cohorts, like UK Biobank, is complicated by incidental structure present when collecting such large cohorts. For instance, UK Biobank comprises 107,162 third degree or closer related participants. Traditionally, GWAS have removed related individuals because they comprised an insignificant proportion of the overall sample size, however, removing related individuals in UK would entail a substantial loss of power. Furthermore, modelling such structure using is computationally expensive, which requires a computational infrastructure that may not be accessible to all researchers. Here we present an atlas of genetic associations for 118 non-binary and 599 binary traits of 408,455 related and unrelated UK Biobank participants of White-British descend. Results are compiled in a publicly accessible database that allows querying genome-wide association summary results for 623,944 genotyped and HapMap2 imputed SNPs, as well downloading whole GWAS summary statistics for over 30 million imputed SNPs from the Haplotype Reference Consortium panel. Our atlas of associations (GeneAtlas, http:/​​​​/​​​​geneatlas.roslin.ed.ac.uk) will help researchers to query UK Biobank results in an easy way without the need to incur in high computational costs.

  196. http://geneatlas.roslin.ed.ac.uk/

  197. 2017-visscher.pdf: ⁠, Peter M. Visscher, Naomi R. Wray, Qian Zhang, Pamela Sklar, Mark I. McCarthy, Matthew A. Brown, and Jian Yang (2017-07-06; genetics  /​ ​​ ​heritable):

    Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.

  198. ⁠, Michael Inouye, Gad Abraham, Christopher P. Nelson, Angela M. Wood, Michael J. Sweeting, Frank Dudbridge, Florence Y. Lai, Stephen Kaptoge, Marta Brozynska, Tingting Wang, Shu Ye, Thomas R. Webb, Martin K. Rutter, Ioanna Tzoulaki, Riyaz S. Patel, Ruth J. F. Loos, Bernard Keavney, Harry Hemingway, John Thompson, Hugh Watkins, Panos Deloukas, Emanuele Di Angelantonio, Adam S. Butterworth, John Danesh, Nilesh J. Samani, for The UK Biobank CardioMetabolic Consortium CHD Working Group (2018-01-19):

    Background: Coronary artery disease (CAD) has substantial heritability and a polygenic architecture; however, genomic risk scores have not yet leveraged the totality of genetic information available nor been externally tested at population-scale to show potential utility in primary prevention.

    Methods: Using a meta-analytic approach to combine large-scale genome-wide and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS), consisting of 1.7 million genetic variants. We externally tested metaGRS, individually and in combination with available conventional risk factors, in 22,242 CAD cases and 460,387 non-cases from UK Biobank.


    In UK Biobank, a standard deviation increase in metaGRS had a hazard ratio (HR) of 1.71 (95% CI 1.68–1.73) for CAD, greater than any other externally tested genetic risk score. Individuals in the top 20% of the metaGRS distribution had a HR of 4.17 (95% CI 3.97–4.38) compared with those in the bottom 20%. The metaGRS had higher C-index (C = 0.623, 95% CI 0.615–0.631) for incident CAD than any of four conventional factors (smoking, diabetes, hypertension, and body mass index), and addition of the metaGRS to a model of conventional risk factors increased C-index by 3.7%. In individuals on lipid-lowering or anti-hypertensive medications at recruitment, metaGRS hazard for incident CAD was significantly but only partially attenuated with HR of 2.83 (95% CI 2.61– 3.07) between the top and bottom 20% of the metaGRS distribution.


    Recent genetic association studies have yielded enough information to meaningfully stratify individuals using the metaGRS for CAD risk in both early and later life, thus enabling targeted primary intervention in combination with conventional risk factors. The metaGRS effect was partially attenuated by lipid and blood pressure-lowering medication, however other prevention strategies will be required to fully benefit from earlier genomic risk stratification.


    National Health and Medical Research Council of Australia, British Heart Foundation, Australian Heart Foundation.

  199. 2018-inouye-cad-riskprediction.png

  200. https://www.nature.com/articles/gim2017246

  201. ⁠, Lauge Farnaes, Amber Hildreth, Nathaly M. Sweeney, Michelle M. Clark, Shimul Chowdhury, Shareef Nahas, Julie A. Cakici, Wendy Benson, Robert H. Kaplan, Richard Kronick, Matthew N. Bainbridge, Jennifer Friedman, Jeffrey J. Gold, Yan Ding, Narayanan Veeraraghavan, David Dimmock, Stephen F. Kingsmore, on behalf of the RCIGM Investigators (2018-01-26):

    Background: Genetic disorders are a leading cause of morbidity and mortality in infants. Rapid Whole Genome Sequencing (rWGS) can diagnose genetic disorders in time to change acute medical or surgical management (clinical utility) and improve outcomes in acutely ill infants.

    Methods: Retrospective cohort study of acutely ill inpatient infants in a regional children’s hospital from July 2016–March 2017. 42 families received rWGS for etiologic diagnosis of genetic disorders. Probands received standard genetic testing as clinically indicated. Primary end-points were rate of diagnosis, clinical utility, and healthcare utilization. The latter was modelled in six infants by comparing actual utilization with matched historical controls and/​​​​or counterfactual utilization had rWGS been performed at different time points.

    Findings: The diagnostic sensitivity was 43% (18 of 42 infants) for rWGS and 10% (4 of 42 infants) for standard of care (p = 0.0005). The rate of clinical utility for rWGS (31%, 13 of 42 infants) was statistically-significantly greater than for standard of care (2%, one of 42; p = 0.0015). 11 (26%) infants with diagnostic rWGS avoided morbidity, one had 43% reduction in likelihood of mortality, and one started palliative care. In 6 of the 11 infants, the changes in management reduced inpatient cost by $800,000 to $2,000,000.

    Discussion: These findings replicate a study of the clinical utility of rWGS in acutely ill inpatient infants, and demonstrate improved outcomes and net healthcare savings. rWGS merits consideration as a first tier test in this setting.

  202. ⁠, Anubha Mahajan, Daniel Taliun, Matthias Thurner, Neil R. Robertson, Jason M. Torres, N. William Rayner, Valgerdur Steinthorsdottir, Robert A. Scott, Niels Grarup, James P. Cook, Ellen M. Schmidt, Matthias Wuttke, Chloé Sarnowski, Reedik Mägi, Jana Nano, Christian Gieger, Stella Trompet, Cécile Lecoeur, Michael Preuss, Bram Peter Prins, Xiuqing Guo, Lawrence F. Bielak, DIAMANTE Consortium, Amanda J. Bennett, Jette Bork-Jensen, Chad M. Brummett, Mickaël Canouil, Kai-Uwe Eckardt, Krista Fischer, Sharon LR Kardia, Florian Kronenberg, Kristi Läll, Ching-Ti Liu, Adam E. Locke, Jian′an Luan, Ioanna Ntalla, Vibe Nylander, Sebastian Sch࿆nherr, Claudia Schurmann, Loïc Yengo, Erwin P. Bottinger, Ivan Brandslund, Cramer Christensen, George Dedoussis, Jose C. Florez, Ian Ford, Oscar H. Franco, Timothy M. Frayling, Vilmantas Giedraitis, Sophie Hackinger, Andrew T. Hattersley, Christian Herder, M. Arfan Ikram, Martin Ingelsson, Marit E. Jørgensen, Torben Jørgensen, Jennifer Kriebel, Johanna Kuusisto, Symen Ligthart, Cecilia M. Lindgren, Allan Linneberg, Valeriya Lyssenko, Vasiliki Mamakou, Thomas Meitinger, Karen L. Mohlke, Andrew D. Morris, Girish Nadkarni, James S. Pankow, Annette Peters, Naveed Sattar, Alena Stančáková, Konstantin Strauch, Kent D. Taylor, Barbara Thorand, Gudmar Thorleifsson, Unnur Thorsteinsdottir, Jaakko Tuomilehto, Daniel R. Witte, Josée Dupuis, Patricia A. Peyser, Eleftheria Zeggini, Ruth J. F Loos, Philippe Froguel, Erik Ingelsson, Lars Lind, Leif Groop, Markku Laakso, Francis S. Collins, J. Wouter Jukema, Colin N. A Palmer, Harald Grallert, Andres Metspalu, Abbas Dehghan, Anna Köttgen, Goncalo Abecasis, James B. Meigs, Jerome I. Rotter, Jonathan Marchini, Oluf Pedersen, Torben Hansen, Claudia Langenberg, Nicholas J. Wareham, Kari Stefansson, Anna L. Gloyn, Andrew P. Morris, Michael Boehnke, Mark I. McCarthy (2018-01-09):

    We aggregated genome-wide genotyping data from 32 European-descent GWAS (74,124 T2D cases, 824,006 controls) imputed to high-density reference panels of >30,000 sequenced haplotypes. Analysis of ˜27M variants (˜21M with minor allele frequency [MAF]<5%), identified 243 genome-wide statistically-significant loci (p<5×10−8; MAF 0.02%-50%; odds ratio [OR] 1.04-8.05), 135 not previously-implicated in T2D-predisposition. Conditional analyses revealed 160 additional distinct association signals (p<10−5) within the identified loci. The combined set of 403 T2D-risk signals includes 56 low-frequency (0.5%≤MAF<5%) and 24 rare (MAF<0.5%) index SNPs at 60 loci, including 14 with estimated allelic OR>2. Forty-one of the signals displayed effect-size heterogeneity between BMI-unadjusted and adjusted analyses. Increased sample size and improved imputation led to substantially more precise localisation of causal variants than previously attained: at 51 signals, the lead variant after fine-mapping accounted for >80% posterior probability of association (PPA) and at 18 of these, PPA exceeded 99%. Integration with islet regulatory annotations enriched for T2D association further reduced median credible set size (from 42 variants to 32) and extended the number of index variants with PPA>80% to 73. Although most signals mapped to regulatory sequence, we identified 18 genes as human validated therapeutic targets through coding variants that are causal for disease. Genome wide chip heritability accounted for 18% of T2D-risk, and individuals in the 2.5% extremes of a polygenic risk score generated from the GWAS data differed >9× in risk. Our observations highlight how increases in sample size and variant diversity deliver enhanced discovery and single-variant resolution of causal T2D-risk alleles, and the consequent impact on mechanistic insights and clinical translation.

  203. https://www.nature.com/articles/ncomms16015

  204. ⁠, Morten Valberg, Mats Julius Stensrud, Odd O. Aalen (2017-06-14):

    Background: A wide range of diseases show some degree of clustering in families; family history is therefore an important aspect for clinicians when making risk predictions. Familial aggregation is often quantified in terms of a familial relative risk (FRR), and although at first glance this measure may seem simple and intuitive as an average risk prediction, its implications are not straightforward.

    Methods: We use two statistical models for the distribution of disease risk in a population: a dichotomous risk model that gives an intuitive understanding of the implication of a given FRR, and a continuous risk model that facilitates a more detailed computation of the inequalities in disease risk. Published estimates of FRRs are used to produce Lorenz curves and Gini indices that quantifies the inequalities in risk for a range of diseases.

    Results: We demonstrate that even a moderate familial association in disease risk implies a very large difference in risk between individuals in the population. We give examples of diseases for which this is likely to be true, and we further demonstrate the relationship between the point estimates of FRRs and the distribution of risk in the population.

    Conclusions: The variation in risk for several severe diseases may be larger than the variation in income in many countries. The implications of familial risk estimates should be recognized by epidemiologists and clinicians.

  205. ⁠, Louis Lello, Steven G. Avery, Laurent Tellier, Ana I. Vazquez, Gustavo de los Campos, Stephen D. H. Hsu (2017-10-07):

    We construct genomic predictors for heritable and extremely complex human quan-titative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ~40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction. The variance captured for height is comparable to the estimated SNP heritability from GCTA (GREML) analysis, and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for the SNPs used. Thus, our results resolve the common SNP portion of the “missing heritability” problem—i.e., the gap between prediction R-squared and SNP heritability. The ~20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common SNPs. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier GWAS for out-of-sample validation of our results.

  206. ⁠, Loic Yengo, Julia Sidorenko, Kathryn E. Kemper, Zhili Zheng, Andrew R. Wood, Michael N. Weedon, Timothy M. Frayling, Joel Hirschhorn, Jian Yang, Peter M. Visscher, the GIANT Consortium (2018-03-22):

    Genome-wide association studies (GWAS) stand as powerful experimental designs for identifying DNA variants associated with complex traits and diseases. In the past decade, both the number of such studies and their sample sizes have increased dramatically. Recent GWAS of height and body mass index (BMI) in ~250,000 European participants have led to the discovery of ~700 and ~100 nearly independent SNPs associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ~450,000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N~700,000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3,290 and 716 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide statistical-significance threshold of p<1 × 10−8), including 1,185 height-associated SNPs and 554 BMI-associated SNPs located within loci not previously identified by these two GWAS. The genome-wide statistically-significant SNPs explain ~24.6% of the variance of height and ~5% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were 0.44 and 0.20, respectively. From analyses of integrating GWAS and eQTL data by Summary-data based Mendelian Randomization (SMR), we identified an enrichment of eQTLs amongst lead height and BMI signals, prioritisting 684 and 134 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow up studies.

  207. ⁠, Evangelos Evangelou, Helen R. Warren, David Mosen-Ansorena, Borbala Mifsud, Raha Pazoki, He Gao, Georgios Ntritsos, Niki Dimou, Claudia P. Cabrera, Ibrahim Karaman, Fu Liang Ng, Marina Evangelou, Katarzyna Witkowska, Evan Tzanis, Jacklyn N. Hellwege, Ayush Giri, Digna R. Velez Edwards, Yan V. Sun, Kelly Cho, J. Michael Gaziano, Peter WF Wilson, Philip S. Tsao, Csaba P. Kovesdy, Tonu Esko, Reedik Mägi, Lili Milani, Peter Almgren, Thibaud Boutin, Stéphanie Debette, Jun Ding, Franco Giulianini, Elizabeth G. Holliday, Anne U. Jackson, Ruifang Li-Gao, Wei-Yu Lin, Jian'an Luan, Massimo Mangino, Christopher Oldmeadow, Bram Prins, Yong Qian, Muralidharan Sargurupremraj, Nabi Shah, Praveen Surendran, Sébastien Thériault, Niek Verweij, Sara M. Willems, Jing-Hua Zhao, Philippe Amouyel, John Connell, Renée de Mutsert, Alex SF Doney, Martin Farrall, Cristina Menni, Andrew D. Morris, Raymond Noordam, Guillaume Paré, Neil R. Poulter, Denis C. Shields, Alice Stanton, Simon Thom, Gonçalo Abecasis, Najaf Amin, Dan E. Arking, Kristin L. Ayers, Caterina M. Barbieri, Chiara Batini, Joshua C. Bis, Tineka Blake, Murielle Bochud, Michael Boehnke, Eric Boerwinkle, Dorret I. Boomsma, Erwin P. Bottinger, Peter S. Braund, Marco Brumat, Archie Campbell, Harry Campbell, Aravinda Chakravarti, John C. Chambers, Ganesh Chauhan, Marina Ciullo, Massimiliano Cocca, Francis Collins, Heather J. Cordell, Gail Davies, Martin H. de Borst, Eco J. de Geus, Ian J. Deary, Joris Deelen, Fabiola M. Del Greco, Cumhur Yusuf Demirkale, Marcus Dörr, Georg B. Ehret, Roberto Elosua, Stefan Enroth, A. Mesut Erzurumluoglu, Teresa Ferreira, Mattias Frånberg, Oscar H. Franco, Ilaria Gandin, Paolo Gasparini, Vilmantas Giedraitis, Christian Gieger, Giorgia Girotto, Anuj Goel, Alan J. Gow, Vilmundur Gudnason, Xiuqing Guo, Ulf Gyllensten, Anders Hamsten, Tamara B. Harris, Sarah E. Harris, Catharina A. Hartman, Aki S. Havulinna, Andrew A. Hicks, Edith Hofer, Albert Hofman, Jouke-Jan Hottenga, Jennifer E. Huffman, Shih-Jen Hwang, Erik Ingelsson, Alan James, Rick Jansen, Marjo-Riitta Jarvelin, Roby Joehanes, Åsa Johansson, Andrew D. Johnson, Peter K. Joshi, Pekka Jousilahti, J. Wouter Jukema, Antti Jula, Mika Kähönen, Sekar Kathiresan, Bernard D. Keavney, Kay-Tee Khaw, Paul Knekt, Joanne Knight, Ivana Kolcic, Jaspal S. Kooner, Seppo Koskinen, Kati Kristiansson, Zoltan Kutalik, Maris Laan, Marty Larson, Lenore J. Launer, Benjamin C. Lehne, Terho Lehtimäki, Daniel Levy, David CM Liewald, Li Lin, Lars Lind, Cecilia M. Lindgren, Yongmei Liu, Ruth JF Loos, Lorna M. Lopez, Lingchan Lu, Leo-Pekka Lyytikäinen, Anubha Mahajan, Chrysovalanto Mamasoula, Jaume Marrugat, Jonathan Marten, Yuri Milaneschi, Anna Morgan, Andrew P. Morris, Alanna C. Morrison, Peter J. Munson, Mike A. Nalls, Priyanka Nandakumar, Christopher P. Nelson, Christopher Newton-Cheh, Teemu Niiranen, Ilja M. Nolte, Teresa Nutile, Albertine J. Oldehinkel, Ben A. Oostra, Paul F. O'Reilly, Elin Org, Sandosh Padmanabhan, Walter Palmas, Arno Palotie, Alison Pattie, Brenda WJH Penninx, Markus Perola, Annette Peters, Ozren Polasek, Peter P. Pramstaller, Nguyen Quang Tri, Olli T. Raitakari, Meixia Ren, Rainer Rettig, Kenneth Rice, Paul M. Ridker, Janina S. Reid, Harriëtte Riese, Samuli Ripatti, Antonietta Robino, Lynda M. Rose, Jerome I. Rotter, Igor Rudan, Daniella Ruggiero, Yasaman Saba, Cinzia F. Sala, Veikko Salomaa, Nilesh J. Samani, Antti-Pekka Sarin, Rheinhold Schmidt, Helena Schmidt, Nick Shrine, David Siscovick, Albert V. Smith, Harold Schneider, Siim Sõber, Rossella Sorice, John M. Starr, David J. Stott, David P. Strachan, Rona J. Strawbridge, Johan Sundström, Morris A. Swertz, Kent D. Taylor, Alexander Teumer, Martin D. Tobin, Daniela Toniolo, Michela Traglia, Stella Trompet, Jaakko Tuomilehto, Christophe Tzourio, André G. Uitterlinden, Ahmad Vaez, Peter J. van der Most, Cornelia M. van Duijn, Anne-Claire Vergnaud, Germaine C. Verwoert, Veronique Vitart, Uwe Völker, Peter Vollenweider, Dragana Vuckovic, Hugh Watkins, Sarah H. Wild, Gonneke Willemsen, James F. Wilson, Alan F. Wright, Jie Yao, Tatijana Zemunik, Weihua Zhang, John R. Attia, Adam S. Butterworth, Dan Chasman, David Conen, Francesco Cucca, John Danesh, Caroline Hayward, Joanna MM Howson, Markku Laakso, Edward G. Lakatta, Claudia Langenberg, Ollie Melander, Dennis O. Mook-Kanamori, Patricia B. Munroe, Colin NA Palmer, Lorenz Risch, Robert A. Scott, Rodney J. Scott, Peter Sever, Tim D. Spector, Pim van der Harst, Nicholas J. Wareham, Eleftheria Zeggini, Morris J. Brown, Andres Metspalu, Adriana M. Hung, Christopher J. O'Donnell, Todd L. Edwards, on behalf of the Million Veteran Program⁠, Bruce M. Psaty, Ioanna Tzoulaki, Michael R. Barnes, Louise V. Wain, Paul Elliott, Mark J. Caulfield (2017-10-11):

    High blood pressure is the foremost heritable global risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits to date (systolic, diastolic, pulse pressure) in over one million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also reveal shared loci influencing lifestyle exposures. Our findings offer the potential for a precision medicine strategy for future cardiovascular disease prevention.

  208. 2017-akiyama.pdf: ⁠, Masato Akiyama, Yukinori Okada, Masahiro Kanai, Atsushi Takahashi, Yukihide Momozawa, Masashi Ikeda, Nakao Iwata, Shiro Ikegawa, Makoto Hirata, Koichi Matsuda, Motoki Iwasaki, Taiki Yamaji, Norie Sawada, Tsuyoshi Hachiya, Kozo Tanno, Atsushi Shimizu, Atsushi Hozawa, Naoko Minegishi, Shoichiro Tsugane, Masayuki Yamamoto, Michiaki Kubo Yoichiro Kamatani (2017-09-11; genetics  /​ ​​ ​correlation):

    Obesity is a risk factor for a wide variety of health problems. In a genome-wide association study (GWAS) of body mass index (BMI) in Japanese people (n = 173,430), we found 85 loci statistically-significantly associated with obesity (p < 5.0 × 10−8), of which 51 were previously unknown. We conducted trans-ancestral meta-analyses by integrating these results with the results from a GWAS of Europeans and identified 61 additional new loci. In total, this study identifies 112 novel loci, doubling the number of previously known BMI-associated loci. By annotating associated variants with cell-type-specific regulatory marks, we found enrichment of variants in CD19+ cells. We also found statistically-significant genetic correlations between BMI and lymphocyte count (p = 6.46 × 10−5, rg = 0.18) and between BMI and multiple complex diseases. These findings provide genetic evidence that lymphocytes are relevant to body weight regulation and offer insights into the pathogenesis of obesity.

  209. https://www.sciencedirect.com/science/article/pii/S0917504016300673

  210. 2017-tropf.pdf: “Hidden heritability due to heterogeneity across seven populations”⁠, Felix C. Tropf, S. Hong Lee, Renske M. Verweij, Gert Stulp, Peter J. van der Most, Ronald de Vlaming, Andrew Bakshi, Daniel A. Briley, Charles Rahal, Robert Hellpap, Anastasia N. Iliadou, Tamp#x000F5;nu Esko, Andres Metspalu, Sarah E. Medland, Nicholas G. Martin, Nicola Barban, Harold Snieder, Matthew R. Robinson, Melinda C. Mills

  211. ⁠, Genevieve L. Wojcik, Mariaelisa Graff, Katherine K. Nishimura, Ran Tao, Jeffrey Haessler, Christopher R. Gignoux, Heather M. Highland, Yesha M. Patel, Elena P. Sorokin, Christy L. Avery, Gillian M. Belbin, Stephanie A. Bien, Iona Cheng, Chani J. Hodonsky, Laura M. Huckins, Janina Jeff, Anne E. Justice, Jonathan M. Kocarnik, Unhee Lim, Bridget M. Lin, Yingchang Lu, Sarah C. Nelson, Sung-Shim L. Park, Michael H. Preuss, Melissa A. Richard, Claudia Schurmann, Veronica W. Setiawan, Karan Vahi, Abhishek Vishnu, Marie Verbanck, Ryan Walker, Kristin L. Young, Niha Zubair, Jose Luis Ambite, Eric Boerwinkle, Erwin Bottinger, Carlos D. Bustamante, Christian Caberto, Matthew P. Conomos, Ewa Deelman, Ron Do, Kimberly Doheny, Lindsay Fernandez-Rhodes, Myriam Fornage, Gerardo Heiss, Lucia A. Hindorff, Rebecca D. Jackson, Regina James, Cecelia A. Laurie, Cathy C. Laurie, Yuqing Li, Dan-Yu Lin, Girish Nadkarni, Loreall C. Pooler, Alexander P. Reiner, Jane Romm, Chiara Sabati, Xin Sheng, Eli A. Stahl, Daniel O. Stram, Timothy A. Thornton, Christina L. Wassel, Lynne R. Wilkens, Sachi Yoneyama, Steven Buyske, Chris Haiman, Charles Kooperberg, Loic Le Marchand, Ruth JF Loos, Tara C. Matise, Kari E. North, Ulrike Peters, Eimear E. Kenny, Christopher S. Carlson (2017-09-15):

    Genome-wide association studies (GWAS) have laid the foundation for many downstream investigations, including the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of human variation and hinders the translation of genetic associations into clinical and public health applications. To demonstrate the benefit of studying underrepresented populations, the Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using novel strategies for multi-ethnic analysis of admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 28 novel loci and 42 residual signals in known loci. Our data show strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, which substantially restricts genetically-guided precision medicine. We advocate for new, large genome-wide efforts in diverse populations to reduce health disparities.

  212. ⁠, Annah B. Wyss, Tamar Sofer, Mi Kyeong Lee, Natalie Terzikhan, Jennifer N. Nguyen, Lies Lahousse, Jeanne C. Latourelle, Albert Vernon Smith, Traci M. Bartz, Mary F. Feitosa, Wei Gao, Tarunveer S. Ahluwalia, Wenbo Tang, Christopher Oldmeadow, Qing Duan, Kim de Jong, Mary K. Wojczynski, Xin-Qun Wang, Raymond Noordam, Fernando Pires Hartwig, Victoria E. Jackson, Tianyuan Wang, Ma’en Obeidat, Brian D. Hobbs, Tianxiao Huan, Gleb Kichaev, Jianping Jin, Mariaelisa Graff, Tamara B. Harris, Ravi Kalhan, Susan R. Heckbert, Lavinia Paternoster, Kristin M. Burkart, Yongmei Liu, Elizabeth G. Holliday, James G. Wilson, Judith M. Vonk, Jason Sanders, R. Graham Barr, Renée de Mutsert, Ana Maria Baptista Menezes, Hieab H. H. Adams, Maarten van den Berge, Roby Joehanes, Lenore J. Launer, Alanna C. Morrison, Colleen M. Sitlani, Juan C. Celedón, Stephen B. Kritchevsky, Rodney J. Scott, Kaare Christensen, Jerome I. Rotter, Tobias N. Bonten, Fernando César Wehrmeister, Yohan Bossé, Nora Franceschini, Jennifer A. Brody, Robert C. Kaplan, Kurt Lohman, Mark McEvoy, Michael A. Province, Frits R. Rosendaal, Kent D. Taylor, David C. Nickle, International COPD Genetics Consortium Investigators, Vilmundur Gudnason, Kari E. North, Myriam Fornage, Bruce M. Psaty, Richard H. Myers, George O’Connor, Torben Hansen, Cathy C. Laurie, Pat Cassano, Joohon Sung, Woo Jin Kim, John R. Attia, Leslie Lange, H. Marike Boezen, Bharat Thyagarajan, Stephen S. Rich, Dennis O. Mook-Kanamori, Bernardo Lessa Horta, André G. Uitterlinden, Don D. Sin, Hae Kyung Im, Michael H. Cho, Guy G. Brusselle, Sina A. Gharib, Josée Dupuis, Ani Manichaikul, Stephanie J. London (2017-10-05):

    Nearly 100 loci have been identified for pulmonary function, almost exclusively in studies of European ancestry populations. We extend previous research by meta-analyzing genome-wide association studies of 1000 Genomes imputed variants in relation to pulmonary function in a multiethnic population of 90,715 individuals of European (n = 60,552), African (n = 8,429), Asian (n = 9,959), and Hispanic/​​​​Latino (n = 11,775) ethnicities. We identified over 50 novel loci at genome-wide statistical-significance in ancestry-specific and/​​​​or multiethnic meta-analyses. Recent fine mapping methods incorporating functional annotation, gene expression, and/​​​​or differences in linkage disequilibrium between ethnicities identified potential causal variants and genes at known and newly identified loci. Sixteen of the novel genes encode proteins with predicted or established drug targets, including KCNK2 and CDK12.

  213. ⁠, Peter K. Joshi, Nicola Pirastu, Katherine A. Kentistou, Krista Fischer, Edith Hofer, Katharina E. Schraut, David W. Clark, Teresa Nutile, Catriona L. K. Barnes, Paul R. H. J. Timmers, Xia Shen, Ilaria Gandin, Aaron F. McDaid, Thomas Folkmann Hansen, Scott D. Gordon, Franco Giulianini, Thibaud S. Boutin, Abdel Abdellaoui, Wei Zhao, Carolina Medina-Gomez, Traci M. Bartz, Stella Trompet, Leslie A. Lange, Laura Raffield, Ashley van der Spek, Tessel E. Galesloot, Petroula Proitsi, Lisa R. Yanek, Lawrence F. Bielak, Antony Payton, Federico Murgia, Maria Pina Concas, Ginevra Biino, Salman M. Tajuddin, Ilkka Seppälä, Najaf Amin, Eric Boerwinkle, Anders D. Børglum, Archie Campbell, Ellen W. Demerath, Ilja Demuth, Jessica D. Faul, Ian Ford, Alessandro Gialluisi, Martin Gögele, MariaElisa Graff, Aroon Hingorani, Jouke-Jan Hottenga, David M. Hougaard, Mikko A. Hurme, M. Arfan Ikram, Marja Jylhä, Diana Kuh, Lannie Ligthart, Christina M. Lill, Ulman Lindenberger, Thomas Lumley, Reedik Mägi, Pedro Marques-Vidal, Sarah E. Medland, Lili Milani, Reka Nagy, William E. R. Ollier, Patricia A. Peyser, Peter P. Pramstaller, Paul M. Ridker, Fernando Rivadeneira, Daniela Ruggiero, Yasaman Saba, Reinhold Schmidt, Helena Schmidt, P. Eline Slagboom, Blair H. Smith, Jennifer A. Smith, Nona Sotoodehnia, Elisabeth Steinhagen-Thiessen, Frank J. A. van Rooij, André L. Verbeek, Sita H. Vermeulen, Peter Vollenweider, Yunpeng Wang, Thomas Werge, John B. Whitfield, Alan B. Zonderman, Terho Lehtimäki, Michele K. Evans, Mario Pirastu, Christian Fuchsberger, Lars Bertram, Neil Pendleton, Sharon L. R. Kardia, Marina Ciullo, Diane M. Becker, Andrew Wong, Bruce M. Psaty, Cornelia M. van Duijn, James G. Wilson, J. Wouter Jukema, Lambertus Kiemeney, André G. Uitterlinden, Nora Franceschini, Kari E. North, David R. Weir, Andres Metspalu, Dorret I. Boomsma, Caroline Hayward, Daniel Chasman, Nicholas G. Martin, Naveed Sattar, Harry Campbell, Tōnu Esko, Zoltán Kutalik & James F. Wilson (2017-10-13):

    Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents’ survival, we discover two regions associated with longevity (HLA-DQA1/​​​​DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/​​​​5, CDKN2A/​​​​B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.

  214. ⁠, Emmi Tikkanen, Stefan Gustafsson, David Amar, Anna Shcherbina, Daryl Waggott, Euan A. Ashley, Erik Ingelsson (2017-10-10):

    Background: Hand grip strength, a simple indicator of muscular strength, has been associated with a range of health conditions, including fractures, disability, cardiovascular disease and premature death risk. Twin studies have suggested a high (50-60%) heritability, but genetic determinants are largely unknown.


    In this study, our aim was to study genetic variation associated with muscular strength in a large sample of 334,925 individuals of European descent from the UK Biobank, and to evaluate shared genetic aetiology with and causal effects of grip strength on physical and cognitive health.

    Methods and Results

    In our discovery analysis of 223,315 individuals, we identified 101 loci associated with grip strength at genome-wide statistical-significance (P<5×10−8). Of these, 64 were associated (P<0.01 and consistent direction) also in the replication dataset (n = 111,610). Many of the lead SNPs were located in or near genes known to have a function in developmental disorders (FTO, SLC39A8, TFAP2B, TGFA, CELF1, TCF4, BDNF, FOXP1, KIF1B, ANTXR2), and one of the most significant genes based on a gene-based analysis (ATP2A1) encodes SERCA1, the critical enzyme in calcium uptake to the sarcoplasmic reticulum, which plays a major role in muscle contraction and relaxation. Further, we demonstrated a significant enrichment of gene expression in brain-related transcripts among grip strength associations. Finally, we observed inverse genetic correlations of grip strength with cardiometabolic traits, and positive correlation with parents’ age of death and education; and showed that grip strength was causally related to fitness, physical activity and other indicators of frailty, including cognitive performance scores.

    Conclusion: In our study of over 330,000 individuals from the general population, the genetic findings for hand grip strength suggest an important role of the central nervous system in strength performance. Further, our results indicate that maintaining good muscular strength is important for physical and cognitive performance and healthy aging.

  215. ⁠, Aurélien Macé, Marcus A. Tuke, Patrick Deelen, Kati Kristiansson, Hannele Mattsson, Margit Nõukas, Yadav Sapkota, Ursula Schick, Eleonora Porcu, Sina Rüeger, Aaron F. McDaid, David Porteous, Thomas W. Winkler, Erika Salvi, Nick Shrine, Xueping Liu, Wei Q. Ang, Weihua Zhang, Mary F. Feitosa, Cristina Venturini, Peter J. van der Most, Anders Rosengren, Andrew R. Wood, Robin N. Beaumont, Samuel E. Jones, Katherine S. Ruth, Hanieh Yaghootkar, Jessica Tyrrell, Aki S. Havulinna, Harmen Boers, Reedik Mägi, Jennifer Kriebel, Martina Müller-Nurasyid, Markus Perola, Markku Nieminen, Marja-Liisa Lokki, Mika Kähönen, Jorma S. Viikari, Frank Geller, Jari Lahti, Aarno Palotie, Päivikki Koponen, Annamari Lundqvist, Harri Rissanen, Erwin P. Bottinger, Saima Afaq, Mary K. Wojczynski, Petra Lenzini, Ilja M. Nolte, Thomas Sparsø, Nicole Schupf, Kaare Christensen, Thomas T. Perls, Anne B. Newman, Thomas Werge, Harold Snieder, Timothy D. Spector, John C. Chambers, Seppo Koskinen, Mads Melbye, Olli T. Raitakari, Terho Lehtimäki, Martin D. Tobin, Louise V. Wain, Juha Sinisalo, Annette Peters, Thomas Meitinger, Nicholas G. Martin, Naomi R. Wray, Grant W. Montgomery, Sarah E. Medland, Morris A. Swertz, Erkki Vartiainen, Katja Borodulin, Satu Männistö, Anna Murray, Murielle Bochud, Sébastien Jacquemont, Fernando Rivadeneira, Thomas F. Hansen, Albertine J. Oldehinkel, Massimo Mangino, Michael A. Province, Panos Deloukas, Jaspal S. Kooner, Rachel M. Freathy, Craig Pennell, Bjarke Feenstra, David P. Strachan, Guillaume Lettre, Joel Hirschhorn, Daniele Cusi, Iris M. Heid, Caroline Hayward, Katrin Männik, Jacques S. Beckmann, Ruth J. F. Loos, Dale R. Nyholt, Andres Metspalu, Johan G. Eriksson, Michael N. Weedon, Veikko Salomaa, Lude Franke, Alexandre Reymond, Timothy M. Frayling, Zoltán Kutalik (2017-09-29):

    There are few examples of robust associations between rare copy number variants (CNVs) and complex continuous human traits. Here we present a large-scale CNV association meta-analysis on anthropometric traits in up to 191,161 adult samples from 26 cohorts. The study reveals five CNV associations at 1q21.1, 3q29, 7q11.23, 11p14.2, and 18q21.32 and confirms two known loci at 16p11.2 and 22q11.21, implicating at least one anthropometric trait. The discovered CNVs are recurrent and rare (0.01–0.2%), with large effects on height (>2.4 cm), weight (>5 kg), and body mass index (BMI) (>3.5 kg/​​​​m2). Burden analysis shows a 0.41 cm decrease in height, a 0.003 increase in waist-to-hip ratio and increase in BMI by 0.14 kg/​​​​m2 for each Mb of total deletion burden (p = 2.5 × 10−10, 6.0 × 10−5, and 2.9 × 10−3). Our study provides evidence that the same genes (eg., MC4R, FIBIN, and FMO5) harbor both common and rare variants affecting body size and that anthropometric traits share genetic loci with developmental and psychiatric disorders.

  216. ⁠, Amy E. Taylor, Hannah J. Jones, Hannah Sallis, Jack Euesden, Evie Stergiakouli, Neil M. Davies, Stanley Zammit, Debbie A. Lawlor, Marcus R. Munafò, George Davey Smith, Kate Tilling (2017-10-20):

    Background: It is often assumed that selection (including participation and dropout) does not represent an important source of bias in genetic studies. However, there is little evidence to date on the effect of genetic factors on participation.

    Methods: Using data on mothers (n = 7,486) and children (n = 7,508) from the Avon Longitudinal Study of Parents and Children, we 1) examined the association of polygenic risk scores for a range of socio-demographic, lifestyle characteristics and health conditions related to continued participation, 2) investigated whether associations of polygenic scores with body mass index (BMI; derived from self-reported weight and height) and self-reported smoking differed in the largest sample with genetic data and a sub-sample who participated in a recent follow-up and 3) determined the proportion of variation in participation explained by common genetic variants using genome-wide data.

    Results: We found evidence that polygenic scores for higher education, agreeableness and openness were associated with higher participation and polygenic scores for smoking initiation, higher BMI, neuroticism, schizophrenia, and depression were associated with lower participation. Associations between the polygenic score for education and self-reported smoking differed between the largest sample with genetic data (OR for ever smoking per SD increase in polygenic score:0.85, 95% CI:0.81,0.89) and sub-sample (OR:0.95, 95% CI:0.88,1.02). In genome-wide analysis, single nucleotide polymorphism based heritability explained 17–31% of variability in participation.

    Conclusion: Genetic association studies, including Mendelian randomization, can be biased by selection, including loss to follow-up. Genetic risk for dropout should be considered in all analyses of studies with selective participation.

  217. 2017-segal-twinmythconceptions-ch12.pdf

  218. 2013-segal-1.pdf

  219. 2013-segal-2.pdf

  220. 2017-hilker.pdf: ⁠, Rikke Hilker, Dorte Helenius, Birgitte Fagerlund, Axel Skytthe, Kaare Christensen, Thomas M. Werge, Merete Nordentoft, Birte Glenthøj (2017-08-30; genetics  /​ ​​ ​heritable):

    Background: Twin studies have provided evidence that both genetic and environmental factors contribute to schizophrenia (SZ) risk. Heritability estimates of SZ in twin samples have varied methodologically. This study provides updated heritability estimates based on nationwide twin data and an improved statistical methodology.

    Methods: Combining 2 nationwide registers, the Danish Twin Register and the Danish Psychiatric Research Register, we identified a sample of twins born between 1951 and 2000 (n = 31,524 twin pairs). Twins were followed until June 1, 2011. Liability threshold models adjusting for with inverse probability weighting were used to estimate proband-wise concordance rates and heritability of the diagnoses of SZ and SZ spectrum disorders.

    Results: The proband-wise concordance rate of SZ is 33% in monozygotic twins and 7% in dizygotic twins. We estimated the heritability of SZ to be 79%. When expanding illness outcome to include SZ spectrum disorders, the heritability estimate was almost similar (73%).

    Conclusions: The key strength of this study is the application of a novel statistical method accounting for censoring in the follow-up period to a nationwide twin sample. The estimated 79% heritability of SZ is congruent with previous reports and indicates a substantial genetic risk. The high genetic risk also applies to a broader phenotype of SZ spectrum disorders. The low concordance rate of 33% in monozygotic twins demonstrates that illness vulnerability is not solely indicated by genetic factors.

    [Keywords: censoring, concordance, heritability, register, schizophrenia, twin study]

  221. 1987-plomin.pdf: “Unconfounding genetic and nonshared environmental effects”⁠, Arthur R. Jensen

  222. ⁠, Amelie Baud, Francesco Paolo Casale, Jerome Nicod, Oliver Stegle (2018-04-17):

    Social genetic effects (SGE, also called indirect genetic effects) are associations between genotypes of one individual and phenotype of another. SGE arise when two individuals interact and heritable traits of one influence the phenotype of the other. Recent studies have shown that SGE substantially contribute to phenotypic variation in humans and laboratory mice, which suggests that SGE, like direct genetic effects (DGE, effects of an individual’s genes on their own phenotype), are amenable to mapping. Using 170 phenotypes including behavioural, physiological and morphological traits measured in outbred laboratory mice, we empirically explored the potential and challenges of genome-wide association study of SGE (sgeGWAS) as a tool to discover novel mechanisms of social effects between unrelated individuals. For each phenotype we performed sgeGWAS, identifying 21 genome-wide statistically-significant SGE associations for 17 phenotypes, and dgeGWAS for comparison. Our results provide three main insights: first, SGE and DGE arise from partially different loci and/​​​​or loci with different effect sizes, which implies that the widely-studied mechanism of phenotypic “contagion” is not sufficient to explain all social effects. Secondly, several DGE associations but no SGE associations had large effects, suggesting sgeGWAS is unlikely to uncover “low hanging fruits”. Finally, a similar number of variants likely contribute to SGE and DGE. The analytical framework we developed in this study and the insights we gained from our analyses will inform the design, implementation and interpretation of sgeGWAS in this and other populations and species.

  223. ⁠, Augustine Kong, Gudmar Thorleifsson, Michael L. Frigge, Bjarni J. Vilhjálmsson, Alexander I. Young, Thorgeir E. Thorgeirsson, Stefania Benonisdottir, Asmundur Oddsson, Bjarni V. Halldórsson, Gísli Masson, Daniel F. Gudbjartsson, Agnar Helgason, Gyda Bjornsdottir, Unnur Thorsteinsdottir, Kari Stefansson (2017-11-14):

    Sequence variants in the parental genomes that are not transmitted to a child/​​​​proband are often ignored in genetic studies. Here we show that non-transmitted alleles can impact a child through their effects on the parents and other relatives, a phenomenon we call genetic nurture. Using results from a meta-analysis of educational attainment, the polygenic score computed for the non-transmitted alleles of 21,637 probands with at least one parent genotyped has an estimated effect on the educational attainment of the proband that is 29.9% (P = 1.6×10−14) of that of the transmitted polygenic score. Genetic nurturing effects of this polygenic score extend to other traits. Paternal and maternal polygenic scores have similar effects on educational attainment, but mothers contribute more than fathers to nutrition/​​​​heath related traits.

    One Sentence Summary

    Nurture has a genetic component, i.e. alleles in the parents affect the parents’ phenotypes and through that influence the outcomes of the child.

  224. ⁠, Alexander I. Young, Michael L. Frigge, Daniel F. Gudbjartsson, Gudmar Thorleifsson, Gyda Bjornsdottir, Patrick Sulem, Gisli Masson, Unnur Thorsteinsdottir, Kari Stefansson, Augustine Kong (2017-11-14):

    Heritability measures the proportion of trait variation that is due to genetic inheritance. Measurement of heritability is of importance to the nature-versus-nurture debate. However, existing estimates of heritability could be biased by environmental effects. Here we introduce relatedness disequilibrium regression (RDR), a novel method for estimating heritability. RDR removes environmental bias by exploiting variation in relatedness due to random segregation. We use a sample of 54,888 Icelanders with both parents genotyped to estimate the heritability of 14 traits, including height (55.4%, S.E. 4.4%) and educational attainment (17.0%, S.E. 9.4%). Our results suggest that some other estimates of heritability could be inflated by environmental effects.

  225. ⁠, Jakob Grove, Stephan Ripke, Thomas D. Als, Manuel Mattheisen, Raymond Walters, Hyejung Won, Jonatan Pallesen, Esben Agerbo, Ole A. Andreassen, Richard Anney, Rich Belliveau, Francesco Bettella, Joseph D. Buxbaum, Jonas Bybjerg-Grauholm, Marie Bækved-Hansen, Felecia Cerrato, Kimberly Chambert, Jane H. Christensen, Claire Churchhouse, Karin Dellenvall, Ditte Demontis, Silvia De Rubeis, Bernie Devlin, Srdjan Djurovic, Ashle Dumont, Jacqueline Goldstein, Christine S. Hansen, Mads Engel Hauberg, Mads V. Hollegaard, Sigrun Hope, Daniel P. Howrigan, Hailiang Huang, Christina Hultman, Lambertus Klei, Julian Maller, Joanna Martin, Alicia R. Martin, Jennifer Moran, Mette Nyegaard, Terje Nærland, Duncan S. Palmer, Aarno Palotie, Carsten B. Pedersen, Marianne G. Pedersen, Timothy Poterba, Jesper B. Poulsen, Beate St Pourcain, Per Qvist, Karola Rehnström, Avi Reichenberg, Jennifer Reichert, Elise B. Robinson, Kathryn Roeder, Panos Roussos, Evald Saemundsen, Sven Sandin, F. Kyle Satterstrom, George D. Smith, Hreinn Stefansson, Kari Stefansson, Stacy Steinberg, Christine Stevens, Patrick F. Sullivan, Patrick Turley, G. Bragi Walters, Xinyi Xu, Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium, BUPGEN, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, 23andMe Research Team, Daniel Geschwind, Merete Nordentoft, David M. Hougaard, Thomas Werge, Ole Mors, Preben Bo Mortensen, Benjamin M. Neale, Mark J. Daly, Anders D. Børglum (2017-11-25):

    Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide statistically-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels. Dissecting the polygenic architecture we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes, in contrast to what is typically seen in other complex disorders. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD, just as it has been in a broad range of important psychiatric and diverse medical phenotypes.

  226. https://www.nature.com/articles/s41467-017-01490-8

  227. 2018-hysi.pdf: “Genome-wide association meta-analysis of individuals of European ancestry identifies new loci explaining a substantial fraction of hair color variation and heritability”⁠, Pirro G. Hysi, Ana M. Valdes, Fan Liu, Nicholas A. Furlotte, David M. Evans, Veronique Bataille, Alessia Visconti, Gibran Hemani, George McMahon, Susan M. Ring, George Davey Smith, David L. Duffy, Gu Zhu, Scott D. Gordon, Sarah E. Medland, Bochao D. Lin, Gonneke Willemsen, Jouke Hottenga, Dragana Vuckovic, Giorgia Girotto, Ilaria Gandin, Cinzia Sala, Maria Pina Concas, Marco Brumat, Paolo Gasparini, Daniela Toniolo, Massimiliano Cocca, Antonietta Robino, Seyhan Yazar, Alex W. Hewitt, Yan Chen, Changqing Zeng, Andre G. Uitterlinden, M. Arfan Ikram, Merel A. Hamer, Cornelia M. Duijn, Tamar Nijsten, David A. Mackey, Mario Falchi, Dorret I. Boomsma, Nicholas G. Martin, David A. Hinds, Manfred Kayser, Timothy D. Spector

  228. 2017-figlio.pdf

  229. ⁠, Ryan D. Hernandez, Lawrence H. Uricchio, Kevin Hartman, Chun Ye, Andrew Dahl, Noah Zaitlen (2017-11-14):

    The vast majority of human mutations have minor allele frequencies (MAF) under 1%, with the plurality observed only once (i.e., “singletons”). While Mendelian diseases are predominantly caused by rare alleles, their role in complex phenotypes remains largely unknown. We develop and rigorously validate an approach to jointly estimate the contribution of alleles with different frequencies, including singletons, to phenotypic variation. We apply our approach to transcriptional regulation, an intermediate between genetic variation and complex disease. Using whole genome DNA and RNA sequencing data from 360 European individuals, we find that singletons alone contribute ~23% of all cis-heritability across genes (dwarfing the contributions of other frequencies). We then integrate external estimates of global MAF from worldwide samples to improve our inference, and find that average cis-heritability is 15.3%. Strikingly, 50.9% of cis-heritability is contributed by globally rare variants (MAF<0.1%), implicating purifying selection as a pervasive force shaping the regulatory architecture of most human genes.

    One Sentence Summary

    The vast majority of variants so far discovered in humans are rare, and together they have a substantial impact on gene regulation.

  230. https://www.sciencedirect.com/science/article/pii/S0040580917300886

  231. ⁠, Zachary D. Stephens, Skylar Y. Lee, Faraz Faghri, Roy H. Campbell, Chengxiang Zhai, Miles J. Efron, Ravishankar Iyer, Michael C. Schatz, Saurabh Sinha, Gene E. Robinson ():

    Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a “four-headed beast”—it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the “genomical” challenges of the next decade.

    This perspective considers the growth of genomics over the next ten years and assesses the computational needs that we will face relative to other “Big Data” activities such as astronomy, YouTube, and Twitter.

  232. https://www.theatlantic.com/science/archive/2017/11/what-happens-when-you-put-500000-peoples-dna-online/543747/

  233. https://www.cnbc.com/2017/11/21/amazon-has-suddenly-become-a-big-marketplace-for-selling-genetic-tests.html

  234. https://www.wired.com/story/ancestrys-genetic-testing-kits-are-heading-for-your-stocking-this-year/

  235. https://techcrunch.com/2018/01/22/myheritage-says-it-sold-over-1m-dna-kits-last-year-annual-revenue-grew-to-133m/

  236. https://www.fastcompany.com/40438376/after-a-comeback-23andme-faces-its-next-test

  237. https://www.technologyreview.com/s/610233/2017-was-the-year-consumer-dna-testing-blew-up/

  238. ⁠, Alon Keinan, Alexandre Lussier (2018-04-12):

    Genealogies are likely the first, centuries-old “big data”, with their construction as old as human civilization itself. Globalization, and the identity crisis that ensued, turned many to online services, building family trees and investigating connections to historical records and other family trees [1]. An explosion has been underway since the beginning of the century in the number and usage of websites offering such genealogical services. About 130 million users combine to have created almost four billion profiles for family members across the three most popular websites of genealogy enthusiasts, Ancestry.com, MyHeritage, and Geni. More recent years have witnessed a similar rapid increase of genetic-based services that address the same need to learn about familial relationships and ancestry. These vast amounts of crowdsourced—and often crowdfunded (as users often pay for these services)—data offers ample scientific research opportunities that would otherwise require expansive collection. In a paper published today in Science, Kaplanis et al. [2, 3] introduce a genealogical dataset based on processing 86 million public Geni profiles. Armed with this crowdsourced dataset, they address fundamental research questions.

  239. https://www.broadinstitute.org/news/broad-institute-sequences-its-100000th-whole-human-genome-national-dna-day

  240. http://www.aging-us.com/article/101334/text

  241. 2017-dudbridge.pdf

  242. ⁠, Wenlong Ma, Zhixu Qiu, Jie Song, Qian Cheng, Chuang Ma (2017-12-31):


    Genomic selection (GS) is a new breeding strategy by which the phenotypes of quantitative traits are usually predicted based on genome-wide markers of genotypes using conventional statistical models. However, the GS prediction models typically make strong assumptions and perform linear regression analysis, limiting their accuracies since they do not capture the complex, non-linear relationships within genotypes, and between genotypes and phenotypes.

    Results: We present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypic markers when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional marker data. We used a large GS dataset to train DeepGS and compare its performance with other methods. In terms of mean normalized discounted cumulative gain value, DeepGS achieves an increase of 27.70%~246.34% over a conventional neural network in selecting top-ranked 1% individuals with high phenotypic values for the eight tested traits. Additionally, compared with the widely used method RR-BLUP, DeepGS still yields a relative improvement ranging from 1.44% to 65.24%. Through extensive simulation experiments, we also demonstrated the effectiveness and robustness of DeepGS for the absent of outlier individuals and subsets of genotypic markers. Finally, we illustrated the complementarity of DeepGS and RR-BLUP with an ensemble learning approach for further improving prediction performance.


    DeepGS is provided as an open source R package available at https:/​​​​/​​​​github.com/​​​​cma2015/​​​​DeepGS.

  243. ⁠, Hassan S. Dashti, Samuel E. Jones, Andrew R. Wood, Jacqueline M. Lane, Vincent T. van Hees, Heming Wang, Jessica A. Rhodes, Yanwei Song, Krunal Patel, Simon G. Anderson, Robin Beaumont, David A. Bechtold, Jack Bowden, Brian E. Cade, Marta Garaulet, Simon D. Kyle, Max A. Little, Andrew S. Loudon, Annemarie I. Luik, Frank AJL Scheer, Kai Spiegelhalder, Jessica Tyrrell, Daniel J. Gottlieb, Henning Tiemeier, David W. Ray, Shaun M. Purcell, Timothy M. Frayling, Susan Redline, Deborah A. Lawlor, Martin K. Rutter, Michael N. Weedon, Richa Saxena (2018-04-19):

    Sleep is an essential homeostatically-regulated state of decreased activity and alertness conserved across animal species, and both short and long sleep duration associate with chronic disease and all-cause mortality1,2. Defining genetic contributions to sleep duration could point to regulatory mechanisms and clarify causal disease relationships. Through genome-wide association analyses in 446,118 participants of European ancestry from the UK Biobank, we discover 78 loci for self-reported sleep duration that further impact accelerometer-derived measures of sleep duration, daytime inactivity duration, sleep efficiency and number of sleep bouts in a subgroup (n = 85,499) with up to 7-day accelerometry. Associations are enriched for genes expressed in several brain regions, and for pathways including striatum and subpallium development, mechanosensory response, binding, synaptic neurotransmission, catecholamine production, synaptic plasticity, and unsaturated fatty acid metabolism. Genetic correlation analysis indicates shared biological links between sleep duration and psychiatric, cognitive, anthropometric and metabolic traits and Mendelian randomization highlights a causal link of longer sleep with schizophrenia.

  244. ⁠, Samuel E. Jones, Jacqueline M. Lane, Andrew R. Wood, Vincent T. van Hees, Jessica Tyrrell, Robin N. Beaumont, Aaron R. Jeffries, Hassan S. Dashti, Melvyn Hillsdon, Katherine S. Ruth, Marcus A. Tuke, Hanieh Yaghootkar, Seth A. Sharp, Yingjie Ji, James W. Harrison, Amy Dawes, Enda M. Byrne, Henning Tiemeier, Karla V. Allebrandt, Jack Bowden, David W. Ray, Rachel M. Freathy, Anna Murray, Diego R. Mazzotti, Philip R. Gehrman, the 23andMe Research Team, Deborah A. Lawlor, Timothy M. Frayling, Martin K. Rutter, David A. Hinds, Richa Saxena, Michael N. Weedon (2018-04-19):

    Using data from 697,828 research participants from 23andMe and UK Biobank, we identified 351 loci associated with being a morning person, a behavioural indicator of a person’s underlying circadian rhythm. These loci were validated in 85,760 individuals with activity-monitor derived measures of sleep timing: the mean sleep timing of the 5% of individuals carrying the most “morningness” alleles was 25.1 minutes (95% CI: 22.5, 27.6) earlier than the 5% carrying the fewest. The loci were enriched for genes involved in circadian rhythm and insulin pathways, and those expressed in the retina, hindbrain, hypothalamus, and pituitary (all FDRw<1%). We provide some evidence that being a morning person was causally associated with reduced risk of schizophrenia (OR: 0.89; 95% CI: 0.82, 0.96), depression (OR: 0.94; 95% CI: 0.91, 0.98) and a lower age at last childbirth in women (β: -046 years; 95% CI: -0.067, -0.025), but was not associated with BMI (β: -4.6×10−4; 95% CI: -0.044, 0.043) or type 2 diabetes (OR: 1.00; 95% CI: 0.91, 1.1). This study offers new insights into the biology of circadian rhythms and disease links in humans.

