Embryo-selection (Link Bibliography)

“Embryo-selection” links:

  1. 2014-shulman.pdf

  2. ⁠, 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 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 I describe some unpublished results concerning the genetic architecture of height and cognitive ability, which suggest that roughly 10k moderately rare causal variants of mostly negative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.

  3. Embryo-editing

  4. #limiting-step-eggs-or-scores

  5. #gamete-selection

  6. ⁠, Cory J. Smith, Oscar Castanon, Khaled Said, Verena Volf, Parastoo Khoshakhlagh, Amanda Hornick, Raphael Ferreira, Chun-Ting Wu, Marc Güell, Shilpa Garg, Hannu Myllykallio, George M. Church (2019-03-15):

    To extend the frontier of genome editing and enable the radical redesign of mammalian genomes, we developed a set of dead-Cas9 base editor (dBEs) variants that allow editing at tens of thousands of loci per cell by overcoming the cell death associated with DNA double-strand breaks (DSBs) and single-strand breaks (SSBs). We used a set of gRNAs targeting repetitive elements—ranging in target copy number from about 31 to 124,000 per cell. dBEs enabled survival after large-scale base editing, allowing targeted mutations at up to ~13,200 and ~2610 loci in 293T and human induced pluripotent stem cells (hiPSCs), respectively, three orders of magnitude greater than previously recorded. These dBEs can overcome current on-target mutation and toxicity barriers that prevent cell survival after large-scale genome engineering.

    One Sentence Summary

    Base editing with reduced DNA nicking allows for the simultaneous editing of >10,000 loci in human cells.

  7. https://static1.squarespace.com/static/549464d0e4b0b59bd666040a/t/5755a9d901dbae3c6d1bdb52/1465231833378/2016_Science_Boeke.pdf

  8. https://science.sciencemag.org/content/sci/suppl/2016/06/01/science.aaf6850.DC1/Boeke.SM.pdf

  9. Forking-Paths

  10. #embryo-selection-versus-alternative-breeding-methods

  11. #multiple-selection

  12. #overview-of-major-approaches

  13. #why-trust-gwases

  14. #gwas-improvements

  15. 2014-muir.pdf#page=21: ⁠, William M. Muir (2014; genetics  /​ ​​ ​selection):

    Many behaviors in poultry can be modified by genetic selection. Selection of laying hens for maximum egg production had the unfortunate side effect of increased rates of beak inflicted damage on other birds. Selective breeding has eliminated broodiness and has either increased or decreased other behaviors, such as hysteria, fearfulness, appetite in broilers, social dominance, ability and damage to other birds. Genetic selection can be used to reduce behaviors that cause welfare problems. However, it must be approached with caution to avoid unintended consequences that would be detrimental to welfare. A calm, docile bird that appears behaviorally calm, may take longer for its heart rate to return to normal after it is frightened. The use of instead of single-bird selection can be effectively used to reduce undesirable behaviors such as feather pecking and to maintain high egg production. An entire group of birds is selected instead of selecting individuals.

    [Keywords: feather pecking, group selection, poultry, welfare]

  16. https://www.edge.org/conversation/stewart_brand-we-are-as-gods-and-have-to-get-good-at-it

  17. 2013-aa.pdf

  18. 2018-mullaart.pdf: “Embryo Biopsies for Genomic Selection”⁠, Erik Mullaart, David Wells

  19. https://www.ivfbabble.com/on-the-40th-anniversary-of-the-first-ivf-in-the-usa-the-first-baby-elizabeth-jordan-carr-looks-at-how-science-today-has-produced-a-new-world-first-baby-aurea/

  20. https://www.theatlantic.com/business/archive/2015/05/the-financial-perks-of-being-tall/393518/

  21. https://genepi.qimr.edu.au/contents/publications/staff/MarouliE_Nature_Height2017Feb1EPUB.pdf

  22. ⁠, 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 () 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 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 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 (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.

  23. ⁠, Louis Lello, Steven G. Avery, Laurent Tellier, Ana I. Vazquez, Gustavo de los Campos, Stephen D. H. Hsu (2017-09-19):

    We construct genomic predictors for heritable and extremely complex human quantitative 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 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 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.

  24. ⁠, Carla Márquez-Luna, Steven Gazal, Po-Ru Loh, Nicholas Furlotte, Adam Auton, 23andMe Research Team, Alkes L. Price (2018-07-24):

    Genetic variants in functional regions of the genome are enriched for complex trait heritability. Here, we introduce a new method for polygenic prediction, LDpred-funct, that leverages trait-specific functional enrichments to increase prediction accuracy. We fit priors using the recently developed baseline-LD model, which includes coding, conserved, regulatory and LD-related anno-tations. We analytically estimate posterior mean causal effect sizes and then use cross-validation to regularize these estimates, improving prediction accuracy for sparse architectures. LDpred-funct attained higher prediction accuracy than other polygenic prediction methods in simulations using real genotypes. We applied LDpred-funct to predict 16 highly heritable traits in the UK Biobank. We used association statistics from British-ancestry samples as training data (avg JV = 365K) and samples of other European ancestries as validation data (avg 7V = 22K), to minimize ⁠. LDpred-funct attained a +27% relative improvement in prediction accuracy (avg prediction R2=0.173; highest R2=0.417 for height) compared to existing methods that do not incorporate functional information, consistent with simulations. For height, meta-analyzing training data from UK Biobank and 23andMe cohorts (total iV = 1107K; higher heritability in UK Biobank cohort) increased prediction R2 to 0.429. Our results show that modeling functional enrichment substantially improves polygenic prediction accuracy, bringing polygenic prediction of complex traits closer to clinical utility.