  245. ⁠, Po-Ru Loh, Gleb Kichaev, Steven Gazal, Armin P. Schoech, Alkes L. Price (2018-01-04):

    Biobank-based genome-wide association studies are enabling exciting insights in complex trait genetics, but much uncertainty remains over best practices for optimizing and computational efficiency in GWAS while controlling confounders. Here, we introduce a much faster version of our BOLT-LMM Bayesian mixed model association method— capable of running analyses of the full UK Biobank cohort in a few days on a single compute node—and show that it produces highly powered, robust test statistics when run on all 459K European samples (retaining related individuals). When used to conduct a GWAS for height in UK Biobank, BOLT-LMM achieved power equivalent to linear regression on 650K samples—a 93% increase in effective sample size versus the common practice of analyzing unrelated British samples using linear regression (UK Biobank documentation; Bycroft et al bioRxiv). Across a broader set of 23 highly heritable traits, the total number of independent GWAS loci detected increased from 5,839 to 10,759, an 84% increase. We recommend the use of BOLT-LMM (retaining related individuals) for biobank-scale analyses, and we have publicly released BOLT-LMM summary association statistics for the 23 traits analyzed as a resource for all researchers.

  246. 2018-kaplanis.pdf: ⁠, Joanna Kaplanis, Assaf Gordon, Tal Shor, Omer Weissbrod, Dan Geiger, Mary Wahl, Michael Gershovits, Barak Markus, Mona Sheikh, Melissa Gymrek, Gaurav Bhatia, Daniel G. MacArthur, Alkes L. Price, Yaniv Erlich (2018-03-01; genetics  /​ ​​ ​heritable):

    Family trees have vast applications in multiple fields from genetics to anthropology and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. Here, we collected 86 million profiles from publicly-available online data shared by genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of longevity by inspecting millions of relative pairs and to provide insights into the geographical dispersion of families. We also report a simple digital procedure to overlay other datasets with our resource in order to empower studies with population-scale genealogical data.

  247. #keinan-lussier-2018

  248. ⁠, B. M. L. Baselmans, M. Bartels (2018-03-15):

    Whether hedonism or eudaimonism are two distinguishable forms of well-being is a topic of ongoing debate. To shed light on the relation between the two, large-scale available molecular genetic data were leveraged to gain more insight into the genetic architecture of the overlap between hedonic and eudaimonic well-being. Hence, we conducted the first genome-wide association studies (GWAS) of eudaimonic well-being (N = ~108K) and linked it to a GWAS of hedonic well-being (N = ~ 222K). We identified the first two genome-wide statistically-significant independent loci for eudaimonic well-being and 6 independent loci for hedonic well-being. Joint analyses revealed a moderate phenotypic correlation (r = 0.53), but a high genetic correlation (rg = 0.78) between eudaimonic and hedonic well-being. For both traits we identified enrichment in the frontal cortex -and cingulate cortex as well as the cerebellum to be top ranked. Bi-directional Mendelian Randomization analyses using two-sample MR indicated some evidence for a causal relationship from hedonic well-being to eudaimonic well-being whereas no evidence was found for the reverse. Additionally, genetic correlations patterns with a range of positive and negative related phenotypes were largely similar for hedonic –and eudaimonic well-being. Our results reveal a large genetic overlap between hedonism and eudaimonism.

  249. 2006-nettle.pdf: ⁠, Daniel Nettle (2006-09-01; genetics  /​ ​​ ​heritable):

    A comprehensive evolutionary framework for understanding the maintenance of heritable behavioral variation in humans is yet to be developed. Some evolutionary psychologists have argued that heritable variation will not be found in important, fitness-relevant characteristics because of the winnowing effect of natural selection. This article propounds the opposite view. Heritable variation is ubiquitous in all species, and there are a number of frameworks for understanding its persistence. The author argues that each of the dimensions of human personality can be seen as the result of a trade-off between different fitness costs and benefits. As there is no unconditionally optimal value of these trade-offs, it is to be expected that genetic diversity will be retained in the population.

  250. ⁠, Lars Penke, Jaap J. A. Denissen, Geoffrey F. Miller (2007-04-27):

    Genetic influences on personality differences are ubiquitous, but their nature is not well understood. A theoretical framework might help, and can be provided by evolutionary genetics.

    We assess three evolutionary genetic mechanisms that could explain genetic variance in personality differences: selective neutrality, mutation-selection balance, and balancing selection. Based on evolutionary genetic theory and empirical results from behaviour genetics and personality psychology, we conclude that selective neutrality is largely irrelevant, that mutation-selection balance seems best at explaining genetic variance in intelligence, and that balancing selection by environmental heterogeneity seems best at explaining genetic variance in personality traits. We propose a general model of heritable personality differences that conceptualises intelligence as fitness components and personality traits as individual reaction norms of genotypes across environments, with different fitness consequences in different environmental niches. We also discuss the place of mental health in the model.

    This evolutionary genetic framework highlights the role of gene-environment interactions in the study of personality, yields new insight into the person-situation-debate and the structure of personality, and has practical implications for both quantitative and molecular genetic studies of personality.

    [Keywords: evolutionary psychology, personality differences, behaviour genetics, intelligence, personality traits, gene-environment interactions, ⁠, mutation-selection balance, mutational cross-section, epistasis, frequency-dependent selection]

  251. ⁠, Lars Penke, Markus Jokela (2016-02):

    • Evolutionary forces that maintain genetic variance in traits can be inferred from their genetic architecture and fitness correlates.
    • A substantial amount of new data on the genomics and reproductive success associated with personality traits and intelligence has recently become available.
    • Intelligence differences seem to have been selected for robustness against mutations.
    • Human tendencies to select, create and adapt to environments might support the maintenance of personality traits through balancing selection.

    Like all human individual differences, personality traits and intelligence are substantially heritable. From an evolutionary perspective, this poses the question what evolutionary forces maintain their genetic variation. Information about the genetic architecture and associations with evolutionary fitness permit inferences about these evolutionary forces. As our understanding of the genomics of personality and its associations with reproductive success have grown considerably in recent years, it is time to revisit this question. While mutations clearly affect the very low end of the intelligence continuum, individual differences in the normal intelligence range seem to be surprisingly robust against mutations, suggesting that they might have been canalized to withstand such perturbations. Most personality traits, by contrast, seem to be neither neutral to selection nor under consistent directional or stabilizing selection. Instead evidence is in line with balancing selection acting on personality traits, probably supported by human tendencies to seek out, construct and adapt to fitting environments.

  252. 2012-lee.pdf: ⁠, James Jung-Hun Lee (2012-07-26; genetics  /​ ​​ ​heritable):

    Personality psychology aims to explain the causes and the consequences of variation in behavioural traits. Because of the observational nature of the pertinent data, this endeavour has provoked many controversies. In recent years, the computer scientist Judea Pearl has used a graphical approach to extend the innovations in causal inference developed by Ronald Fisher and Sewall Wright. Besides shedding much light on the philosophical notion of causality itself, this graphical framework now contains many powerful concepts of relevance to the controversies just mentioned. In this article, some of these concepts are applied to areas of personality research where questions of causation arise, including the analysis of observational data and the genetic sources of individual differences.

  253. 2016-smith.pdf: ⁠, D J. Smith, V. Escott-Price, G. Davies, M. E S. Bailey, L. Colodro-Conde, J. Ward, A. Vedernikov, R. Marioni, B. Cullen, D. Lyall, S. P Hagenaars, D. C M. Liewald, M. Luciano, C. R Gale, S. J Ritchie, C. Hayward, B. Nicholl, B. Bulik-Sullivan, M. Adams, B. Couvy-Duchesne, N. Graham, D. Mackay, J. Evans, B. H Smith, D. J Porteous, S. E Medland, N. G Martin, P. Holmans, A. M McIntosh, J. P Pell, I. J Deary, M. C O'Donovan (2016-01-01; genetics  /​ ​​ ​correlation):

    Neuroticism is a personality trait of fundamental importance for psychological well-being and public health. It is strongly associated with major depressive disorder (MDD) and several other psychiatric conditions. Although neuroticism is heritable, attempts to identify the alleles involved in previous studies have been limited by relatively small sample sizes. Here we report a combined meta-analysis of genome-wide association study (GWAS) of neuroticism that includes 91 370 participants from the UK Biobank cohort, 6659 participants from the Generation Scotland: Scottish Family Health Study (GS:SFHS) and 8687 participants from a QIMR (Queensland Institute of Medical Research) Berghofer Medical Research Institute (QIMR) cohort. All participants were assessed using the same neuroticism instrument, the Personality Questionnaire-Revised (EPQ-R-S) Short Form’s scale. We found a single-nucleotide polymorphism-based heritability estimate for neuroticism of ~15% (s.e. = 0.7%). Meta-analysis identified nine novel loci associated with neuroticism. The strongest evidence for association was at a locus on chromosome 8 (P = 1.5 × 10−15) spanning 4 Mb and containing at least 36 genes. Other associated loci included interesting candidate genes on chromosome 1 (GRIK3 (glutamate receptor ionotropic kainate 3)), chromosome 4 (KLHL2 (Kelch-like protein 2)), chromosome 17 (CRHR1 (corticotropin-releasing hormone receptor 1) and MAPT (microtubule-associated protein Tau)) and on chromosome 18 (CELF4 (CUGBP elav-like family member 4)). We found no evidence for genetic differences in the common allelic architecture of neuroticism by sex. By comparing our findings with those of the Psychiatric Genetics Consortia, we identified a strong genetic correlation between neuroticism and MDD and a less strong but statistically-significant genetic correlation with schizophrenia, although not with bipolar disorder. Polygenic risk scores derived from the primary UK Biobank sample captured ~1% of the variance in neuroticism in the GS:SFHS and QIMR samples, although most of the genome-wide statistically-significant alleles identified within a UK Biobank-only GWAS of neuroticism were not independently replicated within these cohorts. The identification of nine novel neuroticism-associated loci will drive forward future work on the neurobiology of neuroticism and related phenotypes.

  254. ⁠, Varun Warrier, Roberto Toro, Bhismadev Chakrabarti, Nadia Litterman, David A. Hinds, Thomas Bourgeron, Simon Baron-Cohen (2016-04-29):

    Empathy is the drive to identify the mental states of others and respond to these with an appropriate emotion. Systemizing is the drive to analyse or build lawful systems. Difficulties in empathy have been identified in different psychiatric conditions including autism and schizophrenia. In this study, we conducted genome-wide association studies of empathy and systemizing using the Empathy Quotient (EQ) (n = 46,861) and the Systemizing Quotient-Revised (SQ-R) (n = 51,564) in participants from 23andMe, Inc. We confirmed significant sex-differences in performance on both tasks, with a male advantage on the SQ-R and female advantage on the EQ. We found highly significant heritability explained by single nucleotide polymorphisms (SNPs) for both the traits (EQ: 0.11±0.014; p = 1.7 × 10-14 and SQ-R: 0.12±0.012; p = 1.2 × 10-20) and these were similar for males and females. However, genes with higher expression in the male brain appear to contribute to the male advantage for the SQ-R. Finally, we identified statistically-significant genetic correlations between high score for empathy and risk for schizophrenia (p = 2.5 × 10-5), and correlations between high score for systemizing and higher educational attainment (p = 5 × 10-4). These results shed light on the genetic contribution to individual differences in empathy and systemizing, two major cognitive functions of the human brain.

  255. ⁠, Varun Warrier, Katrina Grasby, Florina Uzefovsky, Roberto Toro, Paula Smith, Bhismadev Chakrabarti, Jyoti Khadake, Nadia Litterman, Jouke-Jan Hottenga, Gitta Lubke, Dorret I. Boomsma, Nicholas G. Martin, Peter K. Hatemi, Sarah E. Medland, David A. Hinds, Thomas Bourgeron, Simon Baron-Cohen (2016-10-19):

    We conducted a genome-wide meta-analysis of cognitive empathy using the ‘Reading the Mind in the Eyes’ Test (Eyes Test) in 88,056 Caucasian research participants (44,574 females and 43,482 males) from 23andMe Inc., and an additional 1,497 Caucasian participants (891 females and 606 males) from the Brisbane Longitudinal Twin Study (BLTS). We confirmed a female advantage on the Eyes Test (Cohen’s d = 0.21, p &;t; 0.001), and identified a locus in 3p26.1 that is associated with scores on the Eyes Test in females (rs7641347, pmeta = 1.57 × 10−8). Common single nucleotide polymorphisms (SNPs) explained 20% of the twin heritability and 5.6% (±0.76; p = 1.72 × 10−13) of the total trait variance in both sexes. Finally, we identified statistically-significant genetic correlation between the Eyes Test and measures of empathy (the Empathy Quotient), openness (NEO-Five Factor Inventory), and different measures of educational attainment and cognitive aptitude, and show that the genetic determinants of striatal volumes (caudate nucleus, putamen, and nucleus accumbens) are positively correlated with the genetic determinants of performance on the Eyes Test.

  256. https://www.cambridge.org/core/journals/twin-research-and-human-genetics/article/personality-polygenes-positive-affect-and-life-satisfaction/4DB2BE673BF122FB9A0AF2147EED80C0/core-reader

  257. ⁠, Gustavson, Daniel E. Miyake, Akira Hewitt, John K. Friedman, Naomi P (2014):

    Previous research has revealed a moderate and positive correlation between procrastination and impulsivity. However, little is known about why these two constructs are related. In the present study, we used behavior-genetics methodology to test three predictions derived from an evolutionary account that postulates that procrastination arose as a by-product of impulsivity: (a) Procrastination is heritable, (b) the two traits share considerable genetic variation, and (c) goal-management ability is an important component of this shared variation. These predictions were confirmed. First, both procrastination and impulsivity were moderately heritable (46% and 49%, respectively). Second, although the two traits were separable at the phenotypic level (r = 0.65), they were not separable at the genetic level (r genetic = 1.0). Finally, variation in goal-management ability accounted for much of this shared genetic variation. These results suggest that procrastination and impulsivity are linked primarily through genetic influences on the ability to use high-priority goals to effectively regulate actions.

  258. 2014-cronqvist.pdf

  259. https://www.nature.com/articles/ncomms10448

  260. 2015-hall.pdf: ⁠, Kathryn T. Hall, Joseph Loscalzo, Ted J. Kaptchuk (2015-05-01; nootropic):

    • Predisposition to respond to placebo treatment may be in part a stable heritable trait.
    • Candidate placebo response pathways may interact with drugs to modify outcomes in both the placebo and drug treatment arms of clinical trials.
    • Genomic analysis of randomized placebo and no-treatment controlled trials are needed to fully realize the potential of the placebome.

    Placebos are indispensable controls in randomized clinical trials (RCTs), and placebo responses statistically-significantly contribute to routine clinical outcomes. Recent neurophysiological studies reveal neurotransmitter pathways that mediate placebo effects. Evidence that genetic variations in these pathways can modify placebo effects raises the possibility of using genetic screening to identify placebo responders and thereby increase RCT efficacy and improve therapeutic care. Furthermore, the possibility of interaction between placebo and drug molecular pathways warrants consideration in RCT design. The study of genomic effects on placebo response, ‘the placebome’, is in its infancy. Here, we review evidence from placebo studies and RCTs to identify putative genes in the placebome, examine evidence for placebo-drug interactions, and discuss implications for RCTs and clinical care.

  261. ⁠, Ditte Demontis, Raymond K. Walters, Joanna Martin, Manuel Mattheisen, Thomas D. Als, Esben Agerbo, Rich Belliveau, Jonas Bybjerg-Grauholm, Marie Bækvad-Hansen, Felecia Cerrato, Kimberly Chambert, Claire Churchhouse, Ashley Dumont, Nicholas Eriksson, Michael Gandal, Jacqueline Goldstein, Jakob Grove, Christine S. Hansen, Mads E. Hauberg, Mads V. Hollegaard, Daniel P. Howrigan, Hailiang Huang, Julian Maller, Alicia R. Martin, Jennifer Moran, Jonatan Pallesen, Duncan S. Palmer, Carsten B. Pedersen, Marianne G. Pedersen, Timothy Poterba, Jesper B. Poulsen, Stephan Ripke, Elise B. Robinson, Kyle F. Satterstrom, Christine Stevens, Patrick Turley, Hyejung Won, ADHD Working Group of the Psychiatric Genomics Consortium (PGC), Early Lifecourse &amp, Genetic Epidemiology (EAGLE) Consortium, 23andMe Research Team, Ole A. Andreassen, Christie Burton, Dorret Boomsma, Bru Cormand, Søren Dalsgaard, Barbara Franke, Joel Gelernter, Daniel Geschwind, Hakon Hakonarson, Jan Haavik, Henry Kranzler, Jonna Kuntsi, Kate Langley, Klaus-Peter Lesch, Christel Middeldorp, Andreas Reif, Luis A. Rohde, Panos Roussos, Russell Schachar, Pamela Sklar, Edmund Sonuga-Barke, Patrick F. Sullivan, Anita Thapar, Joyce Tung, Irwin Waldman, Merete Nordentoft, David M. Hougaard, Thomas Werge, Ole Mors, Preben B. Mortensen, Mark J. Daly, Stephen V. Faraone, Anders D. Børglum, Benjamin M. Neale (2017-06-03):

    Attention-Deficit/​​​​Hyperactivity Disorder (ADHD) is a highly heritable childhood behavioral disorder affecting 5% of school-age children and 2.5% of adults. Common genetic variants contribute substantially to ADHD susceptibility, but no individual variants have been robustly associated with ADHD. We report a genome-wide association meta-analysis of 20,183 ADHD cases and 35,191 controls that identifies variants surpassing genome-wide statistical-significance in 12 independent loci, revealing new and important information on the underlying biology of ADHD. Associations are enriched in evolutionarily constrained genomic regions and loss-of-function intolerant genes, as well as around brain-expressed regulatory marks. These findings, based on clinical interviews and/​​​​or medical records are supported by additional analyses of a self-reported ADHD sample and a study of quantitative measures of ADHD symptoms in the population. Meta-analyzing these data with our primary scan yielded a total of 16 genome-wide statistically-significant loci. The results support the hypothesis that clinical diagnosis of ADHD is an extreme expression of one or more continuous heritable traits.

  262. http://journals.sagepub.com/doi/full/10.1177/0956797617707270

  263. ⁠, Michelle Luciano, Saskia P. Hagenaars, Gail Davies, W. David Hill, Toni-Kim Clarke, Masoud Shirali, Riccardo E. Marioni, Sarah E. Harris, David C. Liewald, Chloe Fawns-Ritchie, Mark J. Adams, David M. Howard, Cathryn M. Lewis, Catharine R. Gale, Andrew M. McIntosh, Ian J. Deary (2017-07-28):

    Neuroticism is a stable personality trait 1; twin studies report heritability between 30% and 50% 2, and SNP-based heritability is about 15% 3. Higher levels of neuroticism are associated with poorer mental and physical health 4,5, and the economic burden of neuroticism for societies is high 6. To date, genome-wide association (GWA) studies of neuroticism have identified up to 11 genetic loci 3,7. Here we report 116 significant independent genetic loci from a GWA of neuroticism in 329,821 UK Biobank participants, with replication available in a GWA meta-analysis of neuroticism in 122,867 individuals. Genetic signals for neuroticism were enriched in neuronal genesis and differentiation pathways, and substantial genetic correlations were found between neuroticism and depressive symptoms (rg = 0.82, SE=.03), major depressive disorder (rg = 0.69, SE=.07) and subjective wellbeing (rg = -.68, SE=.03) alongside other mental health traits. These discoveries significantly advance our understanding of neuroticism and its association with major depressive disorder.

  264. ⁠, Naomi R. Wray, Stephan Ripke, Manuel Mattheisen, Maciej Trzaskowski, Enda M. Byrne, Abdel Abdellaoui, Mark J. Adams, Esben Agerbo, Tracy M. Air, Till F. M Andlauer, Silviu-Alin Bacanu, Marie Bækvad-Hansen, Aartjan T. F Beekman, Tim B. Bigdeli, Elisabeth B. Binder, Douglas H. R Blackwood, Julien Bryois, Henriette N. Buttenschøn, Jonas Bybjerg-Grauholm, Na Cai, Enrique Castelao, Jane Hvarregaard Christensen, Toni-Kim Clarke, Jonathan R. I Coleman, Lucía Colodro-Conde, Baptiste Couvy-Duchesne, Nick Craddock, Gregory E. Crawford, Cheynna A. Crowley, Hassan S. Dashti, Gail Davies, Ian J. Deary, Franziska Degenhardt, Eske M. Derks, Nese Direk, Conor V. Dolan, Erin C. Dunn, Thalia C. Eley, Nicholas Eriksson, Valentina Escott-Price, Farnush Farhadi Hassan Kiadeh, Hilary K. Finucane, Andreas J. Forstner, Josef Frank, Héléna A. Gaspar, Michael Gill, Paola Giusti-Rorínguez, Fernando S. Goes, Scott D. Gordon, Jakob Grove, Lynsey S. Hall, Christine Søholm Hansen, Thomas F. Hansen, Stefan Herms, Ian B. Hickie, Per Hoffmann, Georg Homuth, Carsten Horn, Jouke-Jan Hottenga, David M. Hougaard, Ming Hu, Craig L. Hyde, Marcus Ising, Rick Jansen, Fulai Jin, Eric Jorgenson, James A. Knowles, Isaac S. Kohane, Julia Kraft, Warren W. Kretzschmar, Jesper Krogh, Zoltan Kutalik, Jacqueline M. Lane, Yihan Li, Yun Li, Penelope A. Lind, Xiaoxiao Liu, Leina Lu, Donald J. MacIntyre, Dean F. MacKinnon, Robert M. Maier, Wolfgang Maier, Jonathan Marchini, Hamdi Mbarek, Patrick McGrath, Peter McGuffin, Sarah E. Medland, Divya Mehta, Christel M. Middeldorp, Evelin Mihailov, Yuri Milaneschi, Lili Milani, Francis M. Mondimore, Grant W. Montgomery, Sara Mostafavi, Niamh Mullins, Matthias Nauck, Bernard Ng, Michel G. Nivard, Dale R. Nyholt, Paul F. O’Reilly, Hogni Oskarsson, Michael J. Owen, Jodie N. Painter, Carsten Bøcker, Marianne Giørtz Pedersen, Roseann E. Peterson, Erik Pettersson, Wouter J. Peyrot, Giorgio Pistis, Danielle Posthuma, Shaun M. Purcell, Jorge A. Quiroz, Per Qvist, John P. Rice, Brien P. Riley, Margarita Rivera, Saira Saeed Mirza, Richa Saxena, Robert Schoevers, Eva C. Schulte, Ling Shen, Jianxin Shi, Stanley I. Shyn, Engilbert Sigurdsson, Grant C. B Sinnamon, Johannes H. Smit, Daniel J. Smith, Hreinn Stefansson, Stacy Steinberg, Craig A. Stockmeier, Fabian Streit, Jana Strohmaier, Katherine E. Tansey, Henning Teismann, Alexander Teumer, Wesley Thompson, Pippa a Thomson, Thorgeir E. Thorgeirsson, Chao Tian, Matthew Traylor, Jens Treutlein, Vassily Trubetskoy, André G. Uitterlinden, Daniel Umbricht, Sandra Van der Auwera, Albert M. van Hemert, Alexander Viktorin, Peter M. Visscher, Yunpeng Wang, Bradley T. Webb, Shantel Marie Weinsheimer, Jürgen Wellmann, Gonneke Willemsen, Stephanie H. Witt, Yang Wu, Hualin S. Xi, Jian Yang, Futao Zhang, eQTLGen Consortium, 23andMe Research Team, Volker Arolt, Bernhard T. Baune, Klaus Berger, Dorret I. Boomsma, Sven Cichon, udo Dannlowski, EJC de Geus, J. Raymond DePaulo, Enrico Domenici, Katharina Domschke, Tönu Esko, Hans J. Grabe, Steven P. Hamilton, Caroline Hayward, Andrew C. Heath, David A. Hinds, Kenneth S. Kendler, Stefan Kloiber, Glyn Lewis, Qingqin S. Li, Susanne Lucae, Pamela AF Madden, Patrik K. Magnusson, Nicholas G. Martin, Andrew M. McIntosh, Andres Metspalu, Ole Mors, Preben Bo Mortensen, Bertram Müller-Myhsok, Merete Nordentoft, Markus M. Nöthen, Michael C. O’Donovan, Sara A. Paciga, Nancy L. Pedersen, Brenda WJH Penninx, Roy H. Perlis, David J. Porteous, James B. Potash, Martin Preisig, Marcella Rietschel, Catherine Schaefer, Thomas G. Schulze, Jordan W. Smoller, Kari Stefansson, Henning Tiemeier, Rudolf Uher, Henry Völzke, Myrna M. Weissman, Thomas Werge, Ashley R. Winslow, Cathryn M. Lewis, Douglas F. Levinson, Gerome Breen, Anders D. Børglum, Patrick F. Sullivan, for the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (2017-07-24):

    Major depressive disorder (MDD) is a notably complex illness with a lifetime prevalence of 14%.1 It is often chronic or recurrent and is thus accompanied by considerable morbidity, excess mortality, substantial costs, and heightened risk of suicide.2-7 MDD is a major cause of disability worldwide.8 We conducted a genome-wide association (GWA) meta-analysis in 130,664 MDD cases and 330,470 controls, and identified 44 independent loci that met criteria for statistical-significance. We present extensive analyses of these results which provide new insights into the nature of MDD. The genetic findings were associated with clinical features of MDD, and implicated prefrontal and anterior cingulate cortex in the pathophysiology of MDD (regions exhibiting anatomical differences between MDD cases and controls). Genes that are targets of antidepressant medications were strongly enriched for MDD association signals (p = 8.5×10−10), suggesting the relevance of these findings for improved pharmacotherapy of MDD. Sets of genes involved in gene splicing and in creating isoforms were also enriched for smaller MDD GWA p-values, and these gene sets have also been implicated in schizophrenia and autism. Genetic risk for MDD was correlated with that for many adult and childhood onset psychiatric disorders. Our analyses suggested important relations of genetic risk for MDD with educational attainment, body mass, and schizophrenia: the genetic basis of lower educational attainment and higher body mass were putatively causal for MDD whereas MDD and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for MDD, and a continuous measure of risk underlies the observed clinical phenotype. MDD is not a distinct entity that neatly demarcates normalcy from pathology but rather a useful clinical construct associated with a range of adverse outcomes and the end result of a complex process of intertwined genetic and environmental effects. These findings help refine and define the fundamental basis of MDD.

  265. ⁠, Philip R. Jansen, Kyoko Watanabe, Sven Stringer, Nathan Skene, Julien Bryois, Anke R. Hammerschlag, Christiaan A. de Leeuw, Jeroen Benjamins, Ana B. Muñoz-Manchado, Mats Nagel, Jeanne E. Savage, Henning Tiemeier, Tonya White, Joyce Y. Tung, David A. Hinds, Vladimir Vacic, Patrick F. Sullivan, Sophie van der Sluis, Tinca J. C. Polderman, August B. Smit, Jens Hjerling-Leffler, Eus J. W. Van Someren, Danielle Posthuma (2018-01-30):

    Insomnia is the second-most prevalent mental disorder, with no sufficient treatment available. Despite a substantial role of genetic factors, only a handful of genes have been implicated and insight into the associated neurobiological pathways remains limited. Here, we use an unprecedented large genetic association sample (n = 1,331,010) to allow detection of a substantial number of genetic variants and gain insight into biological functions, cell types and tissues involved in insomnia. We identify 202 genome-wide statistically-significant loci implicating 956 genes through positional, eQTL and chromatin interaction mapping. We show involvement of the axonal part of neurons, of specific cortical and subcortical tissues, and of two specific cell-types in insomnia: striatal medium spiny neurons and hypothalamic neurons. These cell-types have been implicated previously in the regulation of reward processing, sleep and arousal in animal studies, but have never been genetically linked to insomnia in humans. We found weak genetic correlations with other sleep-related traits, but strong genetic correlations with psychiatric and metabolic traits. Mendelian randomization identified causal effects of insomnia on specific psychiatric and metabolic traits. Our findings reveal key brain areas and cells implicated in the neurobiology of insomnia and its related disorders, and provide novel targets for treatment.

  266. ⁠, Douglas M. Ruderfer, Stephan Ripke, Andrew McQuillin, James Boocock, Eli A. Stahl, Jennifer M. Whitehead Pavlides, Niamh Mullins, Alexander W. Charney, Anil P. S Ori, Loes M. Olde Loohuis, Enrico Domenici, Arianna Di Florio, Sergi Papiol, Janos L. Kalman, Rolf Adolfsson, Ingrid Agartz, Esben Agerbo, Huda Akil, Diego Albani, Margot Albus, Martin Alda, Madeline Alexander, Judith Allardyce, Ney Alliey-Rodriguez, Thomas D. Als, Farooq Amin, Adebayo Anjorin, Maria J. Arranz, Swapnil Awasthi, Silviu A. Bacanu, Judith A. Badner, Marie Baekvad-Hansen, Steven Bakker, Gavin Band, Jack D. Barchas, Ines Barroso, Nicholas Bass, Michael Bauer, Bernhard T. Baune, Martin Begemann, Celine Bellenguez, Richard A. Belliveau, Frank Bellivier, Stephan Bender, Judit Bene, Sarah E. Bergen, Wade H. Berrettini, Elizabeth Bevilacqua, Joanna M. Biernacka, Tim B. Bigdeli, Donald W. Black, Hannah Blackburn, Jenefer M. Blackwell, Douglas HR Blackwood, Carsten Bocker Pedersen, Michael Boehnke, Marco Boks, Anders D. Borglum, Elvira Bramon, Gerome Breen, Matthew A. Brown, Richard Bruggeman, Nancy G. Buccola, Randy L. Buckner, Monika Budde, Brendan Bulik-Sullivan, Suzannah J. Bumpstead, William Bunney, Margit Burmeister, Joseph D. Buxbaum, Jonas Bybjerg-Grauholm, William Byerley, Wiepke Cahn, Guiqing Cai, Murray J. Cairns, Dominique Campion, Rita M. Cantor, Vaughan J. Carr, Noa Carrera, Juan P. Casas, Miquel Casas, Stanley V. Catts, Pablo Cervantes, Kimberley D. Chambert, Raymond CK Chan, Eric YH Chen, Ronald YL Chen, Wei Cheng, Eric FC Cheung, Siow Ann Chong, Toni-Kim Clarke, C. Robert Cloninger, David Cohen, Nadine Cohen, Jonathan R. I Coleman, David A. Collier, Paul Cormican, William Coryell, Nicholas Craddock, David W. Craig, Benedicto Crespo-Facorro, James J. Crowley, Cristiana Cruceanu, David Curtis, Piotr M. Czerski, Anders M. Dale, Mark J. Daly, Udo Dannlowski, Ariel Darvasi, Michael Davidson, Kenneth L. Davis, Christiaan A. de Leeuw, Franziska Degenhardt, Jurgen Del Favero, Lynn E. DeLisi, Panos Deloukas, Ditte Demontis, J. Raymond DePaulo, Marta di Forti, Dimitris Dikeos, Timothy Dinan, Srdjan Djurovic, Amanda L. Dobbyn, Peter Donnelly, Gary Donohoe, Elodie Drapeau, Serge Dronov, Jubao Duan, Frank Dudbridge, Audrey Duncanson, Howard Edenberg, Sarah Edkins, Hannelore Ehrenreich, Peter Eichhammer, Torbjorn Elvsashagen, Johan Eriksson, Valentina Escott-Price, Tonu Esko, Laurent Essioux, Bruno Etain, Chun Chieh Fan, Kai-How Farh, Martilias S. Farrell, Matthew Flickinger, Tatiana M. Foroud, Liz Forty, Josef Frank, Lude Franke, Christine Fraser, Robert Freedman, Colin Freeman, Nelson B. Freimer, Joseph I. Friedman, Menachem Fromer, Mark A. Frye, Janice M. Fullerton, Katrin Gade, Julie Garnham, Helena A. Gaspar, Pablo V. Gejman, Giulio Genovese, Lyudmila Georgieva, Claudia Giambartolomei, Eleni Giannoulatou, Ina Giegling, Michael Gill, Matthew Gillman, Marianne Giortz Pedersen, Paola Giusti-Rodriguez, Stephanie Godard, Fernando Goes, Jacqueline I. Goldstein, Srihari Gopal, Scott D. Gordon, Katherine Gordon-Smith, Jacob Gratten, Emma Gray, Elaine K. Green, Melissa J. Green, Tiffany A. Greenwood, Maria Grigoroiu-Serbanescu, Jakob Grove, Weihua Guan, Hugh Gurling, Jose Guzman Parra, Rhian Gwilliam, Lieuwe de Haan, Jeremy Hall, Mei-Hua Hall, Christian Hammer, Naomi Hammond, Marian L. Hamshere, Mark Hansen, Thomas Hansen, Vahram Haroutunian, Annette M. Hartmann, Joanna Hauser, Martin Hautzinger, Urs Heilbronner, Garrett Hellenthal, Frans A. Henskens, Stefan Herms, Maria Hipolito, Joel N. Hirschhorn, Per Hoffmann, Mads V. Hollegaard, David M. Hougaard, Hailiang Huang, Laura Huckins, Christina M. Hultman, Sarah E. Hunt, Masashi Ikeda, Nakao Iwata, Conrad Iyegbe, Assen V. Jablensky, Stephane Jamain, Janusz Jankowski, Alagurevathi Jayakumar, Inge Joa, Ian Jones, Lisa A. Jones, Erik G. Jonsson, Antonio Julia, Anders Jureus, Anna K. Kahler, Rene S. Kahn, Luba Kalaydjieva, Radhika Kandaswamy, Sena Karachanak-Yankova, Juha Karjalainen, Robert Karlsson, David Kavanagh, Matthew C. Keller, Brian J. Kelly, John Kelsoe, James L. Kennedy, Andrey Khrunin, Yunjung Kim, George Kirov, Sarah Kittel-Schneider, Janis Klovins, Jo Knight, Sarah V. Knott, James A. Knowles, Manolis Kogevinas, Bettina Konte, Eugenia Kravariti, Vaidutis Kucinskas, Zita Ausrele Kucinskiene, Ralph Kupka, Hana Kuzelova-Ptackova, Mikael Landen, Cordelia Langford, Claudine Laurent, Jacob Lawrence, Stephen Lawrie, William B. Lawson, Markus Leber, Marion Leboyer, Phil H. Lee, Jimmy Lee Chee Keong, Sophie E. Legge, Todd Lencz, Bernard Lerer, Douglas F. Levinson, Shawn E. Levy, Cathryn M. Lewis, Jun Z. Li, Miaoxin Li, Qingqin S. Li, Tao Li, Kung-Yee Liang, Jennifer Liddle, Jeffrey Lieberman, Svetlana Limborska, Kuang Lin, Don H. Linszen, Jolanta Lissowska, Chunyu Liu, Jianjun Liu, Jouko Lonnqvist, Carmel M. Loughland, Jan Lubinski, Susanne Lucae, Milan Macek, Donald J. MacIntyre, Patrik KE Magnusson, Brion S. Maher, Pamela B. Mahon, Wolfgang Maier, Anil K. Malhotra, Jacques Mallet, Ulrik F. Malt, Hugh S. Markus, Sara Marsal, Nicholas G. Martin, Ignacio Mata, Christopher G. Mathew, Manuel Mattheisen, Morten Mattingsdal, Fermin Mayoral, Owen T. McCann, Robert W. McCarley, Steven A. McCarroll, Mark I. McCarthy, Colm McDonald, Susan L. McElroy, Peter McGuffin, Melvin G. Mclnnis, Andrew M. McIntosh, James D. McKay, Francis J. McMahon, Helena Medeiros, Sarah E. Medland, Sandra Meier, Carin J. Meijer, Bela Melegh, Ingrid Melle, Fan Meng, Raquelle I. Mesholam-Gately, Andres Metspalu, Patricia T. Michie, Lili Milani, Vihra Milanova, Philip B. Mitchell, Younes Mokrab, Grant W. Montgomery, Jennifer L. Moran, Gunnar Morken, Derek W. Morris, Ole Mors, Preben B. Mortensen, Bryan J. Mowry, Thomas W. Mühleisen, Bertram Müller-Myhsok, Kieran C. Murphy, Robin M. Murray, Richard M. Myers, Inez Myin-Germeys, Benjamin M. Neale, Mari Nelis, Igor Nenadic, Deborah A. Nertney, Gerald Nestadt, Kristin K. Nicodemus, Caroline M. Nievergelt, Liene Nikitina-Zake, Vishwajit Nimgaonkar, Laura Nisenbaum, Merete Nordentoft, Annelie Nordin, Markus M. Nöthen, Evaristus A. Nwulia, Eadbhard O’Callaghan, Claire O’Donovan, O’Dushlaine Colm, F. Anthony O’Neill, Ketil J. Oedegaard, Sang-Yun Oh, Ann Olincy, Line Olsen, Lilijana Oruc, Jim Van Os, Michael J. Owen, Sara A. Paciga, Colin N. A Palmer, Aarno Palotie, Christos Pantelis, George N. Papadimitriou, Elena Parkhomenko, Carlos Pato, Michele T. Pato, Tiina Paunio, Richard Pearson, Psychosis Endophenotypes International Consortium, Diana O. Perkins, Roy H. Perlis, Amy Perry, Tune H. Pers, Tracey L. Petryshen, Andrea Pfennig, Marco Picchioni, Olli Pietilainen, Jonathan Pimm, Matti Pirinen, Robert Plomin, Andrew J. Pocklington, Danielle Posthuma, James B. Potash, Simon C. Potter, John Powell, Alkes Price, Ann E. Pulver, Shaun M. Purcell, Digby Quested, Josep Antoni Ramos-Quiroga, Henrik B. Rasmussen, Anna Rautanen, Radhi Ravindrarajah, Eline J. Regeer, Abraham Reichenberg, Andreas Reif, Mark A. Reimers, Marta Ribases, John P. Rice, Alexander L. Richards, Michelle Ricketts, Brien P. Riley, Fabio Rivas, Margarita Rivera, Joshua L. Roffman, Guy A. Rouleau, Panos Roussos, Dan Rujescu, Veikko Salomaa, Cristina Sanchez-Mora, Alan R. Sanders, Stephen J. Sawcer, Ulrich Schall, Alan F. Schatzberg, William A. Scheftner, Peter R. Schofield, Nicholas J. Schork, Sibylle G. Schwab, Edward M. Scolnick, Laura J. Scott, Rodney J. Scott, Larry J. Seidman, Alessandro Serretti, Pak C. Sham, Cynthia Shannon Weickert, Tatyana Shehktman, Jianxin Shi, Paul D. Shilling, Engilbert Sigurdsson, Jeremy M. Silverman, Kang Sim, Claire Slaney, Petr Slominsky, Olav B. Smeland, Jordan W. Smoller, Hon-Cheong So, Janet L. Sobell, Erik Soderman, Christine Soholm Hansen, Chris C. A Spencer, Anne T. Spijker, David St Clair, Hreinn Stefansson, Kari Stefansson, Stacy Steinberg, Elisabeth Stogmann, Eystein Stordal, Amy Strange, Richard E. Straub, John S. Strauss, Fabian Streit, Eric Strengman, Jana Strohmaier, T. Scott Stroup, Zhan Su, Mythily Subramaniam, Jaana Suvisaari, Dragan M. Svrakic, Jin P. Szatkiewicz, Szabolcs Szelinger, Avazeh Tashakkori-Ghanbaria, Srinivas Thirumalai, Robert C. Thompson, Thorgeir E. Thorgeirsson, Draga Toncheva, Paul A. Tooney, Sarah Tosato, Timothea Toulopoulou, Richard C. Trembath, Jens Treutlein, Vassily Trubetskoy, Gustavo Turecki, Arne E. Vaaler, Helmut Vedder, Eduard Vieta, John Vincent, Peter M. Visscher, Ananth C. Viswanathan, Damjan Vukcevic, John Waddington, Matthew Waller, Dermot Walsh, Muriel Walshe, James TR Walters, Dai Wang, Qiang Wang, Weiqing Wang, Yunpeng Wang, Stanley J. Watson, Bradley T. Webb, Thomas W. Weickert, Daniel R. Weinberger, Matthias Weisbrod, Mark Weiser, Thomas Werge, Paul Weston, Pamela Whittaker, Sara Widaa, Durk Wiersma, Dieter B. Wildenauer, Nigel M. Williams, Stephanie Williams, Stephanie H. Witt, Aaron R. Wolen, Emily HM Wong, Nicholas W. Wood, Brandon K. Wormley, Wellcome Trust Case-Control Consortium, Jing Qin Wu, Simon Xi, Wei Xu, Allan H. Young, Clement C. Zai, Peter Zandi, Peng Zhang, Xuebin Zheng, Fritz Zimprich, Sebastian Zollner, Aiden Corvin, Ayman H. Fanous, Sven Cichon, Marcella Rietschel, Elliot S. Gershon, Thomas G. Schulze, Alfredo B. Cuellar-Barboza, Andreas J. Forstner, Peter A. Holmans, John I. Nurnberger, Ole A. Andreassen, S. Hong Lee, Michael C. O’Donovan, Patrick F. Sullivan, Roel A. Ophoff, Naomi R. Wray, Pamela Sklar, Kenneth S. Kendler (2017-08-08):

    Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable disorders that share a significant proportion of common risk variation. Understanding the genetic factors underlying the specific symptoms of these disorders will be crucial for improving diagnosis, intervention and treatment. In case-control data consisting of 53,555 cases (20,129 BD, 33,426 SCZ) and 54,065 controls, we identified 114 genome-wide statistically-significant loci (GWS) when comparing all cases to controls, of which 41 represented novel findings. Two genome-wide statistically-significant loci were identified when comparing SCZ to BD and a third was found when directly incorporating functional information. Regional joint association identified a genomic region of overlapping association in BD and SCZ with disease-independent causal variants indicating a fourth region contributing to differences between these disorders. Regional SNP-heritability analyses demonstrated that the estimated heritability of BD based on the SCZ GWS regions was significantly higher than that based on the average genomic region (91 regions, p = 1.2×10−6) while the inverse was not significant (19 regions, p = 0.89). Using our BD and SCZ GWAS we calculated polygenic risk scores and identified several statistically-significant correlations with: 1) SCZ subphenotypes: negative symptoms (SCZ, p = 3.6×10−6) and manic symptoms (BD, p = 2×10−5), 2) BD subphenotypes: psychotic features (SCZ p = 1.2×10−10, BD p = 5.3×10−5) and age of onset (SCZ p = 7.9×10−4). Finally, we show that psychotic features in BD has significant SNP-heritability (h2snp = 0.15, SE = 0.06), and a statistically-significant genetic correlation with SCZ (rg = 0.34) in addition there is a significant sign test result between SCZ GWAS and a GWAS of BD cases contrasting those with and without psychotic features (p = 0.0038, one-side binomial test). For the first time, we have identified specific loci pointing to a potential role of 4 genes (DARS2, ARFGEF2, DCAKD and GATAD2A) that distinguish between BD and SCZ, providing an opportunity to understand the biology contributing to clinical differences of these disorders. Our results provide the best evidence so far of genomic components distinguishing between BD and SCZ that contribute directly to specific symptom dimensions.

  267. ⁠, Eli A. Stahl, Andreas J. Forstner, Andrew McQuillin, Stephan Ripke, Vassily Trubetskoy, Manuel Mattheisen, Weiqing Wang, Yunpeng Wang, Jonathan R. I Coleman, Héléna A. Gaspar, Christiaan A. de Leeuw, Jennifer M. Whitehead Pavlides, Loes M. Olde Loohuis, Anil P. S Ori, Tune H. Pers, Peter A. Holmans, Douglas M. Ruderfer, Phil H. Lee, Alexander W. Charney, Amanda L. Dobbyn, Laura Huckins, James Boocock, Claudia Giambartolomei, Panos Roussos, Niamh Mullins, Swapnil Awasthi, Esben Agerbo, Thomas D. Als, Carsten Bøcker Pedersen, Jakob Grove, Ralph Kupka, Eline J. Regeer, Adebayo Anjorin, Miquel Casas, Cristina Sánchez-Mora, Pamela B. Mahon, Shaun M. Purcell, Steve McCarroll, Judith Allardyce, Valentina Escott-Price, Liz Forty, Christine Fraser, Marian L. Hamshere, George Kirov, Manolis Kogevinas, Josef Frank, Fabian Streit, Jana Strohmaier, Jens Treutlein, Stephanie H. Witt, James L. Kennedy, John S. Strauss, Julie Garnham, Claire O’Donovan, Claire Slaney, Stacy Steinberg, Thorgeir E. Thorgeirsson, Martin Hautzinger, Michael Steffens, Ralph Kupka, Steve McCarroll, Roy H. Perlis, Miquel Casas, Cristina Sánchez-Mora, Maria Hipolito, William B. Lawson, Evaristus A. Nwulia, Shawn E. Levy, Shaun M. Purcell, Tatiana M. Foroud, Stéphane Jamain, Allan H. Young, James D. McKay, Thomas D. Als, Carsten Bøcker Pedersen, Jakob Grove, Diego Albani, Peter Zandi, Pamela B. Mahon, James B. Potash, Peng Zhang, J. Raymond DePaulo, Sarah E. Bergen, Anders Juréus, Robert Karlsson, Radhika Kandaswamy, Peter McGuffin, Margarita Rivera, Jolanta Lissowska, Roy H. Perlis, Cristiana Cruceanu, Susanne Lucae, Pablo Cervantes, Monika Budde, Katrin Gade, Urs Heilbronner, Marianne Giørtz Pedersen, Carsten Bøcker Pedersen, Derek W. Morris, Cynthia Shannon Weickert, Thomas W. Weickert, Donald J. MacIntyre, Jacob Lawrence, Torbjørn Elvsåshagen, Olav B. Smeland, Srdjan Djurovic, Simon Xi, Elaine K. Green, Piotr M. Czerski, Joanna Hauser, Wei Xu, Helmut Vedder, Lilijana Oruc, Anne T. Spijker, Scott D. Gordon, Sarah E. Medland, David Curtis, Thomas W. Mühleisen, Judith Badner, William A. Scheftner, Engilbert Sigurdsson, Nicholas J. Schork, Alan F. Schatzberg, Marie Bækvad-Hansen, Jonas Bybjerg-Grauholm, Christine Søholm Hansen, James A. Knowles, Helena Medeiros, Szabolcs Szelinger, Grant W. Montgomery, Derek W. Morris, Marco Boks, Annelie Nordin Adolfsson, Miquel Casas, Stéphane Jamain, Nicholas Bass, David Curtis, Per Hoffmann, Michael Bauer, Andrea Pfennig, Markus Leber, Sarah Kittel-Schneider, Andreas Reif, Katrin Gade, Jurgen Del-Favero, Sascha B. Fischer, Stefan Herms, Per Hoffmann, Thomas W. Mühleisen, Céline S. Reinbold, Srdjan Djurovic, Franziska Degenhardt, Stefan Herms, Per Hoffmann, Anna C. Koller, Anna Maaser, Wolfgang Maier, Nelson B. Freimer, Anil Ori, Anders M. Dale, Chun Chieh Fan, Tiffany A. Greenwood, Caroline M. Nievergelt, Tatyana Shehktman, Paul D. Shilling, Olav B. Smeland, William Byerley, William Bunney, Ney Alliey-Rodriguez, Douglas H. R Blackwood, Toni-Kim Clarke, Donald J. MacIntyre, Margarita Rivera, Chunyu Liu, William Coryell, Huda Akil, Margit Burmeister, Matthew Flickinger, Jun Z. Li, Melvin G. McInnis, Fan Meng, Robert C. Thompson, Stanley J. Watson, Sebastian Zollner, Weihua Guan, Melissa J. Green, Cynthia Shannon Weickert, Thomas W. Weickert, Olav B. Smeland, David Craig, Janet L. Sobell, Lili Milani, James L. Kennedy, John S. Strauss, Wei Xu, Katherine Gordon-Smith, Sarah V. Knott, Amy Perry, José Guzman Parra, Fermin Mayoral, Fabio Rivas, Miquel Casas, Cristina Sánchez-Mora, Caroline M. Nievergelt, Ralph Kupka, John P. Rice, Jack D. Barchas, Anders D. Børglum, Preben Bo Mortensen, Ole Mors, Maria Grigoroiu-Serbanescu, Frank Bellivier, Bruno Etain, Marion Leboyer, Josep Antoni Ramos-Quiroga, Marta Ribasés, Tõnu Esko, Jordan W. Smoller, Nicholas Craddock, Ian Jones, Michael J. Owen, Marcella Rietschel, Thomas G. Schulze, John Vincent, Tõnu Esko, Eduard Vieta, Merete Nordentoft, Martin Alda, Hreinn Stefansson, Kari Stefansson, Danielle Posthuma, Ingrid Agartz, Frank Bellivier, Tõnunu Esko, Ketil J. Oedegaard, Eystein Stordal, Josep Antoni Ramos-Quiroga, Marta Ribasés, Richard M. Myers, René S. Kahn, Frank Bellivier, Bruno Etain, Marion Leboyer, Bruno Etain, Anders D. Børglum, Ole Mors, Thomas Werge, Qingqin S. Li, Thomas G. Schulze, Fernando Goes, Ingrid Agartz, Christina M. Hultman, Mikael Landén, Patrick F. Sullivan, Cathryn M. Lewis, Susan L. McElroy, Jordan W. Smoller, Bertram Müller-Myhsok, Joanna M. Biernacka, Mark Frye, Gustavo Turecki, Guy A. Rouleau, Thomas G. Schulze, Thomas Werge, Guy A. Rouleau, Bertram Müller-Myhsok, Martin Alda, Francis J. McMahon, Thomas G. Schulze, Janice M. Fullerton, Peter R. Schofield, Eystein Stordal, Gunnar Morken, Ulrik F. Malt, Ingrid Melle, Sara A. Paciga, Nicholas G. Martin, Arne E. Vaaler, Gunnar Morken, David M. Hougaard, Carlos Pato, Michele T. Pato, Nicholas G. Martin, Aiden Corvin, Michael Gill, René S. Kahn, Rolf Adolfsson, Josep Antoni Ramos-Quiroga, Frank Bellivier, Bruno Etain, Marion Leboyer, Thomas G. Schulze, Bernhard T. Baune, Ketil J. Oedegaard, Alessandro Serretti, Markus M. Nöthen, Elliot S. Gershon, Thomas Werge, Andrew M. McIntosh, Mikael Landén, Kari Stefansson, Bertram Müller-Myhsok, Michael Boehnke, Udo Dannlowski, Janice M. Fullerton, Philip B. Mitchell, Peter R. Schofield, Patrick F. Sullivan, Ingrid Agartz, Ingrid Melle, Wade H. Berrettini, Vishwajit Nimgaonkar, Tõnu Esko, Andres Metspalu, Lisa A. Jones, Josep Antoni Ramos-Quiroga, Marta Ribasés, John Nurnberger, Naomi R. Wray, Arianna Di Florio, Michael C. O’Donovan, Howard Edenberg, Roel A. Ophoff, Laura J. Scott, Sven Cichon, Ole A. Andreassen, Pamela Sklar, John Kelsoe, Gerome Breen, for the Bipolar Disorder Working Group of the Psychiatric Genomics Consortium. (2017-08-08):

    Bipolar disorder is a highly heritable psychiatric disorder that features episodes of mania and depression. We performed the largest genome-wide association study to date, including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 881 sentinel variants at loci with p < 1×10−4 in an independent sample of 9,412 cases and 137,760 controls. In the combined analysis, 30 loci achieved genome-wide statistical-significance including 20 novel loci. These statistically-significant loci contain genes encoding ion channels and neurotransmitter transporters (CACNA1C, GRIN2A, SCN2A, SLC4A1), synaptic components (RIMS1, ANK3), immune and energy metabolism components, and multiple potential therapeutic targets for mood stabilizer drugs. Bipolar disorder type I (depressive and manic episodes; ~73% of our cases) is strongly genetically correlated with schizophrenia whereas type II (depressive and hypomanic episodes; ~17% of our cases) correlated more with major depression. Furthermore, bipolar disorder has a positive genetic correlation with educational attainment yet has no statistically-significant genetic correlation with intelligence. These findings address key clinical questions and provide potential new biological mechanisms for bipolar disorder.

  268. https://www.nature.com/articles/s41467-018-03819-3

  269. ⁠, Mats Nagel, Philip R. Jansen, Sven Stringer, Kyoko Watanabe, Christiaan A. de Leeuw, Julien Bryois, Jeanne E. Savage, Anke R. Hammerschlag, Nathan Skene, Ana B. Muñoz-Manchado, the 23andMe Research Team, Sten Linnarsson, Jens Hjerling-Leffler, Tonya JH White, Henning Tiemeier, Tinca JC Polderman, Patrick F. Sullivan, Sophie van der Sluis, Danielle Posthuma (2017-09-05):

    Neuroticism is an important risk factor for psychiatric traits including depression1, anxiety2,3, and schizophrenia4–6. Previous genome-wide association studies7–12 (GWAS) reported 16 genomic loci10–12. Here we report the largest neuroticism GWAS meta-analysis to date (n = 449,484), and identify 136 independent genome-wide statistically-significant loci (124 novel), implicating 599 genes. Extensive functional follow-up analyses show enrichment in several brain regions and involvement of specific cell-types, including dopaminergic neuroblasts (P = 3×10-8), medium spiny neurons (P = 4×10-8) and serotonergic neurons (P = 1×10-7). Gene-set analyses implicate three specific pathways: neurogenesis (P = 4.4×10-9), behavioural response to cocaine processes (P = 1.84×10-7), and axon part (p = 5.26×10-8). We show that neuroticism’s genetic signal partly originates in two genetically distinguishable subclusters13 (depressed affect and worry, the former being genetically strongly related to depression, rg = 0.84), suggesting distinct causal mechanisms for subtypes of individuals. These results vastly enhance our neurobiological understanding of neuroticism, and provide specific leads for functional follow-up experiments.

  270. ⁠, Isabelle Peretz, Dominique T. Vuvan (2016-08-22):

    Congenital amusia (commonly known as tone-deafness) is a lifelong musical disorder that should affect 4% of the population according to a single estimate based on a single test from 1980. Here we present the first large-based measure of prevalence with a sample of 20,000 participants that does not rely on self-referral. On the basis of three objective tests and a questionnaire, we show that (a) the prevalence of congenital amusia is only 1.5% with slightly more females than males, unlike other developmental disorders where males often predominate; (b) self-disclosure is a reliable index of congenital amusia, that suggests that congenital amusia is hereditary with 46% first-degree relatives similarly affected; c) that the deficit is not attenuated by musical training and d) it emerges in relative isolation from other cognitive disorder except for spatial orientation problems. Hence, we suggest that congenital amusia is likely to result from genetic variations that affect musical abilities specifically.

  271. http://www2.psych.ubc.ca/~schaller/528Readings/Tesser1993.pdf

  272. http://humancond.org/_media/papers/bouchard04_genetic_influence_psychological_traits.pdf

  273. http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1006&context=poliscifacpub

  274. 2008-eaves-2.pdf: ⁠, Lindon J. Eaves, Peter K. Hatemi (2008-03-29; genetics  /​ ​​ ​heritable):

    The biological and social transmission of attitudes toward abortion and gay rights are analyzed in a large sample of adult twins, siblings, and their parents. We present a linear model for family resemblance allowing for both genetic and cultural transmission of attitudes from parents to offspring, as well as phenotypic mating (the tendency to marry like) and other environmental sources of twin and sibling resemblance that do not depend on the attitudes of their parents. The model gives a close fit to the patterns of similarity between relatives for the two items. Results are consistent with a substantial role of genetic liability in the transmission of both attitudes. Contrary to the dominant paradigm of the social and political sciences, the kinship data are consistent with a relatively minor non-genetic impact of parental attitudes on the development of adult attitudes in their children. By contrast, the choice of mate is a social action that has a marked impact on the polarization of social attitudes and on the long-term influence that parents exert upon the next generation.