  25. ⁠, 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 () 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.

  26. ⁠, 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]

  27. ⁠, 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.

  28. https://slate.com/articles/life/seed/2001/05/the_genius_babies_grow_up.html

  29. 2019-karavani.pdf: ⁠, Ehud Karavani, Or Zuk, Danny Zeevi, Nir Barzilai, Nikos C. Stefanis, Alex Hatzimanolis, Nikolaos Smyrnis, Dimitrios Avramopoulos, Leonid Kruglyak, Gil Atzmon, Max Lam, Todd Lencz (2019-11-21; genetics  /​ ​​ ​selection):

    • IVF embryos could be profiled with polygenic scores for traits such as height or IQ
    • The top-scoring embryo is expected to be ≈2.5 cm or ≈2.5 IQ points above the average
    • The adult trait value of the top-scoring embryo would remain widely distributed
    • Multiple ethical and other factors impose practical limits on the actual gain

    The increasing proportion of variance in human complex traits explained by polygenic scores, along with progress in preimplantation genetic diagnosis, suggests the possibility of screening embryos for traits such as height or cognitive ability. However, the expected outcomes of embryo screening are unclear, which undermines discussion of associated ethical concerns. Here, we use theory, simulations, and real data to evaluate the potential gain of embryo screening, defined as the difference in trait value between the top-scoring embryo and the average embryo. The gain increases very slowly with the number of embryos but more rapidly with the variance explained by the score. Given current technology, the average gain due to screening would be ≈2.5 cm for height and ≈2.5 IQ points for cognitive ability. These mean values are accompanied by wide prediction intervals, and indeed, in large nuclear families, the majority of children top-scoring for height are not the tallest.

  30. https://github.com/orzuk/EmbryoSelectionCalculator

  31. 2019-karavani-supplement.pdf

  32. ⁠, Todd Lencz, Daniel Backenroth, Einat Granot-Hershkovitz, Adam Green, Kyle Gettler, Judy H. Cho, Omer Weissbrod, Or Zuk, Shai Carmi (2021-06-03):

    Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. Our main finding is that a strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied. Specifically, when only embryos with a very high are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. For example, a relative risk reduction of ≈50% for schizophrenia can be achieved by selecting the embryo with the lowest PRS out of five viable embryos. We systematically examine the impact of several factors on the utility of screening, including the variance explained by the PRS, the number of embryos, the disease prevalence, the parental PRSs, and the parental disease status. When quantifying the utility, we consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions. We also examine the risk of pleiotropic effects. Finally, we confirm our theoretical predictions by simulating “virtual” couples and offspring based on real genomes from schizophrenia and Crohn’s disease studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.

  33. 2021-turley.pdf: ⁠, Patrick Turley, Michelle N. Meyer, Nancy Wang, David Cesarini, Evelynn Hammonds, Alicia R. Martin, Benjamin M. Neale, Heidi L. Rehm, Louise Wilkins-Haug, Daniel J. Benjamin, Steven Hyman, David Laibson, Peter M. Visscher (2021-07-01; genetics  /​ ​​ ​selection):

    Companies have recently begun to sell a new service to patients considering in vitro fertilization: embryo selection based on polygenic scores (ESPS). These scores represent individualized predictions of health and other outcomes derived from genome-wide association studies in adults to partially predict these outcomes.

    This article includes a discussion of many factors that lower the predictive power of polygenic scores in the context of embryo selection and quantifies these effects for a variety of clinical and nonclinical traits. Also discussed are potential unintended consequences of ESPS (including selecting for adverse traits, altering population demographics, exacerbating inequalities in society, and devaluing certain traits).

    Recommendations for the responsible communication about ESPS by practitioners are provided, and a call for a society-wide conversation about this technology is made.

    Recommendations For Responsible Communication Of Expected Gains From ESPS:

    1. Emphasize absolute, not relative, risk reduction.

      Patients have reported greater intention to accept interventions,32 and health care professionals have reported greater willingness to purchase,33 prescribe,34,35 and view interventions as therapeutically effective,35,36 when the benefits are presented in terms of relative rather than absolute risk reduction. Given this consistent trend in the literature,37,38 absolute risk reduction should be the most salient measure of expected gain in tables, figures, and other materials.39,40 Relative risk reduction associated with embryo selection based on polygenic scores (ESPS) should never be presented in isolation.41

    2. Provide phenotype-specific estimates of expected gains.

      In the phenotypes we assessed, expected gains from ESPS differed widely—from an absolute risk reduction of 0.12% to 8.5% and a relative risk reduction of 15% to 80% in persons of European ancestries. Companies should provide expected estimates of gain for each phenotype for which screening is offered as well as for the screening of multiple phenotypes at once. Expected gains from select phenotypes should not be offered as examples from which consumers and clinicians might improperly generalize.41 Further, consumers should be aware that “expected gains” for phenotypes that are defined by clinical cutoff points may not be practically meaningful.