    [Keywords: Abortion, Gay rights, Assortative mating, Political and social attitudes]

  275. 2010-hatemi.pdf: ⁠, Peter K. Hatemi, John R. Hibbing, Sarah E. Medland, Matthew C. Keller, John R. Alford, Kevin B. Smith, Nicholas G. Martin, Lindon J. Eaves (2020-07-01; genetics  /​ ​​ ​heritable):

    Variance components estimates of political and social attitudes suggest a substantial level of genetic influence, but the results have been challenged because they rely on data from twins only. In this analysis, we include responses from parents and nontwin full siblings of twins, account for by using a panel design, and estimate genetic and environmental variance by maximum-likelihood ⁠. By doing so, we address the central concerns of critics, including that the twin-only design offers no verification of either the equal environments or random mating assumptions. Moving beyond the twin-only design leads to the conclusion that for most political and social attitudes, genetic influences account for an even greater proportion of individual differences than reported by studies using more limited data and more elementary estimation techniques. These findings make it increasingly difficult to deny that—however indirectly—genetics plays a role in the formation of political and social attitudes.

  276. https://genepi.qimr.edu.au/contents/publications/staff/hatemi_2011_JrnlPolitics_271-285.pdf

  277. ⁠, Button, Tanya M. M Stallings, Michael C. Rhee, Soo Hyun Corley, Robin P. Hewitt, John K (2011):

    Studies have demonstrated little to no heritability for adolescent religiosity but moderate genetic, shared environmental, and nonshared environmental influences on adult religiosity. Only one longitudinal study of religiosity in female twins has been conducted (Koenig et al., Dev Psychol 44:532-543, 2008), and reported that persistence from mid to late adolescence is due to shared environmental factors, but persistence from late adolescence to early adulthood was due to genetic and shared environmental factors. We examined the etiology of stability and change in religious values and religious attendance in males and females during adolescence and early adulthood. The heritability of both religious values and religious attendance increased from adolescence to early adulthood, although the increase was greater for religious attendance. Both genetic and shared environmental influences contributed to the stability of religious values and religious attendance across adolescence and young adulthood. Change in religious values was due to both genetic and nonshared environmental influences specific to early adulthood, whereas change in religious attendance was due in similar proportions to genetic, shared environmental, and non-shared environmental influences.

  278. ⁠, Peter K. Hatemi, Brad Verhulst (2014-12-22):

    The primary assumption within the recent personality and political orientations literature is that personality traits cause people to develop political attitudes. In contrast, research relying on traditional psychological and developmental theories suggests the relationship between most personality dimensions and political orientations are either not significant or weak. Research from behavioral genetics suggests the covariance between personality and political preferences is not causal, but due to a common, genetic factor that mutually influences both. The contradictory assumptions and findings from these research streams have yet to be resolved. This is in part due to the reliance on cross-sectional data and the lack of longitudinal genetically informative data. Here, using two independent longitudinal genetically informative samples, we examine the joint development of personality traits and attitude dimensions to explore the underlying causal mechanisms that drive the relationship between these features and provide a first step in resolving the causal question. We find change in personality over a ten-year period does not predict change in political attitudes, which does not support a causal relationship between personality traits and political attitudes as is frequently assumed. Rather, political attitudes are often more stable than the key personality traits assumed to be predicting them. Finally, the results from our genetic models find that no additional variance is accounted for by the causal pathway from personality traits to political attitudes. Our findings remain consistent with the original construction of the five-factor model of personality and developmental theories on attitude formation, but challenge recent work in this area.

  279. 2016-kandler.pdf

  280. 2013-ludeke.pdf

  281. http://rstb.royalsocietypublishing.org/content/370/1683/20150015

  282. ⁠, Cornelis, Marilyn C. Byrne, Enda M. Esko, Tõnu Nalls, Michael A. Ganna, Andrea Paynter, Nina Monda, Keri L. Amin, Najaf Fischer, Krista Renstrom, Frida Ngwa, Julius S. Huikari, Ville Cavadino, Alana Nolte, Ilja M. Teumer, Alexander Yu, Kai Marques-Vidal, Pedro Rawal, Rajesh Manichaikul, Ani Wojczynski, Mary K. Vink, Jacqueline M. Zhao, Jing Hua Burlutsky, George Lahti, Jari Mikkilä, Vera Lemaitre, Rozenn N. Eriksson, Joel Musani, Solomon K. Tanaka, Toshiko Geller, Frank Luan, Jian'an Hui, Jennie Mägi, Reedik Dimitriou, Maria Garcia, Melissa E. Ho, Weang-Kee Wright, Margaret J. Rose, Lynda M. Magnusson, Patrik Ke Pedersen, Nancy L. Couper, David Oostra, Ben A. Hofman, Albert Ikram, Mohammad Arfan Tiemeier, Henning W. Uitterlinden, Andre G. van Rooij, Frank Ja Barroso, Inês Johansson, Ingegerd Xue, Luting Kaakinen, Marika Milani, Lili Power, Chris Snieder, Harold Stolk, Ronald P. Baumeister, Sebastian E. Biffar, Reiner Gu, Fangyi Bastardot, François Kutalik, Zoltán Jacobs, David R. Forouhi, Nita G. Mihailov, Evelin Lind, Lars Lindgren, Cecilia Michaëlsson, Karl Morris, Andrew Jensen, Majken Khaw, Kay-Tee Luben, Robert N. Wang, Jie Jin Männistö, Satu Perälä, Mia-Maria Kähönen, Mika Lehtimäki, Terho Viikari, Jorma Mozaffarian, Dariush Mukamal, Kenneth Psaty, Bruce M. Döring, Angela Heath, Andrew C. Montgomery, Grant W. Dahmen, Norbert Carithers, Teresa Tucker, Katherine L. Ferrucci, Luigi Boyd, Heather A. Melbye, Mads Treur, Jorien L. Mellström, Dan Hottenga, Jouke Jan Prokopenko, Inga Tönjes, Anke Deloukas, Panos Kanoni, Stavroula Lorentzon, Mattias Houston, Denise K. Liu, Yongmei Danesh, John Rasheed, Asif Mason, Marc A. Zonderman, Alan B. Franke, Lude Kristal, Bruce S. Karjalainen, Juha Reed, Danielle R. Westra, Harm-Jan Evans, Michele K. Saleheen, Danish Harris, Tamara B. Dedoussis, George Curhan, Gary Stumvoll, Michael Beilby, John Pasquale, Louis R. Feenstra, Bjarke Bandinelli, Stefania Ordovas, Jose M. Chan, Andrew T. Peters, Ulrike Ohlsson, Claes Gieger, Christian Martin, Nicholas G. Waldenberger, Melanie Siscovick, David S. Raitakari, Olli Eriksson, Johan G. Mitchell, Paul Hunter, David J. Kraft, Peter Rimm, Eric B. Boomsma, Dorret I. Borecki, Ingrid B. Loos, Ruth Jf Wareham, Nicholas J. Vollenweider, Peter Caporaso, Neil Grabe, Hans Jörgen Neuhouser, Marian L. Wolffenbuttel, Bruce Hr Hu, Frank B. Hyppönen, Elina Järvelin, Marjo-Riitta Cupples, L. Adrienne Franks, Paul W. Ridker, Paul M. van Duijn, Cornelia M. Heiss, Gerardo Metspalu, Andres North, Kari E. Ingelsson, Erik Nettleton, Jennifer A. van Dam, Rob M. Chasman, Daniel I (2015):

    Coffee, a major dietary source of ⁠, is among the most widely consumed beverages in the world and has received considerable attention regarding health risks and benefits. We conducted a genome-wide (GW) meta-analysis of predominately regular-type coffee consumption (cups per day) among up to 91,462 coffee consumers of European ancestry with top single-nucleotide polymorphisms (SNPs) followed-up in ~30 062 and 7964 coffee consumers of European and African-American ancestry, respectively. Studies from both stages were combined in a trans-ethnic meta-analysis. Confirmed loci were examined for putative functional and biological relevance. Eight loci, including six novel loci, met GW significance (log10Bayes factor (BF)>5.64) with per-allele effect sizes of 0.03-0.14 cups per day. Six are located in or near genes potentially involved in pharmacokinetics (ABCG2, AHR, POR and CYP1A2) and pharmacodynamics (BDNF and SLC6A4) of caffeine. Two map to GCKR and MLXIPL genes related to metabolic traits but lacking known roles in coffee consumption. Enhancer and promoter histone marks populate the regions of many confirmed loci and several potential regulatory SNPs are highly correlated with the lead SNP of each. SNP alleles near GCKR, MLXIPL, BDNF and CYP1A2 that were associated with higher coffee consumption have previously been associated with smoking initiation, higher adiposity and fasting insulin and glucose but lower blood pressure and favorable lipid, inflammatory and liver enzyme profiles (p < 5 × 10−8).Our genetic findings among European and African-American adults reinforce the role of caffeine in mediating habitual coffee consumption and may point to molecular mechanisms underlying inter-individual variability in pharmacological and health effects of coffee.

  283. http://people.virginia.edu/~ent3c/papers2/d%27onofrioAJPH.pdf

  284. ⁠, Lynch, Stacy K. Turkheimer, Eric D'Onofrio, Brian M. Mendle, Jane Emery, Robert E. Slutske, Wendy S. Martin, Nicholas G (2006):

    Conclusions about the effects of harsh parenting on children have been limited by research designs that cannot control for genetic or shared environmental confounds. The present study used a sample of children of twins and a hierarchical linear modeling statistical approach to analyze the consequences of varying levels of punishment while controlling for many confounding influences. The sample of 887 twin pairs and 2,554 children came from the Australian Twin Registry. Although corporal punishment per se did not have statistically-significant associations with negative childhood outcomes, harsher forms of physical punishment did appear to have specific and statistically-significant effects. The observed association between harsh physical punishment and negative outcomes in children survived a relatively rigorous test of its causal status, thereby increasing the authors’ conviction that harsh physical punishment is a serious risk factor for children.

  285. https://www.nytimes.com/2007/05/08/health/08fat.html?pagewanted=all

  286. 2017-wehby.pdf: ⁠, George L. Wehby, Benjamin W. Domingue, Fred Ullrich, and Fredric D. Wolinsky (2017-05-10; genetics  /​ ​​ ​selection):

    Background: The relationship between obesity and health expenditures is not well understood. We examined the relationship between genetic predisposition to obesity measured by a polygenic risk score for body mass index (BMI) and Medicare expenditures. Methods: Biennial interview data from the Health and Retirement Survey for a nationally representative sample of older adults enrolled in fee-for-service Medicare were obtained from 1991 through 2010 and linked to Medicare claims for the same period and to Genome-Wide Association Study (GWAS) data. The study included 6,628 Medicare beneficiaries who provided 68,627 complete person-year observations during the study period. Outcomes were total and service-specific Medicare expenditures and indicators for expenditures exceeding the 75th and 90th percentiles. The BMI polygenic risk score was derived from GWAS data. Regression models were used to examine how the BMI polygenic risk score was related to health expenditures adjusting for demographic factors and GWAS-derived ancestry. Results: Greater genetic predisposition to obesity was associated with higher Medicare expenditures. Specifically, a 1 SD increase in the BMI polygenic risk score was associated with a $805 (p < 0.001) increase in annual Medicare expenditures per person in 2010 dollars (~15% increase), a $370 (p < 0.001) increase in inpatient expenses, and a $246 (p < 0.001) increase in outpatient services. A 1 SD increase in the polygenic risk score was also related to increased likelihood of expenditures exceeding the 75th percentile by 18% (95% CI: 10%–28%) and the 90th percentile by 27% (95% CI: 15%–40%). Conclusion: Greater genetic predisposition to obesity is associated with higher Medicare expenditures.

  287. ⁠, Jessica Tyrrell, Andrew R. Wood, Ryan M. Ames, Hanieh Yaghootkar, Robin N. Beaumont, Samuel E. Jones, Marcus A. Tuke, Katherine S. Ruth, Rachel M. Freathy, George Davey Smith, Stéphane Joost, Idris Guessous, Anna Murray, David P. Strachan, Zoltán Kutalik, Michael N. Weedon, Timothy M. Frayling (2016-09-13):

    Susceptibility to obesity in today’s environment has a strong genetic component. Lower socioeconomic position (SEP) is associated with a higher risk of obesity but it is not known if it accentuates genetic susceptibility to obesity. We aimed to use up to 120,000 individuals from the UK Biobank study to test the hypothesis that measures of socioeconomic position accentuate genetic susceptibility to obesity.

    We used the Townsend deprivation index (TDI) as the main measure of socioeconomic position, and a 69-variant genetic risk score (GRS) as a measure of genetic susceptibility to obesity. We also tested the hypothesis that interactions between BMI genetics and socioeconomic position would result in evidence of interaction with individual measures of the obesogenic environment and behaviours that correlate strongly with socioeconomic position, even if they have no obesogenic role. These measures included self-reported TV watching, diet and physical activity, and an objective measure of activity derived from accelerometers. We performed several negative control tests, including a simulated environment correlated with BMI but not TDI, and sun protection use. We found evidence of gene-environment interactions with TDI (pinteraction = 3×10−10) such that, within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. We also observed evidence of interaction between sun protection use and BMI genetics, suggesting that residual confounding may result in evidence of non-causal interactions [especially given such a weak PGS…].

    Our findings provide evidence that relative social deprivation best captures aspects of the obesogenic environment that accentuate the genetic predisposition to obesity in the UK.

  288. https://academic.oup.com/aje/article/156/11/985/80696

  289. ⁠, Sira Karvinen, Katja Waller, Mika Silvennoinen, Lauren G. Koch, Steven L. Britton, Jaakko Kaprio, Heikki Kainulainen, Urho M. Kujala (2015-12-15):

    Observational studies report a strong inverse relationship between leisure-time physical activity and all-cause mortality. Despite suggestive evidence from population-based associations, scientists have not been able to show a beneficial effect of physical activity on the risk of death in controlled intervention studies among individuals who have been healthy at baseline. On the other hand, high cardiorespiratory fitness is known to be a strong predictor of reduced mortality, even more robust than physical activity level itself.

    Here, in both animals and/​​​​or human twins, we show that the same genetic factors influence physical activity levels, cardiorespiratory fitness and risk of death. Previous observational follow-up studies in humans suggest that increasing fitness through physical activity levels could prolong life; however, our controlled interventional study with laboratory rats bred for low and high intrinsic fitness contrast with these findings. Also, we find no evidence for the suggested association using pairwise analysis among monozygotic twin pairs who are discordant in their physical activity levels.

    Based on both our animal and human findings, we propose that genetic pleiotropy might partly explain the frequently observed associations between high baseline physical activity and later reduced mortality in humans.

    [See also author’s later thesis, ⁠.]

  290. 2015-rottensteiner.pdf: ⁠, Mirva Rottensteiner, Tuija Leskinen, Eini Niskanen, Sari Aaltonen, Sara Mutikainen, Jan Wikgren, Kauko Heikkilä, Vuokko Kovanen, Heikki Kainulainen, Jaakko Kaprio, Ina Tarkka, Urho Kujala (2015; genetics):

    Purpose: The main aim of the present study (FITFATTWIN) was to investigate how physical activity level is associated with body composition, glucose homeostasis, and brain morphology in young adult male monozygotic twin pairs discordant for physical activity.

    Methods: From a population-based twin cohort, we systematically selected 10 young adult male monozygotic twin pairs (age range, 32–36 yr) discordant for leisure time physical activity during the past 3 yr. On the basis of interviews, we calculated a mean sum index for leisure time and commuting activity during the past 3 yr (3-yr LTMET index expressed as MET-hours per day). We conducted extensive measurements on body composition (including fat percentage measured by dual-energy x-ray absorptiometry), glucose homeostasis including homeostatic model assessment index and insulin sensitivity index (Matsuda index, calculated from glucose and insulin values from an oral glucose tolerance test), and whole brain magnetic resonance imaging for regional volumetric analyses.

    Results: According to pairwise analysis, the active twins had lower body fat percentage (p = 0.029) and homeostatic model assessment index (p = 0.031) and higher Matsuda index (p = 0.021) compared with their inactive co-twins. Striatal and prefrontal cortex (subgyral and inferior frontal gyrus) brain gray matter volumes were larger in the nondominant hemisphere in active twins compared with those in inactive co-twins, with a statistical threshold of p < 0.001.

    Conclusions: Among healthy adult male twins in their mid-30s, a greater level of physical activity is associated with improved glucose homeostasis and modulation of striatum and prefrontal cortex gray matter volume, independent of genetic background. The findings may contribute to later reduced risk of type 2 diabetes and mobility limitations.

  291. https://www.sciencedirect.com/science/article/pii/S016028961630023X

  292. https://www.sciencedirect.com/science/article/pii/S0960982214006770

  293. ⁠, Tian Ge, Martin Reuter, Anderson M. Winkler, Avram J. Holmes, Phil H. Lee, Lee S. Tirrell, Joshua L. Roffman, Randy L. Buckner, Jordan W. Smoller, Mert R. Sabuncu (2015-12-01):

    Measurements from structural brain magnetic resonance imaging (MRI) scans have been increasingly analyzed as intermediate phenotypes to bridge the gap between clinical features and genetic variation. To date, most imaging phenotypes are scalar, such as the volume of a brain region, which can miss subtle or localized morphological variation associated with genetics or relevant to disease. Neuroanatomical shape measurements—multidimensional geometric descriptions of a brain structure—provide an alternate class of phenotypes that remain largely unexplored. In this paper, we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide single nucleotide polymorphism (SNP) and MRI data from 1,317 unrelated, young (18-35 years) and healthy individuals. Our results demonstrate that neuroanatomical shape can be significantly heritable, above and beyond volume, and thus can serve as a complementary phenotype to study the genetic underpinnings and clinical relevance of brain structure.

  294. 2014-toro.pdf: ⁠, R. Toro, J-B. Poline, G. Huguet, E. Loth, V. Frouin, T. Banaschewski, G. J. Barker, A. Bokde, C. Büchel, F. M. Carvalho, P. Conrod, M. Fauth-Bühler, H. Flor, J. Gallinat, H. Garavan, P. Gowland, A. Heinz, B. Ittermann, C. Lawrence, H. Lemaître, K. Mann, F. Nees, T. Paus, Z. Pausova, M. Rietschel, T. Robbins, M. N. Smolka, A. Ströhle, G. Schumann, T. Bourgeron (2014-09-16; genetics  /​ ​​ ​correlation):

    Human brain anatomy is strikingly diverse and highly inheritable: genetic factors may explain up to 80% of its variability. Prior studies have tried to detect genetic variants with a large effect on neuroanatomical diversity, but those currently identified account for <5% of the variance.

    Here, based on our analyses of neuroimaging and whole-genome genotyping data from 1765 subjects, we show that up to 54% of this heritability is captured by large numbers of single-nucleotide polymorphisms of small-effect spread throughout the genome, especially within genes and close regulatory regions. The genetic bases of neuroanatomical diversity appear to be relatively independent of those of body size (height), but shared with those of verbal intelligence scores.

    The study of this genomic architecture should help us better understand brain evolution and disease.

  295. ⁠, Hibar, Derrek P. Stein, Jason L. Renteria, Miguel E. Arias-Vasquez, Alejandro Desrivières, Sylvane Jahanshad, Neda Toro, Roberto Wittfeld, Katharina Abramovic, Lucija Andersson, Micael Aribisala, Benjamin S. Armstrong, Nicola J. Bernard, Manon Bohlken, Marc M. Boks, Marco P. Bralten, Janita Brown, Andrew A. Chakravarty, M. Mallar Chen, Qiang Ching, Christopher R. K Cuellar-Partida, Gabriel den Braber, Anouk Giddaluru, Sudheer Goldman, Aaron L. Grimm, Oliver Guadalupe, Tulio Hass, Johanna Woldehawariat, Girma Holmes, Avram J. Hoogman, Martine Janowitz, Deborah Jia, Tianye Kim, Sungeun Klein, Marieke Kraemer, Bernd Lee, Phil H. Olde Loohuis, Loes M. Luciano, Michelle Macare, Christine Mather, Karen A. Mattheisen, Manuel Milaneschi, Yuri Nho, Kwangsik Papmeyer, Martina Ramasamy, Adaikalavan Risacher, Shannon L. Roiz-Santiañez, Roberto Rose, Emma J. Salami, Alireza Sämann, Philipp G. Schmaal, Lianne Schork, Andrew J. Shin, Jean Strike, Lachlan T. Teumer, Alexander van Donkelaar, Marjolein M. J van Eijk, Kristel R. Walters, Raymond K. Westlye, Lars T. Whelan, Christopher D. Winkler, Anderson M. Zwiers, Marcel P. Alhusaini, Saud Athanasiu, Lavinia Ehrlich, Stefan Hakobjan, Marina M. H Hartberg, Cecilie B. Haukvik, Unn K. Heister, Angelien J. G A. M Hoehn, David Kasperaviciute, Dalia Liewald, David C. M Lopez, Lorna M. Makkinje, Remco R. R Matarin, Mar Naber, Marlies A. M McKay, D. Reese Needham, Margaret Nugent, Allison C. Pütz, Benno Royle, Natalie A. Shen, Li Sprooten, Emma Trabzuni, Daniah van der Marel, Saskia S. L van Hulzen, Kimm J. E Walton, Esther Wolf, Christiane Almasy, Laura Ames, David Arepalli, Sampath Assareh, Amelia A. Bastin, Mark E. Brodaty, Henry Bulayeva, Kazima B. Carless, Melanie A. Cichon, Sven Corvin, Aiden Curran, Joanne E. Czisch, Michael de Zubicaray, Greig I. Dillman, Allissa Duggirala, Ravi Dyer, Thomas D. Erk, Susanne Fedko, Iryna O. Ferrucci, Luigi Foroud, Tatiana M. Fox, Peter T. Fukunaga, Masaki Gibbs, J. Raphael Göring, Harald H. H Green, Robert C. Guelfi, Sebastian Hansell, Narelle K. Hartman, Catharina A. Hegenscheid, Katrin Heinz, Andreas Hernandez, Dena G. Heslenfeld, Dirk J. Hoekstra, Pieter J. Holsboer, Florian Homuth, Georg Hottenga, Jouke-Jan Ikeda, Masashi Jack, Clifford R. Jenkinson, Mark Johnson, Robert Kanai, Ryota Keil, Maria Kent, Jack W. Kochunov, Peter Kwok, John B. Lawrie, Stephen M. Liu, Xinmin Longo, Dan L. McMahon, Katie L. Meisenzahl, Eva Melle, Ingrid Mohnke, Sebastian Montgomery, Grant W. Mostert, Jeanette C. Mühleisen, Thomas W. Nalls, Michael A. Nichols, Thomas E. Nilsson, Lars G. Nöthen, Markus M. Ohi, Kazutaka Olvera, Rene L. Perez-Iglesias, Rocio Pike, G. Bruce Potkin, Steven G. Reinvang, Ivar Reppermund, Simone Rietschel, Marcella Romanczuk-Seiferth, Nina Rosen, Glenn D. Rujescu, Dan Schnell, Knut Schofield, Peter R. Smith, Colin Steen, Vidar M. Sussmann, Jessika E. Thalamuthu, Anbupalam Toga, Arthur W. Traynor, Bryan J. Troncoso, Juan Turner, Jessica A. Valdés Hernández, Maria C. van 't Ent, Dennis van der Brug, Marcel van der Wee, Nic J. A van Tol, Marie-Jose Veltman, Dick J. Wassink, Thomas H. Westman, Eric Zielke, Ronald H. Zonderman, Alan B. Ashbrook, David G. Hager, Reinmar Lu, Lu McMahon, Francis J. Morris, Derek W. Williams, Robert W. Brunner, Han G. Buckner, Randy L. Buitelaar, Jan K. Cahn, Wiepke Calhoun, Vince D. Cavalleri, Gianpiero L. Crespo-Facorro, Benedicto Dale, Anders M. Davies, Gareth E. Delanty, Norman Depondt, Chantal Djurovic, Srdjan Drevets, Wayne C. Espeseth, Thomas Gollub, Randy L. Ho, Beng-Choon Hoffmann, Wolfgang Hosten, Norbert Kahn, René S. Le Hellard, Stephanie Meyer-Lindenberg, Andreas Müller-Myhsok, Bertram Nauck, Matthias Nyberg, Lars Pandolfo, Massimo Penninx, Brenda W. J H. Roffman, Joshua L. Sisodiya, Sanjay M. Smoller, Jordan W. van Bokhoven, Hans van Haren, Neeltje E. M Völzke, Henry Walter, Henrik Weiner, Michael W. Wen, Wei White, Tonya Agartz, Ingrid Andreassen, Ole A. Blangero, John Boomsma, Dorret I. Brouwer, Rachel M. Cannon, Dara M. Cookson, Mark R. de Geus, Eco J. C Deary, Ian J. Donohoe, Gary Fernández, Guillén Fisher, Simon E. Francks, Clyde Glahn, David C. Grabe, Hans J. Gruber, Oliver Hardy, John Hashimoto, Ryota Hulshoff Pol, Hilleke E. Jönsson, Erik G. Kloszewska, Iwona Lovestone, Simon Mattay, Venkata S. Mecocci, Patrizia McDonald, Colm McIntosh, Andrew M. Ophoff, Roel A. Paus, Tomas Pausova, Zdenka Ryten, Mina Sachdev, Perminder S. Saykin, Andrew J. Simmons, Andy Singleton, Andrew Soininen, Hilkka Wardlaw, Joanna M. Weale, Michael E. Weinberger, Daniel R. Adams, Hieab H. H Launer, Lenore J. Seiler, Stephan Schmidt, Reinhold Chauhan, Ganesh Satizabal, Claudia L. Becker, James T. Yanek, Lisa van der Lee, Sven J. Ebling, Maritza Fischl, Bruce Longstreth, W. T Greve, Douglas Schmidt, Helena Nyquist, Paul Vinke, Louis N. van Duijn, Cornelia M. Xue, Luting Mazoyer, Bernard Bis, Joshua C. Gudnason, Vilmundur Seshadri, Sudha Ikram, M. Arfan Martin, Nicholas G. Wright, Margaret J. Schumann, Gunter Franke, Barbara Thompson, Paul M. Medland, Sarah E (2015):

    The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; p = 1.08 × 10(-33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

  296. ⁠, Chabris, Christopher F. Hebert, Benjamin M. Benjamin, Daniel J. Beauchamp, Jonathan Cesarini, David van der Loos, Matthijs Johannesson, Magnus Magnusson, Patrik K. E Lichtenstein, Paul Atwood, Craig S. Freese, Jeremy Hauser, Taissa S. Hauser, Robert M. Christakis, Nicholas Laibson, David (2012):

    General intelligence (g) and virtually all other behavioral traits are heritable. Associations between g and specific single-nucleotide polymorphisms (SNPs) in several candidate genes involved in brain function have been reported. We sought to replicate published associations between g and 12 specific genetic variants (in the genes DTNBP1, CTSD, DRD2, ANKK1, CHRM2, SSADH, COMT, BDNF, CHRNA4, DISC1, APOE, and SNAP25) using data sets from three independent, well-characterized longitudinal studies with samples of 5,571, 1,759, and 2,441 individuals. Of 32 independent tests across all three data sets, only 1 was nominally statistically-significant. By contrast, power analyses showed that we should have expected 10 to 15 statistically-significant associations, given reasonable assumptions for genotype effect sizes. For positive controls, we confirmed accepted genetic associations for Alzheimer’s disease and body mass index, and we used SNP-based calculations of genetic relatedness to replicate previous estimates that about half of the variance in g is accounted for by common genetic variation among individuals. We conclude that the molecular genetics of psychology and social science requires approaches that go beyond the examination of candidate genes.

  297. ⁠, G. Davies, A. Tenesa, A. Payton, J. Yang, S. E. Harris, D. Liewald, X. Ke, S. Le Hellard, A. Christoforou, M. Luciano, K. McGhee, L. Lopez, A. J. Gow, J. Corley, P. Redmond, H. C. Fox, P. Haggarty, L. J. Whalley, G. McNeill, M. E. Goddard, T. Espeseth, A. J. Lundervold, I. Reinvang, A. Pickles, V. M. Steen, W. Ollier, D. J. Porteous, M. Horan, J. M. Starr, N. Pendleton, P. M. Visscher, I. J. Deary (2011-08-09):

    General intelligence is an important human quantitative trait that accounts for much of the variation in diverse cognitive abilities. Individual differences in intelligence are strongly associated with many important life outcomes, including educational and occupational attainments, income, health and lifespan. Data from twin and family studies are consistent with a high heritability of intelligence, but this inference has been controversial. We conducted a genome-wide analysis of 3511 unrelated adults with data on 549 692 single nucleotide polymorphisms (SNPs) and detailed phenotypes on cognitive traits. We estimate that 40% of the variation in crystallized-type intelligence and 51% of the variation in fluid-type intelligence between individuals is accounted for by linkage disequilibrium between genotyped common SNP markers and unknown causal variants. These estimates provide lower bounds for the narrow-sense heritability of the traits. We partitioned genetic variation on individual chromosomes and found that, on average, longer chromosomes explain more variation. Finally, using just SNP data we predicted ~1% of the variance of crystallized and fluid cognitive phenotypes in an independent sample (p = 0.009 and 0.028, respectively). Our results unequivocally confirm that a substantial proportion of individual differences in human intelligence is due to genetic variation, and are consistent with many genes of small effects underlying the additive genetic influences on intelligence.

  298. 2012-deary.pdf: “Genetic contributions to stability and change in intelligence from childhood to old age”⁠, Ian J. Deary, Jian Yang, Gail Davies, Sarah E. Harris, Albert Tenesa, David Liewald, Michelle Luciano, Lorna M. Lopez, Alan J. Gow, Janie Corley, Paul Redmond, Helen C. Fox, Suzanne J. Rowe, Paul Haggarty, Geraldine McNeill, Michael E. Goddard, David J. Porteous, Lawrence J. Whalley, John M. Starr, Peter M. Visscher

  299. ⁠, Plomin, Robert Haworth, Claire M. A Meaburn, Emma L. Price, Thomas S. Davis, Oliver S. P (2013):

    For nearly a century, twin and adoption studies have yielded substantial estimates of heritability for cognitive abilities, although it has proved difficult for genomewide-association studies to identify the genetic variants that account for this heritability (i.e., the missing-heritability problem). However, a new approach, genomewide complex-trait analysis (), forgoes the identification of individual variants to estimate the total heritability captured by common DNA markers on genotyping arrays. In the same sample of 3,154 pairs of 12-year-old twins, we directly compared twin-study heritability estimates for cognitive abilities (language, verbal, nonverbal, and general) with GCTA estimates captured by 1.7 million DNA markers. We found that DNA markers tagged by the array accounted for .66 of the estimated heritability, reaffirming that cognitive abilities are heritable. Larger sample sizes alone will be sufficient to identify many of the genetic variants that influence cognitive abilities.

  300. ⁠, Elise B. Robinson, Andrew Kirby, Kosha Ruparel, Jian Yang, Lauren McGrath, Verneri Anttila, Benjamin M. Neale, Kathleen Merikangas, Thomas Lehner, Patrick M. A. Sleiman, Mark J. Daly, Ruben Gur, Raquel Gur, Hakon Hakonarson (2015-04):

    The objective of this analysis was to examine the genetic architecture of diverse cognitive abilities in children and adolescents, including the magnitude of common genetic effects and patterns of shared and unique genetic influences. Subjects included 3689 members of the Philadelphia Neurodevelopmental Cohort, a general population sample comprising those aged 8–21 years who completed an extensive battery of cognitive tests. We used to estimate the SNP-based heritability of each domain, as well as the genetic correlation between all domains that showed substantial genetic influence. Several of the individual domains suggested strong influence of common genetic variants (for example, reading ability, h2g = 0.43, p = 4e-06; emotion identification, h2g = 0.36, p = 1e-05; verbal memory, h2g = 0.24, p = 0.005). The genetic correlations highlighted trait domains that are candidates for joint interrogation in future genetic studies (for example, language reasoning and spatial reasoning, rg = 0.72, p = 0.007). These results can be used to structure future genetic and neuropsychiatric investigations of diverse cognitive abilities.

  301. https://www.sciencedirect.com/science/article/pii/S0160289613001049

  302. https://link.springer.com/article/10.1007/s10519-013-9594-x/fulltext.html

  303. https://www.nature.com/articles/tp201481

  304. ⁠, Robert M. Kirkpatrick, Matt McGue, William G. Iacono, Michael B. Miller, Saonli Basu (2014-05-04):

    We carried out a genome-wide association study (GWAS) for general cognitive ability (GCA) plus three other analyses of GWAS data that aggregate the effects of multiple single-nucleotide polymorphisms (SNPs) in various ways. Our multigenerational sample comprised 7,100 Caucasian participants, drawn from two longitudinal family studies, who had been assessed with an age-appropriate IQ test and had provided DNA samples passing quality screens. We conducted the GWAS across ~2.5 million SNPs (both typed and imputed), using a generalized least-squares method appropriate for the different family structures present in our sample, and subsequently conducted gene-based association tests. We also conducted polygenic prediction analyses under five-fold cross-validation, using two different schemes of weighting SNPs. Using parametric ⁠, we assessed the performance of this prediction procedure under the null. Finally, we estimated the proportion of variance attributable to all genotyped SNPs as random effects with software GCTA. The study is limited chiefly by its power to detect realistic single-SNP or single-gene effects, none of which reached genome-wide statistical-significance, though some genomic inflation was evident from the GWAS. Unit SNP weights performed about as well as least-squares regression weights under cross-validation, but the performance of both increased as more SNPs were included in calculating the polygenic score. Estimates from GCTA were 35% of phenotypic variance at the recommended biological-relatedness ceiling. Taken together, our results concur with other recent studies: they support a substantial heritability of GCA, arising from a very large number of causal SNPs, each of very small effect. We place our study in the context of the literature–both contemporary and historical–and provide accessible explication of our statistical methods.

  305. 2014-benyamin.pdf: ⁠, B. Benyamin, B. St Pourcain, O. S. Davis, G. Davies, N. K. Hansell, M-J. A. Brion, R. M. Kirkpatrick, R. A. M. Cents, S. Franić, M. B. Miller, C.M. A. Haworth, E. Meaburn, T. S. Price, D. M. Evans, N. Timpson, J. Kemp, S. Ring, W. McArdle, S. E. Medland, J. Yang, S. E. Harris, D. C. Liewald, P. Scheet, X. Xiao, J. J. Hudziak, E.J.C. de Geus, Wellcome Trust Case Control Consortium 2 (WTCCC2), V.W. V. Jaddoe, J. M. Starr, F. C. Verhulst, C. Pennell, H. Tiemeier, W. G. Iacono, L. J. Palmer, G. W. Montgomery, N. G. Martin, D. I. Boomsma, D. Posthuma, M. McGue, M. J. Wright, G. Davey Smith, I. J. Deary, R. Plomin, P. M. Visscher (2013-01-29; iq):

    Intelligence in childhood, as measured by psychometric cognitive tests, is a strong predictor of many important life outcomes, including educational attainment, income, health and lifespan. Results from twin, family and adoption studies are consistent with general intelligence being highly heritable and genetically stable throughout the life course. No robustly associated genetic loci or variants for childhood intelligence have been reported. Here, we report the first genome-wide association study (GWAS) on childhood intelligence (age range 6–18 years) from 17 989 individuals in six discovery and three replication samples. Although no individual single-nucleotide polymorphisms (SNPs) were detected with genome-wide statistical-significance, we show that the aggregate effects of common SNPs explain 22–46% of phenotypic variation in childhood intelligence in the three largest cohorts (p = 3.9×10-15, 0.014 and 0.028). FNBP1L, previously reported to be the most statistically-significantly associated gene for adult intelligence, was also statistically-significantly associated with childhood intelligence (p = 0.003). Polygenic prediction analyses resulted in a statistically-significant correlation between predictor and outcome in all replication cohorts. The proportion of childhood intelligence explained by the predictor reached 1.2% (p = 6×10-5), 3.5% (p = 10-3) and 0.5% (p = 6×10-5) in three independent validation cohorts. Given the sample sizes, these genetic prediction results are consistent with expectations if the genetic architecture of childhood intelligence is like that of body mass index or height. Our study provides molecular support for the heritability and polygenic nature of childhood intelligence. Larger sample sizes will be required to detect individual variants with genome-wide statistical-significance.

  306. 2013-trzaskowski.pdf

  307. ⁠, W. David Hill, Ruben C. Arslan, Charley Xia, Michelle Luciano, Amador, Pau Navarro, Caroline Hayward, Reka Nagy, David J. Porteous, Andrew M. McIntosh, Ian J. Deary, Chris S. Haley, Lars Penke (2017-06-05):

    Pedigree-based analyses of intelligence have reported that genetic differences account for 50–80% of the phenotypic variation. For personality traits these effects are smaller, with 34–48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0% and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using ~20 000 individuals in the Generation Scotland family cohort genotyped for ~700 000 single nucleotide polymorphisms (SNPs), we exploit the high levels of (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWASs of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that ~50% of differences in intelligence, and ~40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence and education is consistent with mutation-selection balance.

  308. ⁠, Luke M. Evans, Rasool Tahmasbi, Scott I. Vrieze, Gonçalo R. Abecasis, Sayantan Das, Doug W. Bjelland, Teresa R. deCandia, Haplotype Reference Consortium, Michael E. Goddard, Benjamin M. Neale, Jian Yang, Peter M. Visscher, Matthew C. Keller (2017-03-10):

    Heritability, h2, is a foundational concept in genetics, critical to understanding the genetic basis of complex traits. Recently-developed methods that estimate heritability from genotyped SNPs, h2SNP, explain substantially more genetic variance than genome-wide statistically-significant loci, but less than classical estimates from twins and families. However, h2SNP estimates have yet to be comprehensively compared under a range of genetic architectures, making it difficult to draw conclusions from sometimes conflicting published estimates. Here, we used thousands of real whole genome sequences to simulate realistic phenotypes under a variety of genetic architectures, including those from very rare causal variants. We compared the performance of ten methods across different types of genotypic data (commercial SNP array positions, whole genome sequence variants, and imputed variants) and under differing causal variant frequencies, levels of stratification, and relatedness thresholds. These results provide guidance in interpreting past results and choosing optimal approaches for future studies. We then chose two methods (GREML-MS and GREML-LDMS) that best estimated overall h2SNP and the causal variant frequency spectra to six phenotypes in the UK Biobank using imputed genome-wide variants. Our results suggest that as imputation reference panels become larger and more diverse, estimates of the frequency distribution of causal variants will become increasingly unbiased and the vast majority of trait narrow-sense heritability will be accounted for.

  309. 2013-rietveld-supplementary-revision2.pdf

  310. https://www.pnas.org/content/111/38/13790.full.pdf#page=2

  311. https://www.pnas.org/content/suppl/2014/09/06/1404623111.DCSupplemental/pnas.1404623111.sapp.pdf

  312. ⁠, G. Davies, N. Armstrong, J. C. Bis, J. Bressler, V. Chouraki, S. Giddaluru, E. Hofer, C. A Ibrahim-Verbaas, M. Kirin, J. Lahti, S. J. van der Lee, S. Le Hellard, T. Liu, R. E. Marioni, C. Oldmeadow, I. Postmus, A. V. Smith, J. A Smith, A. Thalamuthu, R. Thomson, V. Vitart, J. Wang, L. Yu, L. Zgaga, W. Zhao, R. Boxall, S. E. Harris, W. D. Hill, D. C. Liewald, M. Luciano, H. Adams, D. Ames, N. Amin, P. Amouyel, A. A Assareh, R. Au, J. T. Becker, A. Beiser, C. Berr, L. Bertram, E. Boerwinkle, B. M. Buckley, H. Campbell, J. Corley, P. L. De Jager, C. Dufouil, J. G. Eriksson, T. Espeseth, J. D. Faul, I. Ford, Generation Scotland, R. F. Gottesman, M. E. Griswold, V. Gudnason, T. B. Harris, G. Heiss, A. Hofman, E. G. Holliday, J. Huffman, S. L. R. Kardia, N. Kochan, D. S. Knopman, J. B. Kwok, J-C Lambert, T. Lee, G. Li, S-C Li, M. Loitfelder, O. L. Lopez, A. J. Lundervold, A. Lundqvist, K. A Mather, S. S. Mirza, L. Nyberg, B. A Oostra, A. Palotie, G. Papenberg, A. Pattie, K. Petrovic, O. Polasek, B. M. Psaty, P. Redmond, S. Reppermund, J. I Rotter, H. Schmidt, M. Schuur, P. W. Schofield, R. J. Scott, V. M. Steen, D. J. Stott, J. C. van Swieten, K. D. Taylor, J. Trollor, S. Trompet, A. G. Uitterlinden, G. Weinstein, E. Widen, B. G. Windham, J. W. Jukema, A. F. Wright, M. J. Wright, Q. Yang, H. Amieva, J. R. Attia, D. A Bennett, H. Brodaty, A. J. M. de Craen, C. Hayward, M. A Ikram, U. Lindenberger, L-G Nilsson, D. J. Porteous, K. Räikkönen, I. Reinvang, I. Rudan, P. S. Sachdev, R. Schmidt, P. R. Schofield, V. Srikanth, J. M. Starr, S. T. Turner, D. R. Weir, J. F. Wilson, C. van Duijn, L. Launer, A. L. Fitzpatrick, S. Seshadri, T. H. Mosley Jr, I. J. Deary (2015-02-03):

    General cognitive function is substantially heritable across the human life course from adolescence to old age. We investigated the genetic contribution to variation in this important, health-related and well-being-related trait in middle-aged and older adults. We conducted a meta-analysis of genome-wide association studies of 31 cohorts (N = 53 949) in which the participants had undertaken multiple, diverse cognitive tests. A general cognitive function phenotype was tested for, and created in each cohort by principal component analysis. We report 13 genome-wide statistically-significant single-nucleotide polymorphism (SNP) associations in three genomic regions, 6q16.1, 14q12 and 19q13.32 (best SNP and closest gene, respectively: rs10457441, p = 3.93 × 10−9, MIR2113; rs17522122, p = 2.55 × 10−8, AKAP6; rs10119, p = 5.67 × 10−9, APOE/​​​​TOMM40). We report one gene-based statistically-significant association with the HMGN1 gene located on chromosome 21 (p = 1 × 10−6). These genes have previously been associated with neuropsychiatric phenotypes. Meta-analysis results are consistent with a polygenic model of inheritance. To estimate SNP-based heritability, the genome-wide complex trait analysis procedure was applied to two large cohorts, the Atherosclerosis Risk in Communities Study (N = 6617) and the Health and Retirement Study (N = 5976). The proportion of phenotypic variation accounted for by all genotyped common SNPs was 29% (s.e. = 5%) and 28% (s.e. = 7%), respectively. Using polygenic prediction analysis, ~1.2% of the variance in general cognitive function was predicted in the Generation Scotland cohort (N = 5487; p = 1.5 × 10−17). In hypothesis-driven tests, there was statistically-significant association between general cognitive function and four genes previously associated with Alzheimer’s disease: TOMM40, APOE, ABCG1 and MEF2C.

  313. https://www.sciencedirect.com/science/article/pii/S0160289614001676

  314. https://www.nature.com/articles/mp2015108

  315. ⁠, D. Zabaneh, E. Krapohl, H. A. Gaspar, C. Curtis, S. H. Lee, H. Patel, S. Newhouse, H. M. Wu, M. A. Simpson, M. Putallaz, David Lubinski, Robert Plomin, G. Breen (2017-07-04):

    We used a case-control genome-wide association (GWA) design with cases consisting of 1238 individuals from the top 0.0003 (~170 mean IQ) of the population distribution of intelligence and 8172 unselected population-based controls. The single-nucleotide polymorphism heritability for the extreme IQ trait was 0.33 (0.02), which is the highest so far for a cognitive phenotype, and statistically-significant genome-wide genetic correlations of 0.78 were observed with educational attainment and 0.86 with population IQ. Three variants in locus ADAM12 achieved genome-wide statistical-significance, although they did not replicate with published GWA analyses of normal-range IQ or educational attainment. A genome-wide polygenic score constructed from the GWA results accounted for 1.6% of the variance of intelligence in the normal range in an unselected sample of 3414 individuals, which is comparable to the variance explained by GWA studies of intelligence with substantially larger sample sizes. The gene family plexins, members of which are mutated in several monogenic neurodevelopmental disorders, was statistically-significantly enriched for associations with high IQ. This study shows the utility of extreme trait selection for genetic study of intelligence and suggests that extremely high intelligence is continuous genetically with normal-range intelligence in the population.

  316. ⁠, G. Davies, R. E. Marioni, D. C. Liewald, W. D. Hill, S. P. Hagenaars, S. E. Harris, S. J. Ritchie, M. Luciano, C. Fawns-Ritchie, D. Lyall, B. Cullen, S. R. Cox, C. Hayward, D. J. Porteous, J. Evans, A. M. McIntosh, J. Gallacher, N. Craddock, J. P. Pell, D. J. Smith, C. R. Gale, I. J. Deary (2016-04-05):

    People’s differences in cognitive functions are partly heritable and are associated with important life outcomes. Previous genome-wide association (GWA) studies of cognitive functions have found evidence for polygenic effects yet, to date, there are few replicated genetic associations. Here we use data from the UK Biobank sample to investigate the genetic contributions to variation in tests of three cognitive functions and in educational attainment. GWA analyses were performed for verbal-numerical reasoning (n = 36 035), memory (n = 112 067), reaction time (n = 111 483) and for the attainment of a college or a university degree (n = 111 114). We report genome-wide statistically-significant single-nucleotide polymorphism (SNP)-based associations in 20 genomic regions, and statistically-significant gene-based findings in 46 regions. These include findings in the ATXN2, CYP2DG, APBA1 and CADM2 genes. We report replication of these hits in published GWA studies of cognitive function, educational attainment and childhood intelligence. There is also replication, in UK Biobank, of SNP hits reported previously in GWA studies of educational attainment and cognitive function. GCTA-GREML analyses, using common SNPs (minor allele frequency>0.01), indicated statistically-significant SNP-based heritabilities of 31% (s.e.m. = 1.8%) for verbal-numerical reasoning, 5% (s.e.m. = 0.6%) for memory, 11% (s.e.m. = 0.6%) for reaction time and 21% (s.e.m. = 0.6%) for educational attainment. Polygenic score analyses indicate that up to 5% of the variance in cognitive test scores can be predicted in an independent cohort. The genomic regions identified include several novel loci, some of which have been associated with intracranial volume, neurodegeneration, Alzheimer’s disease and schizophrenia.

  317. ⁠, Ibrahim-Verbaas, C. A Bressler, J. Debette, S. Schuur, M. Smith, A. V Bis, J. C Davies, G. Trompet, S. Smith, J. A Wolf, C. Chibnik, L. B Liu, Y. Vitart, V. Kirin, M. Petrovic, K. Polasek, O. Zgaga, L. Fawns-Ritchie, C. Hoffmann, P. Karjalainen, J. Lahti, J. Llewellyn, D. J Schmidt, C. O Mather, K. A Chouraki, V. Sun, Q. Resnick, S. M Rose, L. M Oldmeadow, C. Stewart, M. Smith, B. H Gudnason, V. Yang, Q. Mirza, S. S Jukema, J. W deJager, P. L Harris, T. B Liewald, D. C Amin, N. Coker, L. H Stegle, O. Lopez, O. L Schmidt, R. Teumer, A. Ford, I. Karbalai, N. Becker, J. T Jonsdottir, M. K Au, R. Fehrmann, Rsn Herms, S. Nalls, M. Zhao, W. Turner, S. T Yaffe, K. Lohman, K. van Swieten, J. C Kardia, Slr Knopman, D. S Meeks, W. M Heiss, G. Holliday, E. G Schofield, P. W Tanaka, T. Stott, D. J Wang, J. Ridker, P. Gow, A. J Pattie, A. Starr, J. M Hocking, L. J Armstrong, N. J McLachlan, S. Shulman, J. M Pilling, L. C Eiriksdottir, G. Scott, R. J Kochan, N. A Palotie, A. Hsieh, Y-C Eriksson, J. G Penman, A. Gottesman, R. F Oostra, B. A Yu, L. DeStefano, A. L Beiser, A. Garcia, M. Rotter, J. I Nöthen, M. M Hofman, A. Slagboom, P. E Westendorp, Rgj Buckley, B. M Wolf, P. A Uitterlinden, A. G Psaty, B. M Grabe, H. J Bandinelli, S. Chasman, D. I Grodstein, F. Räikkönen, K. Lambert, J-C Porteous, D. J Price, J. F Sachdev, P. S Ferrucci, L. Attia, J. R Rudan, I. Hayward, C. Wright, A. F Wilson, J. F Cichon, S. Franke, L. Schmidt, H. Ding, J. de Craen, Ajm Fornage, M. Bennett, D. A Deary, I. J Ikram, M. A Launer, L. J Fitzpatrick, A. L Seshadri, S. van Duijn, C. M Mosley, T. H (2016):

    To identify common variants contributing to normal variation in two specific domains of cognitive functioning, we conducted a genome-wide association study (GWAS) of and information processing speed in non-demented older adults from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium. Neuropsychological testing was available for 5429-32,070 subjects of European ancestry aged 45 years or older, free of dementia and clinical stroke at the time of cognitive testing from 20 cohorts in the discovery phase. We analyzed performance on the Trail Making Test parts A and B, the Letter Digit Substitution Test (LDST), the Digit Symbol Substitution Task (DSST), semantic and phonemic fluency tests, and the Stroop Color and Word Test. Replication was sought in 1311-21860 subjects from 20 independent cohorts. A statistically-significant association was observed in the discovery cohorts for the single-nucleotide polymorphism (SNP) rs17518584 (discovery p-value = 3.12 × 10−8) and in the joint discovery and replication meta-analysis (p-value = 3.28 × 10−9 after adjustment for age, gender and education) in an intron of the gene cell adhesion molecule 2 (CADM2) for performance on the LDST/​​​​DSST. Rs17518584 is located about 170 kb upstream of the transcription start site of the major transcript for the CADM2 gene, but is within an intron of a variant transcript that includes an alternative first exon. The variant is associated with expression of CADM2 in the cingulate cortex (p-value = 4 × 10−4). The protein encoded by CADM2 is involved in glutamate signaling (p-value = 7.22 × 10−15), gamma-aminobutyric acid (GABA) transport (p-value = 1.36 × 10−11) and neuron cell-cell adhesion (p-value = 1.48 × 10−13). Our findings suggest that genetic variation in the CADM2 gene is associated with individual differences in information processing speed.

  318. 2016-okbay-2.pdf: ⁠, Aysu Okbay, Jonathan P. Beauchamp, Mark Alan Fontana, James J. Lee, Tune H. Pers, Cornelius A. Rietveld, Patrick Turley, Guo-Bo Chen, Valur Emilsson, S. Fleur W. Meddens, Sven Oskarsson, Joseph K. Pickrell, Kevin Thom, Pascal Timshel, Ronald de Vlaming, Abdel Abdellaoui, Tarunveer S. Ahluwalia, Jonas Bacelis, Clemens Baumbach, Gyda Bjornsdottir, Johannes H. Brandsma, Maria Pina Concas, Jaime Derringer, Nicholas A. Furlotte, Tessel E. Galesloot, Giorgia Girotto, Richa Gupta, Leanne M. Hall, Sarah E. Harris, Edith Hofer, Momoko Horikoshi, Jennifer E. Huffman, Kadri Kaasik, Ioanna P. Kalafati, Robert Karlsson, Augustine Kong, Jari Lahti, Sven J. van der Lee, Christiaan de Leeuw, Penelope A. Lind, Karl-Oskar Lindgren, Tian Liu, Massimo Mangino, Jonathan Marten, Evelin Mihailov, Michael B. Miller, Peter J. van der Most, Christopher Oldmeadow, Antony Payton, Natalia Pervjakova, Wouter J. Peyrot, Yong Qian, Olli Raitakari, Rico Rueedi, Erika Salvi, Börge Schmidt, Katharina E. Schraut, Jianxin Shi, Albert V. Smith, Raymond A. Poot, Beate St Pourcain, Alexander Teumer, Gudmar Thorleifsson, Niek Verweij, Dragana Vuckovic, Juergen Wellmann, Harm-Jan Westra, Jingyun Yang, Wei Zhao, Zhihong Zhu, Behrooz Z. Alizadeh, Najaf Amin, Andrew Bakshi, Sebastian E. Baumeister, Ginevra Biino, Klaus Bønnelykke, Patricia A. Boyle, Harry Campbell, Francesco P. Cappuccio, Gail Davies, Jan-Emmanuel De Neve, Panos Deloukas, Ilja Demuth, Jun Ding, Peter Eibich, Lewin Eisele, Niina Eklund, David M. Evans, Jessica D. Faul, Mary F. Feitosa, Andreas J. Forstner, Ilaria Gandin, Bjarni Gunnarsson, Bjarni V. Halldórsson, Tamara B. Harris, Andrew C. Heath, Lynne J. Hocking, Elizabeth G. Holliday, Georg Homuth, Michael A. Horan, Jouke-Jan Hottenga, Philip L. de Jager, Peter K. Joshi, Astanand Jugessur, Marika A. Kaakinen, Mika Kähönen, Stavroula Kanoni, Liisa Keltigangas-Järvinen, Lambertus A. L. M. Kiemeney, Ivana Kolcic, Seppo Koskinen, Aldi T. Kraja, Martin Kroh, Zoltan Kutalik, Antti Latvala, Lenore J. Launer, Maël P. Lebreton, Douglas F. Levinson, Paul Lichtenstein, Peter Lichtner, David C. M. Liewald, LifeLines Cohort Study, Anu Loukola, Pamela A. Madden, Reedik Mägi, Tomi Mäki-Opas, Riccardo E. Marioni, Pedro Marques-Vidal, Gerardus A. Meddens, George McMahon, Christa Meisinger, Thomas Meitinger, Yusplitri Milaneschi, Lili Milani, Grant W. Montgomery, Ronny Myhre, Christopher P. Nelson, Dale R. Nyholt, William E. R. Ollier, Aarno Palotie, Lavinia Paternoster, Nancy L. Pedersen, Katja E. Petrovic, David J. Porteous, Katri Räikkönen, Susan M. Ring, Antonietta Robino, Olga Rostapshova, Igor Rudan, Aldo Rustichini, Veikko Salomaa, Alan R. Sanders, Antti-Pekka Sarin, Helena Schmidt, Rodney J. Scott, Blair H. Smith, Jennifer A. Smith, Jan A. Staessen, Elisabeth Steinhagen-Thiessen, Konstantin Strauch, Antonio Terracciano, Martin D. Tobin, Sheila Ulivi, Simona Vaccargiu, Lydia Quaye, Frank J. A. van Rooij, Cristina Venturini, Anna A. E. Vinkhuyzen, Uwe Völker, Henry Völzke, Judith M. Vonk, Diego Vozzi, Johannes Waage, Erin B. Ware, Gonneke Willemsen, John R. Attia, David A. Bennett, Klaus Berger, Lars Bertram, Hans Bisgaard, Dorret I. Boomsma, Ingrid B. Borecki, Ute Bültmann, Christopher F. Chabris, Francesco Cucca, Daniele Cusi, Ian J. Deary, George V. Dedoussis, Cornelia M. van Duijn, Johan G. Eriksson, Barbara Franke, Lude Franke, Paolo Gasparini, Pablo V. Gejman, Christian Gieger, Hans-Jörgen Grabe, Jacob Gratten, Patrick J. F. Groenen, Vilmundur Gudnason, Pim van der Harst, Caroline Hayward, David A. Hinds, Wolfgang Hoffmann, Elina Hyppönen, William G. Iacono, Bo Jacobsson, Marjo-Riitta Järvelin, Karl-Heinz Jöckel, Jaakko Kaprio, Sharon L. R. Kardia, Terho Lehtimäki, Steven F. Lehrer, Patrik K. E. Magnusson, Nicholas G. Martin, Matt McGue, Andres Metspalu, Neil Pendleton, Brenda W. J. H. Penninx, Markus Perola, Nicola Pirastu, Mario Pirastu, Ozren Polasek, Danielle Posthuma, Christine Power, Michael A. Province, Nilesh J. Samani, David Schlessinger, Reinhold Schmidt, Thorkild I. A. Sørensen, Tim D. Spector, Kari Stefansson, Unnur Thorsteinsdottir, A. Roy Thurik, Nicholas J. Timpson, Henning Tiemeier, Joyce Y. Tung, André G. Uitterlinden, Veronique Vitart, Peter Vollenweider, David R. Weir, James F. Wilson, Alan F. Wright, Dalton C. Conley, Robert F. Krueger, George Davey Smith, Albert Hofman, David I. Laibson, Sarah E. Medland, Michelle N. Meyer, Jian Yang, Magnus Johannesson, Tõnu Esko, Peter M. Visscher, Philipp D. Koellinger, David Cesarini, Daniel J. Benjamin (2016-05-11; iq):

    Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample1, of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide statistically-significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

  319. https://static-content.springer.com/esm/art%3A10.1038%2Fnature17671/MediaObjects/41586_2016_BFnature17671_MOESM48_ESM.pdf

  320. https://www.nature.com/mp/journal/vaop/ncurrent/full/mp2016107a.html

  321. ⁠, J. W. Trampush, M. L. Z. Yang, J. Yu, E. Knowles, G. Davies, D. C. Liewald, J. M. Starr, S. Djurovic, I. Melle, K. Sundet, A. Christoforou, I. Reinvang, P. DeRosse, A. J. Lundervold, V. M. Steen, T. Espeseth, K. Räikkönen, E. Widen, A. Palotie, J. G. Eriksson, I. Giegling, B. Konte, P. Roussos, S. Giakoumaki, K. E. Burdick, A. Payton, W. Ollier, M. Horan, O. Chiba-Falek, D. K. Attix, A. C. Need, E. T. Cirulli, A. N. Voineskos, N. C. Stefanis, D. Avramopoulos, A. Hatzimanolis, D. E. Arking, N. Smyrnis, R. M. Bilder, N. A. Freimer, T. D. Cannon, E. London, R. A. Poldrack, F. W. Sabb, E. Congdon, E. D. Conley, M. A. Scult, D. Dickinson, R. E. Straub, G. Donohoe, D. Morris, A. Corvin, M. Gill, A. R. Hariri, D. R. Weinberger, N. Pendleton, P. Bitsios, D. Rujescu, J. Lahti, S. Le Hellard, M. C. Keller, O. A. Andreassen, I. J. Deary, D. C. Glahn, A. K. Malhotra, T. Lencz (2017-01-17):

    The complex nature of human cognition has resulted in cognitive genomics lagging behind many other fields in terms of gene discovery using genome-wide association study (GWAS) methods. In an attempt to overcome these barriers, the current study utilized GWAS meta-analysis to examine the association of common genetic variation (~8M single-nucleotide polymorphisms (SNP) with minor allele frequency ⩾1%) to general cognitive function in a sample of 35 298 healthy individuals of European ancestry across 24 cohorts in the Cognitive Genomics Consortium (COGENT). In addition, we utilized individual SNP lookups and polygenic score analyses to identify genetic overlap with other relevant neurobehavioral phenotypes. Our primary GWAS meta-analysis identified two novel SNP loci (top SNPs: rs76114856 in the CENPO gene on chromosome 2 and rs6669072 near LOC105378853 on chromosome 1) associated with cognitive performance at the genome-wide statistical-significance level (p<5 × 10−8). Gene-based analysis identified an additional three Bonferroni-corrected statistically-significant loci at chromosomes 17q21.31, 17p13.1 and 1p13.3. Altogether, common variation across the genome resulted in a conservatively estimated SNP heritability of 21.5% (s.e. = 0.01%) for general cognitive function. Integration with prior GWAS of cognitive performance and educational attainment yielded several additional statistically-significant loci. Finally, we found robust polygenic correlations between cognitive performance and educational attainment, several psychiatric disorders, birth length/​​​​weight and smoking behavior, as well as a novel genetic association to the personality trait of openness. These data provide new insight into the genetics of neurocognitive function with relevance to understanding the pathophysiology of neuropsychiatric illness.

  322. 2017-sniekers.pdf: ⁠, Suzanne Sniekers, Sven Stringer, Kyoko Watanabe, Philip R. Jansen, Jonathan R. I. Coleman, Eva Krapohl, Erdogan Taskesen, Anke R. Hammerschlag, Aysu Okbay, Delilah Zabaneh, Najaf Amin, Gerome Breen, David Cesarini, Christopher F. Chabris, William G. Iacono, M. Arfan Ikram, Magnus Johannesson, Philipp Koellinger, James J. Lee, Patrik K. E Magnusson, Matt McGue, Mike B. Miller, William E. R. Ollier, Antony Payton, Neil Pendleton, Robert Plomin, Cornelius A. Rietveld, Henning Tiemeier, Cornelia M. van Duijn, Danielle Posthuma (2017-05-22; iq):

    Intelligence is associated with important economic and health-related life outcomes1. Despite intelligence having substantial heritability2 (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered3,4,5. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL p < 5 × 10−8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA p < 2.73 × 10−6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive p = 3.5 × 10−6). Despite the well-known difference in twin-based heritability2 for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression p = 5.4 × 10−29). These findings provide new insight into the genetic architecture of intelligence.

  323. ⁠, Jeanne E. Savage, Philip R. Jansen, Sven Stringer, Kyoko Watanabe, Julien Bryois, Christiaan A. de Leeuw, Mats Nagel, Swapnil Awasthi, Peter B. Barr, Jonathan R. I Coleman, Katrina L. Grasby, Anke R. Hammerschlag, Jakob Kaminski, Robert Karlsson, Eva Krapohl, Max Lam, Marianne Nygaard, Chandra A. Reynolds, Joey W. Trampush, Hannah Young, Delilah Zabaneh, Sara Hägg, Narelle K. Hansell, Ida K. Karlsson, Sten Linnarsson, Grant W. Montgomery, Ana B. Muñoz-Manchado, Erin B. Quinlan, Gunter Schumann, Nathan Skene, Bradley T. Webb, Tonya White, Dan E. Arking, Deborah K. Attix, Dimitrios Avramopoulos, Robert M. Bilder, Panos Bitsios, Katherine E. Burdick, Tyrone D. Cannon, Ornit Chiba-Falek, Andrea Christoforou, Elizabeth T. Cirulli, Eliza Congdon, Aiden Corvin, Gail Davies, Ian J. Deary, Pamela DeRosse, Dwight Dickinson, Srdjan Djurovic, Gary Donohoe, Emily Drabant Conley, Johan G. Eriksson, Thomas Espeseth, Nelson A. Freimer, Stella Giakoumaki, Ina Giegling, Michael Gill, David C. Glahn, Ahmad R. Hariri, Alex Hatzimanolis, Matthew C. Keller, Emma Knowles, Bettina Konte, Jari Lahti, Stephanie Le Hellard, Todd Lencz, David C. Liewald, Edythe London, Astri J. Lundervold, Anil K. Malhotra, Ingrid Melle, Derek Morris, Anna C. Need, William Ollier, Aarno Palotie, Antony Payton, Neil Pendleton, Russell A. Poldrack, Katri Räikkönen, Ivar Reinvang, Panos Roussos, Dan Rujescu, Fred W. Sabb, Matthew A. Scult, Olav B. Smeland, Nikolaos Smyrnis, John M. Starr, Vidar M. Steen, Nikos C. Stefanis, Richard E. Straub, Kjetil Sundet, Aristotle N. Voineskos, Daniel R. Weinberger, Elisabeth Widen, Jin Yu, Goncalo Abecasis, Ole A. Andreassen, Gerome Breen, Lene Christiansen, Birgit Debrabant, Danielle M. Dick, Andreas Heinz, Jens Hjerling-Leffler, M. Arfan Ikram, Kenneth S. Kendler, Nicholas G. Martin, Sarah E. Medland, Nancy L. Pedersen, Robert Plomin, Tinca JC Polderman, Stephan Ripke, Sophie van der Sluis, Patrick F. Sullivan, Henning Tiemeier, Scott I. Vrieze, Margaret J. Wright, Danielle Posthuma (2017-09-06):

    Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3–7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (n = 279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimer’s dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

  324. ⁠, William D. Hill, Robert E. Marioni, O. Maghzian, Stuart J. Ritchie, Sarah P. Hagenaars, A. M. McIntosh, C. R. Gale, G. Davies, Ian J. Deary (2018-01-11):

    Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence (rg = 0.70). We used these findings as foundations for our use of a novel approach—multi-trait analysis of genome-wide association studies (MTAG; Turley et al. 2017)—to combine two large genome-wide association studies (GWASs) of education and intelligence, increasing statistical power and resulting in the largest GWAS of intelligence yet reported. Our study had four goals: first, to facilitate the discovery of new genetic loci associated with intelligence; second, to add to our understanding of the biology of intelligence differences; third, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predicts phenotypic intelligence in an independent sample. By combining datasets using MTAG, our functional sample size increased from 199,242 participants to 248,482. We found 187 independent loci associated with intelligence, implicating 538 genes, using both SNP-based and gene-based GWAS. We found evidence that neurogenesis and myelination—as well as genes expressed in the synapse, and those involved in the regulation of the nervous system—may explain some of the biological differences in intelligence. The results of our combined analysis demonstrated the same pattern of genetic correlations as those from previous GWASs of intelligence, providing support for the meta-analysis of these genetically-related phenotypes.

  325. ⁠, Gail Davies, Max Lam, Sarah E. Harris, Joey W. Trampush, Michelle Luciano, W. David Hill, Saskia P. Hagenaars, Stuart J. Ritchie, Riccardo E. Marioni, Chloe Fawns-Ritchie, David CM Liewald, Judith A. Okely, Ari V. Ahola-Olli, Catriona LK Barnes, Lars Bertram, Joshua C. Bis, Katherine E. Burdick, Andrea Christoforou, Pamela DeRosse, Srdjan Djurovic, Thomas Espeseth, Stella Giakoumaki, Sudheer Giddaluru, Daniel E. Gustavson, Caroline Hayward, Edith Hofer, M. Arfan Ikram, Robert Karlsson, Emma Knowles, Jari Lahti, Markus Leber, Shuo Li, Karen A. Mather, Ingrid Melle, Derek Morris, Christopher Oldmeadow, Teemu Palviainen, Antony Payton, Raha Pazoki, Katja Petrovic, Chandra A. Reynolds, Muralidharan Sargurupremraj, Markus Scholz, Jennifer A. Smith, Albert V. Smith, Natalie Terzikhan, Anbu Thalamuthu, Stella Trompet, Sven J. van der Lee, Erin B. Ware, B. Gwen Windham, Margaret J. Wright, Jingyun Yang, Jin Yu, David Ames, Najaf Amin, Philippe Amouyel, Ole A. Andreassen, Nicola J. Armstrong, Amelia A. Assareh, John R. Attia, Deborah Attix, Dimitrios Avramopoulos, David A. Bennett, Anne C. Böhmer, Patricia A. Boyle, Henry Brodaty, Harry Campbell, Tyrone D. Cannon, Elizabeth T. Cirulli, Eliza Congdon, Emily Drabant Conley, Janie Corley, Simon R. Cox, Anders M. Dale, Abbas Dehghan, Danielle Dick, Dwight Dickinson, Johan G. Eriksson, Evangelos Evangelou, Jessica D. Faul, Ian Ford, Nelson A. Freimer, He Gao, Ina Giegling, Nathan A. Gillespie, Scott D. Gordon, Rebecca F. Gottesman, Michael E. Griswold, Vilmundur Gudnason, Tamara B. Harris, Annette M. Hartmann, Alex Hatzimanolis, Gerardo Heiss, Elizabeth G. Holliday, Peter K. Joshi, Mika Kähönen, Sharon LR Kardia, Ida Karlsson, Luca Kleineidam, David S. Knopman, Nicole A. Kochan, Bettina Konte, John B. Kwok, Stephanie Le Hellard, Teresa Lee, Terho Lehtimäki, Shu-Chen Li, Tian Liu, Marisa Koini, Edythe London, Will T. Longstreth, Oscar L. Lopez, Anu Loukola, Tobias Luck, Astri J. Lundervold, Anders Lundquist, Leo-Pekka Lyytikäinen, Nicholas G. Martin, Grant W. Montgomery, Alison D. Murray, Anna C. Need, Raymond Noordam, Lars Nyberg, William Ollier, Goran Papenberg, Alison Pattie, Ozren Polasek, Russell A. Poldrack, Bruce M. Psaty, Simone Reppermund, Steffi G. Riedel-Heller, Richard J. Rose, Jerome I. Rotter, Panos Roussos, Suvi P. Rovio, Yasaman Saba, Fred W. Sabb, Perminder S. Sachdev, Claudia Satizabal, Matthias Schmid, Rodney J. Scott, Matthew A. Scult, Jeannette Simino, P. Eline Slagboom, Nikolaos Smyrnis, Aïcha Soumaré, Nikos C. Stefanis, David J. Stott, Richard E. Straub, Kjetil Sundet, Adele M. Taylor, Kent D. Taylor, Ioanna Tzoulaki, Christophe Tzourio, André Uitterlinden, Veronique Vitart, Aristotle N. Voineskos, Jaakko Kaprio, Michael Wagner, Holger Wagner, Leonie Weinhold, K. Hoyan Wen, Elisabeth Widen, Qiong Yang, Wei Zhao, Hieab HH Adams, Dan E. Arking, Robert M. Bilder, Panos Bitsios, Eric Boerwinkle, Ornit Chiba-Falek, Aiden Corvin, Philip L. De Jager, Stéphanie Debette, Gary Donohoe, Paul Elliott, Annette L. Fitzpatrick, Michael Gill, David C. Glahn, Sara Hägg, Narelle K. Hansell, Ahmad R. Hariri, M. Kamran Ikram, J. Wouter Jukema, Eero Vuoksimaa, Matthew C. Keller, William S. Kremen, Lenore Launer, Ulman Lindenberger, Aarno Palotie, Nancy L. Pedersen, Neil Pendleton, David J. Porteous, Katri Räikkönen, Olli T. Raitakari, Alfredo Ramirez, Ivar Reinvang, Igor Rudan, Dan Rujescu, Reinhold Schmidt, Helena Schmidt, Peter W. Schofield, Peter R. Schofield, John M. Starr, Vidar M. Steen, Julian N. Trollor, Steven T. Turner, Cornelia M. Van Duijn, Arno Villringer, Daniel R. Weinberger, David R. Weir, James F. Wilson, Anil Malhotra, Andrew M. McIntosh, Catharine R. Gale, Sudha Seshadri, Thomas H. Mosley, Jan Bressler, Todd Lencz, Ian J. Deary (2017-08-18):

    General cognitive function is a prominent human trait associated with many important life outcomes1,2, including longevity3. The substantial heritability of general cognitive function is known to be polygenic, but it has had little explication in terms of the contributing genetic variants4,5,6. Here, we combined cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total n = 280,360; age range = 16 to 102). We found 9,714 genome-wide statistically-significant SNPs (P<5 x 10−8) in 99 independent loci. Most showed clear evidence of functional importance. Among many novel genes associated with general cognitive function were SGCZ, ATXN1, MAPT, AUTS2, and P2RY6. Within the novel genetic loci were variants associated with neurodegenerative disorders, neurodevelopmental disorders, physical and psychiatric illnesses, brain structure, and BMI. Gene-based analyses found 536 genes statistically-significantly associated with general cognitive function; many were highly expressed in the brain, and associated with neurogenesis and dendrite gene sets. Genetic association results predicted up to 4% of general cognitive function variance in independent samples. There was significant genetic overlap between general cognitive function and information processing speed, as well as many health variables including longevity.

  326. 2018-zhang.pdf: “Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits”⁠, Yan Zhang, Guanghao Qi, Ju-Hyun Park, Nilanjan Chatterjee

  327. ⁠, E. Krapohl, H. Patel, S. Newhouse, C. J. Curtis, S. von Stumm, P. S. Dale, D. Zabaneh, G. Breen, P. F. O'Reilly, R. Plomin (2018-08-08):

    A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWASs, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWASs of cognitive, medical and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body mass index (BMI) and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK representative sample of 6710 unrelated adolescents. The MPS approach predicted 10.9% variance in educational achievement, 4.8% in general cognitive ability and 5.4% in BMI in an independent test set, predicting 1.1%, 1.1%, and 1.6% more variance than the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene-environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

  328. ⁠, Jonathan R. I. Coleman, Julien Bryois, Héléna A. Gaspar, Philip R. Jansen, Jeanne Savage, Nathan Skene, Robert Plomin, Ana B. Muñoz-Manchado, Sten Linnarsson, Greg Crawford, Jens Hjerling-Leffler, Patrick F. Sullivan, Danielle Posthuma, Gerome Breen (2017-07-31):

    Variance in IQ is associated with a wide range of health outcomes, and 1% of the population are affected by intellectual disability. Despite a century of research, the fundamental neural underpinnings of intelligence remain unclear. We integrate results from genome-wide association studies (GWAS) of intelligence with brain tissue and single cell gene expression data to identify tissues and cell types associated with intelligence. GWAS data for IQ (n = 78,308) were meta-analyzed with an extreme-trait cohort of 1,247 individuals with mean IQ ~170 and 8,185 controls. Genes associated with intelligence implicate pyramidal neurons of the somatosensory cortex and CA1 region of the hippocampus, and midbrain embryonic GABAergic neurons. Tissue-specific analyses find the most significant enrichment for frontal cortex brain expressed genes. These results suggest specific neuronal cell types and genes may be involved in intelligence and provide new hypotheses for neuroscience experiments using model systems.

  329. ⁠, Lloyd T. Elliott, Kevin Sharp, Fidel Alfaro-Almagro, Gwenaëlle Douaud, Karla Miller, Jonathan Marchini, Stephen Smith (2017-08-21):

    The genetic basis of brain structure and function is largely unknown. We carried out genome-wide association studies (GWAS) of 3,144 distinct functional and structural brain imaging derived phenotypes (IDPs), using imaging and genetic data from a total of 9,707 participants in UK Biobank. All subjects were imaged on a single scanner, with 6 distinct brain imaging modalities being acquired. We show that most of the IDPs are heritable and we identify patterns of co-heritability within and between IDP sub-classes. We report 1,262 SNP associations with IDPs, based on a discovery sample of 8,426 subjects. Notable significant and interpretable associations include: spatially specific changes in T2* in subcortical regions associated with several genes related to iron transport and storage; spatially extended changes in white matter micro-structure associated with genes coding for proteins of the extracellular matrix and the epidermal growth factor; variations in pontine crossing tract neural organization associated with genes that regulate axon guidance and fasciculation during development; and variations in brain connectivity associated with 14 genes that contribute broadly to brain development, patterning and plasticity. Our results provide new insight into the genetic architecture of the brain with relevance to complex neurological and psychiatric disorders, as well as brain development and aging.

    The most merciful thing in the world, I think, is the inability of the human mind to correlate all its contents. (H.P. Lovecraft, 1890-1937)

  330. 2018-turley.pdf: ⁠, Patrick Turley, Raymond K. Walters, Omeed Maghzian, Aysu Okbay, James J. Lee, Mark Alan Fontana, Tuan Anh Nguyen-Viet, Robbee Wedow, Meghan Zacher, Nicholas A. Furlotte, 23andMe Research Team, Social Science Genetic Association Consortium, Patrik Magnusson, Sven Oskarsson, Magnus Johannesson, Peter M. Visscher, David Laibson, David Cesarini, Benjamin M. Neale, Daniel J. Benjamin (2017-10-23; genetics  /​ ​​ ​correlation):

    We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (Neff = 354,862), neuroticism (n = 168,105), and subjective well-being (n = 388,538). As compared to the 32, 9, and 13 genome-wide statistically-significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

  331. ⁠, W. D. Hill, G. Davies, A. M. McIntosh, C. R. Gale, I. J. Deary (2017-07-07):

    Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including many physical and mental health variables. Both education and household income are strongly genetically correlated with intelligence, at r g = 0.73 and r g = 0.70 respectively. This allowed us to utilize a novel approach, Multi-Trait Analysis of Genome-wide association studies (MTAG; Turley et al 2017), to combine two large genome-wide association studies (GWASs) of education and household income to increase power in the largest GWAS on intelligence so far (Sniekers et al 2017). This study had four goals: firstly, to facilitate the discovery of new genetic loci associated with intelligence; secondly, to add to our understanding of the biology of intelligence differences; thirdly, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predict phenotypic intelligence variance in an independent sample. We apply MTAG to three large GWAS: Sniekers et al 2017 on intelligence, Okbay et al 2016 on Educational attainment, and Hill et al 2016 on household income. By combining these three samples our functional sample size increased from 78 308 participants to 147 194. We found 107 independent loci associated with intelligence, implicating 233 genes, using both SNP-based and gene-based GWAS. We find evidence that neurogenesis may explain some of the biological differences in intelligence as well as genes expressed in the synapse and those involved in the regulation of the nervous system. We show that the results of our combined analysis demonstrate the same pattern of genetic correlations as a single measure/​​​​the simple measure of intelligence, providing support for the meta-analysis of these genetically-related phenotypes. We find that our MTAG meta-analysis of intelligence shows similar genetic correlations to 26 other phenotypes when compared with a GWAS consisting solely of cognitive tests. Finally, using an independent sample of 6 844 individuals we were able to predict 7% of intelligence using SNP data alone.

  332. 2018-plomin.pdf: ⁠, Robert Plomin, Sophie von Stumm (2018; iq):

    Intelligence—the ability to learn, reason and solve problems—is at the forefront of behavioural genetic research. Intelligence is highly heritable and predicts important educational, occupational and health outcomes better than any other trait. Recent genome-wide association studies have successfully identified inherited genome sequence differences that account for 20% of the 50% heritability of intelligence. These findings open new avenues for research into the causes and consequences of intelligence using genome-wide polygenic scores that aggregate the effects of thousands of genetic variants.

  333. ⁠, Tian Ge, Chia-Yen Chen, Richard Vettermann, Lauri J. Tuominen, Daphne J. Holt, Mert R. Sabuncu, Jordan W. Smoller (2018-01-04):

    Human intelligence differences are linked to diverse cognitive abilities and predict important life outcomes. Here we investigate the biological bases of fluid intelligence in a large sample of participants from the UK Biobank. We explore the genetic underpinnings of fluid intelligence via genome-wide association analysis (N = 108,147), and examine brain morphological correlates of fluid intelligence (N = 7, 485). Importantly, we develop novel statistical methods that enable high-dimensional co-heritability analysis, and compute high-resolution surface maps for the co-heritability and genetic correlations between fluid intelligence and cortical thickness measurements. Our analyses reveal the genetic overlap between fluid intelligence and brain morphology in predominately left inferior precentral gyrus, pars opercularis, superior temporal cortex, supramarginal gyrus, and their proximal regions. These results suggest a shared genetic basis between fluid intelligence and Broca’s speech and Wernicke’s language areas and motor regions, and may contribute to our understanding of the biological substrate of human fluid intelligence.

  334. https://www.sciencedirect.com/science/article/pii/S0160289614000178

  335. 2016-hughjones.pdf

  336. ⁠, Konrad Rawlik, Oriol Canela-Xandri, Albert Tenesa (2017-09-07):

    Phenotypic correlations of couples for phenotypes evident at the time of mate choice, like height, are well documented. Similarly, phenotypic correlations among partners for traits not directly observable at the time of mate choice, like longevity or late-onset disease status, have been reported. Partner correlations for longevity and late-onset disease are comparable in magnitude to correlations in 1st degree relatives. These correlations could arise as a consequence of convergence after mate choice, due to initial assortment on observable correlates of one or more risk factors (e.g. BMI), referred to as indirect assortative mating, or both. Using couples from the UK Biobank cohort, we show that longevity and disease history of the parents of white British couples is correlated. The correlations in parental longevity are replicated in the FamiLinx cohort. These correlations exceed what would be expected due to variations in lifespan based on year and location of birth. This suggests the presence of assortment on factors correlated with disease and lifespan, which show correlations across generations. Birth year, birth location, Townsend Deprivation Index, height, waist to hip ratio, BMI and smoking history of UK Biobank couples explained ~70% of the couple correlation in parental lifespan. For cardiovascular diseases, in particular hypertension, we find statistically-significant correlations in genetic values among partners, which support a model where partners assort for risk factors genetically correlated with cardiovascular disease. Identifying the factors that mediate indirect assortment on longevity and human disease risk will help to unravel what factors affect human disease and ultimately longevity.

  337. 2016-ganna.pdf

  338. 2016-kendall.pdf: “Cognitive Performance Among Carriers of Pathogenic Copy Number Variants_ Analysis of 152,000 UK Biobank Subjects”⁠, Kimberley M. Kendall, Elliott Rees, Valentina Escott-Price, Mark Einon, Rhys Thomas, Jonathan Hewitt, Michael C. O’Donovan, Michael J. Owen, James T. R. Walters, George Kirov

  339. 2017-mcconnell.pdf

  340. ⁠, Maxwell L. Elliott, Daniel W. Belsky, Kevin Anderson, David L. Corcoran, Tian Ge, Annchen Knodt, Joseph A. Prinz, Karen Sugden, Benjamin Williams, David Ireland, Richie Poulton, Avshalom Caspi, Avram Holmes, Terrie Moffitt, Ahmad R. Hariri (2018-03-23):

    People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to development of larger brains. We tested this hypothesis with molecular genetic data using discoveries from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We analyzed genetic, brain imaging, and cognitive test data from the UK Biobank, the Dunedin Study, the Brain Genomics Superstruct Project (GSP), and the Duke Neurogenetics Study (DNS) (combined n = 8,271). We measured genetics using polygenic scores based on published GWAS. We conducted meta-analysis to test associations among participants’ genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants’ education polygenic scores and their cognitive test performance. Effect-sizes were larger in the population-based UK Biobank and Dunedin samples than in the GSP and DNS samples. Sensitivity analysis suggested this effect-size difference partly reflected of cognitive performance in the GSP and DNS samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging data to understand neurobiology linking genetics with individual differences in cognitive performance.

  341. 2018-huguet.pdf: “Measuring and Estimating the Effect Sizes of Copy Number Variants on General Intelligence in Community-Based Samples”⁠, American Medical Association

  342. ⁠, Emma C. Johnson, Luke M. Evans, Matthew C. Keller (2018-03-29):

    Inbreeding increases the risk of certain Mendelian disorders in humans but may also reduce fitness through its effects on complex traits and diseases. Such is thought to occur due to increased at causal variants that are recessive with respect to fitness. Until recently it has been difficult to amass large enough sample sizes to investigate the effects of inbreeding depression on complex traits using genome-wide single nucleotide polymorphism (SNP) data in population-based samples. Further, it is difficult to infer causation in analyses that relate degree of inbreeding to complex traits because variables (e.g., education) may influence both the likelihood for parents to outbreed and offspring trait values. The present study used runs of homozygosity in genome-wide SNP data in up to 400,000 individuals in the UK Biobank to estimate the proportion of the autosome that exists in autozygous tracts—stretches of the genome which are identical due to a shared common ancestor. After multiple testing corrections and controlling for possible sociodemographic confounders, we found significant relationships in the predicted direction between estimated autozygosity and three of the 26 traits we investigated: age at first sexual intercourse, fluid intelligence, and forced expiratory volume in 1 second. Our findings for fluid intelligence and forced expiratory volume corroborate those of several published studies while the finding for age at first sexual intercourse was novel. These results may suggest that these traits have been associated with Darwinian fitness over evolutionary time, although there are other possible explanations for these associations that cannot be eliminated. Some of the autozygosity-trait relationships were attenuated after controlling for background sociodemographic characteristics, suggesting that care needs to be taken in the design and interpretation of ROH studies in order to glean reliable information about the genetic architecture and evolutionary history of complex traits.

    Author Summary

    Inbreeding is well known to increase the risk of rare, monogenic diseases, and there has been some evidence that it also affects complex traits, such as cognition and educational attainment. However, difficulties can arise when inferring causation in these types of analyses because of the potential for confounding variables (e.g., socioeconomic status) to bias the observed relationships between distant inbreeding and complex traits. In this investigation, we used single-nucleotide polymorphism data in a very large (N > 400,000) sample of seemingly outbred individuals to quantify the degree to which distant inbreeding is associated with 26 complex traits. We found robust evidence that distant inbreeding is inversely associated with fluid intelligence and a measure of lung function, and is positively associated with age at first sex, while other trait associations with inbreeding were attenuated after controlling for background sociodemographic characteristics. Our findings are consistent with evolutionary predictions that fluid intelligence, lung function, and age at first sex have been under selection pressures over time; however, they also suggest that confounding variables must be accounted for in order to reliably interpret results from these types of analyses.

  343. https://www.nature.com/articles/s41539-018-0019-8

  344. https://www.nature.com/articles/s41539-018-0021-1

  345. 2014-beaver.pdf

  346. 2015-tucker-drob.pdf

  347. https://www.pnas.org/content/112/15/4612.full

  348. https://link.springer.com/article/10.1007/s11065-015-9278-9/fulltext.html

  349. 2014-bouchard.pdf

  350. https://www.nature.com/mp/journal/v20/n1/full/mp2014105a.html

  351. 2015-mcgue.pdf

  352. http://humanvarieties.org/2013/04/03/is-psychometric-g-a-myth/

  353. http://citeseerx.ist.psu.edu/viewdoc/download?doi=

  354. 1969-jensen.pdf: ⁠, Arthur R. Jensen (1969-05-01; iq):

    Arthur Jensen argues that the failure of recent compensatory education efforts to produce lasting effects on children’s IQ and achievement suggests that the premises on which these efforts have been based should be reexamined. He begins by questioning a central notion upon which these and other educational programs have recently been based: that IQ differences are almost entirely a result of environmental differences and the cultural bias of IQ tests. After tracing the history of IQ tests, Jensen carefully defines the concept of IQ, pointing out that it appears as a common factor in all tests that have been devised thus far to tap higher mental processes. Having defined the concept of intelligence and related it to other forms of mental ability, Jensen employs an analysis of variance model to explain how IQ can be separated into genetic and environmental components. He then discusses the concept of “heritability”, a statistical tool for assessing the degree to which individual differences in a trait like intelligence can be accounted for by genetic factors. He analyzes several lines of evidence which suggest that the heritability of intelligence is quite high (ie., genetic factors are much more important than environmental factors in producing IQ differences). After arguing that environmental factors are not nearly as important in determining IQ as are genetic factors, Jensen proceeds to analyze the environmental influences which may be most critical in determining IQ. He concludes that prenatal influences may well contribute the largest environmental influence on IQ. He then discusses evidence which suggests that social class and racial variations in intelligence cannot be accounted for by differences in environment but must be attributed partially to genetic differences. After he has discussed the influence on the distribution of IQ in a society on its functioning, Jensen examines in detail the results of educational programs for young children, and finds that the changes in IQ produced by these programs are generally small. A basic conclusion of Jensen’s discussion of the influence of environment on IQ is that environment acts as a “threshold variable.” Extreme environmental deprivation can keep the child from performing up to his genetic potential, but an enriched educational program cannot push the child above that potential. Finally, Jensen examines other mental abilities that might be capitalized on in an educational program, discussing recent findings on diverse patterns of mental abilities between ethnic groups and his own studies of associative learning abilities that are independent of social class. He concludes that educational attempts to boost IQ have been misdirected and that the educational process should focus on teaching much more specific skills. He argues that this will be accomplished most effectively if educational methods are developed which are based on other mental abilities besides IQ.

  355. http://scottbarrykaufman.com/wp-content/uploads/2012/01/Nisbett-et-al.-2012.pdf

  356. DNB-FAQ

  357. DNB-meta-analysis

  358. https://www.dropbox.com/s/icvdgxhx041mghe/2009-harris-thenurtureassumption.epub?dl=0

  359. https://web.archive.org/web/20130831144130/http://gladwell.com:80/do-parents-matter/

  360. https://slatestarcodex.com/2014/11/14/the-dark-side-of-divorce/

  361. https://slatestarcodex.com/2013/06/25/nature-is-not-a-slate-its-a-series-of-levers/

  362. https://slatestarcodex.com/2016/03/16/non-shared-environment-doesnt-just-mean-schools-and-peers/

  363. https://web.archive.org/web/20130715010036/http://squid314.livejournal.com:80/346391.html

  364. https://jaymans.wordpress.com/2014/03/04/environmental-hereditarianism/

  365. https://web.archive.org/web/20171120104155/http://quillette.com/2015/12/01/why-parenting-may-not-matter-and-why-most-social-science-research-is-probably-wrong/

  366. https://kilthub.cmu.edu/articles/What_Went_Wrong_Reflections_on_Science_by_Observation_and_The_Bell_Curve/6493139/files/11937863.pdf#page=2

  367. http://rstb.royalsocietypublishing.org/content/363/1503/2519

  368. ⁠, M-R. Rautiainen, T. Paunio, E. Repo-Tiihonen, M. Virkkunen, H. M. Ollila, S. Sulkava, O. Jolanki, A. Palotie, J. Tiihonen (2016-09-06):

    The pathophysiology of antisocial personality disorder (ASPD) remains unclear. Although the most consistent biological finding is reduced grey matter volume in the frontal cortex, about 50% of the total liability to developing ASPD has been attributed to genetic factors. The contributing genes remain largely unknown. Therefore, we sought to study the genetic background of ASPD. We conducted a genome-wide association study (GWAS) and a replication analysis of Finnish criminal offenders fulfilling DSM-IV criteria for ASPD (n = 370, n = 5850 for controls, GWAS; n = 173, n = 3766 for controls and replication sample). The GWAS resulted in suggestive associations of two clusters of single-nucleotide polymorphisms at 6p21.2 and at 6p21.32 at the human leukocyte antigen (HLA) region. Imputation of HLA alleles revealed an independent association with DRB1*01:01 (odds ratio (OR) = 2.19 (1.53–3.14), p = 1.9 × 10-5). Two polymorphisms at 6p21.2 LINC00951LRFN2 gene region were replicated in a separate data set, and rs4714329 reached genome-wide statistical-significance (OR = 1.59 (1.37–1.85), p = 1.6 × 10−9) in the meta-analysis. The risk allele also associated with antisocial features in the general population conditioned for severe problems in childhood family (β = 0.68, p = 0.012). Functional analysis in brain tissue in open access GTEx and Braineac databases revealed eQTL associations of rs4714329 with LINC00951 and LRFN2 in cerebellum. In humans, LINC00951 and LRFN2 are both expressed in the brain, especially in the frontal cortex, which is intriguing considering the role of the frontal cortex in behavior and the neuroanatomical findings of reduced gray matter volume in ASPD. To our knowledge, this is the first study showing genome-wide statistically-significant and replicable findings on genetic variants associated with any personality disorder.

  369. ⁠, Guo, Guang Ou, Xiao-Ming Roettger, Michael Shih, Jean C (2008):

    Genetic studies of delinquent and criminal behavior are rare in spite of the wide recognition that individuals may differ in their propensity for delinquency and criminality. Using 2524 participants in Add Health in the United States, the present study demonstrates a link between the rare 2 repeat of the 30-bp VNTR in the MAOA gene and much higher levels of self-reported serious and violent delinquency. The evidence is based on a statistical association analysis and a functional analysis of MAOA promoter activity using two human brain-derived cell lines: neuroblastoma SH-SY5Y and human glioblastoma 1242-MG. The association analysis shows that men with a 2R report a level of serious delinquency and violent delinquency in adolescence and young adulthood that were about twice (CI: (0.21, 3.24), p = 0.025; and CI: (0.37, 2.5), p = 0.008 for serious and violent delinquency, respectively) as high as those for participants with the other variants. The results for women are similar, but weaker. In the functional analysis, the 2 repeat exhibits much lower levels of promoter activity than the 3 or 4 repeat.

  370. 2012-anholt.pdf: “Genetics of Aggression”⁠, Robert R. H. Anholt, Trudy F. C. Mackay

  371. 2013-beaver.pdf

  372. http://www.sakkyndig.com/psykologi/artvit/frisell2010.pdf

  373. 2012-frisell.pdf

  374. http://digitalcommons.unomaha.edu/cgi/viewcontent.cgi?article=1018&context=criminaljusticefacpub

  375. http://www.unav.edu/matrimonioyfamilia/observatorio/uploads/32708_Latvala-etal_PS2014_Paternal.pdf

  376. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842006/

  377. ⁠, Amir Sariaslan, Niklas Langstrom, Brian D’Onofrio, Johan Hallqvist, Johan Franck, Paul Lichtenstein (2013-03-26):

    We found that the adverse effect of neighbourhood deprivation on adolescent violent criminality and substance misuse in Sweden was not consistent with a causal inference. Instead, our findings highlight the need to control for familial confounding in multilevel studies of criminality and substance misuse.

  378. ⁠, Kendler, Kenneth S. Sundquist, Kristina Ohlsson, Henrik Palmér, Karolina Maes, Hermine Winkleby, Marilyn A. Sundquist, Jan (2012; genetics  /​ ​​ ​heritable  /​ ​​ ​adoption):

    Context: Prior research suggests that drug abuse (DA) is strongly influenced by both genetic and familial environmental factors. No large-scale adoption study has previously attempted to verify and integrate these findings.

    Objective: To determine how genetic and environmental factors contribute to the risk for DA.

    Design: Follow-up in 9 public databases (1961-2009) of adopted children and their biological and adoptive relatives.

    Setting: Sweden.

    Participants: The study included 18 115 adopted children born between 1950 and 1993; 78,079 biological parents and siblings; and 51,208 adoptive parents and siblings.

    Main Outcome Measures: Drug abuse recorded in medical, legal, or pharmacy registry records.

    Results: Risk for DA was significantly elevated in the adopted offspring of biological parents with DA (odds ratio, 2.09; 95% CI, 1.66-2.62), in biological full and half siblings of adopted children with DA (odds ratio, 1.84; 95% CI, 1.28-2.64; and odds ratio, 1.41; 95% CI, 1.19-1.67, respectively), and in adoptive siblings of adopted children with DA (odds ratio, 1.95; 95% CI, 1.43-2.65). A genetic risk index (including biological parental or sibling history of DA, criminal activity, and psychiatric or alcohol problems) and an environmental risk index (including adoptive parental history of divorce, death, criminal activity, and alcohol problems, as well as an adoptive sibling history of DA and psychiatric or alcohol problems) both strongly predicted the risk for DA. Including both indices along with sex and age at adoption in a predictive model revealed a significant positive interaction between the genetic and environmental risk indices.

    Conclusions: Drug abuse is an etiologically complex syndrome strongly influenced by a diverse set of genetic risk factors reflecting a specific liability to DA, by a vulnerability to other externalizing disorders, and by a range of environmental factors reflecting marital instability, as well as psychopathology and criminal behavior in the adoptive home. Adverse environmental effects on DA are more pathogenic in individuals with high levels of genetic risk. These results should be interpreted in the context of limitations of the diagnosis of DA from registries.

  379. 2014-sariaslan-1.pdf

  380. http://pubman.mpdl.mpg.de/pubman/item/escidoc:2176524/component/escidoc:2332385/Pappa_etal_AMJMedGenB_2015.pdf

  381. http://ije.oxfordjournals.org/content/44/2/713.full

  382. 2014-yao.pdf

  383. 2014-burt.pdf: ⁠, Callie H. Burt, Ronald L. Simons (2014-03-28; genetics  /​ ​​ ​heritable):

    Unfortunately, the nature-versus-nurture debate continues in criminology. Over the past 5 years, the number of heritability studies in criminology has surged. These studies invariably report sizeable heritability estimates (~50%) and minimal effects of the so-called shared environment for crime and related outcomes. Reports of such high heritabilities for such complex social behaviors are surprising, and findings indicating negligible shared environmental influences (usually interpreted to include parenting and community factors) seem implausible given extensive criminological research demonstrating their importance. Importantly, however, the models on which these estimates are based have fatal flaws for complex social behaviors such as crime. Moreover, the goal of heritability studies—partitioning the effects of nature and nurture—is misguided given the bidirectional, interactional relationship among genes, cells, organisms, and environments. This study provides a critique of heritability study methods and assumptions to illuminate the dubious foundations of heritability estimates and questions the rationale and utility of partitioning genetic and environmental effects. After critiquing the major models, we call for an end to heritability studies. We then present what we perceive to be a more useful biosocial research agenda that is consonant with and informed by recent advances in our understanding of gene function and developmental plasticity.

  384. 2014-barnes.pdf

  385. 2015-burt.pdf

  386. 2015-wright.pdf

  387. 2014-sariaslan-2.pdf: ⁠, Amir Sariaslan, Henrik Larsson, Brian D’Onofrio, Niklas Långström, Seena Fazel, Paul Lichtenstein (2014-07-22; genetics  /​ ​​ ​correlation):

    People living in densely populated and socially disorganized areas have higher rates of psychiatric morbidity, but the potential causal status of such factors is uncertain. We used nationwide Swedish longitudinal registry data to identify all children born 1967–1989 (n = 2361585), including separate datasets for all cousins (n = 1 715 059) and siblings (n = 1667 894). The nature of the associations between population density and neighborhood deprivation and individual risk for a schizophrenia diagnosis was investigated while adjusting for unobserved familial risk factors (through cousin and sibling comparisons) and then compared with similar associations for depression. We generated familial pedigree structures using the Multi-Generation Registry and identified study participants with schizophrenia and depression using the National Patient Registry. Fixed-effects models were used to study within-family estimates. Population density, measured as ln(population size/​​​​km2), at age 15 predicted subsequent schizophrenia in the population (OR = 1.10; 95% CI: 1.09; 1.11). Unobserved familial risk factors shared by cousins within extended families attenuated the association (1.06; 1.03; 1.10), and the link disappeared entirely within nuclear families (1.02; 0.97; 1.08). Similar results were found for neighborhood deprivation as predictor and for depression as outcome. Sensitivity tests demonstrated that timing and accumulation effects of the exposures (mean scores across birth, ages 1–5, 6–10, and 11–15 years) did not alter the findings. Excess risks of psychiatric morbidity, particularly schizophrenia, in densely populated and socioeconomically deprived Swedish neighborhoods appear, therefore, to result primarily from unobserved familial selection factors. Previous studies may have overemphasized the etiological importance of these environmental factors.

  388. https://www.nature.com/articles/tp201662

  389. 2015-agerbo.pdf

  390. https://www.frontiersin.org/articles/10.3389/fgene.2016.00149/full

  391. https://link.springer.com/article/10.1007/s10519-015-9723-9

  392. ⁠, Power, R. A Verweij, K. J H. Zuhair, M. Montgomery, G. W Henders, A. K Heath, A. C Madden, P. A F. Medland, S. E Wray, N. R Martin, N. G (2014):

    Cannabis is the most commonly used illicit drug worldwide. With debate surrounding the legalization and control of use, investigating its health risks has become a pressing area of research. One established association is that between cannabis use and schizophrenia, a debilitating psychiatric disorder affecting ~1% of the population over their lifetime. Although considerable evidence implicates cannabis use as a component cause of schizophrenia, it remains unclear whether this is entirely due to cannabis directly raising risk of psychosis, or whether the same genes that increases psychosis risk may also increase risk of cannabis use. In a sample of 2082 healthy individuals, we show an association between an individual’s burden of schizophrenia risk alleles and use of cannabis. This was significant both for comparing those who have ever versus never used cannabis (p = 2.6 × 10−4), and for quantity of use within users (p = 3.0 × 10−3). Although directly predicting only a small amount of the variance in cannabis use, these findings suggest that part of the association between schizophrenia and cannabis is due to a shared genetic aetiology. This form of gene-environment correlation is an important consideration when calculating the impact of environmental risk factors, including cannabis use.

  393. https://www.pnas.org/content/111/Supplement_3/10796.long

  394. ⁠, Benjamin W. Domingue, Daniel W. Belsky, Jason M. Fletcher, Dalton Conley, Jason D. Boardman, Kathleen Mullan Harris (2017-02-09):

    It has been long known that human relationships are genetically stratified. Whether genetic similarity among those in a relationship is due to complex ancestral patterns linked to historical migration, macro-level social structures in modern society, or individual-level peer selection remains unsettled. We use data from 9,500 adolescents from the National Longitudinal Study of Adolescent to Adult Health to examine genetic similarity among school-based friends. While friends have correlated genotypes, both at the whole-genome level as well as at trait-associated loci (via polygenic scores), the results suggest that macro-level forces, such as school assignment, are a prime source of genetic similarity between friends. Further, we find evidence consistent with the existence of social genetic effects as an individual’s educational attainment is strongly associated with the polygenic scores of those in their broader social environment (e.g., school) and of their friends (net of their own score). In contrast, individual BMI and height are largely unassociated with the genetics of peers.

  395. 1997-rowe.pdf: “Poverty and Behavior: Are Environmental Measures Nature and Nurture?”⁠, Rowe, D. C., et al.

  396. http://paulbingley.com/papers/signals-manuscript.pdf

  397. https://research.facebook.com/blog/do-jobs-run-in-families-/

  398. 2004-gottfredson.pdf: “2004fundamentalcause.pdf”⁠, gottfredson

  399. 1997-gottfredson.pdf

  400. 2016-domingue.pdf

  401. http://bjp.rcpsych.org/content/early/2016/03/17/bjp.bp.114.159079.abstract

  402. ⁠, W David Hill, Alexander Weiss, Andrew M. McIntosh, Catharine R. Gale, Ian J. Deary (2017-06-06):

    Neuroticism is a personality trait that describes the tendency to experience negative emotions. Individual differences in neuroticism are moderately stable across much of the life course1; the trait is heritable2-5, and higher levels are associated with psychiatric disorders6-8, and have been estimated to have an economic burden to society greater than that of substance abuse, mood, or anxiety disorders9. Understanding the genetic architecture of neuroticism therefore has the potential to offer insight into the causes of psychiatric disorders, general wellbeing10, and longevity. The broad trait of neuroticism is composed of narrower traits, or factors. It was recently discovered that, whereas higher scores on the broad trait of neuroticism are associated with earlier death, higher scores on a ‘worry/​​​​vulnerability’ factor are associated with living longer11. Here, we examine the genetic architectures of two neuroticism factors—worry/​​​​vulnerability and anxiety/​​​​tension—and how they contrast with the architecture of the general factor of neuroticism. We show that, whereas the polygenic load for general factor of neuroticism is associated with an increased risk of coronary artery disease (CAD), major depressive disorder, and poorer self-rated health, the genetic variants associated with high levels of the anxiety/​​​​tension and worry/​​​​vulnerability factors are associated with affluence, higher cognitive ability, better self-rated health, and longer life. We also identify the first genes associated with factors of neuroticism that are linked with these positive outcomes that show no relationship with the general factor of neuroticism.

  403. https://journals.sagepub.com/doi/full/10.1177/2332858415599972

  404. ⁠, Trzaskowski, Maciej Harlaar, Nicole Arden, Rosalind Krapohl, Eva Rimfeld, Kaili McMillan, Andrew Dale, Philip S. Plomin, Robert (2014):

    Environmental measures used widely in the behavioral sciences show nearly as much genetic influence as behavioral measures, a critical finding for interpreting associations between environmental factors and children’s development. This research depends on the twin method that compares monozygotic and dizygotic twins, but key aspects of children’s environment such as socioeconomic status (SES) cannot be investigated in twin studies because they are the same for children growing up together in a family. Here, using a new technique applied to DNA from 3000 unrelated children, we show significant genetic influence on family SES, and on its association with children’s IQ at ages 7 and 12. In addition to demonstrating the ability to investigate genetic influence on between-family environmental measures, our results emphasize the need to consider genetics in research and policy on family SES and its association with children’s IQ.

  405. ⁠, Eva Krapohl, Robert Plomin (2015-03-10):

    One of the best predictors of children’s educational achievement is their family’s socioeconomic status (SES), but the degree to which this association is genetically mediated remains unclear. For 3000 UK-representative unrelated children we found that genome-wide single-nucleotide polymorphisms could explain a third of the variance of scores on an age-16 UK national examination of educational achievement and half of the correlation between their scores and family SES. Moreover, genome-wide polygenic scores based on a previously published genome-wide association meta-analysis of total number of years in education accounted for ~3.0% variance in educational achievement and ~2.5% in family SES. This study provides the first molecular evidence for substantial genetic influence on differences in children’s educational achievement and its association with family SES.

  406. https://pdfs.semanticscholar.org/1f91/b7e56a17a672f22c3029f625ecadc1dd43db.pdf

  407. ⁠, W. David Hill, Saskia P. Hagenaars, Riccardo E. Marioni, Sarah E. Harris, David C. M. Liewald, Gail Davies, International Consortium for Blood Pressure, Andrew M. McIntosh, Catharine R. Gale, Ian J. Deary (2016-03-09):

    Individuals with lower socio-economic status (SES) are at increased risk of physical and mental illnesses and tend to die at an earlier age. Explanations for the association between SES and health typically focus on factors that are environmental in origin. However, common single nucleotide polymorphisms (SNPs) have been found collectively to explain around 18% (SE = 5%) of the phenotypic variance of an area-based social deprivation measure of SES. Molecular genetic studies have also shown that physical and psychiatric diseases are at least partly heritable. It is possible, therefore, that phenotypic associations between SES and health arise partly due to a shared genetic etiology.

    We conducted a genome-wide association study (GWAS) on social deprivation and on household income using the 112,151 participants of UK Biobank. We find that common SNPs explain 21% (SE = 0.5%) of the variation in social deprivation and 11% (SE = 0.7%) in household income. 2 independent SNPs attained genome-wide statistical-significance for household income, rs187848990 on chromosome 2, and rs8100891 on chromosome 19. Genes in the regions of these SNPs have been associated with intellectual disabilities, schizophrenia, and synaptic plasticity. Extensive genetic correlations were found between both measures of socioeconomic status and illnesses, anthropometric variables, psychiatric disorders, and cognitive ability.

    These findings show that some SNPs associated with SES are involved in the brain and central nervous system. The genetic associations with SES are probably mediated via other partly-heritable variables, including cognitive ability, education, personality, and health.

    Genetic correlation between household income and health variables.
  408. 2016-hill-ses-health-geneticcorrelations.jpg

  409. https://www.cell.com/current-biology/fulltext/S0960-9822(16)31119-8

  410. https://www.bmj.com/content/352/bmj.i582

  411. ⁠, Neil M. Davies, Matt Dickson, George Davey Smith, Gerard van den Berg, Frank Windmeijer (2016-09-13):

    Educated people are generally healthier, have fewer comorbidities and live longer than people with less education. Previous evidence about the effects of education come from observational studies many of which are affected by residual confounding⁠. Legal changes to the minimum school leave age is a potential which provides a potentially more robust source of evidence about the effects of schooling. Previous studies have exploited this natural experiment using population-level administrative data to investigate mortality, and relatively small surveys to investigate the effect on mortality.

    Here, we add to the evidence using data from a large sample from the UK Biobank. We exploit the raising of the school-leaving age in the UK in September 1972 as a natural experiment and regression discontinuity and instrumental variable estimators to identify the causal effects of staying on in school. Remaining in school was positively associated with 23 of 25 outcomes. After accounting for multiple hypothesis testing, we found evidence of causal effects on 12 outcomes, however, the associations of schooling and intelligence, smoking, and alcohol consumption may be due to genomic and socioeconomic confounding factors. Education affects some, but not all health and socioeconomic outcomes.

    Differences between educated and less educated people may be partially due to residual genetic and socioeconomic confounding.

    Significance Statement: On average people who choose to stay in education for longer are healthier, wealthier, and live longer. We investigated the causal effects of education on health, income, and well-being later in life. This is the largest study of its kind to date and it has objective clinic measures of morbidity and aging. We found evidence that people who were forced to remain in school had higher wages and lower mortality. However, there was little evidence of an effect on intelligence later in life. Furthermore, estimates of the effects of education using conventionally adjusted regression analysis are likely to suffer from genomic confounding. In conclusion, education affects some, but not all health outcomes later in life.

    Funding: The Medical Research Council (MRC) and the University of Bristol fund the MRC Integrative Epidemiology Unit [MC_UU_12013/​​​​1, MC_UU_12013/​​​​9]. NMD is supported by the Economics and Social Research Council (ESRC) via a Future Research Leaders Fellowship [ES/​​​​N000757/​​​​1]. The research described in this paper was specifically funded by a grant from the Economics and Social Research Council for Transformative Social Science. No funding body has influenced data collection, analysis or its interpretations. This publication is the work of the authors, who serve as the guarantors for the contents of this paper. This work was carried out using the computational facilities of the Advanced Computing Research Centre and the Research Data Storage Facility of the University of Bristol. This research was conducted using the UK Biobank Resource.

    Data access: The statistical code used to produce these results can be accessed here⁠. The final analysis dataset used in this study is archived with UK Biobank, which can be accessed by contacting UK Biobank access@biobank.ac.uk.

  412. 2016-belsky.pdf: ⁠, Daniel W. Belsky, Terrie E. Moffitt, David L. Corcoran, Benjamin Domingue, HonaLee Harrington, Sean Hogan, Renate Houts, Sandhya Ramrakha, Karen Sugden, Benjamin S. Williams, Richie Poulton, Avshalom Caspi (2016-06-01; genetics  /​ ​​ ​correlation):

    A previous genome-wide association study (GWAS) of more than 100,000 individuals identified molecular-genetic predictors of educational attainment.

    We undertook in-depth life-course investigation of the polygenic score derived from this GWAS using the 4-decade Dunedin Study (N = 918). There were 5 main findings.

    1. polygenic scores predicted adult economic outcomes even after accounting for educational attainments.
    2. genes and environments were correlated: Children with higher polygenic scores were born into better-off homes.
    3. children’s polygenic scores predicted their adult outcomes even when analyses accounted for their social-class origins; social-mobility analysis showed that children with higher polygenic scores were more upwardly mobile than children with lower scores.
    4. polygenic scores predicted behavior across the life course, from early acquisition of speech and reading skills through geographic mobility and mate choice and on to financial planning for retirement.
    5. polygenic-score associations were mediated by psychological characteristics, including intelligence, self-control, and interpersonal skill. Effect sizes were small.

    Factors connecting GWAS sequence with life outcomes may provide targets for interventions to promote population-wide positive development.