    3. Provide ancestry-specific estimates of expected gains.

      Currently, ESPS is not nearly as effective for consumers with non-European ancestries. Both the expected gains for each ancestral group and the uncertain gains for those of multiple ancestries should be prominently acknowledged, in plain language. Technical statements buried in fine print, such as “in demographics different from the Caucasian training set, sensitivity will be reduced”,42 are inadequate.

    4. Provide risk-specific estimates of expected gains.

      Expected gains will differ depending on the lifetime risk of the phenotype in the embryo “population.” This risk, in turn, will depend on family history and on the environment in which the resulting child is expected to be reared.

    5. Emphasize that expected gains (and risks) are uncertain.

      Companies should make clear that ESPS predictions have very wide prediction intervals that sometimes cross zero and that pleiotropy presents both risks and uncertainties regarding the other traits that do or might correlate with those the parent is selecting.

    6. Avoid exaggerating the benefits of screening additional embryos.

      Claims such as “the more sibling embryos you have to choose from, the greater the relative reduction in risk”41 are misleading. Even for cases in which the expected gains of ESPS increase largely with each additional embryo for the first 5 embryos, the incremental gains will be smaller with each of the next 5 additional embryos and will slow dramatically thereafter.11 This caution will be especially important if progress in stem-cell technologies makes it possible to create sperm or egg cells from a person’s blood or skin cells, yielding many more embryos, noninvasively, than is possible today.43,44

  34. ⁠, Nathan R. Treff, Jennifer Eccles, Lou Lello, Elan Bechor, Jeffrey Hsu, Kathryn Plunkett, Raymond Zimmerman, Bhavini Rana, Artem Samoilenko, Steven Hsu, Laurent C. A. M. Tellier (Genomic Prediction) (2019-12-04):

    For over 2 decades preimplantation genetic testing (PGT) has been in clinical use to reduce the risk of miscarriage and genetic disease in patients with advanced maternal age and risk of transmitting disease. Recently developed methods of genome-wide genotyping and machine learning algorithms now offer the ability to genotype embryos for polygenic disease risk with accuracy equivalent to adults. In addition, contemporary studies on adults indicate the ability to predict polygenic disorders with risk equivalent to monogenic disorders. Existing biobanks provide opportunities to model the clinical utility of polygenic disease risk reduction among sibling adults. Here, we provide a mathematical model for the use of embryo screening to reduce the risk of type 1 diabetes. Results indicate a 45–72% reduced risk with blinded genetic selection of one sibling. The first clinical case of polygenic risk scoring in human preimplantation embryos from patients with a family history of complex disease is reported. In addition to these data, several common and accepted practices place PGT for polygenic disease risk in the applicable context of contemporary reproductive medicine. In addition, prediction of risk for PCOS, endometriosis, and aneuploidy are of particular interest and relevance to patients with infertility and represent an important focus of future research on polygenic risk scoring in embryos.

  35. ⁠, Nathan R. Treff, Jennifer Eccles, Diego Marin, Edward Messick, Louis Lello, Jessalyn Gerber, Jia Xu, Laurent C. A. M. Tellier (2020-06-12):

    Preimplantation genetic testing for polygenic disease risk (PGT-P) represents a new tool to aid in embryo selection. Previous studies demonstrated the ability to obtain necessary genotypes in the embryo with accuracy equivalent to in adults. When applied to select adult siblings with known type I diabetes status, a reduction in disease incidence of 45–72% compared to random selection was achieved. This study extends analysis to 11,883 sibling pairs to evaluate clinical utility of embryo selection with PGT-P. Results demonstrate simultaneous relative risk reduction of all diseases tested in parallel, which included diabetes, cancer, and heart disease, and indicate applicability beyond patients with a known family history of disease.

    [Keywords: preimplantation genetic testing; PGT-P; polygenic risk scoring; genomic index; relative risk reduction]

  36. ⁠, Laurent C. A. M. Tellier, Jennifer Eccles, Nathan R. Treff, Louis Lello, Simon Fishel, Stephen Hsu (2021-07-21):

    [Note one author is ] Machine learning methods applied to large genomic datasets (such as those used in GWAS) have led to the creation of polygenic risk scores (PRSs) that can be used identify individuals who are at highly elevated risk for important disease conditions, such as coronary artery disease (CAD), diabetes, hypertension, breast cancer, and many more.

    PRSs have been validated in large population groups across multiple continents and are under evaluation for widespread clinical use in adult health. It has been shown that PRSs can be used to identify which of 2 individuals is at a lower disease risk, even when these 2 individuals are siblings from a shared family environment. The relative risk reduction (RRR) from choosing an embryo with a lower PRS (with respect to one chosen at random) can be quantified by using these sibling results.

    New technology for precise embryo genotyping allows more sophisticated preimplantation ranking with better results than the current method of selection that is based on morphology.

    We review the advances described above and discuss related ethical considerations.

    [Keywords: genomics, complex trait prediction, PRS, in vitro fertilization, genetic engineering]

  37. ⁠, Louis Lello, Timothy G. Raben, Stephen D. H. Hsu (2020-08-06):

    We test 26 polygenic predictors using tens of thousands of genetic siblings from the UK Biobank (UKB), for whom we have SNP genotypes, health status, and phenotype information in late adulthood. Siblings have typically experienced similar environments during childhood, and exhibit negligible population stratification relative to each other. Therefore, the ability to predict differences in disease risk or complex trait values between siblings is a strong test of genomic prediction in humans. We compare validation results obtained using non-sibling subjects to those obtained among siblings and find that typically most of the predictive power persists in between-sibling designs. In the case of disease risk we test the extent to which higher polygenic risk score (PRS) identifies the affected sibling, and also compute Relative Risk Reduction as a function of risk score threshold. For quantitative traits we examine between-sibling differences in trait values as a function of predicted differences, and compare to performance in non-sibling pairs. Example results: Given 1 sibling with normal-range PRS score (< 84 percentile, < + 1 SD) and 1 sibling with high PRS score (top few percentiles, ie. > + 2 SD), the predictors identify the affected sibling about 70–90% of the time across a variety of disease conditions, including Breast Cancer, Heart Attack, Diabetes, etc. 55–65% of the time the higher PRS sibling is the case. For quantitative traits such as height, the predictor correctly identifies the taller sibling roughly 80% of the time when the (male) height difference is 2 inches or more.

  38. ⁠, Jeffrey S. Zax, Daniel I. Rees (2001-05):

    This paper explores the effects of peers, friends, family, IQ and academic performance, observed in the last year of high school, on earnings at ages 35 and 53. All statistically-significantly affect earnings at both ages. The effects of IQ are much smaller than asserted in, for example, The Bell Curve, and badly overstated in the absence of controls for family, wider context or academic performance. Aspirations appear to be very important. Socialization and role models may be as well, but not ability spillovers. Feasible increases in academic performance and education can compensate for the effects of many cognitive and contextual deficits. [This paper exemplifies the fallacy of controlling for intermediate variables—as if all those “controls” had nothing to do with IQ causing earnings! —Editor]

  39. http://www.albany.edu/faculty/kretheme/PAD705/PastExams/JPE_RolePreMarketBWWageDiff.pdf

  40. 1997-cawley.pdf

  41. http://www.copenhagenconsensus.com/sites/default/files/CP+-+Hunger+FINISHED.pdf

  42. http://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1086&context=econ_workingpaper

  43. ⁠, Grosse, Scott D. Matte, Thomas D. Schwartz, Joel Jackson, Richard J (2002):

    In this study we quantify economic benefits from projected improvements in worker productivity resulting from the reduction in children’s exposure to lead in the United States since 1976. We calculated the decline in blood lead levels (BLLs) from 1976 to 1999 on the basis of nationally representative National Health and Nutrition Examination Survey (NHANES) data collected during 1976 through 1980, 1991 through 1994, and 1999. The decline in mean BLL in 1- to 5-year-old U.S. children from 1976-1980 to 1991-1994 was 12.3 microg/​​​​dL, and the estimated decline from 1976 to 1999 was 15.1 microg/​​​​dL. We assumed the change in cognitive ability resulting from declines in BLLs, on the basis of published meta-analyses, to be between 0.185 and 0.323 IQ points for each 1 g/​​​​dL blood lead concentration. These calculations imply that, because of falling BLLs, U.S. preschool-aged children in the late 1990s had IQs that were, on average, 2.2-4.7 points higher than they would have been if they had the blood lead distribution observed among U.S. preschool-aged children in the late 1970s. We estimated that each IQ point raises worker productivity 1.76-2.38%. With discounted lifetime earnings of $723,300 for each 2-year-old in 2000 dollars, the estimated economic benefit for each year’s cohort of 3.8 million 2-year-old children ranges from $110 billion to $319 billion.

  44. https://rhyclearinghouse.acf.hhs.gov/sites/default/files/docs/18142-Long_run_Economic_Effects_of_Early_Childhood_Programs_on_Adult_Earnings%5Bfull_report%5D.pdf

  45. https://www.bls.gov/data/inflation_calculator.htm

  46. ⁠, Jonathan Anomaly, Garett Jones (2020-02-22):

    A central debate in bioethics is whether parents should try to influence the genetic basis of their children’s traits. We argue that the case for using mate selection, embryo selection, and other interventions to enhance heritable traits like intelligence is strengthened by the fact that they seem to have positive network effects. These network effects include increased cooperation in collective action problems, which contributes to social trust and prosperity. We begin with an overview of evidence for these claims, and then argue that if individual welfare is largely a function of group traits, parents should try to preserve or enhance cognitive traits that have positive network effects.