    [Keywords: genetics, behavior genetics, intelligence, personality, adult development]

  413. ⁠, Caspi, Avshalom Houts, Renate M. Belsky, Daniel W. Harrington, Honalee Hogan, Sean Ramrakha, Sandhya Poulton, Richie Moffitt, Terrie E (2016):

    Policy-makers are interested in early-years interventions to ameliorate childhood risks. They hope for improved adult outcomes in the long run, bringing return on investment. How much return can be expected depends, partly, on how strongly childhood risks forecast adult outcomes. But there is disagreement about whether childhood determines adulthood. We integrated multiple nationwide administrative databases and electronic medical records with the four-decade Dunedin birth-cohort study to test child-to-adult prediction in a different way, by using a population-segmentation approach. A segment comprising one-fifth of the cohort accounted for 36% of the cohort’s injury insurance-claims; 40% of excess obese-kilograms; 54% of cigarettes smoked; 57% of hospital nights; 66% of welfare benefits; 77% of fatherless childrearing; 78% of prescription fills; and 81% of criminal convictions. Childhood risks, including poor age-three brain health, predicted this segment with large effect sizes. Early-years interventions effective with this population segment could yield very large returns on investment.

  414. ⁠, Amelie Baud, Megan K. Mulligan, Francesco Paolo Casale, Jesse F. Ingels, Casey J. Bohl, Jacques Callebert, Jean-Marie Launay, Jon Krohn, Andres Legarra, Robert W. Williams, Oliver Stegle (2016-11-21):

    Assessing the impact of the social environment on health and disease is challenging. As social effects are in part determined by the genetic makeup of social partners, they can be studied from associations between genotypes of one individual and phenotype of another (social genetic effects, SGE, also called indirect genetic effects). For the first time we quantified the contribution of SGE to more than 100 organismal phenotypes and genome-wide gene expression measured in laboratory mice. We find that genetic variation in cage mates (i.e. SGE) contributes to variation in organismal and molecular measures related to anxiety, wound healing, immune function, and body weight. Social genetic effects explained up to 29% of phenotypic variance, and for several traits their contribution exceeded that of direct genetic effects (effects of an individual’s genotypes on its own phenotype). Importantly, we show that ignoring SGE can severely bias estimates of direct genetic effects (heritability). Thus SGE may be an important source of “missing heritability” in studies of complex traits in human populations. In summary, our study uncovers an important contribution of the social environment to phenotypic variation, sets the basis for using SGE to dissect social effects, and identifies an opportunity to improve studies of direct genetic effects.

    Author Summary:

    Daily interactions between individuals can influence their health both in positive and negative ways. Often the mechanisms mediating social effects are unknown, so current approaches to study social effects are limited to a few phenotypes for which the mediating mechanisms are known a priori or suspected. Here we propose to leverage the fact that most traits are genetically controlled to investigate the influence of the social environment. To do so, we study associations between genotypes of one individual and phenotype of another individual (social genetic effects, SGE, also called indirect genetic effects). Importantly, SGE can be studied even when the traits that mediate the influence of the social environment are not known. For the first time we quantified the contribution of SGE to more than 100 organismal phenotypes and genome-wide gene expression measured in laboratory mice. We find that genetic variation in cage mates (i.e. SGE) explains up to 29% of the variation in anxiety, wound healing, immune function, and body weight. Hence our study uncovers an unexpectedly large influence of the social environment. Additionally, we show that ignoring SGE can severely bias estimates of direct genetic effects (effects of an individual’s genotypes on its own phenotype), which has important implications for the study of the genetic basis of complex traits.

  415. ⁠, Yilan Xu, Daniel A. Briley, Jeffrey R. Brown, William G. Karnes, Brent W. Roberts (2017-01-08):

    Heterogeneity of household financial outcomes emerges from various individual and environmental factors, including personality, cognitive ability, and socioeconomic status (SES), among others. Using a genetically informative data set, we decompose the variation in financial management behavior into genetic, shared environmental and non-shared environmental factors. We find that about half of the variation in financial distress is genetically influenced, and personality and cognitive ability are associated with financial distress through genetic and within-family pathways. Moreover, the genetic influences of financial distress are highest at the extremes of SES, which in part can be explained by neuroticism and cognitive ability being more important predictors of financial distress at low and high levels of SES, respectively.

  416. ⁠, Richard Karlsson Linnér, Pietro Biroli, Edward Kong, S. Fleur W. Meddens, Robbee Wedow, Mark Alan Fontana, Maël Lebreton, Abdel Abdellaoui, Anke R. Hammerschlag, Michel G. Nivard, Aysu Okbay, Cornelius A. Rietveld, Pascal N. Timshel, Stephen P. Tino, Maciej Trzaskowski, Ronald de Vlaming, Christian L. Zünd, Yanchun Bao, Laura Buzdugan, Ann H. Caplin, Chia-Yen Chen, Peter Eibich, Pierre Fontanillas, Juan R. Gonzalez, Peter K. Joshi, Ville Karhunen, Aaron Kleinman, Remy Z. Levin, Christina M. Lill, Gerardus A. Meddens, Gerard Muntané, Sandra Sanchez-Roige, Frank J. van Rooij, Erdogan Taskesen, Yang Wu, Futao Zhang, 23andMe Research Team, eQTLgen Consortium, International Cannabis Consortium, Psychiatric Genomics Consortium, Social Science Genetic Association Consortium, Adam Auton, Jason D. Boardman, David W. Clark, Andrew Conlin, Conor C. Dolan, Urs Fischbacher, Patrick JF Groenen, Kathleen Mullan Harris, Gregor Hasler, Albert Hofman, Mohammad A. Ikram, Sonia Jain, Robert Karlsson, Ronald C. Kessler, Maarten Kooyman, James MacKillop, Minna Männikkö, Carlos Morcillo-Suarez, Matthew B. McQueen, Klaus M. Schmidt, Melissa C. Smart, Matthias Sutter, A. Roy Thurik, Andre G. Uitterlinden, Jon White, Harriet de Wit, Jian Yang, Lars Bertram, Dorret Boomsma, Tõnu Esko, Ernst Fehr, David A. Hinds, Magnus Johannesson, M. Kumari, David Laibson, Patrik KE Magnusson, Michelle N. Meyer, Arcadi Navarro, Abraham A. Palmer, Tune H. Pers, Danielle Posthuma, Daniel Schunk, Murray B. Stein, Rauli Svento, Henning Tiemeier, Paul RHJ Timmers, Patrick Turley, Robert J. Ursano, Gert G. Wagner, James F. Wilson, Jacob Gratten, James J. Lee, David Cesarini, Daniel J. Benjamin, Philipp D. Koellinger, Jonathan P. Beauchamp (2018-02-08):

    Humans vary substantially in their willingness to take risks. In a combined sample of over one million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. We identified 611 approximately independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance. We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated () with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

  417. ⁠, Sarah E. Harris, Saskia P. Hagenaars, Gail Davies, W. David Hill, David CM Liewald, Stuart J. Ritchie, Riccardo E. Marioni, METASTROKE consortium, International Consortium for Blood Pressure, CHARGE consortium Aging, Longevity Group, CHARGE consortium Cognitive Group, Cathie LM Sudlow, Joanna M. Wardlaw, Andrew M. McIntosh, Catharine R. Gale, Ian J. Deary (2016-04-12):

    Background: Poorer self-rated health (SRH) predicts worse health outcomes, even when adjusted for objective measures of disease at time of rating. Twin studies indicate SRH has a heritability of up to 60% and that its genetic architecture may overlap with that of personality and cognition.

    Methods: We carried out a genome-wide association study (GWAS) of SRH on 111 749 members of the UK Biobank sample. Univariate genome-wide complex trait analysis (GCTA)-GREML analyses were used to estimate the proportion of variance explained by all common autosomal SNPs for SRH. Linkage Disequilibrium (LD) score regression and polygenic risk scoring, two complementary methods, were used to investigate pleiotropy between SRH in UK Biobank and up to 21 health-related and personality and cognitive traits from published GWAS consortia.

    Results: The GWAS identified 13 independent signals associated with SRH, including several in regions previously associated with diseases or disease-related traits. The strongest signal was on chromosome 2 (rs2360675, p = 1.77×10−10) close to KLF7, which has previously been associated with obesity and type 2 diabetes. A second strong peak was identified on chromosome 6 in the major histocompatibility region (rs76380179, p = 6.15×10−10). The proportion of variance in SRH that was explained by all common genetic variants was 13%. Polygenic scores for the following traits and disorders were associated with SRH: cognitive ability, education, neuroticism, BMI, longevity, ADHD, major depressive disorder, schizophrenia, lung function, blood pressure, coronary artery disease, large vessel disease stroke, and type 2 diabetes.

    Conclusion: Individual differences in how people respond to a single item on SRH are partly explained by their genetic propensity to many common psychiatric and physical disorders and psychological traits.

    Key Messages

    Genetic variants associated with common diseases and psychological traits are associated with self-rated health.

    The SNP-based heritability of self-rated health is 0.13 (SE 0.006).

    There is pleiotropy between self-rated health and psychiatric and physical diseases and psychological traits.

  418. 2016-liu.pdf: “The MC1R Gene and Youthful Looks”⁠, Fan Liu, Merel A. Hamer, Joris Deelen, Japal S. Lall, Leonie Jacobs, Diana van Heemst, Peter G. Murray, Andreas Wollstein, Anton J. M. de Craen, Hae-Won Uh, Changqing Zeng, Albert Hofman, André G. Uitterlinden, Jeanine J. Houwing-Duistermaat, Luba M. Pardo, Marian Beekman, P. Eline Slagboom, Tamar Nijsten, Manfred Kayser, David A. Gunn

  419. ⁠, Saskia P. Hagenaars, W. David Hill, Sarah E. Harris, Stuart J. Ritchie, Gail Davies, David C. Liewald, Catharine R. Gale, David J. Porteous, Ian J. Deary, Riccardo E. Marioni (2016-08-31):

    Male pattern baldness can have substantial psychosocial effects, and it has been phenotypically linked to adverse health outcomes such as prostate cancer and cardiovascular disease. We explored the genetic architecture of the trait using data from over 52,000 male participants of UK Biobank, aged 40–69 years. We identified over 250 independent novel genetic loci associated with severe hair loss. By developing a prediction algorithm based entirely on common genetic variants, and applying it to an independent sample, we could discriminate accurately (AUC = 0.82) between those with no hair loss from those with severe hair loss. The results of this study might help identify those at the greatest risk of hair loss and also potential genetic targets for intervention.

  420. ⁠, John R. Shaffer, Ekaterina Orlova, Myoung Keun Lee, Elizabeth J. Leslie, Zachary D. Raffensperger, Carrie L. Heike, Michael L. Cunningham, Jacqueline T. Hecht, Chung How Kau, Nichole L. Nidey, Lina M. Moreno, George L. Wehby, Jeffrey C. Murray, Cecelia A. Laurie, Cathy C. Laurie, Joanne Cole, Tracey Ferrara, Stephanie Santorico, Ophir Klein, Washington Mio, Eleanor Feingold, Benedikt Hallgrimsson, Richard A. Spritz, Mary L. Marazita, Seth M. Weinberg (2016-06-08):

    Numerous lines of evidence point to a genetic basis for facial morphology in humans, yet little is known about how specific genetic variants relate to the phenotypic expression of many common facial features. We conducted genome-wide association meta-analyses of 20 quantitative facial measurements derived from the 3D surface images of 3118 healthy individuals of European ancestry belonging to two US cohorts. Analyses were performed on just under one million genotyped SNPs (Illumina OmniExpress+Exome v1.2 array) imputed to the 1000 Genomes reference panel (Phase 3). We observed genome-wide statistically-significant associations (p < 5 x 10−8) for cranial base width at 14q21.1 and 20q12, intercanthal width at 1p13.3 and Xq13.2, nasal width at 20p11.22, nasal ala length at 14q11.2, and upper facial depth at 11q22.1. Several genes in the associated regions are known to play roles in craniofacial development or in syndromes affecting the face: MAFB, PAX9, MIPOL1, ALX3, HDAC8, and PAX1. We also tested genotype-phenotype associations reported in two previous genome-wide studies and found evidence of replication for nasal ala length and SNPs in CACNA2D3 and PRDM16. These results provide further evidence that common variants in regions harboring genes of known craniofacial function contribute to normal variation in human facial features. Improved understanding of the genes associated with facial morphology in healthy individuals can provide insights into the pathways and mechanisms controlling normal and abnormal facial morphogenesis.

    Author Summary:

    There is a great deal of evidence that genes influence facial appearance. This is perhaps most apparent when we look at our own families, since we are more likely to share facial features in common with our close relatives than with unrelated individuals. Nevertheless, little is known about how variation in specific regions of the genome relates to the kinds of distinguishing facial characteristics that give us our unique identities, e.g., the size and shape of our nose or how far apart our eyes are spaced. In this paper, we investigate this question by examining the association between genetic variants across the whole genome and a set of measurements designed to capture key aspects of facial form. We found evidence of genetic associations involving measures of eye, nose, and facial breadth. In several cases, implicated regions contained genes known to play roles in embryonic face formation or in syndromes in which the face is affected. Our ability to connect specific genetic variants to ubiquitous facial traits can inform our understanding of normal and abnormal craniofacial development, provide potential predictive models of evolutionary changes in human facial features, and improve our ability to create forensic facial reconstructions from DNA.

  421. http://blogs.discovermagazine.com/gnxp/2011/06/heritability-and-genomics-of-facial-characteristics/

  422. http://www.wired.co.uk/article/craig-venter-human-longevity-genome-diseases-ageing

  423. http://www.unz.com/gnxp/the-genetic-architecture-natural-history-of-pigmentation/

  424. ⁠, Lu Qiao, Yajun Yang, Pengcheng Fu, Sile Hu, Hang Zhou, Shouneng Peng, Jingze Tan, Yan Lu, Haiyi Lou, Dongsheng Lu, Sijie Wu, Jing Guo, Li Jin, Yaqun Guan, Sijia Wang, Shuhua Xu, Kun Tang (2016-07-25):

    It is a long standing question as to which genes define the characteristic facial features among different ethnic groups. In this study, we use Uyghurs, an ancient admixed population to query the genetic bases why Europeans and Han Chinese look different. Facial traits were analyzed based on high-dense 3D facial images; numerous biometric spaces were examined for divergent facial features between European and Han Chinese, ranging from inter-landmark distances to dense shape geometrics. Genome-wide association analyses were conducted on a discovery panel of Uyghurs. Six significant loci were identified four of which, rs1868752, rs118078182, rs60159418 at or near UBASH3B, COL23A1, PCDH7 and rs17868256 were replicated in independent cohorts of Uyghurs or Southern Han Chinese. A quantitative model was developed to predict 3D faces based on 277 top GWAS SNPs. In hypothetic forensic scenarios, this model was found to significantly enhance the verification rate, suggesting a practical potential of related research.

  425. 2010-wade.pdf

  426. https://bmcmedgenet.biomedcentral.com/articles/10.1186/1471-2350-13-53

  427. ⁠, Li, Jingmei Foo, Jia Nee Schoof, Nils Varghese, Jajini S. Fernandez-Navarro, Pablo Gierach, Gretchen L. Quek, Swee Tian Hartman, Mikael Nord, Silje Kristensen, Vessela N. Pollán, Marina Figueroa, Jonine D. Thompson, Deborah J. Li, Yi Khor, Chiea Chuen Humphreys, Keith Liu, Jianjun Czene, Kamila Hall, Per (2013):

    Background: Individual differences in breast size are a conspicuous feature of variation in human females and have been associated with fecundity and advantage in selection of mates. To identify common variants that are associated with breast size, we conducted a large-scale genotyping association meta-analysis in 7169 women of European descent across three independent sample collections with digital or screen film mammograms.

    Methods: The samples consisted of the Swedish KARMA, LIBRO-1 and SASBAC studies genotyped on iCOGS, a custom illumina iSelect genotyping array comprising of 211 155 single nucleotide polymorphisms (SNPs) designed for replication and fine mapping of common and rare variants with relevance to breast, ovary and prostate cancer. Breast size of each subject was ascertained by measuring total breast area (mm(2)) on a mammogram.

    Results: We confirm genome-wide statistically-significant associations at 8p11.23 (rs10086016, p = 1.3×10(-14)) and report a new locus at 22q13 (rs5995871, p = 3.2×10−8). The latter region contains the MKL1 gene, which has been shown to impact endogenous oestrogen receptor α transcriptional activity and is recruited on oestradiol sensitive genes. We also replicated previous genome-wide association study findings for breast size at four other loci.

    Conclusions: A new locus at 22q13 may be associated with female breast size.

  428. 2015-krapohl.pdf

  429. ⁠, Brendan Bulik-Sullivan, Hilary K. Finucane, Verneri Anttila, Alexander Gusev, Felix R. Day, ReproGen Consortium, Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Laramie Duncan, John R. B. Perry, Nick Patterson, Elise B. Robinson, Mark J. Daly, Alkes L. Price, Benjamin M. Neale (2015-04-06):

    Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use our method to estimate 300 genetic correlations among 25 traits, totaling more than 1.5 million unique phenotype measurements. Our results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no genome-wide statistically-significant SNPs for anorexia nervosa and only three for educational attainment.

  430. ⁠, Huwenbo Shi, Gleb Kichaev, Bogdan Pasaniuc (2016-01-14):

    Variance components methods that estimate the aggregate contribution of large sets of variants to the heritability of complex traits have yielded important insights into the disease architecture of common diseases. Here, we introduce new methods that estimate the total variance in trait explained by a single locus in the genome (local heritability) from summary GWAS data while accounting for linkage disequilibrium (LD) among variants. We apply our new estimator to ultra large-scale GWAS summary data of 30 common traits and diseases to gain insights into their local genetic architecture. First, we find that common SNPs have a high contribution to the heritability of all studied traits. Second, we identify traits for which the majority of the SNP heritability can be confined to a small percentage of the genome. Third, we identify GWAS risk loci where the entire locus explains statistically-significantly more variance in the trait than the GWAS reported variants. Finally, we identify 55 loci that explain a large proportion of heritability across multiple traits.

  431. ⁠, S. P. Hagenaars, S. E. Harris, G. Davies, W. D. Hill, D. C. M. Liewald, S. J. Ritchie, R. E. Marioni, C. Fawns-Ritchie, B. Cullen, R. Malik, GWAS Consortium, International Consortium for Blood Pressure GWAS, SpiroMeta Consortium, CHARGE Consortium Pulmonary Group, CHARGE Consortium Aging and Longevity Group, B. B. Worrall, C. L. M. Sudlow, J. M. Wardlaw, J. Gallacher, J. Pell, A. M. McIntosh, D. J. Smith, C. R. Gale, Ian J. Deary (2016-01-26):

    Causes of the well-documented association between low levels of cognitive functioning and many adverse neuropsychiatric outcomes, poorer physical health and earlier death remain unknown. We used linkage disequilibrium regression and polygenic profile scoring to test for shared genetic aetiology between cognitive functions and neuropsychiatric disorders and physical health. Using information provided by many published genome-wide association study consortia, we created polygenic profile scores for 24 vascular-metabolic, neuropsychiatric, physiological-anthropometric and cognitive traits in the participants of UK Biobank, a very large population-based sample (n = 112 151). Pleiotropy between cognitive and health traits was quantified by deriving genetic correlations using summary genome-wide association study statistics and to the method of linkage disequilibrium score regression. Substantial and statistically-significant genetic correlations were observed between cognitive test scores in the UK Biobank sample and many of the mental and physical health-related traits and disorders assessed here. In addition, highly statistically-significant associations were observed between the cognitive test scores in the UK Biobank sample and many polygenic profile scores, including coronary artery disease, stroke, Alzheimer’s disease, schizophrenia, autism, major depressive disorder, body mass index, intracranial volume, infant head circumference and childhood cognitive ability. Where disease diagnosis was available for UK Biobank participants, we were able to show that these results were not confounded by those who had the relevant disease. These findings indicate that a substantial level of pleiotropy exists between cognitive abilities and many human mental and physical health disorders and traits and that it can be used to predict phenotypic variance across samples.

  432. 2016-pickrell.pdf: “Detection and interpretation of shared genetic influences on 42 human traits”⁠, Tomaz Berisa, Jimmy Z. Liu, Laure Ségurel, Joyce Y. Tung, David A. Hinds, Joseph K. Pickrell

  433. ⁠, Anna R. Docherty, Arden Moscati, Danielle Dick, Jeanne E. Savage, Jessica E. Salvatore, Megan Cooke, Fazil Aliev, Ashlee A. Moore, Alexis C. Edwards, Brien P. Riley, Daniel E. Adkins, Roseann Peterson, Bradley T. Webb, Silviu A. Bacanu, and Kenneth S. Kendler (2017-11-27):

    Background: Identifying genetic relationships between complex traits in emerging adulthood can provide useful etiological insights into risk for psychopathology. College-age individuals are under-represented in genomic analyses thus far, and the majority of work has focused on the clinical disorder or cognitive abilities rather than normal-range behavioral outcomes.

    Methods: This study examined a sample of emerging adults 18–22 years of age (n = 5947) to construct an atlas of polygenic risk for 33 traits predicting relevant phenotypic outcomes. 28 hypotheses were tested based on the previous literature on samples of European ancestry, and the availability of rich assessment data allowed for polygenic predictions across 55 psychological and medical phenotypes.

    Results: Polygenic risk for schizophrenia (SZ) in emerging adults predicted anxiety, depression, nicotine use, trauma, and family history of psychological disorders. Polygenic risk for neuroticism predicted anxiety, depression, phobia, panic, neuroticism, and was correlated with polygenic risk for cardiovascular disease.

    Conclusions: These results demonstrate the extensive impact of genetic risk for SZ, neuroticism, and major depression on a range of health outcomes in early adulthood. Minimal cross-ancestry replication of these phenomic patterns of polygenic influence underscores the need for more genome-wide association studies of non-European populations.

  434. ⁠, Jie Zheng, A. Mesut Erzurumluoglu, Benjamin L. Elsworth, Laurence Howe, Philip C. Haycock, Gibran Hemani, Katherine Tansey, Charles Laurin, Early Genetics, Lifecourse Epidemiology (EAGLE) Eczema Consortium, Beate St. Pourcain, Nicole M. Warrington, Hilary K. Finucane, Alkes L. Price, Brendan K. Bulik-Sullivan, Verneri Anttila, Lavinia Paternoster, Tom R. Gaunt, David M. Evans, Benjamin M. Neale (2016-05-03):


    LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.

    Results: In this manuscript, we describe LD Hub—a centralized database of summary-level GWAS results for 177 diseases/​​​​traits from different publicly available resources/​​​​consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/​​​​diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies.

    Availability and implementation

    The web interface and instructions for using LD Hub are available at http:/​​​​/​​​​ldsc.broadinstitute.org/​​​​

  435. http://ldsc.broadinstitute.org/about/

  436. ⁠, Bulik-Sullivan, Brendan K. Loh, Po-Ru Finucane, Hilary K. Ripke, Stephan Yang, Jian Patterson, Nick Daly, Mark J. Price, Alkes L. Neale, Benjamin M (2015):

    Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

  437. ⁠, Vincent Deary, Saskia P. Hagenaars, Sarah E. Harris, W. David Hill, Gail Davies, David CM Liewald, International Consortium for Blood Pressure GWAS, CHARGE consortium Aging, Longevity Group, Andrew M. McIntosh, Catharine R. Gale, Ian J. Deary (2016-04-05):

    Self-reported tiredness and low energy, often called fatigue, is associated with poorer physical and mental health. Twin studies have indicated that this has a heritability between 6% and 50%. In the UK Biobank sample (n = 108 976) we carried out a genome-wide association study of responses to the question, “Over the last two weeks, how often have you felt tired or had little energy?” Univariate GCTA-GREML found that the proportion of variance explained by all common SNPs for this tiredness question was 8.4% (SE = 0.6%). GWAS identified one genome-wide statistically-significant hit (Affymetrix id 1:64178756_C_T; p = 1.36 x 10−11). LD score regression and polygenic profile analysis were used to test for pleiotropy between tiredness and up to 28 physical and mental health traits from GWAS consortia. Significant genetic correlations were identified between tiredness and BMI, HDL cholesterol, forced expiratory volume, grip strength, HbA1c, longevity, obesity, self-rated health, smoking status, triglycerides, type 2 diabetes, waist-hip ratio, ADHD, bipolar disorder, major depressive disorder, neuroticism, schizophrenia, and verbal-numerical reasoning (absolute rg effect sizes between 0.11 and 0.78). Significant associations were identified between tiredness phenotypic scores and polygenic profile scores for BMI, HDL cholesterol, LDL cholesterol, coronary artery disease, HbA1c, height, obesity, smoking status, triglycerides, type 2 diabetes, and waist-hip ratio, childhood cognitive ability, neuroticism, bipolar disorder, major depressive disorder, and schizophrenia (standardised β’s between −0.016 and 0.03). These results suggest that tiredness is a partly-heritable, heterogeneous and complex phenomenon that is phenotypically and genetically associated with affective, cognitive, personality, and physiological processes.

    "Hech, sirs! But I’m wabbit, I’m back frae the toon;

    I ha’ena dune pechin’—jist let me sit doon.

    From Glesca’

    By William Dixon Cocker (1882-1970)

  438. https://www.bmj.com/content/349/bmj.g6330.long

  439. https://www.pnas.org/content/early/2014/10/02/1408777111.full.pdf

  440. ⁠, Rimfeld, Kaili Kovas, Yulia Dale, Philip S. Plomin, Robert (2015):

    Research has shown that genes play an important role in educational achievement. A key question is the extent to which the same genes affect different academic subjects before and after controlling for general intelligence. The present study investigated genetic and environmental influences on, and links between, the various subjects of the age-16 UK-wide standardized GCSE (General Certificate of Secondary Education) examination results for 12,632 twins. Using the twin method that compares identical and non-identical twins, we found that all GCSE subjects were substantially heritable, and that various academic subjects correlated substantially both phenotypically and genetically, even after controlling for intelligence. Further evidence for pleiotropy in academic achievement was found using a method based directly on DNA from unrelated individuals. We conclude that performance differences for all subjects are highly heritable at the end of compulsory education and that many of the same genes affect different subjects independent of intelligence.

  441. https://www.nature.com/articles/srep26373

  442. 2016-tuckerdrob.pdf

  443. ⁠, René Mõttus, Anu Realo, Uku Vainik, Jüri Allik, Tõnu Esko (2016-09-28):

    It is possible that heritable variance in personality characteristics does not reflect (only) genetic and biological processes specific to personality per se. We tested the possibility that Five-Factor Model personality domains and facets, as rated by people themselves and their knowledgeable informants, reflect polygenic influences that have been previously associated with educational attainment. In a sample of over 3,000 adult Estonians, polygenic scores for educational attainment, based on small contributions from more than 150,000 genetic variants, were correlated with various personality traits, mostly from the Neuroticism and domains. The correlations of personality characteristics with educational attainment-related polygenic influences reflected almost entirely their correlations with phenotypic educational attainment. Structural equation modeling of the associations between polygenic risk, personality (a weighed aggregate of education-related facets) and educational attainment lent relatively strongest support to the possibility of educational attainment mediating (explaining) some of the heritable variance in personality traits.

  444. http://ije.oxfordjournals.org/content/early/2015/07/24/ije.dyv112.full.pdf

  445. ⁠, The Brainstorm Consortium (2018-06-22):

    Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified statistically-significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

  446. ⁠, Lee, S. Hong Ripke, Stephan Neale, Benjamin M. Faraone, Stephen V. Purcell, Shaun M. Perlis, Roy H. Mowry, Bryan J. Thapar, Anita Goddard, Michael E. Witte, John S. Absher, Devin Agartz, Ingrid Akil, Huda Amin, Farooq Andreassen, Ole A. Anjorin, Adebayo Anney, Richard Anttila, Verneri Arking, Dan E. Asherson, Philip Azevedo, Maria H. Backlund, Lena Badner, Judith A. Bailey, Anthony J. Banaschewski, Tobias Barchas, Jack D. Barnes, Michael R. Barrett, Thomas B. Bass, Nicholas Battaglia, Agatino Bauer, Michael Bayés, Mònica Bellivier, Frank Bergen, Sarah E. Berrettini, Wade Betancur, Catalina Bettecken, Thomas Biederman, Joseph Binder, Elisabeth B. Black, Donald W. Blackwood, Douglas H. R Bloss, Cinnamon S. Boehnke, Michael Boomsma, Dorret I. Breen, Gerome Breuer, René Bruggeman, Richard Cormican, Paul Buccola, Nancy G. Buitelaar, Jan K. Bunney, William E. Buxbaum, Joseph D. Byerley, William F. Byrne, Enda M. Caesar, Sian Cahn, Wiepke Cantor, Rita M. Casas, Miguel Chakravarti, Aravinda Chambert, Kimberly Choudhury, Khalid Cichon, Sven Cloninger, C. Robert Collier, David A. Cook, Edwin H. Coon, Hilary Cormand, Bru Corvin, Aiden Coryell, William H. Craig, David W. Craig, Ian W. Crosbie, Jennifer Cuccaro, Michael L. Curtis, David Czamara, Darina Datta, Susmita Dawson, Geraldine Day, Richard De Geus, Eco J. Degenhardt, Franziska Djurovic, Srdjan Donohoe, Gary J. Doyle, Alysa E. Duan, Jubao Dudbridge, Frank Duketis, Eftichia Ebstein, Richard P. Edenberg, Howard J. Elia, Josephine Ennis, Sean Etain, Bruno Fanous, Ayman Farmer, Anne E. Ferrier, I. Nicol Flickinger, Matthew Fombonne, Eric Foroud, Tatiana Frank, Josef Franke, Barbara Fraser, Christine Freedman, Robert Freimer, Nelson B. Freitag, Christine M. Friedl, Marion Frisén, Louise Gallagher, Louise Gejman, Pablo V. Georgieva, Lyudmila Gershon, Elliot S. Geschwind, Daniel H. Giegling, Ina Gill, Michael Gordon, Scott D. Gordon-Smith, Katherine Green, Elaine K. Greenwood, Tiffany A. Grice, Dorothy E. Gross, Magdalena Grozeva, Detelina Guan, Weihua Gurling, Hugh De Haan, Lieuwe Haines, Jonathan L. Hakonarson, Hakon Hallmayer, Joachim Hamilton, Steven P. Hamshere, Marian L. Hansen, Thomas F. Hartmann, Annette M. Hautzinger, Martin Heath, Andrew C. Henders, Anjali K. Herms, Stefan Hickie, Ian B. Hipolito, Maria Hoefels, Susanne Holmans, Peter A. Holsboer, Florian Hoogendijk, Witte J. Hottenga, Jouke-Jan Hultman, Christina M. Hus, Vanessa Ingason, Andrés Ising, Marcus Jamain, Stéphane Jones, Edward G. Jones, Ian Jones, Lisa Tzeng, Jung-Ying Kähler, Anna K. Kahn, René S. Kandaswamy, Radhika Keller, Matthew C. Kennedy, James L. Kenny, Elaine Kent, Lindsey Kim, Yunjung Kirov, George K. Klauck, Sabine M. Klei, Lambertus Knowles, James A. Kohli, Martin A. Koller, Daniel L. Konte, Bettina Korszun, Ania Krabbendam, Lydia Krasucki, Robert Kuntsi, Jonna Kwan, Phoenix Landén, Mikael Långström, Niklas Lathrop, Mark Lawrence, Jacob Lawson, William B. Leboyer, Marion Ledbetter, David H. Lee, Phil H. Lencz, Todd Lesch, Klaus-Peter Levinson, Douglas F. Lewis, Cathryn M. Li, Jun Lichtenstein, Paul Lieberman, Jeffrey A. Lin, Dan-Yu Linszen, Don H. Liu, Chunyu Lohoff, Falk W. Loo, Sandra K. Lord, Catherine Lowe, Jennifer K. Lucae, Susanne MacIntyre, Donald J. Madden, Pamela A. F Maestrini, Elena Magnusson, Patrik K. E Mahon, Pamela B. Maier, Wolfgang Malhotra, Anil K. Mane, Shrikant M. Martin, Christa L. Martin, Nicholas G. Mattheisen, Manuel Matthews, Keith Mattingsdal, Morten McCarroll, Steven A. McGhee, Kevin A. McGough, James J. McGrath, Patrick J. McGuffin, Peter McInnis, Melvin G. McIntosh, Andrew McKinney, Rebecca McLean, Alan W. McMahon, Francis J. McMahon, William M. McQuillin, Andrew Medeiros, Helena Medland, Sarah E. Meier, Sandra Melle, Ingrid Meng, Fan Meyer, Jobst Middeldorp, Christel M. Middleton, Lefkos Milanova, Vihra Miranda, Ana Monaco, Anthony P. Montgomery, Grant W. Moran, Jennifer L. Moreno-De-Luca, Daniel Morken, Gunnar Morris, Derek W. Morrow, Eric M. Moskvina, Valentina Muglia, Pierandrea Mühleisen, Thomas W. Muir, Walter J. Müller-Myhsok, Bertram Murtha, Michael Myers, Richard M. Myin-Germeys, Inez Neale, Michael C. Nelson, Stan F. Nievergelt, Caroline M. Nikolov, Ivan Nimgaonkar, Vishwajit Nolen, Willem A. Nöthen, Markus M. Nurnberger, John I. Nwulia, Evaristus A. Nyholt, Dale R. O'Dushlaine, Colm Oades, Robert D. Olincy, Ann Oliveira, Guiomar Olsen, Line Ophoff, Roel A. Osby, Urban Owen, Michael J. Palotie, Aarno Parr, Jeremy R. Paterson, Andrew D. Pato, Carlos N. Pato, Michele T. Penninx, Brenda W. Pergadia, Michele L. Pericak-Vance, Margaret A. Pickard, Benjamin S. Pimm, Jonathan Piven, Joseph Posthuma, Danielle Potash, James B. Poustka, Fritz Propping, Peter Puri, Vinay Quested, Digby J. Quinn, Emma M. Ramos-Quiroga, Josep Antoni Rasmussen, Henrik B. Raychaudhuri, Soumya Rehnström, Karola Reif, Andreas Ribasés, Marta Rice, John P. Rietschel, Marcella Roeder, Kathryn Roeyers, Herbert Rossin, Lizzy Rothenberger, Aribert Rouleau, Guy Ruderfer, Douglas Rujescu, Dan Sanders, Alan R. Sanders, Stephan J. Santangelo, Susan L. Sergeant, Joseph A. Schachar, Russell Schalling, Martin Schatzberg, Alan F. Scheftner, William A. Schellenberg, Gerard D. Scherer, Stephen W. Schork, Nicholas J. Schulze, Thomas G. Schumacher, Johannes Schwarz, Markus Scolnick, Edward Scott, Laura J. Shi, Jianxin Shilling, Paul D. Shyn, Stanley I. Silverman, Jeremy M. Slager, Susan L. Smalley, Susan L. Smit, Johannes H. Smith, Erin N. Sonuga-Barke, Edmund J. S St Clair, David State, Matthew Steffens, Michael Steinhausen, Hans-Christoph Strauss, John S. Strohmaier, Jana Stroup, T. Scott Sutcliffe, James S. Szatmari, Peter Szelinger, Szabocls Thirumalai, Srinivasa Thompson, Robert C. Todorov, Alexandre A. Tozzi, Federica Treutlein, Jens Uhr, Manfred van den Oord, Edwin J. C G. Van Grootheest, Gerard Van Os, Jim Vicente, Astrid M. Vieland, Veronica J. Vincent, John B. Visscher, Peter M. Walsh, Christopher A. Wassink, Thomas H. Watson, Stanley J. Weissman, Myrna M. Werge, Thomas Wienker, Thomas F. Wijsman, Ellen M. Willemsen, Gonneke Williams, Nigel Willsey, A. Jeremy Witt, Stephanie H. Xu, Wei Young, Allan H. Yu, Timothy W. Zammit, Stanley Zandi, Peter P. Zhang, Peng Zitman, Frans G. Zöllner, Sebastian Devlin, Bernie Kelsoe, John R. Sklar, Pamela Daly, Mark J. O'Donovan, Michael C. Craddock, Nicholas Sullivan, Patrick F. Smoller, Jordan W. Kendler, Kenneth S. Wray, Naomi R (2013):

    Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/​​​​hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17–29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn’s disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.

  447. 2016-lo.pdf

  448. 2016-hyde.pdf: ⁠, Craig L. Hyde, Michael W. Nagle, Chao Tian, Xing Chen, Sara A. Paciga, Jens R. Wendland, Joyce Y. Tung, David A. Hinds, Roy H. Perlis, Ashley R. Winslow (2016-08-01; genetics  /​ ​​ ​correlation):

    Despite strong evidence supporting the heritability of major depressive disorder (MDD), previous genome-wide studies were unable to identify risk loci among individuals of European descent. We used self-report data from 75,607 individuals reporting clinical diagnosis of depression and 231,747 individuals reporting no history of depression through 23andMe and carried out meta-analysis of these results with published MDD genome-wide association study results. We identified five independent variants from four regions associated with self-report of clinical diagnosis or treatment for depression. Loci with a p value <1.0 × 10−5 in the meta-analysis were further analyzed in a replication data set (45,773 cases and 106,354 controls) from 23andMe. A total of 17 independent SNPs from 15 regions reached genome-wide statistical-significance after joint analysis over all three data sets. Some of these loci were also implicated in genome-wide association studies of related psychiatric traits. These studies provide evidence for large-scale consumer genomic data as a powerful and efficient complement to data collected from traditional means of ascertainment for neuropsychiatric disease genomics.

  449. ⁠, Joey Ward, Rona J. Strawbridge, Mark E. S. Bailey, Nicholas Graham, Ferguson Amy, Donald M. Lyall, Breda Cullen DClinPsy, Laura M. Pidgeon, Jonathan Cavanagh, Daniel F. Mackay, Jill P. Pell, Michael O’Donovan, Valentina Escott-Price, Daniel J. Smith (2017-06-15):

    Mood instability is a core clinical feature of affective and psychotic disorders. In keeping with the Research Domain Criteria (RDoC) approach, it may be a useful construct for identifying biology that cuts across psychiatric categories. We aimed to investigate the biological validity of a simple measure of mood instability and evaluate its genetic relationship with several psychiatric disorders, including major depressive disorder (MDD), bipolar disorder (BD), schizophrenia, attention deficit hyperactivity disorder (ADHD), anxiety disorder and post-traumatic stress disorder (). We conducted a genome-wide association study (GWAS) of mood instability in 53,525 cases and 60,443 controls from UK Biobank, identifying four independently-associated loci (on chromosomes eight, nine, 14 and 18), and a common single nucleotide polymorphism (SNP)-based heritability estimate of approximately 8%. We found a strong genetic correlation between mood instability and MDD (rg = 0.60, SE = 0.07, p = 8.95 × 10−17) and a small but statistically-significant genetic correlation with both schizophrenia (rg = 0.11, SE = 0.04, p = 0.01) and anxiety disorders (rg = 0.28, SE = 0.14, p = 0.04), although no genetic correlation with BD, ADHD or PTSD. Several genes at the associated loci may have a role in mood instability, including the DCC netrin 1 receptor (DCC) gene, eukaryotic translation initiation factor 2B subunit beta (eIF2B2), placental growth factor (PGF), and protein tyrosine phosphatase, receptor type D (PTPRD). Strengths of this study include the very large sample size, but our measure of mood instability may be limited by the use of a single question. Overall, this work suggests a polygenic basis for mood instability. This simple measure can be obtained in very large samples; our findings suggest that doing so may offer the opportunity to illuminate the fundamental biology of mood regulation.

  450. 2013-pettersson.pdf

  451. https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2519364

  452. 2016-rees.pdf

  453. 2016-riglin.pdf: “Schizophrenia risk alleles and neurodevelopmental outcomes in childhood: a population-based cohort study”⁠, Lucy Riglin, Stephan Collishaw, Alexander Richards, Ajay K. Thapar MRCGP, Barbara Maughan, Michael C. O'Donovansych, Anita Thaparsych

  454. 2014-arnedo.pdf

  455. https://web.archive.org/web/20140924070551/https://news.wustl.edu/news/Pages/27358.aspx

  456. https://www.nature.com/nature/journal/v511/n7510/full/nature13595.html

  457. http://archpsyc.jamanetwork.com/article.aspx?articleid=2494707

  458. ⁠, Po-Ru Loh, Gaurav Bhatia, Alexander Gusev, Hilary K. Finucane, Brendan K. Bulik-Sullivan, Samuela J. Pollack, Schizophrenia Working Group of the Psychiatric Genomics Consortiumy, Teresa R. de Candia, Sang Hong Lee, Naomi R. Wray, Kenneth S. Kendler, Michael C. O’Donovan, Benjamin M. Neale, Nick Patterson, Alkes L. Price (2015-06-05):

    Heritability analyses of GWAS cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further discoveries. Here, we analyze the genetic architecture of schizophrenia in 49,806 samples from the PGC, and nine complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which ≥71% of 1Mb genomic regions harbor at least one variant influencing schizophrenia risk. We also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe statistically-significant genetic correlations (ranging from 0.18 to 0.85) among several pairs of GERA diseases; genetic correlations were on average 1.3× stronger than correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multi-component, multi-trait variance components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.

  459. 2016-okbay.pdf: ⁠, Naomi R. Wray, Stephan Ripke, Manuel Mattheisen, Maciej Trzaskowski, Enda M. Byrne, Abdel Abdellaoui, Mark J. Adams, Esben Agerbo, Tracy M. Air, Till M. F. Andlauer, Silviu-Alin Bacanu, Marie Bækvad-Hansen, Aartjan F. T. Beekman, Tim B. Bigdeli, Elisabeth B. Binder, Douglas R. H. Blackwood, Julien Bryois, Henriette N. Buttenschøn, Jonas Bybjerg-Grauholm, Na Cai, Enrique Castelao, Jane Hvarregaard Christensen, Toni-Kim Clarke, Jonathan I. R. Coleman, Lucía Colodro-Conde, Baptiste Couvy-Duchesne, Nick Craddock, Gregory E. Crawford, Cheynna A. Crowley, Hassan S. Dashti, Gail Davies, Ian J. Deary, Franziska Degenhardt, Eske M. Derks, Nese Direk, Conor V. Dolan, Erin C. Dunn, Thalia C. Eley, Nicholas Eriksson, Valentina Escott-Price, Farnush Hassan Farhadi Kiadeh, Hilary K. Finucane, Andreas J. Forstner, Josef Frank, Héléna A. Gaspar, Michael Gill, Paola Giusti-Rodríguez, Fernando S. Goes, Scott D. Gordon, Jakob Grove, Lynsey S. Hall, Eilis Hannon, Christine Søholm Hansen, Thomas F. Hansen, Stefan Herms, Ian B. Hickie, Per Hoffmann, Georg Homuth, Carsten Horn, Jouke-Jan Hottenga, David M. Hougaard, Ming Hu, Craig L. Hyde, Marcus Ising, Rick Jansen, Fulai Jin, Eric Jorgenson, James A. Knowles, Isaac S. Kohane, Julia Kraft, Warren W. Kretzschmar, Jesper Krogh, Zoltán Kutalik, Jacqueline M. Lane, Yihan Li, Yun Li, Penelope A. Lind, Xiaoxiao Liu, Leina Lu, Donald J. MacIntyre, Dean F. MacKinnon, Robert M. Maier, Wolfgang Maier, Jonathan Marchini, Hamdi Mbarek, Patrick McGrath, Peter McGuffin, Sarah E. Medland, Divya Mehta, Christel M. Middeldorp, Evelin Mihailov, Yuri Milaneschi, Lili Milani, Jonathan Mill, Francis M. Mondimore, Grant W. Montgomery, Sara Mostafavi, Niamh Mullins, Matthias Nauck, Bernard Ng, Michel G. Nivard, Dale R. Nyholt, Paul F. O’Reilly, Hogni Oskarsson, Michael J. Owen, Jodie N. Painter, Carsten Bøcker Pedersen, Marianne Giørtz Pedersen, Roseann E. Peterson, Erik Pettersson, Wouter J. Peyrot, Giorgio Pistis, Danielle Posthuma, Shaun M. Purcell, Jorge A. Quiroz, Per Qvist, John P. Rice, Brien P. Riley, Margarita Rivera, Saira Saeed Mirza, Richa Saxena, Robert Schoevers, Eva C. Schulte, Ling Shen, Jianxin Shi, Stanley I. Shyn, Engilbert Sigurdsson, Grant B. C. Sinnamon, Johannes H. Smit, Daniel J. Smith, Hreinn Stefansson, Stacy Steinberg, Craig A. Stockmeier, Fabian Streit, Jana Strohmaier, Katherine E. Tansey, Henning Teismann, Alexander Teumer, Wesley Thompson, Pippa A. Thomson, Thorgeir E. Thorgeirsson, Chao Tian, Matthew Traylor, Jens Treutlein, Vassily Trubetskoy, André G. Uitterlinden, Daniel Umbricht, Sandra Van der Auwera, Albert M. van Hemert, Alexander Viktorin, Peter M. Visscher, Yunpeng Wang, Bradley T. Webb, Shantel Marie Weinsheimer, Jürgen Wellmann, Gonneke Willemsen, Stephanie H. Witt, Yang Wu, Hualin S. Xi, Jian Yang, Futao Zhang, eQTLGen, 23andMe, Volker Arolt, Bernhard T. Baune, Klaus Berger, Dorret I. Boomsma, Sven Cichon, Udo Dannlowski, E. C. J. de Geus, J. Raymond DePaulo, Enrico Domenici, Katharina Domschke, Tõnu Esko, Hans J. Grabe, Steven P. Hamilton, Caroline Hayward, Andrew C. Heath, David A. Hinds, Kenneth S. Kendler, Stefan Kloiber, Glyn Lewis, Qingqin S. Li, Susanne Lucae, Pamela F. A. Madden, Patrik K. Magnusson, Nicholas G. Martin, Andrew M. McIntosh, Andres Metspalu, Ole Mors, Preben Bo Mortensen, Bertram Müller-Myhsok, Merete Nordentoft, Markus M. Nöthen, Michael C. O’Donovan, Sara A. Paciga, Nancy L. Pedersen, Brenda W. J. H. Penninx, Roy H. Perlis, David J. Porteous, James B. Potash, Martin Preisig, Marcella Rietschel, Catherine Schaefer, Thomas G. Schulze, Jordan W. Smoller, Kari Stefansson, Henning Tiemeier, Rudolf Uher, Henry Völzke, Myrna M. Weissman, Thomas Werge, Ashley R. Winslow, Cathryn M. Lewis, Douglas F. Levinson, Gerome Breen, Anders D. Børglum, Patrick F. Sullivan, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (2018-04-26; genetics  /​ ​​ ​correlation):

    Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide.

    We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and statistically-significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology.

    All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

  460. 2016-robinson.pdf: “Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population”⁠, Elise B. Robinson, Beate St Pourcain, Verneri Anttila, Jack A. Kosmicki, Brendan Bulik-Sullivan, Jakob Grove, Julian Maller, Kaitlin E. Samocha, Stephan J. Sanders, Stephan Ripke, Joanna Martin, Mads V. Hollegaard, Thomas Werge, David M. Hougaard, Benjamin M. Neale, David M. Evans, David Skuse, Preben Bo Mortensen, Anders D. Børglum, Angelica Ronald, George Davey Smith, Mark J. Daly

  461. 2015-johnson.pdf

  462. ⁠, Gratten, Jacob Wray, Naomi R. Keller, Matthew C. Visscher, Peter M (2014):

    Family study results are consistent with genetic effects making substantial contributions to risk of psychiatric disorders such as schizophrenia, yet robust identification of specific genetic variants that explain variation in population risk had been disappointing until the advent of technologies that assay the entire genome in large samples. We highlight recent progress that has led to a better understanding of the number of risk variants in the population and the interaction of allele frequency and effect size. The emerging genetic architecture implies a large number of contributing loci (that is, a high genome-wide mutational target) and suggests that genetic risk of psychiatric disorders involves the combined effects of many common variants of small effect, as well as rare and de novo variants of large effect. The capture of a substantial proportion of genetic risk facilitates new study designs to investigate the combined effects of genes and the environment.

  463. 2015-power.pdf: ⁠, Robert A. Power, Stacy Steinberg, Gyda Bjornsdottir, Cornelius A. Rietveld, Abdel Abdellaoui, Michel M. Nivard, Magnus Johannesson, Tessel E. Galesloot, Jouke J. Hottenga, Gonneke Willemsen, David Cesarini, Daniel J. Benjamin, Patrik K. E Magnusson, Fredrik Ullén, Henning Tiemeier, Albert Hofman, Frank J. A van Rooij, G. Bragi Walters, Engilbert Sigurdsson, Thorgeir E. Thorgeirsson, Andres Ingason, Agnar Helgason, Augustine Kong, Lambertus A. Kiemeney, Philipp Koellinger, Dorret I. Boomsma, Daniel Gudbjartsson, Hreinn Stefansson & Kari Stefansson (2015-06-08; genetics  /​ ​​ ​correlation):

    We tested whether polygenic risk scores for schizophrenia and bipolar disorder would predict creativity. Higher scores were associated with artistic society membership or creative profession in both Icelandic (p = 5.2 × 10−6 and 3.8 × 10−6 for schizophrenia and bipolar disorder scores, respectively) and replication cohorts (p = 0.0021 and 0.00086). This could not be accounted for by increased relatedness between creative individuals and those with psychoses, indicating that creativity and psychosis share genetic roots.

  464. ⁠, Patrick F. Sullivan, Arpana Agrawal, Cynthia M. Bulik, Ole A. Andreassen, Anders D. Børglum, Gerome Breen, Sven Cichon, Howard J. Edenberg, Stephen V. Faraone, Joel Gelernter, Carol A. Mathews, Caroline M. Nievergelt, Jordan Smoller, Michael C. O’Donovan, for the Psychiatric Genomics Consortium (2017-03-10):

    The Psychiatric Genomics Consortium (PGC) is the largest consortium in the history of psychiatry. In the past decade, this global effort has delivered a rapidly increasing flow of new knowledge about the fundamental basis of common psychiatric disorders, particularly given its dedication to rapid progress and open science. The PGC has recently commenced a program of research designed to deliver “actionable” findings—genomic results that (a) reveal the fundamental biology, (b) inform clinical practice, and (c) deliver new therapeutic targets. This is the central idea of the PGC: to convert the family history risk factor into biologically, clinically, and therapeutically meaningful insights. The emerging findings suggest that we are entering into a phase of accelerated translation of genetic discoveries to impact psychiatric practice within a precision medicine framework.


    PGC Coordinating Committee: Mark Daly, Michael Gill, John Kelsoe, Karestan Koenen, Douglas Levinson, Cathryn Lewis, Ben Neale, Danielle Posthuma, Jonathan Sebat, and Pamela Sklar.

  465. ⁠, Joëlle A. Pasman, Karin J. H. Verweij, Zachary Gerring, Sven Stringer, Sandra Sanchez-Roige, Jorien L. Treur, Abdel Abdellaoui, Michel G. Nivard, Bart M. L. Baselmans, Jue-Sheng Ong, Hill F. Ip, Matthijs D. van der Zee, Meike Bartels, Felix R. Day, Pierre Fontanillas, Sarah L. Elson, the 23andMe Research Team, Harriet de Wit, Lea K. Davis, James MacKillop, International Cannabis Consortium, Jaime L. Derringer, Susan J. T. Branje, Catharina A. Hartman, Andrew C. Heath, Pol A. C. van Lier, Pamela A. F. Madden, Reedik Mägi, Wim Meeus, Grant W. Montgomery, A. J. Oldehinkel, Zdenka Pausova, Josep A. Ramos-Quiroga, Tomas Paus, Marta Ribases, Jaakko Kaprio, Marco P. M. Boks, Jordana T. Bell, Tim D. Spector, Joel Gelernter, Dorret I. Boomsma, Nicholas G. Martin, Stuart MacGregor, John R. B. Perry, Abraham A. Palmer, Danielle Posthuma, Marcus R. Munafò, Nathan A. Gillespie, Eske M. Derks, Jacqueline M. Vink (2018-01-08):

    Cannabis use is a heritable trait [1] that has been associated with adverse mental health outcomes. To identify risk variants and improve our knowledge of the genetic etiology of cannabis use, we performed the largest genome-wide association study (GWAS) meta-analysis for lifetime cannabis use (n = 184,765) to date. We identified 4 independent loci containing genome-wide statistically-significant SNP associations. Gene-based tests revealed 29 genome-wide statistically-significant genes located in these 4 loci and 8 additional regions. All SNPs combined explained 10% of the variance in lifetime cannabis use. The most statistically-significantly associated gene, CADM2, has previously been associated with substance use and risk-taking phenotypes [2–4]. We used S-PrediXcan to explore gene expression levels and found 11 unique eGenes. LD-score regression uncovered genetic correlations with smoking, alcohol use and mental health outcomes, including schizophrenia and bipolar disorder. Mendelian randomisation analysis provided evidence for a causal positive influence of schizophrenia risk on lifetime cannabis use.

  466. ⁠, Rona J. Strawbridge, Joey Ward, Breda Cullen, Elizabeth M. Tunbridge, Sarah Hartz, Laura Bierut, Amy Horton, Mark E. S. Bailey, Nicholas Graham, Amy Ferguson, Donald M. Lyall, Daniel Mackay, Laura M. Pidgeon, Jonathan Cavanagh, Jill P. Pell, Michael O’Donovan, Valentina Escott-Price, Paul J. Harrison, Daniel J. Smith (2017-08-16):

    Risk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use and diet. Risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby elucidation of the genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome wide association study in 116 255 UK Biobank participants who responded yes/​​​​no to the question “would you consider yourself a risk-taker?” Risk-takers (compared to controls) were more likely to be men, smokers and have a history of mental illness. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention deficit hyperactivity disorder and post-traumatic stress disorder, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait which has a major impact on a range of common physical and mental health disorders.

  467. ⁠, Yann C. Klimentidis, David A. Raichlen, Jennifer Bea, David O. Garcia, Lawrence J. Mandarino, Gene E. Alexander, Zhao Chen, Scott B. Going (2017-08-22):

    Physical activity (PA) protects against a wide range of diseases. Engagement in habitual PA has been shown to be heritable, motivating the search for specific genetic variants that explain variation in habitual PA and may ultimately improve efforts to promote PA and target the best type of PA for each individual. We used data from the UK Biobank to perform the largest genome-wide association study of PA, using four measures based on self-report (n = 277,656) and accelerometry (n = 67,808). Replication was then sought in the Atherosclerosis Risk in Communities (ARIC) study (n = 8,556). In the UK Biobank, we identified 17 genome-wide loci across the four PA measures. Interestingly, rs429358 of the APOE gene was the most strongly associated variant with any single PA measure and was at least nominally associated with three of the four PA measures examined. We also identified three loci (DNAJC1, DCAF5, and PML) consistently associated with PA across all four measures. Tissue enrichment analyses implicate the brain and pituitary gland as locations where PA-associated loci may exert their actions. Genetic correlation analyses suggest a positive genetic correlation of PA with early-morning chronotype and psychiatric traits, and a negative genetic correlation of PA with obesity-related traits. Using data from the GIANT consortium, we identify several loci that are associated with both increased waist circumference and decreased PA. Although very small effect sizes precluded replication of individual loci in ARIC, we found consistent overall genetic correlations of PA with other traits. These results provide new insight into the genetic basis of habitual PA, and the genetic links connecting PA and obesity.