  47. IQ-Income

  48. ⁠, Weiss, B (2000):

    For much of the history of toxicology, the sensitivity of the developing organism to chemical perturbation attracted limited attention. Several tragic episodes and new insights finally taught us that the course of early brain development incurs unique risks. Although the process is exquisitely controlled, its lability renders it highly susceptible to damage from environmental chemicals. Such disturbances, as recognized by current testing protocols and legislation such as the Food Quality Protection Act, can result in outcomes ranging from death to malformations to functional impairment. The latter are the most difficult to determine. First, they require a variety of measures to assay their extent. Second, adult responses may prove an inadequate guide to the response of the developing brain, which is part of the reason for proposing additional safety factors for children. Third, neuropsychological tests are deployed in complex circumstances in which many factors, including economic status, combine to produce a particular effect such as lowered intelligence quotient score. Fourth, the magnitude of the effect, for most environmental exposure levels, may be relatively small but extremely significant for public health. Fifth, changes in brain function occur throughout life, and some consequences of early damage may not even emerge until advanced age. Such factors need to be addressed in estimating the influence of a particular agent or group of agents on brain development and its functional expression. It is especially important to consider ways of dealing with multiple risks and their combinations in addition to the prevailing practice of estimating risks in isolation.

  49. 2003-gottfredson.pdf

  50. 1997-gottfredson.pdf

  51. ⁠, 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 ⁠. Early-years interventions effective with this population segment could yield very large returns on investment.

  52. http://www.hungrymindlab.com/wp-content/uploads/2015/10/Krapohl-et-al-2015.pdf

  53. ⁠, 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.

  54. ⁠, 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 (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.
  55. ⁠, W. David Hill, Neil M. Davies, Stuart J. Ritchie, Nathan G. Skene, Julien Bryois, Steven Bell, Emanuele Di Angelantonio, David J. Roberts, Shen Xueyi, Gail Davies, David C. M. Liewald, David J. Porteous, Caroline Hayward, Adam S. Butterworth, Andrew M. McIntosh, Catharine R. Gale, Ian J. Deary (2019-12-16):

    Socioeconomic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. In a sample of 286,301 participants from UK Biobank, we identify 30 (29 previously unreported) independent-loci associated with income. Using a method to meta-analyze data from genetically-correlated traits, we identify an additional 120 income-associated loci. These loci show clear evidence of functionality, with transcriptional differences identified across multiple cortical tissues, and links to GABA-ergic and serotonergic neurotransmission. By combining our genome wide association study on income with data from eQTL studies and chromatin interactions, 24 genes are prioritized for follow up, 18 of which were previously associated with intelligence. We identify intelligence as one of the likely causal, partly-heritable phenotypes that might bridge the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. These results indicate that, in modern era Great Britain, genetic effects contribute towards some of the observed socioeconomic inequalities.

  56. 2016-hill-ses-health-geneticcorrelations.jpg

  57. ⁠, Adam Socrates, Tom Bond, Ville Karhunen, Juha Auvinen, Cornelius A. Rietveld, Juha Veijola, Marjo-Riitta Jarvelin, Paul F. O’Reilly (2017-10-14):

    Background: There is now convincing evidence that pleiotropy across the genome contributes to the correlation between human traits and comorbidity of diseases. The recent availability of genome-wide association study (GWAS) results have made the polygenic risk score (PRS) approach a powerful way to perform genetic prediction and identify genetic overlap among phenotypes.

    Methods and findings

    Here we use the PRS method to assess evidence for shared genetic aetiology across hundreds of traits within a single epidemiological study—the Northern Finland Birth Cohort 1966 (NFBC1966). We replicate numerous recent findings, such as a genetic association between Alzheimer’s disease and lipid levels, while the depth of phenotyping in the NFBC1966 highlights a range of novel significant genetic associations between traits.

    Conclusion: This study illustrates the power in taking a hypothesis-free approach to the study of shared genetic aetiology between human traits and diseases. It also demonstrates the potential of the PRS method to provide important biological insights using only a single well-phenotyped epidemiological study of moderate sample size (~5k), with important advantages over evaluating genetic correlations from GWAS summary statistics only.

  58. ⁠, Kyoko Watanabe, Sven Stringer, Oleksandr Frei, Maša Umićević Mirkov, Tinca J. C. Polderman, Sophie van der Sluis, Ole A. Andreassen, Benjamin M. Neale, Danielle Posthuma (2018-12-19):

    After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics are still unanswered, such as the extent of pleiotropy across the genome, the nature of trait-associated genetic variants and the disparate genetic architecture across human traits. The current availability of hundreds of GWAS results provide the unique opportunity to gain insight into these questions. In this study, we harmonized and systematically analysed 4,155 publicly available GWASs. For a subset of well-powered GWAS on 558 unique traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait associated loci cover more than half of the genome, and 90% of those loci are associated with multiple trait domains. We further show that potential causal genetic variants are enriched in coding and flanking regions, as well as in regulatory elements, and how trait-polygenicity is related to an estimate of the required sample size to detect 90% of causal genetic variants. Our results provide novel insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource (http:/​​​​/​​​​atlas.ctglab.nl).

  59. ⁠, 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.

  60. 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 body mass index (BMI), and we find evidence that height-associated and BMI-associated variants have been under ⁠. 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 SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.

  61. ⁠, 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 (2017-02-06):

    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. -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 ~520 000 single nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (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. From an evolutionary genetic perspective, a substantial contribution of genetic variants that are not common within the population to individual differences in intelligence, education, and neuroticism is consistent with mutation-selection balance.