  468. 2017-silventoinen.pdf

  469. ⁠, Varun Warrier, the 23andMe Research Team, Thomas Bourgeron, Simon Baron-Cohen (2017-10-05):

    Dissatisfaction in social relationships is reported widely across many psychiatric conditions. We investigated the genetic architecture of family relationship satisfaction and friendship satisfaction in the UK Biobank. We leveraged the high genetic correlation between the two phenotypes (rg = 0.87±0.03; P < 2.2×10-16) to conduct multi-trait analysis of Genome Wide Association Study (GWAS) (Neffective family = 164,112; Neffective friendship = 158,116). We identified two genome-wide statistically-significant associations for both the phenotypes: rs1483617 on chromosome 3 and rs2189373 on chromosome 6, a region previously implicated in schizophrenia. eQTL and chromosome conformation capture in neural tissues prioritizes several genes including NLGN1. Gene-based association studies identified several significant genes, with highest expression in brain tissues. Genetic correlation analysis identified significant negative correlations for multiple psychiatric conditions including highly significant negative correlation with cross-psychiatric disorder GWAS, underscoring the central role of social relationship dissatisfaction in psychiatric diagnosis. The two phenotypes were enriched for genes that are loss of function intolerant. Both phenotypes had modest, significant additive SNP heritability of approximately 6%. Our results underscore the central role of social relationship satisfaction in mental health and identify genes and tissues associated with it.

  470. https://nutritionj.biomedcentral.com/articles/10.1186/s12937-017-0269-y

  471. ⁠, Eero Vuoksimaa, Matthew S. Panizzon, Carol E. Franz, Christine Fennema-Notestine, Donald J. Hagler, Michael J. Lyons, Anders M. Dale, William S. Kremen (2017-09-01):

    Height and general cognitive ability (GCA) are positively associated, but the underlying mechanisms of this relationship are unclear. We used a sample of 515 middle-aged male twins with structural magnetic resonance imaging data to study if the association between height and cognitive ability is mediated by cortical size. We used genetically, ontogenetically and phylogenetically distinct cortical metrics of cortical surface area (SA) and cortical thickness (CT). Our results indicate that the well-replicated height-GCA association is accounted for by individual differences in total cortical SA (highly heritable metric related to global brain size), and not mean CT, and that the genetic association between SA and GCA underlies the phenotypic height-GCA relationship.

  472. https://www.nature.com/articles/s41398-018-0217-4

  473. https://www.amazon.com/Sports-Gene-Extraordinary-Athletic-Performance/dp/161723012X

  474. http://www.outsideonline.com/outdoor-adventure/media/books/How-Athletes-Get-Great.html?page=all

  475. https://www.newyorker.com/magazine/2013/09/09/man-and-superman

  476. https://www.newyorker.com/science/maria-konnikova/practice-doesnt-make-perfect

  477. https://story.californiasunday.com/superhero-gene-euan-ashley-stanford

  478. https://www.cell.com/ajhg/abstract/S0002-9297(15)00245-1

  479. ⁠, Iossifov, Ivan O'Roak, Brian J. Sanders, Stephan J. Ronemus, Michael Krumm, Niklas Levy, Dan Stessman, Holly A. Witherspoon, Kali T. Vives, Laura Patterson, Karynne E. Smith, Joshua D. Paeper, Bryan Nickerson, Deborah A. Dea, Jeanselle Dong, Shan Gonzalez, Luis E. Mandell, Jeffrey D. Mane, Shrikant M. Murtha, Michael T. Sullivan, Catherine A. Walker, Michael F. Waqar, Zainulabedin Wei, Liping Willsey, A. Jeremy Yamrom, Boris Lee, Yoon-ha Grabowska, Ewa Dalkic, Ertugrul Wang, Zihua Marks, Steven Andrews, Peter Leotta, Anthony Kendall, Jude Hakker, Inessa Rosenbaum, Julie Ma, Beicong Rodgers, Linda Troge, Jennifer Narzisi, Giuseppe Yoon, Seungtai Schatz, Michael C. Ye, Kenny McCombie, W. Richard Shendure, Jay Eichler, Evan E. State, Matthew W. Wigler, Michael (2014):

    Whole exome sequencing has proven to be a powerful tool for understanding the genetic architecture of human disease. Here we apply it to more than 2,500 simplex families, each having a child with an autistic spectrum disorder. By comparing affected to unaffected siblings, we show that 13% of de novo missense mutations and 43% of de novo likely gene-disrupting (LGD) mutations contribute to 12% and 9% of diagnoses, respectively. Including copy number variants, coding de novo mutations contribute to about 30% of all simplex and 45% of female diagnoses. Almost all LGD mutations occur opposite wild-type alleles. LGD targets in affected females significantly overlap the targets in males of lower intelligence quotient (IQ), but neither overlaps significantly with targets in males of higher IQ. We estimate that LGD mutation in about 400 genes can contribute to the joint class of affected females and males of lower IQ, with an overlapping and similar number of genes vulnerable to contributory missense mutation. LGD targets in the joint class overlap with published targets for intellectual disability and schizophrenia, and are enriched for chromatin modifiers, FMRP-associated genes and embryonically expressed genes. Most of the significance for the latter comes from affected females.

  480. http://www.spring.org.uk/2014/11/autism-new-studies-identify-dozens-more-associated-genes.php

  481. https://www.npr.org/blogs/health/2014/10/29/359818102/scientists-implicate-more-than-100-genes-in-causing-autism

  482. ⁠, Daniel J. Weiner, Emilie M. Wigdor, Stephan Ripke, Raymond K. Walters, Jack A. Kosmicki, Jakob Grove, Kaitlin E. Samocha, Jacqueline Goldstein, Aysu Okbay, Jonas Bybjerg-Gauholm, Thomas Werge, David M. Hougaard, Jacob Taylor, David Skuse, Bernie Devlin, Richard Anney, Stephan Sanders, Somer Bishop, Preben Bo Mortensen, Anders D. Børglum, George Davey Smith, Mark J. Daly, Elise B. Robinson, iPSYCH-Broad Autism Group, Psychiatric Genomics Consortium Autism Group, Psychiatric Genomics Consortium Autism Group (2016-11-23):

    Autism spectrum disorder (ASD) risk is influenced by both common polygenic and de novo variation. The purpose of this analysis was to clarify the influence of common polygenic risk for ASDs and to identify subgroups of cases, including those with strong acting de novo variants, in which different types of polygenic risk are relevant. To do so, we extend the transmission disequilibrium approach to encompass polygenic risk scores, and introduce the polygenic transmission disequilibrium test. Using data from more than 6,400 children with ASDs and 15,000 of their family members, we show that polygenic risk for ASDs, schizophrenia, and greater educational attainment is over transmitted to children with ASDs in two independent samples, but not to their unaffected siblings. These findings hold independent of proband IQ. We find that common polygenic variation contributes additively to risk in ASD cases that carry a very strong acting de novo variant. Lastly, we find evidence that elements of polygenic risk are independent and differ in their relationship with proband phenotype. These results confirm that ASDs’ genetic influences are highly additive and suggest that they create risk through at least partially distinct etiologic pathways.

  483. https://www.newyorker.com/magazine/2014/07/21/one-of-a-kind-2

  484. https://www.propublica.org/article/muscular-dystrophy-patient-olympic-medalist-same-genetic-mutation

  485. https://psmag.com/diy-diagnosis-how-an-extreme-athlete-uncovered-her-genetic-flaw-d3111a5dd376

  486. 2016-bagnall.pdf: ⁠, Richard D. Bagnall, Robert G. Weintraub, Jodie Ingles, Johan Duflou, Laura Yeates, Lien Lam, Andrew M. Davis, Tina Thompson, Vanessa Connell, Jennie Wallace, Charles Naylor, Jackie Crawford, Donald R. Love, Lavinia Hallam, Jodi White, Christopher Lawrence, Matthew Lynch, Natalie Morgan, Paul James, Desirée du Sart, Rajesh Puranik, Neil Langlois, Jitendra Vohra, Ingrid Winship, John Atherton, Julie McGaughran, Jonathan R. Skinner, Christopher Semsarian (2016-06-23; genetics  /​ ​​ ​heritable):

    Background: Sudden cardiac death among children and young adults is a devastating event. We performed a prospective, population-based, clinical and genetic study of sudden cardiac death among children and young adults.

    Methods: We prospectively collected clinical, demographic, and autopsy information on all cases of sudden cardiac death among children and young adults 1 to 35 years of age in Australia and New Zealand from 2010 through 2012. In cases that had no cause identified after a comprehensive autopsy that included toxicologic and histologic studies (unexplained sudden cardiac death), at least 59 cardiac genes were analyzed for a clinically relevant cardiac gene mutation.

    Results: A total of 490 cases of sudden cardiac death were identified. The annual incidence was 1.3 cases per 100,000 persons 1 to 35 years of age; 72% of the cases involved boys or young men. Persons 31 to 35 years of age had the highest incidence of sudden cardiac death (3.2 cases per 100,000 persons per year), and persons 16 to 20 years of age had the highest incidence of unexplained sudden cardiac death (0.8 cases per 100,000 persons per year). The most common explained causes of sudden cardiac death were coronary artery disease (24% of cases) and inherited cardiomyopathies (16% of cases). Unexplained sudden cardiac death (40% of cases) was the predominant finding among persons in all age groups, except for those 31 to 35 years of age, for whom coronary artery disease was the most common finding. Younger age and death at night were independently associated with unexplained sudden cardiac death as compared with explained sudden cardiac death. A clinically relevant cardiac gene mutation was identified in 31 of 113 cases (27%) of unexplained sudden cardiac death in which genetic testing was performed. During follow-up, a clinical diagnosis of an inherited cardiovascular disease was identified in 13% of the families in which an unexplained sudden cardiac death occurred.

    Conclusions: The addition of genetic testing to autopsy investigation substantially increased the identification of a possible cause of sudden cardiac death among children and young adults.

  487. ⁠, Sisodiya, Sanjay M. Thompson, Pamela J. Need, Anna Harris, Sarah E. Weale, Michael E. Wilkie, Susan E. Michaelides, Michel Free, Samantha L. Walley, Nicole Gumbs, Curtis Gerrelli, Dianne Ruddle, Piers Whalley, Lawrence J. Starr, John M. Hunt, David M. Goldstein, David B. Deary, Ian J. Moore, Anthony T (2007):

    Background: The genetic basis of variation in human cognitive abilities is poorly understood. RIMS1 encodes a synapse active-zone protein with important roles in the maintenance of normal synaptic function: mice lacking this protein have greatly reduced learning ability and memory function.

    Objective: An established paradigm examining the structural and functional effects of mutations in genes expressed in the eye and the brain was used to study a kindred with an inherited retinal dystrophy due to RIMS1 mutation.

    Materials and Methods: Neuropsychological tests and high-resolution MRI brain scanning were undertaken in the kindred. In a population cohort, neuropsychological scores were associated with common variation in RIMS1. Additionally, RIMS1 was sequenced in top-scoring individuals. Evolution of RIMS1 was assessed, and its expression in developing human brain was studied.

    Results: Affected individuals showed significantly enhanced cognitive abilities across a range of domains. Analysis suggests that factors other than RIMS1 mutation were unlikely to explain enhanced cognition. No association with common variation and verbal IQ was found in the population cohort, and no other mutations in RIMS1 were detected in the highest scoring individuals from this cohort. RIMS1 protein is expressed in developing human brain, but RIMS1 does not seem to have been subjected to accelerated evolution in man.

    Conclusions: A possible role for RIMS1 in the enhancement of cognitive function at least in this kindred is suggested. Although further work is clearly required to explore these findings before a role for RIMS1 in human cognition can be formally accepted, the findings suggest that genetic mutation may enhance human cognition in some cases.

  488. https://www.newyorker.com/science/maria-konnikova/a-gene-makes-you-need-less-sleep

  489. ⁠, Kuna, Samuel T. Maislin, Greg Pack, Frances M. Staley, Bethany Hachadoorian, Robert Coccaro, Emil F. Pack, Allan I (2012):

    Study Objectives: To determine if the large and highly reproducible interindividual differences in rates of performance deficit accumulation during sleep deprivation, as determined by the number of lapses on a sustained reaction time test, the Psychomotor Vigilance Task (PVT), arise from a heritable trait.

    Design: Prospective, observational cohort study.

    Setting: Academic medical center.

    Participants: There were 59 monozygotic (mean age 29.2 ± 6.8 [SD] yr; 15 male and 44 female pairs) and 41 dizygotic (mean age 26.6 ± 7.6 yr; 15 male and 26 female pairs) same-sex twin pairs with a normal polysomnogram.

    Interventions: Thirty-eight hr of monitored, continuous sleep deprivation.

    Measurements and Results: Patients performed the 10-min PVT every 2 hr during the sleep deprivation protocol. The primary outcome was change from baseline in square root transformed total lapses (response time ≥ 500 ms) per trial. Patient-specific linear rates of performance deficit accumulation were separated from circadian effects using multiple linear regression. Using the classic approach to assess heritability, the intraclass correlation coefficients for accumulating deficits resulted in a broad sense heritability (h2) estimate of 0.834. The mean within-pair and among-pair heritability estimates determined by analysis of variance-based methods was 0.715. When variance components of mixed-effect multilevel models were estimated by maximum likelihood estimation and used to determine the proportions of phenotypic variance explained by genetic and nongenetic factors, 51.1% (standard error = 8.4%, P < 0.0001) of twin variance was attributed to combined additive and dominance genetic effects.

    Conclusion: Genetic factors explain a large fraction of interindividual variance among rates of performance deficit accumulations on PVT during sleep deprivation.

  490. ⁠, He, Ying Jones, Christopher R. Fujiki, Nobuhiro Xu, Ying Guo, Bin Holder, Jimmy L. Rossner, Moritz J. Nishino, Seiji Fu, Ying-Hui (2009):

    Sleep deprivation can impair human health and performance. Habitual total sleep time and homeostatic sleep response to sleep deprivation are quantitative traits in humans. Genetic loci for these traits have been identified in model organisms, but none of these potential animal models have a corresponding human genotype and phenotype.

    We have identified a mutation in a transcriptional repressor (hDEC2-P385R) that is associated with a human short sleep phenotype. Activity profiles and sleep recordings of transgenic mice carrying this mutation showed increased vigilance time and less sleep time than control mice in a zeitgeber time-dependent and sleep deprivation-dependent manner.

    These mice represent a model of human sleep homeostasis that provides an opportunity to probe the effect of sleep on human physical and mental health.

  491. ⁠, Pellegrino, Renata Kavakli, Ibrahim Halil Goel, Namni Cardinale, Christopher J. Dinges, David F. Kuna, Samuel T. Maislin, Greg Van Dongen, Hans P. A Tufik, Sergio Hogenesch, John B. Hakonarson, Hakon Pack, Allan I (2014):

    Study Objectives: Earlier work described a mutation in DEC2 also known as BHLHE41 (basic helix-loophelix family member e41) as causal in a family of short sleepers, who needed just 6 h sleep per night. We evaluated whether there were other variants of this gene in two well-phenotyped cohorts.

    Design: Sequencing of the BHLHE41 gene, electroencephalographic data, and delta power analysis and functional studies using cell-based luciferase.

    Results: We identified new variants of the BHLHE41 gene in two cohorts who had either acute sleep deprivation (n = 200) or chronic partial sleep deprivation (n = 217). One variant, Y362H, at another location in the same exon occurred in one twin in a dizygotic twin pair and was associated with reduced sleep duration, less recovery sleep following sleep deprivation, and fewer performance lapses during sleep deprivation than the homozygous twin. Both twins had almost identical amounts of non rapid eye movement (NREM) sleep. This variant reduced the ability of BHLHE41 to suppress CLOCK/​​​​BMAL1 and NPAS2/​​​​BMAL1 transactivation in vitro. Another variant in the same exome had no effect on sleep or response to sleep deprivation and no effect on CLOCK/​​​​BMAL1 transactivation. Random mutagenesis identified a number of other variants of BHLHE41 that affect its function.

    Conclusions: There are a number of mutations of BHLHE41. Mutations reduce total sleep while maintaining NREM sleep and provide resistance to the effects of sleep loss. Mutations that affect sleep also modify the normal inhibition of BHLHE41 of CLOCK/​​​​BMAL1 transactivation. Thus, clock mechanisms are likely involved in setting sleep length and the magnitude of sleep homeostasis.

    Citation: Pellegrino R, Kavakli IH, Goel N, Cardinale CJ, Dinges DF, Kuna ST, Maislin G, Van Dongen HP, Tufik S, Hogenesch JB, Hakonarson H, Pack AI. A novel BHLHE41 variant is associated with short sleep and resistance to sleep deprivation in humans. SLEEP 2014;37(8):1327-1336.

  492. ⁠, Andrea Ganna, F. Kyle Satterstrom, Seyedeh M. Zekavat, Indraniel Das, Mitja I. Kurki, Claire Churchhouse, Jessica Alfoldi, Alicia R. Martin, Aki S. Havulinna, Andrea Byrnes, Wesley K. Thompson, Philip R. Nielsen, Konrad J. Karczewski, Elmo Saarentaus, Manuel A. Rivas, Namrata Gupta, Olli Pietiläinen, Connor A. Emdin, Francesco Lescai, Jonas Bybjerg-Grauholm, Jason Flannick, on behalf of GoT2D/​​T2D-GENES consortium, Josep Mercader, Miriam Udlerg, on behalf of SIGMA consortium, Helmsley IBD Exome Sequencing Project, FinMetSeq Consortium, iPSYCH-Broad Consortium, Markku Laakso, Veikko Salomaa, Christina Hultman, Samuli Ripatti, Eija Hämäläinen, Jukka S. Moilanen, Jarmo Körkkö, Outi Kuismin, Merete Nordentoft, David M. Hougaard, Ole Mors, Thomas Werge, Preben Bo Mortensen, Daniel MacArthur, Mark J. Daly, Patrick F. Sullivan, Adam E. Locke, Aarno Palotie, Anders D. Børglum, Sekar Kathiresan, Benjamin M. Neale (2017-06-09):

    Protein truncating variants (PTVs) are likely to modify gene function and have been linked to hundreds of Mendelian disorders1,2. However, the impact of PTVs on complex traits has been limited by the available sample size of whole-exome sequencing studies (WES) 3. Here we assemble WES data from 100,304 individuals to quantify the impact of rare PTVs on 13 quantitative traits and 10 diseases. We focus on those PTVs that occur in PTV-intolerant (PI) genes, as these are more likely to be pathogenic. Carriers of at least one PI-PTV were found to have an increased risk of autism, schizophrenia, bipolar disorder, intellectual disability and ADHD (p-value (p) range: 5×10−3−9×10−12). In controls, without these disorders, we found that this burden associated with increased risk of mental, behavioral and neurodevelopmental disorders as captured by electronic health record information. Furthermore, carriers of PI-PTVs tended to be shorter (p = 2×10−5), have fewer years of education (p = 2×10−4) and be younger (p = 2×10−7); the latter observation possibly reflecting reduced survival or study participation. While other gene-sets derived from in vivo experiments did not show any associations with PTV-burden, gene sets implicated in GWAS of cardiovascular-related traits and inflammatory bowel disease showed a significant PTV-burden with corresponding traits, mainly driven by established genes involved in familial forms of these disorders. We leveraged population health registries from 14,117 individuals to study the phenome-wide impact of PIPTVs and identified an increase in the number of hospital visits among PI-PTV carriers. In conclusion, we provide the most thorough investigation to date of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.


    Helmsley IBD Exome Sequencing Project: Dermot McGovern, Judy H Cho, Ann Pulver, Vincent Plagnol, Tony Segal, Gil Atzmon, Dan Turner, Ben Glaser, Inga Peter, Ramnik Xavier, Harry Sokol, Rinse Weersma, Andre Franke, John Rioux, Tariq Ahmad, Martti Färkkilä, Kimmo Kontula.

    FinMetSeq Consortium: Haley J Abel, Michael Boehnke, Lei Chen, Charleston WK Chiang, Colby C Chiang, Susan K Dutcher, Nelson B Freimer, Robert S Fulton, Liron Ganel, Ira M Hall, Anne U Jackson, Krishna L Kanchi, Chul Joo Kang, Daniel C Koboldt, Hannele Laivuori, David E Larson, Karyn Meltz Steinberg, Joanne Nelson, Thomas J Nicholas, Arto Pietilä, Matti Pirinen, Vasily Ramensky, Debashree Ray, Chiara Sabatti, Laura J Scott, Susan Service, Laurel Stell, Nathan O Stitziel, Heather M Stringham, Ryan Welch, Richard K Wilson, Pranav Yajnik.

    iPSYCH-Broad Consortium: Marianne G Pedersen, Marie Bækvad-Hansen, Christine S Hansen.

  493. https://fivethirtyeight.com/features/thoroughbreds-are-running-as-fast-as-they-can/

  494. https://www.nytimes.com/2006/07/25/health/25rats.html?pagewanted=all

  495. https://www.sciencenews.org/article/tameness-genes?mode=magazine&context=190312

  496. http://www.unz.com/gnxp/through-the-wormhole-are-we-here-for-a-reason-premier-may-13th/

  497. ⁠, Adam R. Boyko, Pascale Quignon, Lin Li, Jeffrey J. Schoenebeck, Jeremiah D. Degenhardt, Kirk E. Lohmueller, Keyan Zhao, Abra Brisbin, Heidi G. Parker, Bridgett M. vonHoldt, Michele Cargill, Adam Auton, Andy Reynolds, Abdel G. Elkahloun, Marta Castelhano, Dana S. Mosher, Nathan B. Sutter, Gary S. Johnson, John Novembre, Melissa J. Hubisz, Adam Siepel, Robert K. Wayne, Carlos D. Bustamante, Elaine A. Ostrander (2010-07-02):

    Domestic dogs exhibit tremendous phenotypic diversity, including a greater variation in body size than any other terrestrial mammal. Here, we generate a high density map of canine genetic variation by genotyping 915 dogs from 80 domestic dog breeds, 83 wild canids, and 10 outbred African shelter dogs across 60,968 single-nucleotide polymorphisms (SNPs).

    Coupling this genomic resource with external measurements from breed standards and individuals as well as skeletal measurements from museum specimens, we identify 51 regions of the dog genome associated with phenotypic variation among breeds in 57 traits. The complex traits include average breed body size and external body dimensions and cranial, dental, and long bone shape and size with and without ⁠. In contrast to the results from association mapping of quantitative traits in humans and domesticated plants, we find that across dog breeds, a small number of (≤3) explain the majority of phenotypic variation for most of the traits we studied. In addition, many genomic regions show signatures of recent selection, with most of the highly differentiated regions being associated with breed-defining traits such as body size, coat characteristics, and ear floppiness.

    Our results demonstrate the efficacy of mapping multiple traits in the domestic dog using a database of genotyped individuals and highlight the important role human-directed selection has played in altering the genetic architecture of key traits in this important species.

    Author Summary: Dogs offer a unique system for the study of genes controlling morphology. DNA from 915 dogs from 80 domestic breeds, as well as a set of feral dogs, was tested at over 60,000 points of variation and the dataset analyzed using novel methods to find loci regulating body size, head shape, leg length, ear position, and a host of other traits. Because each dog breed has undergone strong selection by breeders to have a particular appearance, there is a strong footprint of selection in regions of the genome that are important for controlling traits that define each breed. These analyses identified new regions of the genome, or loci, that are important in controlling body size and shape. Our results, which feature the largest number of domestic dogs studied at such a high level of genetic detail, demonstrate the power of the dog as a model for finding genes that control the body plan of mammals. Further, we show that the remarkable diversity of form in the dog, in contrast to some other species studied to date, appears to have a simple genetic basis dominated by genes of major effect.

  498. 2014-montague.pdf: ⁠, Michael J. Montague, Gang Li, Barbara Gandolfi, Razib Khan, Bronwen L. Aken, Steven M. J. Searle, Patrick Minx, LaDeana W. Hillier, Daniel C. Koboldt, Brian W. Davis, Carlos A. Driscoll, Christina S. Barr, Kevin Blackistone, Javier Quilez, Belen Lorente-Galdos, Tomas Marques-Bonet, Can Alkan, Gregg W. C. Thomas, Matthew W. Hahn, Marilyn Menotti-Raymond, Stephen J. O’Brien, Richard K. Wilson, Leslie A. Lyons, William J. Murphy, and Wesley C. Warren (2014-10-03; genetics  /​ ​​ ​selection):

    Little is known about the genetic changes that distinguish domestic populations from their wild progenitors. Here we describe a high-quality domestic cat reference genome assembly and comparative inferences made with other cat breeds, wildcats, and other mammals. Based upon these comparisons, we identified positively selected genes enriched for genes involved in lipid metabolism that underpin adaptations to a hypercarnivorous diet. We also found positive selection signals within genes underlying sensory processes, especially those affecting vision and hearing in the carnivore lineage. We observed an evolutionary tradeoff between functional olfactory and vomeronasal receptor gene repertoires in the cat and dog genomes, with an expansion of the feline chemosensory system for detecting pheromones at the expense of odorant detection. Genomic regions harboring signatures of natural selection that distinguish domestic cats from their wild congeners are enriched in neural crest-related genes associated with behavior and reward in mouse models, as predicted by the domestication syndrome hypothesis. Our description of a previously unidentified allele for the gloving pigmentation pattern found in the Birman breed supports the hypothesis that cat breeds experienced strong selection on specific mutations drawn from random bred populations. Collectively, these findings provide insight into how the process of domestication altered the ancestral wildcat genome and build a resource for future disease mapping and phylogenomic studies across all members of the Felidae.

  499. http://www.sciencepubs.org/content/345/6200/1074.full.pdf

  500. http://www.genetics.org/content/early/2016/07/11/genetics.116.191106

  501. https://www.nature.com/nature/journal/v464/n7288/full/nature08832.html

  502. ⁠, Brandon M. Lind, Christopher J. Friedline, Jill L. Wegrzyn, Patricia E. Maloney, Detlev R. Vogler, David B. Neale, Andrew J. Eckert (2016-05-31):

    For populations exhibiting high levels of gene flow, the genetic architecture of fitness-related traits is expected to be polygenic and underlain by many small-effect loci that covary across a network of linked genomic regions. For most coniferous taxa, studies describing this architecture have been limited to single-locus approaches, possibly leaving the vast majority of the underlying genetic architecture undescribed. Even so, molecular investigations rarely search for patterns indicative of an underlying polygenic basis, despite prior expectations for this signal. Here, using a polygenic perspective, we employ single and multilocus analyses of genome-wide data (n = 116,231 SNPs) to describe the genetic architecture of adaptation within whitebark pine (Pinus albicaulis Engelm.) across the local extent of the environmentally heterogeneous Lake Tahoe Basin, USA. We show that despite highly shared genetic variation (FST = 0.0069) there is strong evidence for polygenic adaptation to the rain shadow experienced across the eastern Sierra Nevada. Specifically, we find little evidence for large-effect loci and that the frequencies of loci associated with 4⁄5 phenotypes (mean = 236 SNPs), 18 environmental variables (mean = 99 SNPs), and those detected through genetic differentiation (n = 110 SNPs) exhibit significantly higher covariance than random SNPs. We also provide evidence that this covariance tracks environmental measures related to soil water availability through subtle allele frequency shifts across populations. Our results provide replicative support for theoretical expectations and highlight advantages of a polygenic perspective, as unremarkable loci when viewed from a single-locus perspective are noteworthy when viewed through a polygenic lens, particularly when considering protective measures such as conservation guidelines and restoration strategies.

  503. https://www.nytimes.com/2015/04/26/magazine/the-rat-paths-of-new-york.html

  504. http://www.wired.com/2012/07/flies-learn-math/

  505. https://www.nature.com/articles/ncomms12684

  506. 2017-librado.pdf

  507. https://www.nytimes.com/2017/04/27/science/horses-genetics-domestication-scythians.html

  508. ⁠, Amanda L. Pendleton, Feichen Shen, Angela M. Taravella, Sarah Emery, Krishna R. Veeramah, Adam R. Boyko, Jeffrey M. Kidd (2017-03-21):

    Dogs (Canis lupus familiaris) were domesticated from gray wolves between 20–40kya in Eurasia, yet details surrounding the process of domestication remain unclear. The vast array of phenotypes exhibited by dogs mirror numerous other domesticated animal species, a phenomenon known as the Domestication Syndrome. Here, we use signatures persisting in the dog genome to identify genes and pathways altered by the intensive selective pressures of domestication. We identified 37 candidate domestication regions containing 17.5Mb of genome sequence and 172 genes through whole-genome SNP analysis of 43 globally distributed village dogs and 10 wolves. Comparisons with three ancient dog genomes indicate that these regions reflect signatures of domestication rather than breed formation. Analysis of genes within these regions revealed a significant enrichment of gene functions linked to neural crest cell migration, differentiation and development. Genome copy number analysis identified regions of localized sequence and structural diversity, and discovered additional copy number variation at the amylase-2b locus. Overall, these results indicate that primary selection pressures targeted genes in the neural crest as well as components of the minor spliceosome, rather than genes involved in starch metabolism. Smaller jaw sizes, hairlessness, floppy ears, tameness, and diminished craniofacial development distinguish wolves from domesticated dogs, phenotypes of the Domestication Syndrome that can result from decreased neural crest cells at these sites. We propose that initial selection acted on key genes in the neural crest and minor splicing pathways during early dog domestication, giving rise to the phenotypes of modern dogs.

  509. https://www.edge.org/conversation/jonathan_b_losos-urban-evolution

  510. https://www.nytimes.com/2016/07/24/opinion/sunday/evolution-is-happening-faster-than-we-thought.html

  511. http://citeseerx.ist.psu.edu/viewdoc/download?doi=

  512. https://www.pnas.org/content/114/7/1613.full

  513. https://www.atlasobscura.com/articles/seaweed-sheep-north-ronaldsay-orkney-festival

  514. 2017-ostrander.pdf: “Demographic history, selection and functional diversity of the canine genome”⁠, Elaine A. Ostrander, Robert K. Wayne, Adam H. Freedman, Brian W. Davis

  515. ⁠, Stephen E. Harris, Jason Munshi-South (2017-09-26):

    Urbanization significantly alters natural ecosystems and has accelerated globally. Urban wildlife populations are often highly fragmented by human infrastructure, and isolated populations may adapt in response to local urban pressures. However, relatively few studies have identified genomic signatures of adaptation in urban animals. We used a landscape genomics approach to examine signatures of selection in urban populations of white-footed mice (Peromyscus leucopus) in New York City. We analyzed 154,770 SNPs identified from transcriptome data from 48 P. leucopus individuals from three urban and three rural populations, and used outlier tests to identify evidence of urban adaptation. We accounted for demography by simulating a neutral SNP dataset under an inferred demographic history as a null model for outlier analysis. We also tested whether candidate genes were associated with environmental variables related to urbanization. In total, we detected 381 outlier loci and after stringent filtering, identified and annotated 19 candidate loci. Many of the candidate genes were involved in metabolic processes, and have well-established roles in metabolizing lipids and carbohydrates. Our results indicate that white-footed mice in NYC are adapting at the biomolecular level to local selective pressures in urban habitats. Annotation of outlier loci suggest selection is acting on metabolic pathways in urban populations, likely related to novel diets in cities that differ from diets in less disturbed areas.

  516. http://genome.cshlp.org/content/19/5/711.long

  517. 2010-stearns.pdf

  518. ⁠, Chris M. Rands, Stephen Meader, Chris P. Ponting, Gerton Lunter (2014-06-05):

    Ten years on from the finishing of the human reference genome sequence, it remains unclear what fraction of the human genome confers function, where this sequence resides, and how much is shared with other mammalian species. When addressing these questions, functional sequence has often been equated with pan-mammalian conserved sequence. However, functional elements that are short-lived, including those contributing to species-specific biology, will not leave a footprint of long-lasting negative selection. Here, we address these issues by identifying and characterising sequence that has been constrained with respect to insertions and deletions for pairs of eutherian genomes over a range of divergences. Within noncoding sequence, we find increasing amounts of mutually constrained sequence as species pairs become more closely related, indicating that noncoding constrained sequence turns over rapidly. We estimate that half of present-day noncoding constrained sequence has been gained or lost in approximately the last 130 million years (half-life in units of divergence time, d1/​​​​2 = 0.25–0.31). While enriched with ENCODE biochemical annotations, much of the short-lived constrained sequences we identify are not detected by models optimized for wider pan-mammalian conservation. Constrained DNase 1 hypersensitivity sites, promoters and untranslated regions have been more evolutionarily stable than long noncoding RNA loci which have turned over especially rapidly. By contrast, protein coding sequence has been highly stable, with an estimated half-life of over a billion years (d1/​​​​2 = 2.1–5.0). From extrapolations we estimate that 8.2% (7.1–9.2%) of the human genome is presently subject to negative selection and thus is likely to be functional, while only 2.2% has maintained constraint in both human and mouse since these species diverged. These results reveal that the evolutionary history of the human genome has been highly dynamic, particularly for its noncoding yet biologically functional fraction.

    Author Summary:

    Nearly 99% of the human genome does not encode proteins, and while there recently has been extensive biochemical annotation of the remaining noncoding fraction, it remains unclear whether or not the bulk of these DNA sequences have important functional roles. By comparing the genome sequences of different species we identify genomic regions that have evolved unexpectedly slowly, a signature of natural selection upon functional sequence. Using a high resolution evolutionary approach to find sequence showing evolutionary signatures of functionality we estimate that a total of 8.2% (7.1–9.2%) of the human genome is presently functional, more than three times as much than is functional and shared between human and mouse. This implies that there is an abundance of sequences with short lived lineage-specific functionality. As expected, most of the sequence involved in this functional “turnover” is noncoding, while protein coding sequence is stably preserved over longer evolutionary timescales. More generally, we find that the rate of functional turnover varies significantly across categories of functional noncoding elements. Our results provide a pan-mammalian and whole genome perspective on how rapidly different classes of sequence have gained and lost functionality down the human lineage.

  519. ⁠, Hubbard, Troy D. Murray, Iain A. Bisson, William H. Sullivan, Alexis P. Sebastian, Aswathy Perry, George H. Jablonski, Nina G. Perdew, Gary H (2016):

    We have identified a fixed nonsynonymous sequence difference between humans (Val381; derived variant) and Neandertals (Ala381; ancestral variant) in the ligand-binding domain of the aryl hydrocarbon receptor (AHR) gene. In an exome sequence analysis of four Neandertal and Denisovan individuals compared with nine modern humans, there are only 90 total nucleotide sites genome-wide for which archaic hominins are fixed for the ancestral nonsynonymous variant and the modern humans are fixed for the derived variant. Of those sites, only 27, including Val381 in the AHR, also have no reported variability in the human dbSNP database, further suggesting that this highly conserved functional variant is a rare event. Functional analysis of the amino acid variant Ala381 within the AHR carried by Neandertals and nonhuman primates indicate enhanced polycyclic aromatic hydrocarbon (PAH) binding, DNA binding capacity, and AHR mediated transcriptional activity compared with the human AHR. Also relative to human AHR, the Neandertal AHR exhibited 150–1000 times greater sensitivity to induction of Cyp1a1 and Cyp1b1 expression by PAHs (e.g., benzo(a)pyrene). The resulting CYP1A1/​​​​CYP1B1 enzymes are responsible for PAH first pass metabolism, which can result in the generation of toxic intermediates and perhaps AHR-associated toxicities. In contrast, the human AHR retains the ancestral sensitivity observed in primates to nontoxic endogenous AHR ligands (e.g., indole, indoxyl sulfate). Our findings reveal that a functionally significant change in the AHR occurred uniquely in humans, relative to other primates, that would attenuate the response to many environmental pollutants, including chemicals present in smoke from fire use during cooking.

  520. https://www.nytimes.com/2016/08/09/science/fire-smoke-evolution-tuberculosis.html

  521. ⁠, Katarzyna Bozek, Yuning Wei, Zheng Yan, Xiling Liu, Jieyi Xiong, Masahiro Sugimoto, Masaru Tomita, Svante Pääbo, Raik Pieszek, Chet C. Sherwood, Patrick R. Hof, John J. Ely, Dirk Steinhauser, Lothar Willmitzer, Jens Bangsbo, Ola Hansson, Josep Call, Patrick Giavalisco, Philipp Khaitovich (2014-04-17):

    Metabolite concentrations reflect the physiological states of tissues and cells. However, the role of metabolic changes in species evolution is currently unknown. Here, we present a study of metabolome evolution conducted in three brain regions and two non-neural tissues from humans, chimpanzees, macaque monkeys, and mice based on over 10,000 hydrophilic compounds. While chimpanzee, macaque, and mouse metabolomes diverge following the genetic distances among species, we detect remarkable acceleration of metabolome evolution in human prefrontal cortex and skeletal muscle affecting neural and energy metabolism pathways. These metabolic changes could not be attributed to environmental conditions and were confirmed against the expression of their corresponding enzymes. We further conducted muscle strength tests in humans, chimpanzees, and macaques. The results suggest that, while humans are characterized by superior cognition, their muscular performance might be markedly inferior to that of chimpanzees and macaque monkeys.

    Author Summary: Physiological processes that maintain our tissues’ functionality involve the generation of multiple products and intermediates known as metabolites—small molecules with a weight of less than 1,500 Daltons. Changes in concentrations of these metabolites are thought to be closely related to changes in phenotype. Here, we assessed concentrations of more than 10,000 metabolites in three brain regions and two non-neural tissues (skeletal muscle and kidney) of humans, chimpanzees, macaque monkeys, and mice using mass spectrometry-based approaches. We found that the evolution of the metabolome largely reflects genetic divergence between species and is not greatly affected by environmental factors. In the human lineage, however, we observed an exceptional acceleration of metabolome evolution in the prefrontal cortical region of the brain and in skeletal muscle. Based on additional behavioral tests, we further show that metabolic changes in human muscle seem to be paralleled by a drastic reduction in muscle strength. The observed rapid metabolic changes in brain and muscle, together with the unique human cognitive skills and low muscle performance, might reflect parallel mechanisms in human evolution.

  522. ⁠, Constantina Theofanopoulou, Simone Gastaldon, Thomas O’Rourke, Bridget D. Samuels, Angela Messner, Pedro Tiago Martins, Francesco Delogu, Saleh Alamri, Boeckx Cedric (2017-04-09):

    This study identifies and analyzes statistically-significant overlaps between selective sweep screens in anatomically modern humans and several domesticated species. The results obtained suggest that (paleo-) genomic data can be exploited to complement the fossil record and support the idea of self-domestication in Homo sapiens, a process that likely intensified as our species populated its niche. Our analysis lends support to attempts to capture the “domestication syndrome” in terms of alterations to certain signaling pathways and cell lineages, such as the neural crest.

  523. 2016-simonti.pdf

  524. http://www.genetics.org/content/203/2/881

  525. ⁠, Srinivasan, Saurabh Bettella, Francesco Mattingsdal, Morten Wang, Yunpeng Witoelar, Aree Schork, Andrew J. Thompson, Wesley K. Zuber, Verena Winsvold, Bendik S. Zwart, John-Anker Collier, David A. Desikan, Rahul S. Melle, Ingrid Werge, Thomas Dale, Anders M. Djurovic, Srdjan Andreassen, Ole A (2016):

    Background: Why schizophrenia has accompanied humans throughout our history despite its negative effect on fitness remains an evolutionary enigma. It is proposed that schizophrenia is a by-product of the complex evolution of the human brain and a compromise for humans’ language, creative thinking, and cognitive abilities.

    Methods: We analyzed recent large genome-wide association studies of schizophrenia and a range of other human phenotypes (anthropometric measures, cardiovascular disease risk factors, immune-mediated diseases) using a statistical framework that draws on polygenic architecture and ancillary information on genetic variants. We used information from the evolutionary proxy measure called the Neanderthal selective sweep (NSS) score.

    Results: Gene loci associated with schizophrenia are significantly (p = 7.30 × 10−9) more prevalent in genomic regions that are likely to have undergone recent positive selection in humans (i.e., with a low NSS score). Variants in brain-related genes with a low NSS score confer significantly higher susceptibility than variants in other brain-related genes. The enrichment is strongest for schizophrenia, but we cannot rule out enrichment for other phenotypes. The false discovery rate conditional on the evolutionary proxy points to 27 candidate schizophrenia susceptibility loci, 12 of which are associated with schizophrenia and other psychiatric disorders or linked to brain development.

    Conclusions: Our results suggest that there is a polygenic overlap between schizophrenia and NSS score, a marker of human evolution, which is in line with the hypothesis that the persistence of schizophrenia is related to the evolutionary process of becoming human.

  526. ⁠, Jacob A. Tennessen, Joshua M. Akey (2011-04-27):

    Few genetic differences between human populations conform to the classic model of positive selection, in which a newly arisen mutation rapidly approaches in one lineage, suggesting that adaptation more commonly occurs via moderate changes in standing variation at many loci. Detecting and characterizing this type of complex selection requires integrating individually ambiguous signatures across genomically and geographically extensive data. Here, we develop a novel approach to test the hypothesis that selection has favored modest divergence at particular loci multiple times in independent human populations. We find an excess of SNPs showing non-neutral parallel divergence, enriched for genic and nonsynonymous polymorphisms in genes encompassing diverse and often disease related functions. Repeated parallel evolution in the same direction suggests common selective pressures in disparate habitats. We test our method with extensive coalescent simulations and show that it is robust to a wide range of demographic events. Our results demonstrate phylogenetically orthogonal patterns of local adaptation caused by subtle shifts at many widespread polymorphisms that likely underlie substantial phenotypic diversity.

    Author Summary:

    Identifying regions of the human genome that differ among populations because of natural selection is both essential for understanding evolutionary history and a powerful method for finding functionally important variants that contribute to phenotypic diversity and disease. Adaptive events on timescales corresponding to the human diaspora may often manifest as relatively small changes in allele frequencies at numerous loci that are difficult to distinguish from stochastic changes due to genetic drift, rather than the more dramatic selective sweeps described by classic models of natural selection. In order to test whether a substantial proportion of interpopulation genetic differences are indeed adaptive, we identify loci that have undergone moderate allele frequency changes in multiple independent human lineages, and we test whether these parallel divergence events are more frequent than expected by chance. We report a statistically-significant excess of polymorphisms showing parallel divergence, especially within genes, a pattern that is best explained by geographically varying natural selection. Our results indicate that local adaptation in humans has occurred by subtle, repeated changes at particular genes that are likely to be associated with important morphological and physiological differences among human populations.

  527. ⁠, Kelsey Elizabeth Johnson, Benjamin F. Voight (2017-02-17):

    Scans for positive selection in human populations have identified hundreds of sites across the genome with evidence of recent adaptation. These signatures often overlap across populations, but the question of how often these overlaps represent a single ancestral event remains unresolved. If a single positive selection event spread across many populations, the same sweeping haplotype should appear in each population and the selective pressure could be common across diverse populations and environments. Identifying such shared selective events would be of fundamental interest, pointing to genomic loci and human traits important in recent history across the globe. Additionally, genomic annotations that recently became available could help attach these signatures to a potential gene and molecular phenotype that may have been selected across multiple populations. We performed a scan for positive selection using the integrated haplotype score on 20 populations, and compared sweeping haplotypes using the haplotype-clustering capability of fastPHASE to create a catalog of shared and unshared overlapping selective sweeps in these populations. Using additional genomic annotations, we connect these multi-population sweep overlaps with potential biological mechanisms at several loci, including potential new sites of adaptive introgression, the glycophorin locus associated with malarial resistance, and the alcohol dehydrogenase cluster associated with alcohol dependency.

  528. ⁠, Daniel R. Schrider, Andrew D. Kern (2017-04-27):

    The degree to which adaptation in recent human evolution shapes genetic variation remains controversial. This is in part due to the limited evidence in humans for classic “hard selective sweeps,” wherein a novel beneficial mutation rapidly sweeps through a population to fixation. However, positive selection may often proceed via “soft sweeps” acting on mutations already present within a population. Here we examine recent positive selection across six human populations using a powerful machine learning approach that is sensitive to both hard and soft sweeps. We found evidence that soft sweeps are widespread and account for the vast majority of recent human adaptation. Surprisingly, our results also suggest that linked positive selection affects patterns of variation across much of the genome, and may increase the frequencies of deleterious mutations. Our results also reveal insights into the role of ⁠, cancer risk, and central nervous system development in recent human evolution.

  529. 2015-mathieson.pdf: “Genome-wide patterns of selection in 230 ancient Eurasians”⁠, Iosif Lazaridis, Nadin Rohland, Swapan Mallick, Nick Patterson, Songül Alpaslan Roodenberg, Eadaoin Harney, Kristin Stewardson, Daniel Fernandes, Mario Novak, Kendra Sirak, Cristina Gamba, Eppie R. Jones, Bastien Llamas, Stanislav Dryomov, Joseph Pickrell, Juan Luís Arsuaga, José María Bermúdez de Castro, Eudald Carbonell, Fokke Gerritsen, Aleksandr Khokhlov, Pavel Kuznetsov, Marina Lozano, Harald Meller, Oleg Mochalov, Vyacheslav Moiseyev, Manuel A. Rojo Guerra, Jacob Roodenberg, Josep Maria Vergès, Johannes Krause, Alan Cooper, Kurt W. Alt, Dorcas Brown, David Anthony, Carles Lalueza-Fox, Iain Mathieson, Wolfgang Haak, Ron Pinhasi, David Reich

  530. https://www.nytimes.com/2015/11/24/science/agriculture-linked-to-dna-changes-in-ancient-europe.html

  531. ⁠, Iain Mathieson, Iosif Lazaridis, Nadin Rohland, Swapan Mallick, Nick Patterson, Songül Alpaslan Roodenberg, Eadaoin Harney, Kristin Stewardson, Daniel Fernandes, Mario Novak, Kendra Sirak, Cristina Gamba, Eppie R. Jones, Bastien Llamas, Stanislav Dryomov, Joseph Pickrell, Juan Luís Arsuaga, José María Bermúdez de Castro, Eudald Carbonell, Fokke Gerritsen, Aleksandr Khokhlov, Pavel Kuznetsov, Marina Lozano, Harald Meller, Oleg Mochalov, Vayacheslav Moiseyev, Manuel A. Rojo Guerra, Jacob Roodenberg, Josep Maria Vergès, Johannes Krause, Alan Cooper, Kurt W. Alt, Dorcas Brown, David Anthony, Carles Lalueza-Fox, Wolfgang Haak, Ron Pinhasi, David Reich (2015-10-10):

    The arrival of farming in Europe around 8,500 years ago necessitated adaptation to new environments, pathogens, diets, and social organizations. While indirect evidence of adaptation can be detected in patterns of genetic variation in present-day people, ancient DNA makes it possible to witness selection directly by analyzing samples from populations before, during and after adaptation events. Here we report the first genome-wide scan for selection using ancient DNA, capitalizing on the largest genome-wide dataset yet assembled: 230 West Eurasians dating to between 6500 and 1000 BCE, including 163 with newly reported data. The new samples include the first genome-wide data from the Anatolian Neolithic culture, who we show were members of the population that was the source of Europe’s first farmers, and whose genetic material we extracted by focusing on the DNA-rich petrous bone. We identify genome-wide statistically-significant signatures of selection at loci associated with diet, pigmentation and immunity, and two independent episodes of selection on height.

  532. http://www.unz.com/gnxp/selection-in-europeans-but-it-still-sweeps/

  533. ⁠, Field, Yair Boyle, Evan A. Telis, Natalie Gao, Ziyue Gaulton, Kyle J. Golan, David Yengo, Loic Rocheleau, Ghislain Froguel, Philippe McCarthy, Mark I. Pritchard, Jonathan K (2016):

    Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans.

  534. ⁠, Kevin J. Galinsky, Po-Ru Loh, Mallick Swapan, Nick J. Patterson, Alkes L. Price (2016-05-27):

    Analyzing genetic differences between closely related populations can be a powerful way to detect recent adaptation. The very large sample size of the UK Biobank is ideal for detecting selection using population differentiation, and enables an analysis of UK population structure at fine resolution. In analyses of 113,851 UK Biobank samples, population structure in the UK is dominated by 5 principal components (PCs) spanning 6 clusters: Northern Ireland, Scotland, northern England, southern England, and two Welsh clusters. Analyses with ancient Eurasians show that populations in the northern UK have higher levels of Steppe ancestry, and that UK population structure cannot be explained as a simple mixture of Celts and Saxons. A scan for unusual population differentiation along top PCs identified a genome-wide statistically-significant signal of selection at the coding variant rs601338 in FUT2 (p = 9.16 × 10−9). In addition, by combining evidence of unusual differentiation within the UK with evidence from ancient Eurasians, we identified new genome-wide statistically-significant (p < 5 × 10−8) signals of recent selection at two additional loci: CYP1A2/​​​​CSK and F12. We detected strong associations to diastolic blood pressure in the UK Biobank for the variants with new selection signals at CYP1A2/​​​​CSK (p = 1.10 × 10−19)) and for variants with ancient Eurasian selection signals in the ATXN2/​​​​SH2B3 locus (p = 8.00 × 10−33), implicating recent adaptation related to blood pressure.

  535. 2015-fumagalli.pdf

  536. ⁠, Casper-Emil T. Pedersen, Kirk E. Lohmueller, Niels Grarup, Peter Bjerregaard, Torben Hansen, Hans R. Siegismund, Ida Moltke, Anders Albrechtsen (2016-06-30):

    The genetic consequences of a severe bottleneck on genetic load in humans are widely disputed. Based on exome sequencing of 18 Greenlandic Inuit we show that the Inuit have undergone a severe ~20,000 yearlong bottleneck. This has led to a markedly more extreme distribution of deleterious alleles than seen for any other human population. Compared to populations with much larger population sizes, we see an overall reduction in the number of variable sites, increased numbers of fixed sites, a lower heterozygosity, and increased mean allele frequency as well as more homozygous deleterious genotypes. This means, that the Inuit population is the perfect population to examine the effect of a bottleneck on genetic load. Compared to the European, Asian and African populations, we do not observe a difference in the overall number of derived alleles. In contrast, using proxies for genetic load we find that selection has acted less efficiently in the Inuit, under a recessive model. This fits with our simulations that predict a similar number of derived alleles but a true higher genetic load for the Inuit regardless of the genetic model. Finally, we find that the Inuit population has a great potential for mapping of disease-causing variants that are rare in large populations. In fact, we show that these alleles are more likely to be common, and thus easy to map, in the Inuit than in the Finnish and Latino populations; populations considered highly valuable for mapping studies due to recent bottleneck events.

  537. https://academic.oup.com/mbe/article/doi/10.1093/molbev/msx103/3062804/Selection-in-Europeans-on-fatty-acid-desaturases

  538. http://mbe.oxfordjournals.org/content/early/2016/12/20/molbev.msw283.full.pdf+html

  539. https://www.nytimes.com/2016/12/23/science/inuit-greenland-denisovans.html

  540. http://mbe.oxfordjournals.org/content/32/6/1507.full

  541. http://mbe.oxfordjournals.org/content/32/6/1544.full

  542. https://science.sciencemag.org/content/344/6189/1280

  543. 2017-owers.pdf

  544. ⁠, Arslan A. Zaidi, Brooke C. Mattern, Peter Claes, Brian McEcoy, Cris Hughes, Mark D. Shriver (2017-02-03):

    The evolutionary reasons for variation in nose shape across human populations have been subject to continuing debate. An import function of the nose and nasal cavity is to condition inspired air before it reaches the lower respiratory tract. For this reason, it is thought the observed differences in nose shape among populations are not simply the result of ⁠, but may be adaptations to climate. To address the question of whether local adaptation to climate is responsible for nose shape divergence across populations, we use Qst–Fst comparisons to show that nares width and alar base width are more differentiated across populations than expected under genetic drift alone. To test whether this differentiation is due to climate adaptation, we compared the spatial distribution of these variables with the global distribution of temperature, absolute humidity, and relative humidity. We find that width of the nares is correlated with temperature and absolute humidity, but not with relative humidity. We conclude that some aspects of nose shape may indeed have been driven by local adaptation to climate. However, we think that this is a simplified explanation of a very complex evolutionary history, which possibly also involved other non-neutral forces such as sexual selection.

    Author summary:

    The study of human adaptation is essential to our understanding of disease etiology. Evolutionary investigations into why certain disease phenotypes such as sickle-cell anemia and lactose intolerance occur at different rates in different populations have led to a better understanding of the genetic and environmental risk factors involved. Similarly, research into the geographical distribution of skin pigmentation continues to yield important clues regarding risk of vitamin D deficiency and skin cancer. Here, we investigate whether variation in the shape of the external nose across populations has been driven by regional differences in climate. We find that variation in both nares width and alar base width appear to have experienced accelerated divergence across human populations. We also find that the geospatial distribution of nares width is correlated with temperature, and absolute humidity, but not with relative humidity. Our results support the claim that local adaptation to climate may have had a role in the evolution of nose shape differences across human populations.

  545. https://web-beta.archive.org/web/20161021035631/http://mobile.the-scientist.com/article/46651/humans-never-stopped-evolving

  546. 2016-fan.pdf

  547. 2017-horscroft.pdf

  548. http://www.bbc.co.uk/news/science-environment-40006803

  549. ⁠, Renato Polimanti, Joel Gelernter (2017-02-07):

    The human brain is the outcome of innumerable evolutionary processes; the systems genetics of psychiatric disorders could bear their signatures. On this basis, we analyzed five psychiatric disorders, attention deficit hyperactivity disorder, autism spectrum disorder (ASD), bipolar disorder, major depressive disorder, and schizophrenia (SCZ), using GWAS summary statistics from the Psychiatric Genomics Consortium. Machine learning-derived scores were used to investigate two natural-selection scenarios: complete selection (loci where a selected allele reached fixation) and incomplete selection (loci where a selected allele has not yet reached fixation). ASD GWAS results positively correlated with incomplete-selection (p = 3.53×10−4). Variants with ASD GWAS p < 0.1 were shown to have a 19%-increased probability to be in the top-5% for incomplete-selection score (OR = 1.19, 95%CI = 1.11–1.8, p = 9.56×10−7). Investigating the effect directions of minor alleles, we observed an enrichment for positive associations in SNPs with ASD GWAS p < 0.1 and top-5% incomplete-selection score (permutation p < 10−4). Considering the set of these ASD-positive-associated variants, we observed gene-expression enrichments for brain and pituitary tissues (p = 2.3×10−5 and p = 3×10−5, respectively) and 53 gene ontology (GO) enrichments, such as nervous system development (GO:0007399, p = 7.57×10−12), synapse organization (GO:0050808, p = 8.29×10−7), and axon guidance (GO:0007411, p = 1.81×10−7). Previous genetic studies demonstrated that ASD positively correlates with childhood intelligence, college completion, and years of schooling. Accordingly, we hypothesize that certain ASD risk alleles were under positive selection during human evolution due to their involvement in neurogenesis and cognitive ability.

    Author summary:

    Predisposition to psychiatric disorders is due to the contribution of many genes involved in numerous molecular mechanisms. Since brain evolution has played a pivotal role in determining the success of the human species, the molecular pathways involved with the onset of mental illnesses are likely to be informative as we seek an understanding of the mechanisms involved in the evolution of human brain. Accordingly, we tested whether the genetics of psychiatric disorders is enriched for signatures of positive selection. We observed a strong finding related to the genetics of autism spectrum disorders (ASD): common risk alleles are enriched for genomic signatures of incomplete selection (loci where a selected allele has not yet reached fixation). The genes where these alleles map tend to be expressed in brain and pituitary tissues, to be involved in molecular mechanisms related to nervous system development, and surprisingly, to be associated with increased cognitive ability. Previous studies identified signatures of purifying selection in genes affected by ASD rare alleles. Accordingly, at least two different evolutionary mechanisms appear to be present in relation to ASD genetics: 1) rare disruptive alleles eliminated by purifying selection; 2) common alleles selected for their beneficial effects on cognitive skills. This scenario would explain ASD prevalence, which is higher than that expected for a trait under purifying selection, as the evolutionary cost of polygenic adaptation related to cognitive ability.

  550. https://www.nature.com/articles/ncomms14238/

  551. https://www.nature.com/articles/ncomms13175

  552. https://www.pnas.org/content/111/36/13010.long

  553. 2000-cochran.pdf: ⁠, Gregory M. Cochran, Paul W. Ewald, Kyle D. Cochran (2000-01-01; genetics  /​ ​​ ​selection):

    Over the past two centuries, diseases have been separated into three categories: infectious diseases, genetic diseases, and diseases caused by too much or too little of some noninfectious environmental constituent. At the end of the 19th century, the most rapid development was in the first of these categories; within three decades after the first cause-effect linkage of a bacterium to a disease, most of the bacterial causes of common acute infectious diseases had been identified. This rapid progress can be attributed in large part to Koch’s postulates, a rigorous systematic approach to identification of microbes as causes of disease. Koch’s postulates were useful because they could generate conclusive evidence of infectious causation, particularly when (1) the causative organisms could be isolated and experimentally transmitted, and (2) symptoms occurred soon after the onset of infection in a high proportion of infected individuals. While guiding researchers down one path, however, the postulates directed them away from alternative paths: researchers attempting to document infectious causation were guided away from diseases that had little chance of fulfilling the postulates, even though they might have been infectious. During the first half of the 20th century, when the study of infectious agents was shifting from bacteria to viruses, Mendel’s genetics was being integrated into the study of disease. Some diseases could not be ascribed to infectious causes using Koch’s postulates but could be shown to have genetic bases, particularly if they were inherited according to Mendelian ratios. Mendel’s genetics and Koch’s postulates thus helped create a conceptual division of diseases into genetic and infectious categories, a division that persists today.The third category—diseases resulting from noninfectious environmental causes—has a longer history. The known associations of poisons with illness provided a basis for understanding physical agents as causes of disease. The apparent “contagiousness” of some chemical agents, such as the irritant of poison ivy, led experts to consider that diseases could be contagious without being infectious. Even after the discovery of causative microbes during the last quarter of the 19th century, many infectious diseases were considered contagious through the action of poisons, but not necessarily infectious [1].

    …This tendency to dismiss infectious causation has occurred in spite of the recognition that (1) infectious diseases are typically influenced by both host genetic and noninfectious environmental factors, and (2) some chronic diseases, such as tuberculosis and syphilis, have long been recognized as being caused by infection. In this essay we analyze the present conceptions of disease etiology from an historical perspective and within the framework offered by evolutionary biology. We begin by analyzing the degree to which infectious causation has been accepted for different categories of disease over the past two centuries with an emphasis on (1) characteristics that make the infectious causes of different diseases conspicuous or cryptic, and (2) the need to detect ever more cryptic infectious causes as a legacy of the more rapid recognition of the conspicuous infectious causes. We then consider principles and approaches that could facilitate recognition of infectious diseases and other phenomena that are not normally considered to be of infectious origin.

  554. 2016-trumble.pdf

  555. https://www.theatlantic.com/science/archive/2017/01/why-does-a-gene-that-increases-alzheimers-risk-still-exist/512396/

  556. https://entitledtoanopinion.wordpress.com/2013/02/11/rising-plague/

  557. http://nautil.us/issue/29/scaling/the-rhythm-of-the-tide-rp

  558. http://www.econ.ucdavis.edu/faculty/gclark/Farewell%20to%20Alms/Clark%20-Surnames.pdf

  559. https://www.pnas.org/content/108/41/17040.long

  560. ⁠, Joseph Christopher Lee (2013-06-12):

    Quantitative genetics is primarily concerned with two subjects: the correlation between relatives and the response to selection. The correlation between relatives is used to determine the heritability of a trait—the key quantity that addresses the question of nature vs. nurture. Heritability, in turn, is used to predict the response to selection—the main driver of improvements in crops and livestock. The theory of quantitative genetics has been thoroughly tested and applied in plants and animals, but heritability and selection remain open questions in humans due to limited natural experimental designs.

    The Donor Sibling Registry (DSR) is an organization that helps individuals conceived as a result of sperm, egg, or embryo donation make contact with genetically related individuals. Families who conceived children via anonymous sperm donation join the DSR and match with other families who used the same donor ID at the same sperm bank. The resulting donor pedigree consists of heterosexual, lesbian, and single mother families who are connected through the common anonymous sperm donor used to conceive their children.

    Here, we introduce a new quantitative genetic study design based on the unprecedented family relationships found in the donor pedigree. We surveyed 945 individual families constituting 159 donor pedigrees from the Donor Sibling Registry and used their demographic, physical, and behavioral characteristics to conduct a quantitative genetic study of selection and heritability. A direct measurement of phenotypic assortment showed mothers actively selected mates for height, eye color, and religion. Artificial selection for donor height increased mean child height in a manner consistent with the selection differential. Reared-apart donor-conceived paternal half-siblings provided unbiased heritability estimates for traits influenced by maternal and contrast effects. Maternal effects were important in determining the variance of birth weight while eliminating contrast effects revealed sociability to be a highly heritable childhood temperament. Thus, the unprecedented family relationships in the donor pedigree enable a universal model for quantitative genetics.

  561. 2016-whyte.pdf: ⁠, Stephen Whyte, Benno Torgler, Keith L. Harrison (2016-12-01; genetics  /​ ​​ ​selection):

    • Women choose younger and more highly educated sperm donors faster.
    • Education may be a proxy for resources even in the absence of paternal investment.
    • Behavioural research in reproductive medical settings is in its infancy.
    • The sperm donor market is a relevant and high stakes domain for behavioural research.

    Reproductive medicine and commercial sperm banking have facilitated an evolutionary shift in how women are able to choose who fathers their offspring, by notionally expanding women’s opportunity set beyond former constraints.

    This study analyses 1546 individual reservations of semen by women from a private Australian assisted reproductive health facility across a 10 year period from 2006 to 2015. Using the time that each sample was available at the facility until reservation, we explore women’s preference for particular male characteristics.

    We find that younger donors, and those who hold a higher formal education compared to those with no academic qualifications are more quickly selected for reservation by women. Both age and education as proxies for resources are at the centre of Parental Investment theory, and our findings further build on this standard evolutionary construct in relation to female mate preferences.

    Reproductive medicine not only provides women the opportunity to become a parent, where previously they would not have been able to, it also reveals that female preference for resources of their potential mate (sperm donor) remain, even when the notion of paternal investment becomes redundant.

    These findings build on behavioural science’s understanding of large-scale decisions and human behaviour in reproductive medical settings.

    [Keywords: sperm donor market, characteristics & preferences, large scale decision making, mate choice, evolutionary psychology, reproductive medicine]

  562. 2016-berg.pdf: “Genetic Associations Between Personality Traits and Lifetime Reproductive Success in Humans”⁠, Venla Berg, Virpi Lummaa, Ian J. Rickard, Karri Silventoinen, Jaakko Kaprio, Markus Jokela

  563. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2002-3-7-comment2007

  564. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2694957/

  565. https://www.economist.com/node/6795348

  566. http://mosaicscience.com/story/brazils-cancer-curse

  567. https://www.nytimes.com/2014/11/02/opinion/sunday/a-natural-fix-for-adhd.html

  568. https://growthecon.wordpress.com/2015/03/16/genetic-origins-of-economic-development/

  569. ⁠, Kevin M. Waters, Daniel O. Stram, Mohamed T. Hassanein, Loïc Le Marchand, Lynne R. Wilkens, Gertraud Maskarinec, Kristine R. Monroe, Laurence N. Kolonel, David Altshuler, Brian E. Henderson, Christopher A. Haiman (2010-07-21):

    It has been recently hypothesized that many of the signals detected in genome-wide association studies (GWAS) to T2D and other diseases, despite being observed to common variants, might in fact result from causal mutations that are rare. One prediction of this hypothesis is that the allelic associations should be population-specific, as the causal mutations arose after the migrations that established different populations around the world. We selected 19 common variants found to be reproducibly associated to T2D risk in European populations and studied them in a large multiethnic case-control study (6,142 cases and 7,403 controls) among men and women from 5 racial/​​​​ethnic groups (European Americans, African Americans, Latinos, Japanese Americans, and Native Hawaiians). In analysis pooled across ethnic groups, the allelic associations were in the same direction as the original report for all 19 variants, and 14 of the 19 were statistically-significantly associated with risk. In summing the number of risk alleles for each individual, the per-allele associations were highly statistically-significant (p < 10−4) and similar in all populations (odds ratios 1.09–1.12) except in Japanese Americans the estimated effect per allele was larger than in the other populations (1.20; p het = 3.8×10−4). We did not observe ethnic differences in the distribution of risk that would explain the increased prevalence of type 2 diabetes in these groups as compared to European Americans. The consistency of allelic associations in diverse racial/​​​​ethnic groups is not predicted under the hypothesis of Goldstein regarding “synthetic associations” of rare mutations in T2D.

    Author Summary:

    Single rare causal alleles and/​​​​or collections of multiple rare alleles have been suggested to create “synthetic associations” with common variants in genome-wide association studies (GWAS). This model predicts that associations with common variants will not be consistent across populations. In this study, we examined 19 T2D variants for association with T2D risk in 6,142 cases and 7,403 controls from five racial/​​​​ethnic populations in the Multiethnic Cohort (European Americans, African Americans, Latinos, Japanese Americans, and Native Hawaiians). In racial/​​​​ethnic pooled analysis, all 19 variants were associated with T2D risk in the same direction as previous reports in Europeans, and the sum total of risk variants was statistically-significantly associated with T2D risk in each racial/​​​​ethnic group. The consistent associations across populations do not support the Goldstein hypothesis that rare causal alleles underlie GWAS signals. We also did not find evidence that these markers underlie racial/​​​​ethnic disparities in T2D prevalence. Large-scale GWAS and sequencing studies in these populations are necessary in order to both improve the current set of markers at these risk loci and identify new risk variants for T2D that may be difficult, or impossible, to detect in European populations.

  570. https://www.sciencedirect.com/science/article/pii/S000292971300325X

  571. ⁠, Christopher S. Carlson, Tara C. Matise, Kari E. North, Christopher A. Haiman, Megan D. Fesinmeyer, Steven Buyske, Fredrick R. Schumacher, Ulrike Peters, Nora Franceschini, Marylyn D. Ritchie, David J. Duggan, Kylee L. Spencer, Logan Dumitrescu, Charles B. Eaton, Fridtjof Thomas, Alicia Young, Cara Carty, Gerardo Heiss, Loic Le Marchand, Dana C. Crawford, Lucia A. Hindorff, Charles L. Kooperberg (PAGE Consortium) (2013-08-08):

    The vast majority of genome-wide association study (GWAS) findings reported to date are from populations with European Ancestry (EA), and it is not yet clear how broadly the genetic associations described will generalize to populations of diverse ancestry. The Population Architecture Using Genomics and Epidemiology (PAGE) study is a consortium of multi-ancestry, population-based studies formed with the objective of refining our understanding of the genetic architecture of common traits emerging from GWAS.

    In the present analysis of five common diseases and traits, including body mass index, type 2 diabetes, and lipid levels, we compare direction and magnitude of effects for GWAS-identified variants in multiple non-EA populations against EA findings.

    We demonstrate that, in all populations analyzed, a statistically-significant majority of GWAS-identified variants have allelic associations in the same direction as in EA, with none showing a statistically-significant effect in the opposite direction, after adjustment for multiple testing. However, 25% of tagSNPs identified in EA GWAS have statistically-significantly different effect sizes in at least one non-EA population, and these differential effects were most frequent in African Americans where all differential effects were diluted toward the null. We demonstrate that differential LD between tagSNPs and functional variants within populations contributes statistically-significantly to dilute effect sizes in this population.

    Although most variants identified from GWAS in EA populations generalize to all non-EA populations assessed, genetic models derived from GWAS findings in EA may generate spurious results in non-EA populations due to differential effect sizes. Regardless of the origin of the differential effects, caution should be exercised in applying any genetic risk prediction model based on tagSNPs outside of the ancestry group in which it was derived. Models based directly on functional variation may generalize more robustly, but the identification of functional variants remains challenging.

    Author Summary: The number of known associations between human diseases and common genetic variants has grown dramatically in the past decade, most being identified in large-scale genetic studies of people of Western European origin. But because the frequencies of genetic variants can differ substantially between continental populations, it’s important to assess how well these associations can be extended to populations with different continental ancestry. Are the correlations between genetic variants, disease endpoints, and risk factors consistent enough for genetic risk models to be reliably applied across different ancestries? Here we describe a systematic analysis of disease outcome and risk-factor-associated variants (tagSNPs) identified in European populations, in which we test whether the effect size of a tagSNP is consistent across six populations with statistically-significant non-European ancestry. We demonstrate that although nearly all such tagSNPs have effects in the same direction across all ancestries (ie., variants associated with higher risk in Europeans will also be associated with higher risk in other populations), roughly a quarter of the variants tested have statistically-significantly different magnitude of effect (usually lower) in at least one non-European population. We therefore advise caution in the use of tagSNP-based genetic disease risk models in populations that have a different genetic ancestry from the population in which original associations were first made. We then show that this differential strength of association can be attributed to population-dependent variations in the correlation between tagSNPs and the variant that actually determines risk—the so-called functional variant. Risk models based on functional variants are therefore likely to be more robust than tagSNP-based models.

  572. 2012-ntzani.pdf

  573. https://www.sciencedirect.com/science/article/pii/S0002929716301355

  574. 2015-robinson.pdf: ⁠, Matthew R. Robinson, Gibran Hemani, Carolina Medina-Gomez, Massimo Mezzavilla, Tonu Esko, Konstantin Shakhbazov, Joseph E. Powell, Anna Vinkhuyzen, Sonja I. Berndt, Stefan Gustafsson, Anne E. Justice, Bratati Kahali, Adam E. Locke, Tune H. Pers, Sailaja Vedantam, Andrew R. Wood, Wouter van Rheenen, Ole A. Andreassen, Paolo Gasparini, Andres Metspalu, Leonard H. van den Berg, Jan H. Veldink, Fernando Rivadeneira, Thomas M. Werge, Goncalo R. Abecasis, Dorret I. Boomsma, Daniel I. Chasman, Eco J. C. de Geus, Timothy M. Frayling, Joel N. Hirschhorn, Jouke Jan Hottenga, Erik Ingelsson, Ruth J. F. Loos, Patrik K. E. Magnusson, Nicholas G. Martin, Grant W. Montgomery, Kari E. North, Nancy L. Pedersen, Timothy D. Spector, Elizabeth K. Speliotes, Michael E. Goddard, Jian Yang, Peter M. Visscher (2015-09-14; genetics  /​ ​​ ​selection):

    Across-nation differences in the mean values for complex traits are common1–8, but the reasons for these differences are unknown. Here we find that many independent loci contribute to population genetic differences in height and body mass index (BMI) in 9,416 individuals across 14 European countries. Using discovery data on over 250,000 individuals and unbiased effect size estimates from 17,500 sibling pairs, we estimate that 24% (95% credible interval (CI) = 9%, 41%) and 8% (95% CI = 4%, 16%) of the captured additive genetic variance for height and BMI, respectively, reflect differences. Population genetic divergence differed statistically-significantly from that in a null model (height, p < 3.94 × 10−8; BMI, p < 5.95 × 10−4), and we find an among-population genetic correlation for tall and slender individuals (r = −0.80, 95% CI = −0.95, −0.60), consistent with correlated selection for both phenotypes. Observed differences in height among populations reflected the predicted genetic means (r = 0.51; p < 0.001), but environmental differences across Europe masked genetic differentiation for BMI (p < 0.58).

  575. https://slatestarcodex.com/2015/09/28/links-915-linkua-franca/

  576. ⁠, Leslie, Stephen Winney, Bruce Hellenthal, Garrett Davison, Dan Boumertit, Abdelhamid Day, Tammy Hutnik, Katarzyna Royrvik, Ellen C. Cunliffe, Barry Lawson, Daniel J. Falush, Daniel Freeman, Colin Pirinen, Matti Myers, Simon Robinson, Mark Donnelly, Peter Bodmer, Walter (2015):

    Fine-scale genetic variation between human populations is interesting as a signature of historical demographic events and because of its potential for confounding disease studies. We use haplotype-based statistical methods to analyse genome-wide single nucleotide polymorphism (SNP) data from a carefully chosen geographically diverse sample of 2,039 individuals from the United Kingdom. This reveals a rich and detailed pattern of genetic differentiation with remarkable concordance between genetic clusters and geography. The regional genetic differentiation and differing patterns of shared ancestry with 6,209 individuals from across Europe carry clear signals of historical demographic events. We estimate the genetic contribution to southeastern England from Anglo-Saxon migrations to be under half, and identify the regions not carrying genetic material from these migrations. We suggest significant pre-Roman but post-Mesolithic movement into southeastern England from continental Europe, and show that in non-Saxon parts of the United Kingdom, there exist genetically differentiated subgroups rather than a general ‘Celtic’ population.

  577. http://www.sciencemag.org/news/2014/08/pygmies-small-stature-evolved-many-times

  578. https://www.pnas.org/content/111/35/E3596.full

  579. 2015-proto.pdf

  580. 2011-ciani.pdf

  581. https://jaymans.wordpress.com/2015/07/04/demography-is-destiny/

  582. https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S002193201600002X

  583. http://evp.sagepub.com/content/13/1/147470491501300114.full.pdf+html

  584. 2015-piffer.pdf: “A review of intelligence GWAS hits: Their relationship to country IQ and the issue of spatial autocorrelation”⁠, Davide Piffer

  585. https://web.archive.org/web/20150404205305/https://occidentalascent.wordpress.com/2012/06/10/the-facts-that-need-to-be-explained/

  586. 2016-fuerst.pdf

  587. http://humanvarieties.org/2013/01/15/100-years-of-testing-negro-intelligence/

  588. http://humanvarieties.org/2013/01/15/secular-changes-in-the-black-white-cognitive-ability-gap/

  589. ⁠, Felix M. Key, Muslihudeen A. Abdul-Aziz, Roger Mundry, Benjamin M. Peter, Aarthi Sekar, Mauro D’Amato, Megan Y. Dennis, Joshua M. Schmidt, Aida M. Andrés (2018-01-19):

    Ambient temperature is a critical environmental factor for all living organisms. It was likely an important selective force as modern humans recently colonized temperate and cold Eurasian environments. Nevertheless, as of yet we have limited evidence of local adaptation to ambient temperature in populations from those environments. To shed light on this question, we exploit the fact that humans are a cosmopolitan species that inhabits territories under a wide range of temperatures. Focusing on cold perception—which is central to thermoregulation and survival in cold environments— we show evidence of recent local adaptation on TRPM8. This gene encodes for a cation channel that is, to date, the only temperature receptor known to mediate an endogenous response to moderate cold. The upstream variant rs10166942 shows extreme population differentiation, with frequencies that range from 5% in Nigeria to 88% in Finland (placing this SNP in the 0.02% tail of the FST empirical distribution). When all populations are jointly analysed, allele frequencies correlate with latitude and temperature beyond what can be explained by shared ancestry and population substructure. Using a ⁠, we infer that the allele originated and evolved neutrally in Africa, while positive selection raised its frequency to different degrees in Eurasian populations, resulting in allele frequencies that follow a latitudinal cline. We infer strong positive selection, in agreement with ancient DNA showing high frequency of the allele in Europe 3,000 to 8,000 years ago. rs10166942 is important phenotypically because its ancestral allele is protective of migraine. This debilitating disorder varies in prevalence across human populations, with highest prevalence in individuals of European descent –precisely the population with the highest frequency of rs10166942 derived allele. We thus hypothesize that local adaptation on previously neutral standing variation may have contributed to the genetic differences that exist in the prevalence of migraine among human populations today.

    Author Summary

    Some human populations were likely under strong pressure to adapt biologically to cold climates during their colonization of non-African territories in the last 50,000 years. Such putative adaptations required genetic variation in genes that could mediate adaptive responses to cold. TRPM8 is potentially one such gene, being the only known receptor for the sensation of moderate cold temperature. We show that a likely regulatory genetic variant nearby TRPM8 has several signatures of positive selection rising its frequency in Eurasian populations during the last 25,000 years. While the genetic variant was and is rare in Africa, it is now common outside of Africa, with frequencies that strongly correlate with latitude and are highest in northern European populations. Interestingly, this same genetic variant has previously been strongly associated with migraine. This suggests that adaptation to cold has potentially contributed to the variation in migraine prevalence that exists among human groups today.

  590. ⁠, Jeremy J. Berg, Xinjun Zhang, Graham Coop (2017-11-06):

    Our understanding of the genetic basis of human adaptation is biased toward loci of large phenotypic effect. Genome wide association studies (GWAS) now enable the study of genetic adaptation in highly polygenic phenotypes. Here we test for polygenic adaptation among 187 world-wide human populations using polygenic scores constructed from GWAS of 34 complex traits. Comparing these polygenic scores to a null distribution under genetic drift, we identify strong signals of selection for a suite of anthropometric traits including height, infant head circumference (IHC), hip circumference and waist-to-hip ratio (WHR), as well as type 2 diabetes (T2D). In addition to the known north-south gradient of polygenic height scores within Europe, we find that natural selection has contributed to a gradient of decreasing polygenic height scores from West to East across Eurasia. Analyzing a set of ancient DNA samples from across Eurasia, we show that much of this gradient can be explained by selection for increased height in two long diverged hunter-gatherer populations living in western and west-central Eurasia sometime during or shortly after the last glacial maximum. We find that the signal of selection on hip circumference can largely be explained as a correlated response to selection on height. However, our signals in IHC and WHR cannot, suggesting that these patterns are the result of selection along multiple axes of body shape variation. Our observation that IHC and WHR polygenic scores follow a strong latitudinal cline in Western Eurasia support the role of natural selection in establishing Bergmann’s Rule in humans, and are consistent with thermoregulatory adaptation in response to latitudinal temperature variation.

    One Sentence Summary

    Natural selection has lead to divergence in multiple quantitative traits in humans across Eurasian populations.

  591. ⁠, Jeremy J. Berg, Graham Coop (2014-04-17):

    Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We use a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model, we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of QST ⁄ FST comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also substantially outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results.

    Author Summary:

    The process of adaptation is of fundamental importance in evolutionary biology. Within the last few decades, genotyping technologies and new statistical methods have given evolutionary biologists the ability to identify individual regions of the genome that are likely to have been important in this process. When adaptation occurs in traits that are underwritten by many genes, however, the genetic signals left behind are more diffuse, and no individual region of the genome is likely to show strong signatures of selection. Identifying this signature therefore requires a detailed annotation of sites associated with a particular phenotype. Here we develop and implement a suite of statistical methods to integrate this sort of annotation from genome wide association studies with allele frequency data from many populations, providing a powerful way to identify the signal of adaptation in polygenic traits. We apply our methods to test for the impact of selection on human height, skin pigmentation, body mass index, type 2 diabetes risk, and inflammatory bowel disease risk. We find relatively strong signals for height and skin pigmentation, moderate signals for inflammatory bowel disease, and comparatively little evidence for body mass index and type 2 diabetes risk.

  592. 2017-rotimi.pdf

  593. https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006328

  594. ⁠, Lawrence H. Uricchio, Hugo C. Kitano, Alexander Gusev, Noah A. Zaitlen (2017-08-08):

    Selection alters human genetic variation, but the evolutionary mechanisms shaping complex traits and the extent of selection’s impact on polygenic trait evolution remain largely unknown. Here, we develop a novel polygenic selection inference method (Polygenic Ancestral Selection Test Encompassing Linkage, or PASTEL) relying on GWAS summary data from a single population. We use model-based simulations of complex traits that incorporate human demography, stabilizing selection, and polygenic adaptation to show how shifts in the fitness landscape generate distinct signals in GWAS summary data. Our test retains power for relatively ancient selection events and controls for potential confounding from linkage disequilibrium. We apply PASTEL to nine complex traits, and find evidence for selection acting on five of them (height, BMI, schizophrenia, Crohn’s disease, and educational attainment). This study provides evidence that selection modulates the relationship between frequency and effect size of trait-altering alleles for a wide range of traits, and provides a flexible framework for future investigations of selection on complex traits using GWAS data.

  595. ⁠, Hakhamanesh Mostafavi, Tomaz Berisa, Felix R. Day, John R. B. Perry, Molly Przeworski, Joseph K. Pickrell (2017-08-03):

    A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits, we detected a number of strong signals. In participants of the UK Biobank of British ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (P~6.2 × 10−6 for fathers and P~2.0 × 10−3 for mothers), consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associated with a longer maternal life span (P~1.4 × 10−3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD), body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans.

    Author summary:

    Our global understanding of adaptation in humans is limited to indirect statistical inferences from patterns of genetic variation, which are sensitive to past selection pressures. We introduced a method that allowed us to directly observe ongoing selection in humans by identifying genetic variants that affect survival to a given age (i.e., viability selection). We applied our approach to the GERA cohort and parents of the UK Biobank participants. We found viability effects of variants near the APOE and CHRNA3 genes, which are associated with the risk of Alzheimer disease and smoking behavior, respectively. We also tested for the joint effect of sets of genetic variants that influence quantitative traits. We uncovered an association between longer life span and genetic variants that delay puberty timing and age at first birth. We also detected detrimental effects of higher genetically predicted cholesterol levels, body mass index, risk of coronary artery disease (CAD), and risk of asthma on survival. Some of the observed effects differ between males and females, most notably those at the CHRNA3 gene and variants associated with risk of CAD and cholesterol levels. Beyond this application, our analysis shows how large biomedical data sets can be used to study natural selection in humans.

  596. https://www.nature.com/news/massive-genetic-study-shows-how-humans-are-evolving-1.22565

  597. 2017-gazal.pdf: ⁠, Steven Gazal, Hilary K. Finucane, Nicholas A. Furlotte, Po-Ru Loh, Pier Francesco Palamara, Xuanyao Liu, Armin Schoech, Brendan Bulik-Sullivan, Benjamin M. Neale, Alexander Gusev, Alkes L. Price (2017-09-11; genetics  /​ ​​ ​selection):

    Recent work has hinted at the linkage disequilibrium (LD)-dependent architecture of human complex traits, where SNPs with low levels of LD (LLD) have larger per-SNP heritability. Here we analyzed summary statistics from 56 complex traits (average n = 101,401) by extending stratified LD score regression to continuous annotations. We determined that SNPs with low LLD have statistically-significantly larger per-SNP heritability and that roughly half of this effect can be explained by functional annotations negatively correlated with LLD, such as DNase I hypersensitivity sites (DHSs). The remaining signal is largely driven by our finding that more recent common variants tend to have lower LLD and to explain more heritability (p = 2.38 × 10−104); the youngest 20% of common SNPs explain 3.9 times more heritability than the oldest 20%, consistent with the action of negative selection. We also inferred jointly statistically-significant effects of other LD-related annotations and confirmed via forward simulations that they jointly predict deleterious effects.

  598. ⁠, Armin P. Schoech, Daniel Jordan, Po-Ru Loh, Steven Gazal, Luke O’Connor, Daniel J. Balick, Pier F. Palamara, Hilary K. Finucane, Shamil R. Sunyaev, Alkes L. Price (2017-09-13):

    Understanding the role of rare variants is important in elucidating the genetic basis of human diseases and complex traits. It is widely believed that negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1−p)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α by maximizing its profile likelihood in a linear mixed model framework using imputed genotypes, including rare variants (MAF >0.07%). We applied this method to 25 UK Biobank diseases and complex traits (n = 113,851). All traits produced negative α estimates with 20 significantly negative, implying larger rare variant effect sizes. The inferred best-fit distribution of true α values across traits had mean −0.38 (s.e. 0.02) and standard deviation 0.08 (s.e. 0.03), with statistically-significant heterogeneity across traits (p = 0.0014). Despite larger rare variant effect sizes, we show that for most traits analyzed, rare variants (MAF <1%) explain less than 10% of total SNP-heritability. Using evolutionary modeling and forward simulations, we validated the α model of MAF-dependent trait effects and estimated the level of coupling between fitness effects and trait effects. Based on this analysis an average genome-wide negative selection coefficient on the order of 10−4 or stronger is necessary to explain the α values that we inferred.

  599. 2017-marciniak.pdf: “Harnessing ancient genomes to study the history of human adaptation”⁠, Stephanie Marciniak, George H. Perry

  600. https://www.cell.com/cell/fulltext/S0092-8674(17)31008-5

  601. https://www.cell.com/ajhg/fulltext/S0002-9297(17)30379-8

  602. ⁠, Tim Beissinger, Jochen Kruppa, David Cavero, Ngoc-Thuy Ha, Malena Erbe, Henner Simianer (2017-12-21):

    Important traits in agricultural, natural, and human populations are increasingly being shown to be under the control of many genes that individually contribute only a small proportion of genetic variation. However, the majority of modern tools in quantitative and population genetics, including genome wide association studies and selection mapping protocols, are designed to target the identification of individual genes with large effects. We have developed an approach to identify traits that have been under selection and are controlled by large numbers of loci. In contrast to existing methods, our technique utilizes additive effects estimates from all available markers, and relates these estimates to allele frequency change over time. Using this information, we generate a composite statistic, denoted Ĝ, which can be used to test for significant evidence of selection on a trait. Our test requires genotypic data from multiple time points but only a single time point with phenotypic information. Simulations demonstrate that Ĝ is powerful for identifying selection, particularly in situations where the trait being tested is controlled by many genes, which is precisely the scenario where classical approaches for selection mapping are least powerful. We apply this test to breeding populations of maize and chickens, where we demonstrate the successful identification of selection on traits that are documented to have been under selection.

  603. ⁠, Christina M. Bergey, Marie Lopez, Genelle F. Harrison, Etienne Patin, Jacob Cohen, Lluis Quintana-Murci, Luis Barreiro, George H. Perry (2018-04-13):

    Different human populations facing similar environmental challenges have sometimes evolved convergent biological adaptations, for example hypoxia resistance at high altitudes and depigmented skin in northern latitudes on separate continents. The pygmy phenotype (small adult body size), a characteristic of hunter-gatherer populations inhabiting both African and Asian tropical rainforests, is often highlighted as another case of convergent adaptation in humans. However, the degree to which phenotypic convergence in this polygenic trait is due to convergent vs. population-specific genetic changes is unknown. To address this question, we analyzed high-coverage sequence data from the protein-coding portion of the genomes (exomes) of two pairs of populations, Batwa rainforest hunter-gatherers and neighboring Bakiga agriculturalists from Uganda, and Andamanese rainforest hunter-gatherers (Jarawa and Onge) and Brahmin agriculturalists from India. We observed signatures of convergent positive selection between the Batwa and Andamanese rainforest hunter-gatherers across the set of genes with annotated ‘growth factor binding’ functions (p< 0.001). Unexpectedly, for the rainforest groups we also observed convergent and population-specific signatures of positive selection in pathways related to cardiac development (e.g. ‘cardiac muscle tissue development’; p = 0.003). We hypothesize that the growth hormone sub-responsiveness likely underlying the pygmy phenotype may have led to compensatory changes in cardiac pathways, in which this hormone also plays an essential role. Importantly, we did not observe similar patterns of positive selection on sets of genes associated with either growth or cardiac development in the agriculturalist populations, indicating that our results most likely reflect a history of convergent adaptation to the similar ecology of rainforest hunter-gatherers rather than a more common or general evolutionary pattern for human populations.

  604. 2018-ilardo.pdf: “Physiological and Genetic Adaptations to Diving in Sea Nomads”⁠, Melissa A. Ilardo, Ida Moltke, Thorfinn S. Korneliussen, Jade Cheng, Aaron J. Stern, Fernando Racimo, Peter de Barros Damgaard, Martin Sikora, Andaine Seguin-Orlando, Simon Rasmussen, Inge C. L. van den Munckhof, Rob ter Horst, Leo A. B. Joosten, Mihai G. Netea, Suhartini Salingkat, Rasmus Nielsen, Eske Willerslev

  605. https://www.gnxp.com/WordPress/2018/04/20/so-merfolk-are-a-real-thing-now/

  606. http://www.unz.com/gnxp/sexual-selection-as-a-justification-for-sex/

  607. ⁠, Michael A. Woodley Menie, Shameem Younuskunja, Balan Bipin, Piffer Davide (2017-02-21):

    Human populations living in Eurasia during the Holocene experienced significant evolutionary change. It has been predicted that the transition of Holocene populations into agrarianism and urbanization brought about culture-gene co-evolution that favoured via directional selection genetic variants associated with higher general cognitive ability (GCA). Population expansion and replacement has also been proposed as an important source of GCA gene-frequency change during this time period. To examine whether GCA might have risen during the Holocene, we compare a sample of 99 ancient Eurasian genomes (ranging from 4,557 to 1,208 years of age) with a sample of 503 modern European genomes, using three different cognitive polygenic scores. Significant differences favouring the modern genomes were found for all three polygenic scores (Odds Ratio = 0.92, p = 0.037; 0.81, p = 0.001 and 0.81, p = 0.02). Furthermore, a statistically-significant increase in positive allele count over 3,249 years was found using a sample of 66 ancient genomes (r = 0.217, pone-tailed = 0.04). These observations are consistent with the that GCA rose during the Holocene.

  608. ⁠, Ali J. Berens, Taylor L. Cooper, Joseph Lachance (2017-06-02):

    The genomes of ancient humans, Neandertals, and Denisovans contain many alleles that influence disease risks. Using genotypes at 3180 disease-associated loci, we estimated the disease burden of 147 ancient genomes. After correcting for missing data, genetic risk scores were generated for nine disease categories and the set of all combined diseases. These genetic risk scores were used to examine the effects of different types of subsistence, geography, and sample age on the number of risk alleles in each ancient genome. On a broad scale, hereditary disease risks are similar for ancient hominins and modern-day humans, and the GRS percentiles of ancient individuals span the full range of what is observed in present day individuals. In addition, there is evidence that ancient pastoralists may have had healthier genomes than hunter-gatherers and agriculturalists. We also observed a temporal trend whereby genomes from the recent past are more likely to be healthier than genomes from the deep past. This calls into question the idea that modern lifestyles have caused genetic load to increase over time. Focusing on individual genomes, we find that the overall genomic health of the Altai Neandertal is worse than 97% of present day humans and that Ötzi the Tyrolean Iceman had a genetic predisposition to gastrointestinal and cardiovascular diseases. As demonstrated by this work, ancient genomes afford us new opportunities to diagnose past human health, which has previously been limited by the quality and completeness of remains.

  609. ⁠, Fernando Racimo, Jeremy J. Berg, Joseph K. Pickrell (2017-06-07):

    An open question in human evolution is the importance of polygenic adaptation: adaptive changes in the mean of a multifactorial trait due to shifts in allele frequencies across many loci. In recent years, several methods have been developed to detect polygenic adaptation using loci identified in genome-wide association studies (GWAS). Though powerful, these methods suffer from limited interpretability: they can detect which sets of populations have evidence for polygenic adaptation, but are unable to reveal where in the history of multiple populations these processes occurred. To address this, we created a method to detect polygenic adaptation in an admixture graph, which is a representation of the historical divergences and admixture events relating different populations through time. We developed a Markov chain (MCMC) algorithm to infer branch-specific parameters reflecting the strength of selection in each branch of a graph. Additionally, we developed a set of summary statistics that are fast to compute and can indicate which branches are most likely to have experienced polygenic adaptation. We show via simulations that this method—which we call PolyGraph—has good power to detect polygenic adaptation, and applied it to human population genomic data from around the world. We also provide evidence that variants associated with several traits, including height, educational attainment, and self-reported unibrow, have been influenced by polygenic adaptation in different human populations.

  610. http://www.genetics.org/content/190/2/295.full

  611. 2016-goldmann.pdf

  612. ⁠, Ruben C. Arslan, Kai P. Willführ, Emma Frans, Karin J. H. Verweij, Mikko Myrskylä, Eckart Voland, Catarina Almqvist, Brendan P. Zietsch, Lars Penke (2016-03-08; genetics  /​ ​​ ​heritable⁠, genetics  /​ ​​ ​selection  /​ ​​ ​dysgenics):

    Higher paternal age at offspring conception increases de novo genetic mutations (Kong et al., 2012). Based on evolutionary genetic theory we predicted that the offspring of older fathers would be less likely to survive and reproduce, i.e. have lower fitness. In a sibling control study, we find clear support for negative paternal age effects on offspring survival, mating and reproductive success across four large populations with an aggregate N > 1.3 million in main analyses. Compared to a sibling born when the father was 10 years younger, individuals had 4–13% fewer surviving children in the four populations. Three populations were pre-industrial (1670-1850) Western populations and showed a pattern of paternal age effects across the offspring’s lifespan. In 20th-century Sweden, we found no negative paternal age effects on child survival or marriage odds. Effects survived tests for competing explanations, including maternal age and parental loss. To the extent that we succeeded in isolating a mutation-driven effect of paternal age, our results can be understood to show that de novo mutations reduce offspring fitness across populations and time. We can use this understanding to predict the effect of increasingly delayed reproduction on offspring genetic load, mortality and fertility.

  613. http://www.unz.com/gnxp/the-cost-of-inbreeding-in-terms-of-health/

  614. http://citeseerx.ist.psu.edu/viewdoc/download?doi=

  615. http://www.unz.com/gnxp/heritability-autism-fear-of-breeding/

  616. ⁠, Mohd Fareed, Mohammad Afzal (2014-10-14):

    Background: Cognitive ability tests are widely assumed to measure maximal intellectual performance and predictive associations between intelligence quotient (IQ) scores and later mental health problems. Very few epidemiologic studies have been done to demonstrate the relationship between familial inbreeding and modest cognitive impairments in children.

    Objective: We aimed to estimate the effect of inbreeding on children’s cognitive behavior in comparison with non-inbred children.

    Methodology: A cohort of 408 children (6 to 15 years of age) was selected from inbred and non-inbred families of five Muslim populations of Jammu region. The Wechsler Intelligence Scales for Children () was used to measure the verbal IQ (VIQ), performance IQ (PIQ) and full scale IQ (FSIQ). Family pedigrees were drawn to access the family history and children’s inbred status in terms of coefficient of inbreeding (F).

    Results: We found substantial decline in child cognitive abilities due to inbreeding and high frequency of mental retardation among offspring from inbred families. The mean differences (95% C.I.) were reported for the VIQ, being −22.00 (−24.82, −19.17), PIQ −26.92 (−29.96, −23.87) and FSIQ −24.47 (−27.35, −21.59) for inbred as compared to non-inbred children (p>0.001). The higher risk of being mentally retarded was found to be more obvious among inbred categories corresponding to the degree of inbreeding and the same accounts least for non-inbred children (p < 0.0001). We observed an increase in the difference in mean values for VIQ, PIQ and FSIQ with the increase of inbreeding coefficient and these were found to be statistically-significant (p < 0.05). The regression analysis showed a fitness decline (depression) for VIQ (R2 = 0.436), PIQ (R2 = 0.468) and FSIQ (R2 = 0.464) with increasing inbreeding coefficients (p < 0.01).

    Conclusions: Our comprehensive assessment provides the evidence for inbreeding depression on cognitive abilities among children.

  617. http://blogs.discovermagazine.com/gnxp/2013/07/genetic-diversity-and-intellectual-disability/

  618. http://www.genetics.org/content/202/3/869

  619. ⁠, Melinda C. Mills, Felix C. Tropf (2015-09-21; genetics  /​ ​​ ​heritable⁠, genetics  /​ ​​ ​correlation⁠, genetics  /​ ​​ ​selection  /​ ​​ ​dysgenics):

    The social sciences have been reticent to integrate a biodemographic approach to the study of fertility choice and behaviour, resulting in theories and findings that are largely socially-deterministic. The aim of this paper is to first reflect on reasons for this lack of integration, provide a review of previous examinations, take stock of what we have learned until now and propose future research frontiers. We review the early foundations of proximate determinants followed by behavioural genetic (family and twin) studies that isolated the extent of genetic influence on fertility traits. We then discuss research that considers gene and environment interaction and the importance of cohort and country-specific estimates, followed by multivariate models that explore motivational precursors to fertility and education. The next section on molecular genetics reviews fertility-related candidate gene studies and their shortcomings and on-going work on genome wide association studies. Work in evolutionary anthropology and biology is then briefly examined, focusing on evidence for natural selection. Biological and genetic factors are relevant in explaining and predicting fertility traits, with socio-environmental factors and their interaction still key in understanding outcomes. Studying the interplay between genes and the environment, new data sources and integration of new methods will be central to understanding and predicting future fertility trends.

    [Keywords: fertility, age at first birth, number of children ever born, genetics, behavioural genetics, molecular genetics, natural selection]

  620. 2016-day.pdf: ⁠, Felix R. Day, Hannes Helgason, Daniel I. Chasman, Lynda M. Rose, Po-Ru Loh, Robert A. Scott, Agnar Helgason, Augustine Kong, Gisli Masson, Olafur Th Magnusson, Daniel Gudbjartsson, Unnur Thorsteinsdottir, Julie E. Buring, Paul M. Ridker, Patrick Sulem, Kari Stefansson, Ken K. Ong & John R. B. Perry (2016-04-18; genetics  /​ ​​ ​correlation):

    The ages of puberty, first sexual intercourse and first birth signify the onset of reproductive ability, behavior and success, respectively. In a genome-wide association study of 125,667 UK Biobank participants, we identify 38 loci associated (p < 5 × 10−8) with age at first sexual intercourse. These findings were taken forward in 241,910 men and women from Iceland and 20,187 women from the Women’s Genome Health Study. Several of the identified loci also exhibit associations (p < 5 × 10−8) with other reproductive and behavioral traits, including age at first birth (variants in or near ESR1 and RBM6-SEMA3F), number of children (CADM2 and ESR1), irritable temperament (MSRA) and risk-taking propensity (CADM2). Mendelian randomization analyses infer causal influences of earlier puberty timing on earlier first sexual intercourse, earlier first birth and lower educational attainment. In turn, likely causal consequences of earlier first sexual intercourse include reproductive, educational, psychiatric and cardiometabolic outcomes.

  621. https://www.nature.com/articles/ncomms9842

  622. ⁠, Felix R. Day, Deborah J. Thompson, Hannes Helgason, Daniel I. Chasman, Hilary Finucane, Patrick Sulem, Katherine S. Ruth, Sean Whalen, Abhishek K. Sarkar, Eva Albrecht, Elisabeth Altmaier, Marzyeh Amini, Caterina M. Barbieri, Thibaud Boutin, Archie Campbell, Ellen Demerath, Ayush Giri, Chunyan He, Jouke J. Hottenga, Robert Karlsson, Ivana Kolcic, Po-Ru Loh, Kathryn L. Lunetta, Massimo Mangino, Brumat Marco, George McMahon, Sarah E. Medland, Ilja M. Nolte, Raymond Noordam, Teresa Nutile, Lavinia Paternoster, Natalia Perjakova, Eleonora Porcu, Lynda M. Rose, Katharina E. Schraut, Ayellet V. Segrè, Albert V. Smith, Lisette Stolk, Alexander Teumer, Irene L. Andrulis, Stefania Bandinelli, Matthias W. Beckmann, Javier Benitez, Sven Bergmann, Murielle Bochud, Eric Boerwinkle, Stig E. Bojesen, Manjeet K. Bolla, Judith S. Brand, Hiltrud Brauch, Hermann Brenner, Linda Broer, Thomas Brüning, Julie E. Buring, Harry Campbell, Eulalia Catamo, Stephen Chanock, Georgia Chenevix-Trench, Tanguy Corre, Fergus J. Couch, Diana L. Cousminer, Angela Cox, Laura Crisponi, Kamila Czene, George Davey-Smith, Eco J. C.N de Geus, Renée de Mutsert, Immaculata De Vivo, Joe Dennis, Peter Devilee, Isabel dos-Santos-Silva, Alison M. Dunning, Johan G. Eriksson, Peter A. Fasching, Lindsay Fernández-Rhodes, Luigi Ferrucci, Dieter Flesch-Janys, Lude Franke, Marike Gabrielson, Ilaria Gandin, Graham G. Giles, Harald Grallert, Daniel F. Gudbjartsson, Pascal Guénel, Per Hall, Emily Hallberg, Ute Hamann, Tamara B. Harris, Catharina A. Hartman, Gerardo Heiss, Maartje J. Hooning, John L. Hopper, Frank Hu, David Hunter, M. Arfan Ikram, Hae Kyung Im, Marjo-Riitta Järvelin, Peter K. Joshi, David Karasik, Zoltan Kutalik, Genevieve LaChance, Diether Lambrechts, Claudia Langenberg, Lenore J. Launer, Joop S. E. Laven, Stefania Lenarduzzi, Jingmei Li, Penelope A. Lind, Sara Lindstrom, YongMei Liu, Jian'an Luan, Reedik Mägi, Arto Mannermaa, Hamdi Mbarek, Mark I. McCarthy, Christa Meisinger, Thomas Meitinger, Cristina Menni, Andres Metspalu, Kyriaki Michailidou, Lili Milani, Roger L. Milne, Grant W. Montgomery, Anna M. Mulligan, Mike A. Nalls, Pau Navarro, Heli Nevanlinna, Dale R. Nyholt, Albertine J. Oldehinkel, Tracy A. O'Mara, Aarno Palotie, Nancy Pedersen, Annette Peters, Julian Peto, Paul D. P. Pharoah, Anneli Pouta, Paolo Radice, Iffat Rahman, Susan M. Ring, Antonietta Robino, Frits R. Rosendaal, Igor Rudan, Rico Rueedi, Daniela Ruggiero, Cinzia F. Sala, Marjanka K. Schmidt, Robert A. Scott, Mitul Shah, Rossella Sorice, Melissa C. Southey, Ulla Sovio, Meir Stampfer, Maristella Steri, Konstantin Strauch, Toshiko Tanaka, Emmi Tikkanen, Nicholas J. Timpson, Michela Traglia, Thérèse Truong, Jonathan P. Tyrer, André G. Uitterlinden, Digna R. Velez Edwards, Veronique Vitart, Uwe Völker, Peter Vollenweider, Qin Wang, Elisabeth Widen, Ko Willems van Dijk, Gonneke Willemsen, Robert Winqvist, Bruce H. R Wolffenbuttel, Jing Hua Zhao, Magdalena Zoledziewska, Marek Zygmunt, Behrooz Z. Alizadeh, Dorret I. Boomsma, Marina Ciullo, Francesco Cucca, Tõnu Esko, Nora Franceschini, Christian Gieger, Vilmundur Gudnason, Caroline Hayward, Peter Kraft, Debbie A. Lawlor, Patrik K. E Magnusson, Nicholas G. Martin, Dennis O. Mook-Kanamori, Ellen A. Nohr, Ozren Polasek, David Porteous, Alkes L. Price, Paul M. Ridker, Harold Snieder, Tim D. Spector, Doris Stöckl, Daniela Toniolo, Sheila Ulivi, Jenny A. Visser, Henry Völzke, Nicholas J. Wareham, James F. Wilson, The LifeLines Cohort Study, The InterAct Consortium, kConFab/​​AOCS Investigators, Endometrial Cancer Association Consortium, Ovarian Cancer Association Consortium, PRACTICAL consortium, Amanda B. Spurdle, Unnur Thorsteindottir, Katherine S. Pollard, Douglas F. Easton, Joyce Y. Tung, Jenny Chang-Claude, David Hinds, Anna Murray, Joanne M. Murabito, Kari Stefansson, Ken K. Ong, John R. B Perry (2016-09-23):

    The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Here, we analyse 1000-Genome reference panel imputed genotype data on up to ~370,000 women and identify 389 independent signals (all p < 5×10−8) for age at menarche, a notable milestone in female pubertal development. In Icelandic data from deCODE, these signals explain ~7.4% of the population variance in age at menarche, corresponding to one quarter of the estimated heritability. We implicate over 250 genes via coding variation or associated gene expression, and demonstrate enrichment across genes active in neural tissues. We identify multiple rare variants near the imprinted genes MKRN3 and DLK1 that exhibit large effects on menarche only when paternally inherited. Disproportionate effects of variants on early or late puberty timing are observed: single variant and heritability estimates are larger for early than late puberty timing in females. The opposite pattern is seen in males, with larger estimates for late than early puberty timing. Mendelian randomization analyses indicate causal inverse associations, independent of BMI, between puberty timing and risks for breast and endometrial cancers in women, and prostate cancer in men. In aggregate, our findings reveal new complexity in the genetic regulation of puberty timing and support new causal links with adult cancer risks.

  623. ⁠, Lauren Gaydosh, Daniel W. Belsky, Benjamin W. Domingue, Jason D. Boardman, Kathleen Mullan Harris (2017-04-04):

    Evidence shows that girls who experience father absence in childhood experience accelerated reproductive development in comparison to peers with present fathers. One hypothesis advanced to explain this empirical pattern is genetic confounding, wherein gene-environment correlation (rGE) causes a spurious relationship between father absence and reproductive timing. We test this hypothesis by constructing polygenic scores for age at menarche and first birth using recently available genome wide association study results and molecular genetic data on a sample of non-Hispanic white females from the National Longitudinal Study of Adolescent to Adult Health. Young women’s accelerated menarche polygenic scores were unrelated to their exposure to father absence. In contrast, earlier first-birth polygenic scores tended to be higher in young women raised in homes with absent fathers. Nevertheless, father absence and the polygenic scores independently and additively predict reproductive timing. We find limited evidence in support of the gene-environment correlation hypothesis.

  624. ⁠, Jonathan Beauchamp (2016-05-05):

    Recent findings from molecular genetics now make it possible to test directly for natural selection by analyzing whether genetic variants associated with various phenotypes have been under selection. I leverage these findings to construct polygenic scores that use individuals’ genotypes to predict their body mass index, educational attainment (EA), glucose concentration, height, schizophrenia, total cholesterol, and (in females) age at menarche. I then examine associations between these scores and fitness to test whether natural selection has been occurring. My study sample includes individuals of European ancestry born between 1931 and 1953 in the Health and Retirement Study, a representative study of the US population. My results imply that natural selection has been slowly favoring lower EA in both females and males, and are suggestive that natural selection may have favored a higher age at menarche in females. For EA, my estimates imply a rate of selection of about -1.5 months of education per generation (which pales in comparison with the increases in EA observed in contemporary times). Though they cannot be projected over more than one generation, my results provide additional evidence that humans are still evolving—albeit slowly, especially when compared to the rapid secular changes that have occurred over the past few generations due to cultural and environmental factors.

  625. ⁠, Dalton Conley, Thomas Laidley, Daniel W. Belsky, Jason M. Fletcher, Jason D. Boardman, Benjamin W. Domingue (2016-05-31; genetics  /​ ​​ ​selection  /​ ​​ ​dysgenics):

    We describe dynamics in assortative mating and fertility patterns by polygenic scores associated with anthropometric traits, depression, and educational attainment across birth cohorts from 1920 to 1955. We find that, for example, increases in assortative mating at the phenotypic level for education are not matched at the genotypic level. We also show that genes related to height are positively associated with fertility and that, despite a widening gap between the more and less educated with respect to fertility, there is no evidence that this trend is associated with genes. These findings are important to our understanding of the roots of shifting distributions of health and behavior across generations in US society.

    This study asks two related questions about the shifting landscape of marriage and reproduction in US society over the course of the last century with respect to a range of health and behavioral phenotypes and their associated genetic architecture: (1) Has assortment on measured genetic factors influencing reproductive and social fitness traits changed over the course of the 20th century? (2) Has the genetic covariance between fitness (as measured by total fertility) and other traits changed over time? The answers to these questions inform our understanding of how the genetic landscape of American society has changed over the past century and have implications for population trends. We show that husbands and wives carry similar loadings for genetic factors related to education and height. However, the magnitude of this similarity is modest and has been fairly consistent over the course of the 20th century. This consistency is particularly notable in the case of education, for which phenotypic similarity among spouses has increased in recent years. Likewise, changing patterns of the number of children ever born by phenotype are not matched by shifts in genotype-fertility relationships over time. Taken together, these trends provide no evidence that social sorting is becoming increasingly genetic in nature or that dysgenic dynamics have accelerated.

    [Keywords: assortative mating, fertility, polygenic scores, cohort trends]

  626. https://www.pnas.org/content/suppl/2016/05/25/1523592113.DCSupplemental/pnas.1523592113.sapp.pdf

  627. https://www.nature.com/articles/srep30348

  628. 2017-kong.pdf: ⁠, Augustine Kong, Michael L. Frigge, Gudmar Thorleifsson, Hreinn Stefansson, Alexander I. Young, Florian Zink, Gudrun A. Jonsdottir, Aysu Okbay, Patrick Sulem, Gisli Masson, Daniel F. Gudbjartsson, Agnar Helgason, Gyda Bjornsdottir, Unnur Thorsteinsdottir, Kari Stefansson (2017-01-11; genetics  /​ ​​ ​selection  /​ ​​ ​dysgenics):

    Epidemiological studies suggest that educational attainment is affected by genetic variants. Results from recent genetic studies allow us to construct a score from a person’s genotypes that captures a portion of this genetic component. Using data from Iceland that include a substantial fraction of the population we show that individuals with high scores tend to have fewer children, mainly because they have children later in life. Consequently, the average score has been decreasing over time in the population. The rate of decrease is small per generation but marked on an evolutionary timescale. Another important observation is that the association between the score and fertility remains highly statistically-significant after adjusting for the educational attainment of the individuals.

    Epidemiological and genetic association studies show that genetics play an important role in the attainment of education. Here, we investigate the effect of this genetic component on the reproductive history of 109,120 Icelanders and the consequent impact on the gene pool over time. We show that an educational attainment polygenic score, POLYEDU, constructed from results of a recent study is associated with delayed reproduction (p < 10−100) and fewer children overall. The effect is stronger for women and remains highly statistically-significant after adjusting for educational attainment. Based on 129,808 Icelanders born between 1910 and 1990, we find that the average POLYEDU has been declining at a rate of ~0.010 standard units per decade, which is substantial on an evolutionary timescale. Most importantly, because POLYEDU only captures a fraction of the overall underlying genetic component the latter could be declining at a rate that is two to three times faster.