  62. ⁠, Jiemin Liao, Xiang Li, Tien-Yin Wong, Jie Jin Wang, Chiea Chuen Khor, E. Shyong Tai, Tin Aung, Yik-Ying Teo, Ching-Yu Cheng (2013-12-17):

    Measurement error of a phenotypic trait reduces the power to detect genetic associations. We examined the impact of sample size, allele frequency and effect size in presence of measurement error for quantitative traits. The to detect genetic association with phenotype mean and variability was investigated analytically. The non-centrality parameter for a non-central F distribution was derived and verified using computer simulations. We obtained equivalent formulas for the cost of phenotype measurement error. Effects of differences in measurements were examined in a genome-wide association study (GWAS) of two grading scales for cataract and a replication study of genetic variants influencing blood pressure. The mean absolute difference between the analytic power and simulation power for comparison of phenotypic means and variances was less than 0.005, and the absolute difference did not exceed 0.02. To maintain the same power, a one standard deviation (SD) in measurement error of a standard normal distributed trait required a one-fold increase in sample size for comparison of means, and a three-fold increase in sample size for comparison of variances. GWAS results revealed almost no overlap in the statistically-significant SNPs (p<10−5) for the two cataract grading scales while replication results in genetic variants of blood pressure displayed no statistically-significant differences between averaged blood pressure measurements and single blood pressure measurements. We have developed a framework for researchers to quantify power in the presence of measurement error, which will be applicable to studies of phenotypes in which the measurement is highly variable.

  63. ⁠, David Steinsaltz, Andrew Dahl, Kenneth W. Wachter (2016-11-14):

    Random-effects models are a popular tool for analysing total narrow-sense heritability for simple quantitative phenotypes on the basis of large-scale SNP data. Recently, there have been disputes over the validity of conclusions that may be drawn from such analysis. We derive some of the fundamental statistical properties of heritability estimates arising from these models, showing that the bias will generally be small. We show that that the score function may be manipulated into a form that facilitates intelligible interpretations of the results. We use this score function to explore the behavior of the model when certain key assumptions of the model are not satisfied—shared environment, measurement error, and genetic effects that are confined to a small subset of sites—as well as to elucidate the meaning of negative heritability estimates that may arise.

    The variance and bias depend crucially on the variance of certain functionals of the singular values of the genotype matrix. A useful baseline is the singular value distribution associated with genotypes that are completely independent—that is, with no linkage and no relatedness—for a given number of individuals and sites. We calculate the corresponding variance and bias for this setting.

    MSC 2010 subject classifications: Primary 92D10; secondary 62P10; 62F10; 60B20.

  64. https://www.nature.com/mp/journal/v16/n10/extref/mp201185x1.doc

  65. ⁠, 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.

  66. 2012-deary.pdf: ⁠, Ian J. Deary (2011-09-19; iq):

    Individual differences in human intelligence are of interest to a wide range of psychologists and to many people outside the discipline. This overview of contributions to intelligence research covers the first decade of the twenty-first century. There is a survey of some of the major books that appeared since 2000, at different levels of expertise and from different points of view.

    Contributions to the phenotype of intelligence differences are discussed, as well as some contributions to causes and consequences of intelligence differences. The major causal issues covered concern the environment and genetics, and how intelligence differences are being mapped to brain differences. The major outcomes discussed are health, education, and socioeconomic status. Aging and intelligence are discussed, as are sex differences in intelligence and whether twins and singletons differ in intelligence.

    More generally, the degree to which intelligence has become a part of broader research in neuroscience, health, and social science is discussed.

    [Keywords: IQ, cognitive ability, psychometrics, behavior genetics, cognitive epidemiology, twins, education, health]

  67. ⁠, 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 (GCTA), 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.

  68. 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.

  69. http://www.tweelingenregister.org/fileadmin/user_upload/publicaties/verslaggeving/NTR-publicaties_2014/Benyamin_MP_2014_sup.pdf

  70. 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 genome-wide association study (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 polygenic score from all measured SNPs accounts for ≈2% of the variance 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 effect size 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.]

  71. 2013-rietveld-supplementary-revision2.pdf

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

  73. ⁠, 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.

  74. 2013-trzaskowski.pdf

  75. ⁠, 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.

  76. 2014-toro.pdf

  77. https://neuroanatomy.github.io/pdfs/2014Toro-Bourgeron;GenomicArchitecture;Supplement.pdf

  78. https://www.nature.com/mp/journal/v20/n2/full/mp2014188a.html

  79. https://static-content.springer.com/esm/art%3A10.1038%2Fmp.2014.188/MediaObjects/41380_2015_BFmp2014188_MOESM469_ESM.pdf

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

  81. http://www.impactaging.com/papers/v7/n12/full/100864.html

  82. ⁠, 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.

  83. https://www.nature.com/mp/journal/v21/n6/extref/mp201645x11.doc

  84. ⁠, 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 genome-wide complex trait analysis 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.

  85. ⁠, 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.

  86. ⁠, 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.

  87. ⁠, 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 C. M. Liewald, Judith A. Okely, Ari V. Ahola-Olli, Catriona L. K. 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, Anbupalam 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 L. R. 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, Christina M. Lill, Tian Liu, Marisa Koini, Edythe London, Will T. Longstreth Jr, 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 L. 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 H. H. 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 Jr, Jan Bressler, Todd Lencz, Ian J. Deary (2018-05-29):

    General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total n = 300,486; age 16–102) and find 148 genome-wide statistically-significant independent loci (p <5 × 10−8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect statistically-significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.