  629. 2017-kong-iceland-education-dysgenics.png

  630. https://www.theguardian.com/science/2017/jan/16/natural-selection-making-education-genes-rarer-says-icelandic-study

  631. ⁠, Benjamin W. Domingue, Daniel W. Belsky, Amal Harrati, Dalton Conley, David Weir, Jason Boardman (2016-04-21; genetics  /​ ​​ ​heritable⁠, genetics  /​ ​​ ​selection  /​ ​​ ​dysgenics):

    Mortality selection is a general concern in the social and health sciences. Recently, existing health and social science cohorts have begun to collect genomic data. Causes of selection into a genomic dataset can influence results from genomic analyses. Selective non-participation, which is specific to a particular study and its participants, has received attention in the literature. But mortality selection—the very general phenomenon that genomic data collected at a particular age represents selective participation by only the subset of birth cohort members who have survived to the time of data collection—has been largely ignored. Here we test the hypothesis that such mortality selection may significantly alter estimates in polygenetic association studies of both health and non-health traits. We demonstrate mortality selection into genome-wide SNP data collection at older ages using the U.S.-based Health and Retirement Study (HRS). We then model the selection process. Finally, we test whether mortality selection alters estimates from genetic association studies. We find evidence for mortality selection. Healthier and more socioeconomically advantaged individuals are more likely to survive to be eligible to participate in the genetic sample of the HRS. Mortality selection leads to modest drift in estimating time-varying genetic effects, a drift that is enhanced when estimates are produced from data that has additional mortality selection. There is no general solution for correcting for mortality selection in a birth cohort prior to entry into a longitudinal study. We illustrate how genetic association studies using HRS data can adjust for mortality selection from study entry to time of genetic data collection by including probability weights that account for mortality selection. Mortality selection should be investigated more broadly in genetically-informed samples from other cohort studies.

  632. 2016-domingue-usa-education-dysgenics.png

  633. 2018-zeng.pdf⁠, Jian Zeng, Ronald Vlaming, Yang Wu, Matthew R. Robinson, Luke R. Lloyd-Jones, Loic Yengo, Chloe X. Yap, Angli Xue, Julia Sidorenko, Allan F. McRae, Joseph E. Powell, Grant W. Montgomery, Andres Metspalu, Tonu Esko, Greg Gibson, Naomi R. Wray, Peter M. Visscher, Jian Yang

  634. 2016-barban.pdf: ⁠, Nicola Barban, Rick Jansen, Ronald de Vlaming, Ahmad Vaez, Jornt J. Mandemakers, Felix C. Tropf, Xia Shen, James F. Wilson, Daniel I. Chasman, Ilja M. Nolte, Vinicius Tragante, Sander W. van der Laan, John R. B. Perry, Augustine Kong, BIOS Consortium, Tarunveer S. Ahluwalia, Eva Albrecht, Laura YergesArmstrong, Gil Atzmon, Kirsi Auro, Kristin Ayers, Andrew Bakshi, Danny BenAvraham, Klaus Berger, Aviv Bergman, Lars Bertram, Lawrence F. Bielak, Gyda Bjornsdottir, Marc Jan Bonder, Linda Broer, Minh Bui, Caterina Barbieri, Alana Cavadino, Jorge E. Chavarro, Constance Turman, Maria Pina Concas, Heather J. Cordell, Gail Davies, Peter Eibich, Nicholas Eriksson, Tõnu Esko, Joel Eriksson, Fahimeh Falahi, Janine F. Felix, Mark Alan Fontana, Lude Franke, Ilaria Gandin, Audrey J. Gaskins, Christian Gieger, Erica P. Gunderson, Xiuqing Guo, Caroline Hayward, Chunyan He, Edith Hofer, Hongyan Huang, Peter K. Joshi, Stavroula Kanoni, Robert Karlsson, Stefan Kiechl, Annette Kifley, Alexander Kluttig, Peter Kraft, Vasiliki Lagou, Cecile Lecoeur, Jari Lahti, Ruifang LiGao, Penelope A. Lind, Tian Liu, Enes Makalic, Crysovalanto Mamasoula, Lindsay Matteson, Hamdi Mbarek, Patrick F. McArdle, George McMahon, S. Fleur W. Meddens, Evelin Mihailov, Mike Miller, Stacey A. Missmer, Claire Monnereau, Peter J. van der Most, Ronny Myhre, Mike A. Nalls, Teresa Nutile, Ioanna Panagiota Kalafati, Eleonora Porcu, Inga Prokopenko, Kumar B. Rajan, Janet RichEdwards, Cornelius A. Rietveld, Antonietta Robino, Lynda M. Rose, Rico Rueedi, Kathleen A. Ryan, Yasaman Saba, Daniel Schmidt, Jennifer A. Smith, Lisette Stolk, Elizabeth Streeten, Anke Tönjes, Gudmar Thorleifsson, Sheila Ulivi, Juho Wedenoja, Juergen Wellmann, Peter Willeit, Jie Yao, Loic Yengo, Jing Hua Zhao, Wei Zhao, Daria V. Zhernakova, Najaf Amin, Howard Andrews, Beverley Balkau, Nir Barzilai, Sven Bergmann, Ginevra Biino, Hans Bisgaard, Klaus Bønnelykke, Dorret I. Boomsma, Julie E. Buring, Harry Campbell, Stefania Cappellani, Marina Ciullo, Simon R. Cox, Francesco Cucca, Daniela Toniolo, George DaveySmith, Ian J. Deary, George Dedoussis, Panos Deloukas, Cornelia M. van Duijn, Eco J. C. de Geus, Johan G. Eriksson, Denis A. Evans, Jessica D. Faul, Cinzia Felicita Sala, Philippe Froguel, Paolo Gasparini, Giorgia Girotto, HansJörgen Grabe, Karin Halina Greiser, Patrick J. F. Groenen, Hugoline G. de Haan, Johannes Haerting, Tamara B. Harris, Andrew C. Heath, Kauko Heikkilä, Albert Hofman, Georg Homuth, Elizabeth G. Holliday, John Hopper, Elina Hyppönen, Bo Jacobsson, Vincent W. V. Jaddoe, Magnus Johannesson, Astanand Jugessur, Mika Kähönen, Eero Kajantie, Sharon L. R. Kardia, Bernard Keavney, Ivana Kolcic, Päivikki Koponen, Peter Kovacs, Florian Kronenberg, Zoltan Kutalik, Martina La Bianca, Genevieve Lachance, William G. Iacono, Sandra Lai, Terho Lehtimäki, David CLiewald, LifeLines Cohort Study, Cecilia M. Lindgren, Yongmei Liu, Robert Luben, Michael Lucht, Riitta Luoto, Per Magnus, Patrik K. E Magnusson, Nicholas G. Martin, Matt McGue, Ruth McQuillan, Sarah E. Medland, Christa Meisinger, Dan Mellström, Andres Metspalu, Michela Traglia, Lili Milani, Paul Mitchell, Grant W. Montgomery, Dennis MookKanamori, Renée de Mutsert, Ellen A. Nohr, Claes Ohlsson, Jørn Olsen, Ken K. Ong, Lavinia Paternoster, Alison Pattie, Brenda W. J. H. Penninx, Markus Perola, Patricia A. Peyser, Mario Pirastu, Ozren Polasek, Chris Power, Jaakko Kaprio, Leslie J. Raffel, Katri Räikkönen, Olli Raitakari, Paul M. Ridker, Susan M. Ring, Kathryn Roll, Igor Rudan, Daniela Ruggiero, Dan Rujescu, Veikko Salomaa, David Schlessinger, Helena Schmidt, Reinhold Schmidt, Nicole Schupf, Johannes Smit, Rossella Sorice, Tim D. Spector, John M. Starr, Doris Stöckl, Konstantin Strauch, Michael Stumvoll, Morris A. Swertz, Unnur Thorsteinsdottir, A. Roy Thurik, Nicholas J. Timpson, Joyce Y. Tung, André G. Uitterlinden, Simona Vaccargiu, Jorma Viikari, Veronique Vitart, Henry Völzke, Peter Vollenweider, Dragana Vuckovic, Johannes Waage, Gert G. Wagner, Jie Jin Wang, Nicholas J. Wareham, David R. Weir, Gonneke Willemsen, Johann Willeit, Alan F. Wright, Krina T. Zondervan, Kari Stefansson, Robert F. Krueger, James J. Lee, Daniel J. Benjamin, David Cesarini, Philipp D. Koellinger, Marcel den Hoed, Harold Snieder, Melinda C. Mills (2016-10-31; genetics  /​ ​​ ​correlation⁠, genetics  /​ ​​ ​selection  /​ ​​ ​dysgenics):

    The genetic architecture of human reproductive behavior—age at first birth (AFB) and number of children ever born (NEB)—has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are statistically-significantly associated with AFB and/​​​​or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits.

  635. https://www.nature.com/ng/journal/v48/n12/extref/ng.3698-S2.xls

  636. ⁠, Michael A. Woodley Menie, Joseph A. Schwartz, Kevin M. Beaver (2016-08-18; genetics  /​ ​​ ​heritable⁠, genetics  /​ ​​ ​selection  /​ ​​ ​dysgenics⁠, iq):

    Utilizing a newly released cognitive Polygenic Score (PGS) from Wave IV of Add Health (n = 1,886), structural equation models (SEMs) examining the relationship between PGS and fertility (which is approximately 50% complete in the present sample), utilizing measures of verbal IQ and educational attainment as potential mediators, were estimated. The results of indirect pathway models revealed that verbal IQ mediates the positive relationship between PGS and educational attainment, and educational attainment in turn mediates the negative relationship between IQ and a latent fertility measure. The direct path from PGS to fertility was non-significant. The model was robust to controlling for age, sex and race, furthermore the results of a multi-group SEM revealed no statistically-significant differences in the estimated path coefficients across sex. These results indicate that those predisposed towards higher IQ by virtue of higher PGS values are also predisposed towards trading fertility against time spent in education, which contributes to those with higher PGS values producing fewer offspring.

  637. 2016-dutton.pdf

  638. 2015-harpending.pdf

  639. 2017-sanjak.pdf

  640. 2017-sanjak-dysgenics-malefemale.png

  641. https://academic.oup.com/gbe/advance-article/doi/10.1093/gbe/evy004/4794728

  642. 2018-bastarache.pdf

  643. https://www.nytimes.com/2018/03/15/health/genetic-mutations-diagnosis.html

  644. 2018-pardinas.pdf: ⁠, Antonio F. Pardiñas, Peter Holmans, Andrew J. Pocklington, Valentina Escott-Price, Stephan Ripke, Noa Carrera, Sophie E. Legge, Sophie Bishop, Darren Cameron, Marian L. Hamshere, Jun Han, Leon Hubbard, Amy Lynham, Kiran Mantripragada, Elliott Rees, James H. MacCabe, Steven A. McCarroll, Bernhard T. Baune, Gerome Breen, Enda M. Byrne, Udo Dannlowski, Thalia C. Eley, Caroline Hayward, Nicholas G. Martin, Andrew M. McIntosh, Robert Plomin, David J. Porteous, Naomi R. Wray, Armando Caballero, Daniel H. Geschwind, Laura M. Huckins, Douglas M. Ruderfer, Enrique Santiago, Pamela Sklar, Eli A. Stahl, Hyejung Won, Esben Agerbo, Thomas D. Als, Ole A. Andreassen, Marie Bækvad-Hansen, Preben Bo Mortensen, Carsten Bøcker Pedersen, Anders D. Børglum, Jonas Bybjerg-Grauholm, Srdjan Djurovic, Naser Durmishi, Marianne Giørtz Pedersen, Vera Golimbet, Jakob Grove, David M. Hougaard, Manuel Mattheisen, Espen Molden, Ole Mors, Merete Nordentoft, Milica Pejovic-Milovancevic, Engilbert Sigurdsson, Teimuraz Silagadze, Christine Søholm Hansen, Kari Stefansson, Hreinn Stefansson, Stacy Steinberg, Sarah Tosato, Thomas Werge, GERAD1 Consortium, CRESTAR Consortium, David A. Collier, Dan Rujescu, George Kirov, Michael J. Owen, Michael C. O’Donovan and James T. R. Walters (2018; genetics  /​ ​​ ​selection):

    Schizophrenia is a debilitating psychiatric condition often associated with poor quality of life and decreased life expectancy. Lack of progress in improving treatment outcomes has been attributed to limited knowledge of the underlying biology, although large-scale genomic studies have begun to provide insights. We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci. We also show for the first time that the common variant association signal is highly enriched among genes that are under strong selective pressures. These findings provide new insights into the biology and genetic architecture of schizophrenia, highlight the importance of mutation-intolerant genes and suggest a mechanism by which common risk variants persist in the population.

  645. ⁠, Stéphane Aris-Brosou (2018-04-25):

    The role played by natural selection in shaping present-day human populations has received extensive scrutiny [1, 2, 3], especially in the context of local adaptations [4]. However, most studies to date assume, either explicitly or not, that populations have been in their current locations long enough to adapt to local conditions [5], and that population sizes were large enough to allow for the action of selection [6]. If these conditions were satisfied, not only should selection be effective at promoting local adaptations, but deleterious alleles should also be eliminated over time. To assess this prediction, the genomes of 2,062 individuals, including 1,179 ancient humans, were reanalyzed to reconstruct how frequencies of risk alleles and their homozygosity changed through space and time in Europe. While the overall deleterious homozygosity consistently decreased through space and time, risk alleles have shown a steady increase in frequency. Even the mutations that are predicted to be most deleterious fail to exhibit any significant decrease in frequency. These conclusions do not deny the existence of local adaptations, but highlight the limitations imposed by drift and range expansions on the strength of selection in purging the mutational load affecting human populations.

  646. https://pdfs.semanticscholar.org/a508/8eccfb9efc69d6df64dc151aba0b4658023f.pdf

  647. https://slatestarcodex.com/2014/09/10/society-is-fixed-biology-is-mutable/

  648. https://www.sciencedaily.com/releases/2015/08/150813130242.htm

  649. https://www.washingtonpost.com/local/scientists-breed-a-better-seed-trait-by-trait/2014/04/16/ec8ce8c8-9a4b-11e3-80ac-63a8ba7f7942_story.html

  650. http://www.wired.com/wiredscience/2014/01/new-monsanto-vegetables/

  651. ⁠, M. J. Zuidhof, B. L. Schneider, V. L. Carney, D. R. Korver, F. E. Robinson (2014-12):

    The effect of commercial selection on the growth, efficiency, and yield of broilers was studied using 2 University of Alberta Meat Control strains unselected since 1957 and 1978, and a commercial Ross 308 strain (2005). Mixed-sex chicks (n = 180 per strain) were placed into 4 replicate pens per strain, and grown on a current nutritional program to 56 d of age. Weekly front and side profile photographs of 8 birds per strain were collected. Growth rate, feed intake, and measures of feed efficiency including feed conversion ratio, residual feed intake, and residual maintenance energy requirements were characterized. A nonlinear mixed Gompertz growth model was used to predict BW and BW variation, useful for subsequent stochastic growth simulation. Dissections were conducted on 8 birds per strain semiweekly from 21 to 56 d of age to characterize allometric growth of pectoralis muscles, leg meat, abdominal fat pad, liver, gut, and heart. A novel nonlinear analysis of covariance was used to test the hypothesis that allometric growth patterns have changed as a result of commercial selection pressure. From 1957 to 2005, broiler growth increased by over 400%, with a concurrent 50% reduction in feed conversion ratio, corresponding to a compound annual rate of increase in 42 d live BW of 3.30%. Forty-two-day FCR decreased by 2.55% each year over the same 48-yr period. Pectoralis major growth potential increased, whereas abdominal fat decreased due to genetic selection pressure over the same time period. From 1957 to 2005, pectoralis minor yield at 42 d of age was 30% higher in males and 37% higher in females; pectoralis major yield increased by 79% in males and 85% in females. Over almost 50 yr of commercial quantitative genetic selection pressure, intended beneficial changes have been achieved. Unintended changes such as enhanced sexual dimorphism are likely inconsequential, though musculoskeletal, immune function, and parent stock management challenges may require additional attention in future selection programs.

    [Keywords: broiler, genetic change, efficiency, yield dynamics]

    Age-related changes in size (mixed-sex BW and front view photos) of University of Alberta Meat Control strains unselected since 1957 and 1978, and Ross 308 broilers (2005). Within each strain, images are of the same bird at 0, 28, and 56 d of age.
  652. http://www.vox.com/xpress/2014/10/2/6875031/chickens-breeding-farming-boilers-giant

  653. 2014-wolc.pdf

  654. http://www.thebullvine.com/genomics/genetic-super-cow-myth-reality/

  655. http://news.nationalgeographic.com/news/2014/10/141015-better-beef-genetics-science-agriculture-environment-ngfood/

  656. http://infoproc.blogspot.com/2015/07/frontiers-in-cattle-genomics.html

  657. https://infoproc.blogspot.com/2014/08/its-all-in-gene-cows.html

  658. https://www.newyorker.com/magazine/2014/12/08/ride-lives

  659. 1992-innis.pdf: ⁠, N. K. Innis (1992; genetics  /​ ​​ ​selection):

    Few psychologists today are aware of the seminal role played by learning theorist Edward C. Tolman in the early development of the field of behavior genetics. Tolman was the first to publish a study of selective breeding for maze-learning ability in rats. He continued to foster research in this field by supporting the work of his students, particularly Robert C. Tryon. Tryon carried out the first major long-term study of maze-bright and maze-dull rats. This article focuses on Tolman’s early years at Berkeley and the events culminating in the inheritance project, as well as on the evolution of this research under Tryon’s direction.

  660. 2014-blasco.pdf

  661. 2015-gianola.pdf: ⁠, Daniel Gianola, Guilherme J. M. Rosa (2014-11-03; genetics  /​ ​​ ​selection):

    Statistical methodology has played a key role in scientific animal breeding. Approximately one hundred years of statistical developments in animal breeding are reviewed. Some of the scientific foundations of the field are discussed, and many milestones are examined from historical and critical perspectives. The review concludes with a discussion of some future challenges and opportunities arising from the massive amount of data generated by livestock, plant, and human genome projects.

  662. http://apsychoserver.psych.arizona.edu/JJBAReprints/PSYC621/Johnson%20Current%20Directions%20Psych%20Science%202010%20%28G%20and%20E%20in%20IQ%29.pdf

  663. ⁠, William G. Hill, Michael E. Goddard, Peter M. Visscher (2008-01-23):

    The relative proportion of additive and non-additive variation for complex traits is important in evolutionary biology, medicine, and agriculture. We address a long-standing controversy and paradox about the contribution of non-additive genetic variation, namely that knowledge about biological pathways and gene networks imply that epistasis is important. Yet empirical data across a range of traits and species imply that most genetic variance is additive. We evaluate the evidence from empirical studies of genetic variance components and find that additive variance typically accounts for over half, and often close to 100%, of the total genetic variance. We present new theoretical results, based upon the distribution of allele frequencies under neutral and other population genetic models, that show why this is the case even if there are non-additive effects at the level of gene action. We conclude that interactions at the level of genes are not likely to generate much interaction at the level of variance.

    Author Summary:

    Genetic variation in quantitative or complex traits can be partitioned into many components due to additive, dominance, and interaction effects of genes. The most important is the additive genetic variance because it determines most of the correlation of relatives and the opportunities for genetic change by natural or artificial selection. From reviews of the literature and presentation of a summary analysis of human twin data, we show that a high proportion, typically over half, of the total genetic variance is additive. This is surprising as there are many potential interactions of gene effects within and between loci, some revealed in recent QTL analyses. We demonstrate that under the standard model of neutral mutation, which leads to a U-shaped distribution of gene frequencies with most near 0 or 1, a high proportion of additive variance would be expected regardless of the amount of dominance or epistasis at the individual loci. We also show that the model is compatible with observations in populations undergoing selection and results of QTL analyses on F2 populations.

  664. ⁠, Crow, James F (2010):

    There is a difference in viewpoint of developmental and evo-devo geneticists versus breeders and students of quantitative evolution. The former are interested in understanding the developmental process; the emphasis is on identifying genes and studying their action and interaction. Typically, the genes have individually large effects and usually show substantial dominance and epistasis. The latter group are interested in quantitative phenotypes rather than individual genes. Quantitative traits are typically determined by many genes, usually with little dominance or epistasis. Furthermore, epistatic variance has minimum effect, since the selected population soon arrives at a state in which the rate of change is given by the additive variance or covariance. Thus, the breeder’s custom of ignoring epistasis usually gives a more accurate prediction than if epistatic variance were included in the formulae.

  665. ⁠, Stephen D. H. Hsu (2014-08-14):

    How do genes affect cognitive ability or other human quantitative traits such as height or disease risk? Progress on this challenging question is likely to be significant in the near future. I begin with a brief review of psychometric measurements of intelligence, introducing the idea of a “general factor” or g score. The main results concern the stability, validity (predictive power), and heritability of adult g. The largest component of genetic variance for both height and intelligence is additive (linear), leading to important simplifications in predictive modeling and statistical estimation. Due mainly to the rapidly decreasing cost of genotyping, it is possible that within the coming decade researchers will identify loci which account for a significant fraction of total g variation. In the case of height analogous efforts are well under way. I describe some unpublished results concerning the genetic architecture of height and cognitive ability, which suggest that roughly 10k moderately rare causal variants of mostly negative effect are responsible for normal population variation. Using results from Compressed Sensing (L1-penalized regression), I estimate the statistical power required to characterize both linear and nonlinear models for quantitative traits. The main unknown parameter s (sparsity) is the number of loci which account for the bulk of the genetic variation. The required sample size is of order 100s, or roughly a million in the case of cognitive ability.

  666. ⁠, Dominic Holland, Chun-Chieh Fan, Oleksandr Frei, Alexey A. Shadrin, Olav B. Smeland, V. S. Sundar, Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, Ole A. Andreassen, Anders M. Dale (2017-05-24):

    Of signal interest in the genetics of traits are estimating the proportion, π1, of causally associated single nucleotide polymorphisms (SNPs), and their effect size variance, σ[^2^~β~]{.supsub}, which are components of the mean heritabilities captured by the causal SNP.

    Here we present the first model, using detailed linkage disequilibrium structure, to estimate these quantities from genome-wide association studies (GWAS) summary statistics, assuming a of SNP effect sizes, β. We apply the model to three diverse phenotypes—schizophrenia, putamen volume, and educational attainment—and validate it with extensive simulations. We find that schizophrenia is highly polygenic, with ~5 × 104 causal SNPs distributed with small effect size variance, σ[^2^~β~]{.supsub} = 3.5 × 10−5 (in units where the phenotype variance is normalized to 1), requiring a GWAS study with more than 0.5 million samples in each arm for full discovery. In contrast, putamen volume involves only ≃ 3 × 102 causal SNPs, but with σ[^2^~β~]{.supsub} = 1.2 × 10−3, indicating a much larger proportion of the causal SNPs that are strongly associated. Educational attainment has similar polygenicity to schizophrenia, but with effects that are substantially weaker, σ[^2^~β~]{.supsub} = 5 × 10−6, leading to much lower heritability.

    Thus the model is able to describe the broad genetic architecture of phenotypes where both polygenicity and effect size variance range over several orders of magnitude, shows why only small proportions of heritability have been explained for discovered SNPs, and provides a roadmap for future GWAS discoveries.

  667. https://www.nytimes.com/2015/01/20/dining/animal-welfare-at-risk-in-experiments-for-meat-industry.html

  668. http://nautil.us/blog/the-disease-that-turned-us-into-genetic_information-junkies

  669. http://www.openthemagazine.com/article/living/father-son-and-the-double-helix#all

  670. https://www.washingtonpost.com/news/morning-mix/wp/2016/04/15/this-couple-says-everything-they-were-told-about-their-sperm-donor-was-a-lie/

  671. 2013-aa.pdf

  672. 2017-mcrae.pdf: ⁠, Jeremy F. McRae, Stephen Clayton, Tomas W. Fitzgerald, Joanna Kaplanis, Elena Prigmore, Diana Rajan, Alejandro Sifrim, Stuart Aitken, Nadia Akawi, Mohsan Alvi, Kirsty Ambridge, Daniel M. Barrett, Tanya Bayzetinova, Philip Jones, Wendy D. Jones, Daniel King, Netravathi Krishnappa, Laura E. Mason, Tarjinder Singh, Adrian R. Tivey, Munaza Ahmed, Uruj Anjum, Hayley Archer, Ruth Armstrong, Jana Awada, M. Balasubramanian, Siddharth Banka, Diana Baralle, Angela Barnicoat, Paul Batstone, David Baty, Chris Bennett, Jonathan Berg, Birgitta Bernhard, A. Paul Bevan, Maria BitnerGlindzicz, Edward Blair, Moira Blyth, David Bohanna, Louise Bourdon, David Bourn, Lisa Bradley, Angela Brady, Simon Brent, Carole Brewer, Kate Brunstrom, David J. Bunyan, John Burn, Natalie Canham, Bruce Castle, Kate Chandler, Elena Chatzimichali, Deirdre Cilliers, Angus Clarke, Susan Clasper, Jill ClaytonSmith, Virginia Clowes, Andrea Coates, Trevor Cole, Irina Colgiu, Amanda Collins, Morag N. Collinson, Fiona Connell, Nicola Cooper, Helen Cox, Lara Cresswell, Gareth Cross, Yanick Crow, Mariella DAlessandro, Tabib Dabir, Rosemarie Davidson, Sally Davies, Dylan de Vries, John Dean, Charu Deshpande, Gemma Devlin, Abhijit Dixit, Angus Dobbie, Alan Donaldson, Dian Donnai, Deirdre Donnelly, Carina Donnelly, Angela Douglas, Sofia Douzgou, Alexis Duncan, Jacqueline Eason, Sian Ellard, Ian Ellis, Frances Elmslie, Karenza Evans, Sarah Everest, Tina Fendick, Richard Fisher, Frances Flinter, Nicola Foulds, Andrew Fry, Alan Fryer, Carol Gardiner, Lorraine Gaunt, Neeti Ghali, Richard Gibbons, Harinder Gill, Judith Goodship, David Goudie, Emma Gray, Andrew Green, Philip Greene, Lynn Greenhalgh, Susan Gribble, Rachel Harrison, Lucy Harrison, Victoria Harrison, Rose Hawkins, Liu He, Stephen Hellens, Alex Henderson, Sarah Hewitt, Lucy Hildyard, Emma Hobson, Simon Holden, Muriel Holder, Susan Holder, Georgina Hollingsworth, Tessa Homfray, Mervyn Humphreys, Jane Hurst, Ben Hutton, Stuart Ingram, Melita Irving, Lily Islam, Andrew Jackson, Joanna Jarvis, Lucy Jenkins, Diana Johnson, Elizabeth Jones, Dragana Josifova, Shelagh Joss, Beckie Kaemba, Sandra Kazembe, Rosemary Kelsell, Bronwyn Kerr, Helen Kingston, Usha Kini, Esther Kinning, Gail Kirby, Claire Kirk, Emma Kivuva, Alison Kraus, Dhavendra Kumar, V. K. Ajith Kumar, Katherine Lachlan, Wayne Lam, Anne Lampe, Caroline Langman, Melissa Lees, Derek Lim, Cheryl Longman, Gordon Lowther, Sally A. Lynch, Alex Magee, Eddy Maher, Alison Male, Sahar Mansour, Karen Marks, Katherine Martin, Una Maye, Emma McCann, Vivienne McConnell, Meriel McEntagart, Ruth McGowan, Kirsten McKay, Shane McKee, Dominic J. McMullan, Susan McNerlan, Catherine McWilliam, Sarju Mehta, Kay Metcalfe, Anna Middleton, Zosia Miedzybrodzka, Emma Miles, Shehla Mohammed, Tara Montgomery, David Moore, Sian Morgan, Jenny Morton, Hood Mugalaasi, Victoria Murday, Helen Murphy, Swati Naik, Andrea Nemeth, Louise Nevitt, Ruth NewburyEcob, Andrew Norman, Rosie OShea, Caroline Ogilvie, KaiRen Ong, SooMi Park, Michael J. Parker, Chirag Patel, Joan Paterson, Stewart Payne, Daniel Perrett, Julie Phipps, Daniela T. Pilz, Martin Pollard, Caroline Pottinger, Joanna Poulton, Norman Pratt, Katrina Prescott, Sue Price, Abigail Pridham, Annie Procter, Hellen Purnell, Oliver Quarrell, Nicola Ragge, Raheleh Rahbari, Josh Randall, Julia Rankin, Lucy Raymond, Debbie Rice, Leema Robert, Eileen Roberts, Jonathan Roberts, Paul Roberts, Gillian Roberts, Alison Ross, Elisabeth Rosser, Anand Saggar, Shalaka Samant, Julian Sampson, Richard Sandford, Ajoy Sarkar, Susann Schweiger, Richard Scott, Ingrid Scurr, Ann Selby, Anneke Seller, Cheryl Sequeira, Nora Shannon, Saba Sharif, Charles ShawSmith, Emma Shearing, Debbie Shears, Eamonn Sheridan, Ingrid Simonic, Roldan Singzon, Zara Skitt, Audrey Smith, Kath Smith, Sarah Smithson, Linda Sneddon, Miranda Splitt, Miranda Squires, Fiona Stewart, Helen Stewart, Volker Straub, Mohnish Suri, Vivienne ⁠, Ganesh Jawahar Swaminathan, Elizabeth Sweeney, Kate TattonBrown, Cat Taylor, Rohan Taylor, Mark Tein, I. Karen Temple, Jenny Thomson, Marc Tischkowitz, Susan Tomkins, Audrey Torokwa, Becky Treacy, Claire Turner, Peter Turnpenny, Carolyn Tysoe, Anthony Vandersteen, Vinod Varghese, Pradeep Vasudevan, Parthiban Vijayarangakannan, Julie Vogt, Emma Wakeling, Sarah Wallwark, Jonathon Waters, Astrid Weber, Diana Wellesley, Margo Whiteford, Sara Widaa, Sarah Wilcox, Emily Wilkinson, Denise Williams, Nicola Williams, Louise Wilson, Geoff Woods, Christopher Wragg, Michael Wright, Laura Yates, Michael Yau, Chris Nellker, Michael Parker, Helen V. Firth, Caroline F. Wright, David R. FitzPatrick, Jeffrey C. Barrett Matthew E. Hurles (2017-01-25; genetics  /​ ​​ ​selection):

    The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximately half of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year.

  673. https://www.nickbostrom.com/papers/embryo.pdf

  674. https://slate.com/articles/life/seed/2001/04/the_rise_of_the_smart_sperm_shopper.single.html

  675. https://slate.com/articles/life/seed/2001/05/the_genius_babies_grow_up.html

  676. https://www.dropbox.com/s/1kktbj9ptzt9svy/2005-plotz-geniusfactory.epub?dl=0

  677. 2015-hofman.pdf: ⁠, Michel A. Hofman (2015; iq):

    Design principles and operational modes are explored that underlie the information processing capacity of the human brain.

    The hypothesis is put forward that in higher organisms, especially in primates, the complexity of the neural circuitry of the is the neural correlate of the brain’s coherence and predictive power, and, thus, a measure of intelligence. It will be argued that with the evolution of the human brain we have nearly reached the limits of biological intelligence.

    [Keywords: biological intelligence, cognition, consciousness, cerebral cortex, primates, information processing, neural networks, cortical design, human brain evolution]

  678. https://www.technologyreview.com/s/535661/engineering-the-perfect-baby/

  679. https://quadrant.org.au/magazine/2015/05/eugenics-ready/

  680. ⁠, Yanjun Zan, Zheya Sheng, Lars Rönnegård, Christa F. Honaker, Paul B. Siegel, Örjan Carlborg (2017-01-04):

    The ability of a population to adapt to changes in their living conditions, whether in nature or captivity, often depends on polymorphisms in multiple genes across the genome. In-depth studies of such polygenic adaptations are difficult in natural populations, but can be approached using the resources provided by artificial selection experiments. Here, we dissect the genetic mechanisms involved in long-term selection responses of the Virginia chicken lines, populations that after 40 generations of divergent selection for 56-day body weight display a nine-fold difference in the selected trait. In the F15 generation of an intercross between the divergent lines, 20 loci explained more than 60% of the additive genetic variance for the selected trait. We focused particularly on seven major QTL and found that only two fine-mapped to single, bi-allelic loci; the other five contained linked loci, multiple alleles or were epistatic. This detailed dissection of the polygenic adaptations in the Virginia lines provides a deeper understanding of genome-wide mechanisms involved in the long-term selection responses. The results illustrate that long-term selection responses, even from populations with a limited genetic diversity, can be polygenic and influenced by a range of genetic mechanisms.

  681. https://psmag.com/social-justice/diy-diagnosis-extreme-athlete-uncovered-genetic-flaw-88763

  682. https://slate.com/blogs/atlas_obscura/2013/06/21/the_ussr_s_moose_domestication_projects_yield_mixed_results.html

  683. https://www.nature.com/articles/548272a

  684. 2017-hickey.pdf

  685. 2017-latham.pdf: “Mothers want extraversion over conscientiousness or intelligence for their children”⁠, Rachel M. Latham, Sophie von Stumm

  686. https://www.newyorker.com/magazine/2017/08/21/how-driscolls-reinvented-the-strawberry

  687. ⁠, Moira Verbelen, Michael E. Weale, Cathryn M. Lewis (2016-07-23):

    Pharmacogenetics (PGx) has the potential to personalize pharmaceutical treatments. Many relevant gene-drug associations have been discovered, but PGx guided treatment needs to be cost-effective as well as clinically beneficial to be incorporated into standard healthcare. Progress in this area can be assessed by reviewing economic evaluations to determine the cost-effectiveness of PGx testing versus standard treatment. We performed a review of economic evaluations for PGx associations listed in the US Food and Drug Administration (FDA) Table of Pharmacogenomic Biomarkers in Drug Labeling (http:/​​​​/​​​​www.fda.gov/​​​​Drugs/​​​​ScienceResearch/​​​​ResearchAreas/​​​​Pharmacogenetics/​​​​ucm083378.htm). We determined the proportion of evaluations that found PGx guided treatment to be cost-effective or dominant over the alternative strategies, and we estimated the impact on this proportion of removing the cost of genetic testing. Of the 130 PGx associations in the FDA table, 44 economic evaluations, relating to 10 drugs, were identified. Of these evaluations, 57% drew conclusions in favour of PGx testing, of which 30% were cost-effective and 27% were dominant (cost-saving). If genetic information was freely available, 75% of economic evaluations would support PGx guided treatment, of which 25% would be cost-effective and 50% would be dominant. Thus, PGx guided treatment can be a cost-effective and even cost-saving strategy. Having genetic information readily available in the clinical health record is a realistic future prospect, and would make more genetic tests economically worthwhile. However, few drugs with PGx associations have been studied and more economic evaluations are needed to underpin the uptake of genetic testing in clinical practice.

  688. ⁠, Laura Spinney (2017-09-27):

    In the three decades since the first predictive genetic tests became available, a great deal of data has accumulated to show how people respond to knowing previously unknowable things. The rise of genetic testing has presented scientists with a 30-year experiment that has yielded some surprising insights into human behavior. The data suggest that the vast majority react in ways that at first seem counterintuitive, or at least flout what experts predicted. But as genetic testing becomes more widespread, the irrational behavior of a frightened few might start to look like the rational behavior of an enlightened majority. Doctors’ repeatedly failed attempts to anticipate people’s responses to genetic testing is not for want of preparation. Starting in the 1980s, they conducted surveys in which they asked how people might approach the test, were one available. They noted the answers and planned accordingly. The trouble was, when the test became a reality, their respondents didn’t do what they had said they would.

    …In those preparatory surveys, roughly 70% of those at risk of Huntington’s said they would take a test if it existed. In fact, only around 15% do—a proportion that has proved stable across countries and decades. A similar pattern emerged when tests became available for other incurable brain diseases…Prenatal genetic testing is widely available, but the uptake by expecting couples in which one partner is a known carrier of an incurable disease is even lower than that of testing among at-risk adults. Most opt to have a child whose risk of developing that disease is the same as theirs was at birth. Why do people act in this seemingly irresponsible way with respect to their offspring?

    and colleagues at the Pitié-Salpêtrière Hospital in Paris unpacks that decision-making process. They interviewed 54 women—either Huntington’s carriers or wives of carriers—and found that if a couple received a favorable result in a first prenatal test, the majority had the child and stopped there. Most of those who got an unfavorable result terminated the pregnancy and tried again. If a second prenatal test produced a “good” result, they had the child and stopped. But if it produced a “bad” result and another termination, most changed strategy. Some opted for preimplantation genetic diagnosis, removing the need for termination, since only mutation-free embryos are implanted. Some abandoned the idea of having a child altogether. But nearly half, 45%, conceived naturally again, and this time they did not seek prenatal testing. Summarizing the findings, the geneticist on the team, Alexandra Dürr, says, “The desire to have a child overrides all else.”

    …In a study that has yet to be published, Tibben has corroborated the French group’s conclusion. He followed 13 couples who, following counseling but prior to taking a prenatal test, agreed they would terminate in the case of an unfavorable result. None of them did so when they got that result. “That means there are 13 children alive in the Netherlands today, whom we can be 100% sure are [Huntington’s] carriers”, he says.

  689. https://www.technologyreview.com/s/609204/eugenics-20-were-at-the-dawn-of-choosing-embryos-by-health-height-and-more/

  690. https://www.statnews.com/2017/10/23/ivf-embryo-genetic-testing/

  691. https://www.thecut.com/2017/11/raising-child-with-cystic-fibrosis.html

  692. https://www.washingtonpost.com/national/health-science/donor-eggs-sperm-banks-and-the-quest-for-good-genes/2017/10/21/64b9bdd0-aaa6-11e7-b3aa-c0e2e1d41e38_story.html

  693. 2017-weigel.pdf: “A 100-Year Review: Methods and impact of genetic selection in dairy cattle - From daughter-dam comparisons to deep learning algorithms”⁠, K. A. Weigel, P. M. VanRaden, H. D. Norman, H. Grosu

  694. http://pulitzercenter.org/reporting/right-not-know-when-ignorance-bliss-deadly

  695. https://www.sciencemag.org/news/2016/12/six-cloned-horses-help-rider-win-prestigious-polo-match

  696. https://www.cbsnews.com/news/the-clones-of-polo/

  697. ⁠, Renaud Rincent, Jean-Paul Charpentier, Patricia Faivre-Rampant, Etienne Paux, Jacques Le Gouis, Catherine Bastien, Vincent Segura (2018-04-16):

    Genomic selection—the prediction of breeding values using DNA polymorphisms—is a disruptive method that has widely been adopted by animal and plant breeders to increase crop, forest and livestock productivity and ultimately secure food and energy supplies. It improves breeding schemes in different ways, depending on the biology of the species and genotyping and phenotyping constraints. However, both genomic selection and classical phenotypic selection remain difficult to implement because of the high genotyping and phenotyping costs that typically occur when selecting large collections of individuals, particularly in early breeding generations. To specifically address these issues, we propose a new conceptual framework called phenomic selection, which consists of a prediction approach based on low-cost and high-throughput phenotypic descriptors rather than DNA polymorphisms. We applied phenomic selection on two species of economic interest (wheat and poplar) using near-infrared spectroscopy on various tissues. We showed that one could reach accurate predictions in independent environments for developmental and productivity traits and tolerance to disease. We also demonstrated that under realistic scenarios, one could expect much higher genetic gains with phenomic selection than with genomic selection. Our work constitutes a proof of concept and is the first attempt at phenomic selection; it clearly provides new perspectives for the breeding community, as this approach is theoretically applicable to any organism and does not require any genotypic information.

  698. https://old.reddit.com/r/genomics/comments/8cz7fu/phenomic_selection_a_lowcost_and_highthroughput/dxiy4d0/

  699. http://www.businessweek.com/articles/2014-10-22/koreas-sooam-biotech-is-the-worlds-first-animal-cloning-factory

  700. https://www.nature.com/articles/ncomms12359

  701. ⁠, Robert Sparrow (2014-11):

    A series of recent scientific results suggest that, in the not-too-distant future, it will be possible to create viable human gametes from human stem cells. This paper discusses the potential of this technology to make possible what I call “in vitro eugenics”: the deliberate breeding of human beings in vitro by fusing sperm and egg derived from different stem-cell lines to create an embryo and then deriving new gametes from stem cells derived from that embryo. Repeated iterations of this process would allow scientists to proceed through multiple human generations in the laboratory. In vitro eugenics might be used to study the heredity of genetic disorders and to produce cell lines of a desired character for medical applications. More controversially, it might also function as a powerful technology of ‘human enhancement’ by allowing researchers to use all the techniques of selective breeding to produce individuals with a desired genotype.

  702. https://jlb.oxfordjournals.org/content/3/1/87

  703. https://www.washingtonpost.com/news/speaking-of-science/wp/2018/01/24/researchers-clone-the-first-primates-from-monkey-tissue-cells/

  704. https://www.bloomberg.com/news/articles/2017-08-07/horse-clones-start-heading-to-the-races

  705. http://www.nationalgeographic.com/magazine/2017/04/evolution-genetics-medicine-brain-technology-cyborg/

  706. https://www.nytimes.com/2017/05/16/health/ivg-reproductive-technology.html

  707. https://www.technologyreview.com/2017/08/07/105540/a-new-way-to-reproduce/

  708. https://www.wired.com/story/reverse-infertility/

  709. https://academic.oup.com/molehr/advance-article/doi/10.1093/molehr/gay002/4829657

  710. https://www.sciencemag.org/news/2018/02/these-lab-grown-human-eggs-could-combat-infertility-if-they-prove-healthy

  711. https://www.nature.com/news/monkey-kingdom-1.19762

  712. https://www.nature.com/news/welcome-to-the-crispr-zoo-1.19537

  713. http://www.pbs.org/wgbh/nova/next/nature/crispr-grapes/

  714. https://www.newyorker.com/magazine/2015/11/16/the-gene-hackers

  715. http://infoproc.blogspot.com/2016/01/improved-crisprcas9-safe-and-effective.html

  716. 2016-kleinstiver.pdf: “High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects⁠, Benjamin P. Kleinstiver, Vikram Pattanayak, Michelle S. Prew, Shengdar Q. Tsai, Nhu T. Nguyen, Zongli Zheng, J. Keith Joung

  717. 2016-slaymaker.pdf

  718. https://www.nytimes.com/2017/02/14/health/human-gene-editing-panel.html

  719. https://www.nap.edu/read/24623/chapter/1

  720. https://www.nature.com/articles/nature.2016.20302

  721. 2016-kang.pdf: “Introducing precise genetic modifications into human 3PN embryos by CRISPR  /​ ​​ ​Cas-mediated genome editing”⁠, Xiangjin Kang, Wenyin He, Yuling Huang, Qian Yu, Yaoyong Chen, Xingcheng Gao, Xiaofang Sun, Yong Fan

  722. ⁠, Puping Liang, Yanwen Xu, Xiya Zhang, Chenhui Ding, Rui Huang, Zhen Zhang, Jie Lv, Xiaowei Xie, Yuxi Chen, Yujing Li, Ying Sun, Yaofu Bai, Zhou Songyang, Wenbin Ma, Canquan Zhou, Junjiu Huang (2015-04-01):

    Genome editing tools such as the clustered regularly interspaced short palindromic repeat (CRISPR)-associated system (Cas) have been widely used to modify genes in model systems including animal zygotes and human cells, and hold tremendous promise for both basic research and clinical applications. To date, a serious knowledge gap remains in our understanding of DNA repair mechanisms in human early embryos, and in the efficiency and potential off-target effects of using technologies such as CRISPR/​​​​Cas9 in human pre-implantation embryos. In this report, we used tripronuclear(3PN) zygotes to further investigate CRISPR/​​​​Cas9-mediated gene editing in human cells. We found that CRISPR/​​​​Cas9 could effectively cleave the endogenousβ-globin gene (HBB). However, the efficiency of homologous recombination directed repair (HDR) of HBB was low and the edited embryos were mosaic. Off-target cleavage was also apparent in these 3PN zygotes as revealed by the T7E1 assay and ⁠. Furthermore, the endogenous delta-globin gene (HBD), which is homologous to HBB, competed with exogenous donor oligos to act as the repair template, leading to untoward mutations. Our data also indicated that repair of the HBB locus in these embryos occurred preferentially through the non-crossover HDR pathway. Taken together, our work highlights the pressing need to further improve the fidelity and specificity of the platform, a prerequisite for any clinical applications of CRISPR/​​​​Cas9-mediated editing.

  723. 2017-tang.pdf: “CRISPR  /​ ​​ ​Cas9-mediated gene editing in human zygotes using Cas9 protein⁠, Lichun Tang

  724. ⁠, Hong Ma, Nuria Marti-Gutierrez, Sang-Wook Park, Jun Wu, Yeonmi Lee, Keiichiro Suzuki, Amy Koski, Dongmei Ji, Tomonari Hayama, Riffat Ahmed, Hayley Darby, Crystal Van Dyken, Ying Li, Eunju Kang, A.-Reum Park, Daesik Kim, Sang-Tae Kim, Jianhui Gong, Ying Gu, Xun Xu, David Battaglia, Sacha A. Krieg, David M. Lee, Diana H. Wu, Don P. Wolf, Stephen B. Heitner, Juan Carlos Izpisua Belmonte, Paula Amato, Jin-Soo Kim, Sanjiv Kaul, Shoukhrat Mitalipov (2017-08-02):

    Genome editing has potential for the targeted correction of germline mutations. Here we describe the correction of the heterozygous MYBPC3 mutation in human preimplantation embryos with precise CRISPR-Cas9-based targeting accuracy and high homology-directed repair efficiency by activating an endogenous, germline-specific DNA repair response. Induced double-strand breaks (DSBs) at the mutant paternal allele were predominantly repaired using the homologous wild-type maternal gene instead of a synthetic DNA template. By modulating the cell cycle stage at which the DSB was induced, we were able to avoid mosaicism in cleaving embryos and achieve a high yield of homozygous embryos carrying the wild-type MYBPC3 gene without evidence of off-target mutations. The efficiency, accuracy and safety of the approach presented suggest that it has potential to be used for the correction of heritable mutations in human embryos by complementing preimplantation genetic diagnosis. However, much remains to be considered before clinical applications, including the reproducibility of the technique with other heterozygous mutations.

  725. https://www.wsj.com/articles/china-unhampered-by-rules-races-ahead-in-gene-editing-trials-1516562360

  726. http://sciencebulletin.org/archives/9946.html

  727. https://www.technologyreview.com/s/608350/first-human-embryos-edited-in-us/

  728. https://www.nytimes.com/2017/07/12/health/fda-novartis-leukemia-gene-medicine.html

  729. 2017-niu.pdf: ⁠, Dong Niu, HongJiang Wei, Lin Lin, Haydy George, Tao Wang, IHsiu Lee, HongYe Zhao, Yong Wang, Yinan Kan, Ellen Shrock, Emal Lesha, Gang Wang, Yonglun Luo, Yubo Qing, Deling Jiao, Heng Zhao, Xiaoyang Zhou, Shouqi Wang, Hong Wei, Marc Gell, George M. Church, Luhan Yang (2017-08-10; genetics  /​ ​​ ​editing):

    Xenotransplantation is a promising strategy to alleviate the shortage of organs for human transplantation. In addition to the concern on pig-to-human immunological compatibility, the risk of cross-species transmission of porcine endogenous retroviruses (PERVs) has impeded the clinical application of this approach. Earlier, we demonstrated the feasibility of inactivating PERV activity in an immortalized pig cell line. Here, we confirmed that PERVs infect human cells, and observed the horizontal transfer of PERVs among human cells. Using CRISPR-Cas9, we inactivated all the PERVs in a porcine primary cell line and generated PERV-inactivated pigs via somatic cell nuclear transfer. Our study highlighted the value of PERV inactivation to prevent cross-species viral transmission and demonstrated the successful production of PERV-inactivated animals to address the safety concern in clinical xenotransplantation.

  730. https://www.nytimes.com/2017/08/10/health/gene-editing-pigs-organ-transplants.html

  731. 2017-scheufele.pdf: “Science Magazine”

  732. https://www.motherjones.com/politics/2017/08/a-future-of-genetically-engineered-children-is-closer-than-youd-think/

  733. 2017-normile.pdf: “Science Magazine”

  734. https://apnews.com/4ae98919b52e43d8a8960e0e260feb0a/AP-Exclusive:-US-scientists-try-1st-gene-editing-in-the-body

  735. https://www.nejm.org/doi/full/10.1056/NEJMp1710370

  736. https://www.bbc.com/news/health-42337396

  737. https://www.nejm.org/doi/full/10.1056/NEJMoa1708483

  738. https://www.wired.com/story/crispr-therapeutics-plans-its-first-clinical-trial-for-genetic-disease/

  739. https://www.theatlantic.com/magazine/archive/2017/04/pleistocene-park/517779/

  740. https://gsejournal.biomedcentral.com/articles/10.1186/s12711-016-0280-3

  741. https://www.foreignaffairs.com/articles/2018-04-10/gene-editing-good

  742. https://science.sciencemag.org/content/early/2016/06/01/science.aaf6850.full

  743. https://www.nytimes.com/2016/06/03/science/human-genome-project-write-synthetic-dna.html

  744. https://archive.today/tZpBO

  745. 2017-chari.pdf: “Beyond editing to writing large genomes”⁠, Raj Chari, George M. Church

  746. https://www.wired.com/story/live-forever-synthetic-human-genome/

  747. https://medium.com/neodotlife/andrew-hessel-human-genome-project-write-d15580dd0885

  748. https://www.chemistryworld.com/features/step-by-step-synthesis-of-dna/3008753.article

  749. http://www.paulgraham.com/say.html

  750. http://journals.cambridge.org/images/fileUpload/documents/Duarte-Haidt_BBS-D-14-00108_preprint.pdf

  751. https://www.gnxp.com/WordPress/2017/12/12/most-people-say-they-think-nurture-is-more-important-than-nature-especially-white-americans/

  752. http://citeseerx.ist.psu.edu/viewdoc/download?doi=

  753. 2014-horowitz.pdf

  754. https://www.washingtonpost.com/news/wonk/wp/2014/10/28/liberals-deny-science-too/

  755. ⁠, Wagner, Jennifer K. Yu, Joon-Ho Ifekwunigwe, Jayne O. Harrell, Tanya M. Bamshad, Michael J. Royal, Charmaine D (2017):

    Null: Controversies over race conceptualizations have been ongoing for centuries and have been shaped, in part, by anthropologists.

    Objective: To assess anthropologists’ views on race, genetics, and ancestry.

    Methods: In 2012 a broad national survey of anthropologists examined prevailing views on race, ancestry, and genetics.

    Results: Results demonstrate consensus that there are no human biological races and recognition that race exists as lived social experiences that can have important effects on health.

    Discussion: Racial privilege affects anthropologists’ views on race, underscoring the importance that anthropologists be vigilant of biases in the profession and practice. Anthropologists must mitigate racial biases in society wherever they might be lurking and quash any sociopolitical attempts to normalize or promote racist rhetoric, sentiment, and behavior.

  756. http://citeseerx.ist.psu.edu/viewdoc/download?doi=

  757. https://www.nytimes.com/2013/02/17/magazine/napoleon-chagnon-americas-most-controversial-anthropologist.html?pagewanted=all

  758. https://slatestarcodex.com/2015/08/09/contrarians-crackpots-and-consensus/

  759. 2010-stigler.pdf: “rssa_a0157 469..482”

  760. https://www.newyorker.com/magazine/2005/01/24/measure-for-measure-5?currentPage=all

  761. 1988-snyderman-theiqcontroversythemediaandpublicpolicy.pdf

  762. http://citeseerx.ist.psu.edu/viewdoc/download?doi=

  763. ⁠, Rindermann, Heiner Becker, David Coyle, Thomas R (2016):

    Following Snyderman and Rothman (1987, 1988), we surveyed expert opinions on the current state of intelligence research. This report examines expert opinions on causes of international differences in student assessment and psychometric IQ test results. Experts were surveyed about the importance of culture, genes, education (quantity and quality), wealth, health, geography, climate, politics, modernization, sampling error, test knowledge, discrimination, test bias, and migration. The importance of these factors was evaluated for diverse countries, regions, and groups including Finland, East Asia, sub-Saharan Africa, Southern Europe, the Arabian-Muslim world, Latin America, Israel, Jews in the West, Roma (gypsies), and Muslim immigrants. Education was rated by n = 71 experts as the most important cause of international ability differences. Genes were rated as the second most relevant factor but also had the highest variability in ratings. Culture, health, wealth, modernization, and politics were the next most important factors, whereas other factors such as geography, climate, test bias, and sampling error were less important. The paper concludes with a discussion of limitations of the survey (e.g., response rates and validity of expert opinions).

  764. https://www.nature.com/news/ethics-taboo-genetics-1.13858

  765. 2015-cofnas.pdf

  766. http://www.pewinternet.org/2016/07/26/u-s-public-wary-of-biomedical-technologies-to-enhance-human-abilities/

  767. http://boards.straightdope.com/sdmb/showpost.php?p=13724510&postcount=133

  768. http://www.prospectmagazine.co.uk/magazine/we-cant-ignore-the-evidence-genes-affect-social-mobility

  769. http://blogs.discovermagazine.com/gnxp/2011/08/the-end-of-environmental-inequality-means-the-rise-of-genetic-inequality/

  770. http://www1.udel.edu/educ/gottfredson/reprints/2007gottfredsoninterview.pdf

  771. http://www.politico.com/story/2013/08/opinion-jason-richwine-95353_Page2.html

  772. http://freakonomics.com/2014/03/26/dalton-conley-answers-your-parentology-questions/

  773. https://medium.com/matter/tutankhamuns-blood-9fb62a68597b

  774. https://www.nytimes.com/2016/05/17/science/eske-willerslev-ancient-dna-scientist.html

  775. https://web.archive.org/web/20141215181704/https://www.wsj.com/news/articles/SB10001424052702303380004579521482247869874

  776. https://www.bostonglobe.com/ideas/2016/03/06/crime-genetic-scientists-don-know-because-they-afraid-ask/3lhGUVuNsfdJXjvhtaxPHN/story.html

  777. http://www.unz.com/gnxp/r-a-fisher-on-race-and-human-genetic-variation/

  778. http://radishmag.wordpress.com/2014/04/21/cosmic-horror/

  779. https://www.newstatesman.com/2018/04/iq-trap-how-study-genetics-could-transform-education

  780. http://people.virginia.edu/~ent3c/papers2/three_laws.pdf

  781. http://www.unz.com/gnxp/the-fourth-law-of-behavior-genetics/

  782. http://ije.oxfordjournals.org/content/40/3/537.full

  783. ⁠, Suzanne H. Gage, George Davey Smith, Jennifer J. Ware, Jonathan Flint, Marcus R. Munafò ():

    As our understanding of genetics has improved, genome-wide association studies (GWAS) have identified numerous variants associated with lifestyle behaviours and health outcomes. However, what is sometimes overlooked is the possibility that genetic variants identified in GWAS of disease might reflect the effect of modifiable risk factors as well as direct genetic effects. We discuss this possibility with illustrative examples from tobacco and alcohol research, in which genetic variants that predict behavioural phenotypes have been seen in GWAS of diseases known to be causally related to these behaviours. This consideration has implications for the interpretation of GWAS findings.

  784. 2001-rowe.pdf

  785. 2016-plomin.pdf

  786. 2016-lee-2.pdf

  787. 1987-rossi

  788. http://www.wired.com/2015/09/power-1000-genomes/

  789. https://www.nytimes.com/2009/01/11/magazine/11Genome-t.html?_r=2&pagewanted=all

  790. https://www.edge.org/conversation/robert_plomin-why-were-different

  791. https://www.amazon.com/Singularity-Rising-Surviving-Thriving-Dangerous/dp/1936661659/