  88. http://gcta.freeforums.net/thread/213/analysis-greml-results-multiple-cohorts

  89. 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 meta-analysis 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.

  90. ⁠, 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 UK Biobank, comprising both quantitative phenotypes and disease codes, and examine the moderating effect of three major demographic variables (age, sex and socioeconomic status) 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.

  91. https://www.biorxiv.org/highwire/filestream/19425/field_highwire_adjunct_files/0/070177-1.xlsx

  92. ⁠, 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 Biobank 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.

  93. http://images.pearsonclinical.com/images/pdf/wisciv/WISCIVTechReport2.pdf

  94. https://cran.r-project.org/package=lmomco

  95. Order-statistics

  96. orderstatistics-increasedvarianceadvantage-n100.png

  97. orderstatistics-increasedvarianceadvantage-n1000.png

  98. orderstatistics-increasedvarianceadvantage-n10000.png

  99. https://gwern.shinyapps.io/orderStatisticsIncreasedVariance/

  100. 1939-taylor.pdf

  101. 1979-schmidt.pdf: ⁠, Frank L. Schmidt, J. E. Hunter, R. C. McKenzie, T. W. Muldrow (1979-01-01; statistics  /​ ​​ ​decision):

    Used decision theoretic equations to estimate the impact of the Programmer Aptitude Test (PAT) on productivity if used to select new computer programmers for 1 yr in the federal government and the national economy. A newly developed technique was used to estimate the standard deviation of the dollar value of employee job performance, which in the past has been the most difficult and expensive item of required information. For the federal government and the US economy separately, results are presented for different selection ratios and for different assumed values for the validity of previously used selection procedures. The impact of the PAT on programmer productivity was substantial for all combinations of assumptions. Results support the conclusion that hundreds of millions of dollars in increased productivity could be realized by increasing the validity of selection decisions in this occupation. Similarities between computer programmers and other occupations are discussed. It is concluded that the impact of valid selection procedures on work-force productivity is considerably greater than most personnel psychologists have believed.

  102. 1996-lubinski-2.pdf: ⁠, David Lubinski, Lloyd G. Humphreys (1996; statistics  /​ ​​ ​order):

    When measures of individual differences are used to predict group performance, the reporting of correlations computed on samples of individuals invites misinterpretation and dismissal of the data. In contrast, if regression equations, in which the correlations required are computed on bivariate means, as are the distribution statistics, it is difficult to underappreciate or lightly dismiss the utility of psychological predictors.

    Given sufficient sample size and linearity of regression, this technique produces cross-validated regression equations that forecast criterion means with almost perfect accuracy. This level of accuracy is provided by correlations approaching unity between bivariate samples of predictor and criterion means, and this holds true regardless of the magnitude of the “simple” correlation (eg., rxy = 0.20, or rxy = 0.80).

    We illustrate this technique empirically using a measure of general intelligence as the predictor and other measures of individual differences and socioeconomic status as criteria. In addition to theoretical applications pertaining to group trends, this methodology also has implications for applied problems aimed at developing policy in numerous fields.

    …To summarize, psychological variables generating modest correlations frequently are discounted by those who focus on the magnitude of unaccounted for criterion variance, large standard errors, and frequent false positive and false negative errors in predicting individuals. Dismissal of modest correlations (and the utility of their regressions) by professionals based on this psychometric-statistical reasoning has spread to administrators, journalists, and legislative policy makers. Some examples of this have been compiled by Dawes (1979, 1988) and Linn (1982). They range from squaring a correlation of 0.345 (ie., 0.12) and concluding that for 88% of students, “An SAT score will predict their grade rank no more accurately than a pair of dice” (cf. Linn, 1982, p. 280) to evaluating the differential utility of two correlations 0.20 and 0.40 (based on different procedures for selecting graduate students) as “twice of nothing is nothing” (cf. Dawes, 1979, p. 580).

    …Tests are used, however, in ways other than the prediction of individuals or of a specific outcome for Johnny or Jane. And policy decisions based on tests frequently have broader implications for individuals beyond those directly involved in the assessment and selection context (see the discussion later in this article). For example, selection of personnel in education, business, industry, and the military focuses on the criterion performance of groups of applicants whose scores on selection instruments differ. Selection psychologists have long made use of modest predictive correlations when the ratio of applicants to openings becomes large. The relation of utility to size of correlation, relative to the selection ratio and base rate for success (if one ignores the test scores), is incorporated in the well-known Taylor-Russell (1939) tables. These tables are examples of how psychological tests have revealed convincingly economic and societal benefits (), even when a correlation of modest size remains at center stage. For example, given a base rate of 30% for adequate performance and a predictive validity coefficient of 0.30 within the applicant population, selecting the top 20% on the predictor test will result in 46% of hires ultimately achieving adequate performance (a 16% gain over base rate). To be sure, the prediction for individuals within any group is not strong—about 9% of the variance in job performance. Yet, when training is expensive or time-consuming, this can result in huge savings. For analyses of groups composed of anonymous persons, however, there is a more unequivocal way of illustrating the importance of modest correlations than even the Taylor-Russell tables provide.

    Rationale for an Alternative Approach: Applied psychologists discovered decades ago that it is more advantageous to report correlations between a continuous predictor and a dichotomous criterion graphically rather than as a number that varies between zero and one. For example, the correlation () of about 0.40 with the pass-fail pilot training criterion and an ability- predictor looks quite impressive when graphed in the manner of Figure 1a. In contrast, in Figure 1b, a scatter plot of a correlation of 0.40 between two continuous measures looks at first glance like the pattern of birdshot on a target. It takes close scrutiny to perceive that the pattern in Figure 1b is not quite circular for the small correlation. Figure 1a communicates the information more effectively than Figure 1b. When the data on the predictive validity of the pilot ability-stanine were presented in the form of Figure 1a (rather than, say, as a scatter plot of a correlation of 0.40; Figure 1b), general officers in recruitment, training, logistics, and operations immediately grasped the importance of the data for their problems. Because the Army Air Forces were an attractive career choice, there were many more applicants for pilot training than could be accommodated and selection was required…A small gain on a criterion for a unit of gain on the predictor, as long as it is predicted with near-perfect accuracy, can have high utility.

    Figure 1. a: Percentage of pilots eliminated from a training class as a function of pilot aptitude rating in stanines. Number of trainees in each stanine is shown on each bar. (From DuBois 1947). b: A synthetic example of a correlation of 0.40 (n = 400).
  103. https://humanvarieties.org/2014/03/31/what-does-it-mean-to-have-a-low-r-squared-a-warning-about-misleading-interpretation/

  104. Order-statistics#probability-of-bivariate-maximum

  105. orderstatistics-maximums.png

  106. https://journals.ametsoc.org/doi/pdf/10.1175/JCLI3593.1

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

  108. https://science.sciencemag.org/content/sci/suppl/2013/05/29/science.1235488.DC1/Rietveld.SM.revision.2.pdf

  109. ⁠, Rietveld, Cornelius A. Conley, Dalton Eriksson, Nicholas Esko, Tõnu Medland, Sarah E. Vinkhuyzen, Anna A. E Yang, Jian Boardman, Jason D. Chabris, Christopher F. Dawes, Christopher T. Domingue, Benjamin W. Hinds, David A. Johannesson, Magnus Kiefer, Amy K. Laibson, David Magnusson, Patrik K. E Mountain, Joanna L. Oskarsson, Sven Rostapshova, Olga Teumer, Alexander Tung, Joyce Y. Visscher, Peter M. Benjamin, Daniel J. Cesarini, David Koellinger, Philipp D (2014):

    A recent genome-wide-association study of educational attainment identified three single-nucleotide polymorphisms (SNPs) whose associations, despite their small effect sizes (each R2 ≈ 0.02%), reached genome-wide statistical-significance (p < 5 × 10−8) in a large discovery sample and were replicated in an independent sample (p < .05). The study also reported associations between educational attainment and indices of SNPs called “polygenic scores.” In three studies, we evaluated the robustness of these findings. Study 1 showed that the associations with all three SNPs were replicated in another large (n = 34,428) independent sample. We also found that the scores remained predictive (R2 ≈ 2%) in regressions with stringent controls for stratification (Study 2) and in new within-family analyses (Study 3). Our results show that large and therefore well-powered genome-wide-association studies can identify replicable genetic associations with behavioral traits. The small effect sizes of individual SNPs are likely to be a major contributing factor explaining the striking contrast between our results and the disappointing replication record of most candidate-gene studies.

  110. https://media.nature.com/original/nature-assets/mp/journal/v19/n2/extref/mp2012184x1.doc

  111. https://www.pnas.org/content/early/2014/09/05/1404623111.full.pdf

  112. ⁠, Cornelius Rietveld (2015-01-08):

    This document provides further details about materials, methods and additional analyses to accompany the research report “Proxy-Phenotype Method Identifies Common Genetic Variants Associated with Cognitive Performance.”

  113. http://ero.sagepub.com/content/1/3/2332858415599972?full

  114. 2015-zhu.pdf

  115. ⁠, 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.

  116. http://www.tweelingenregister.org/nederlands/verslaggeving/NTR-publicaties_2014/Zeeuw_AJMG_2014.pdf

  117. ⁠, Dalton Conley, Benjamin W. Domingue, David Cesarini, Christopher Dawes, Cornelius A. Rietveld, Jason D. Boardman (2015-02-25):

    Parental education is the strongest measured predictor of offspring education, and thus many scholars see the parent-child correlation in educational attainment as an important measure of social mobility. But if social changes or policy interventions are going to have dynastic effects, we need to know what accounts for this intergenerational association, that is, whether it is primarily environmental or genetic in origin. Thus, to understand whether the estimated social influence of parental education on offspring education is biased owing to genetic inheritance (or moderated by it), we exploit the findings from a recent large genome-wide association study of educational attainment to construct a genetic score designed to predict educational attainment. Using data from two independent samples, we find that our genetic score statistically-significantly predicts years of schooling in both between-family and within-family analyses. We report three findings that should be of interest to scholars in the stratification and education fields. First, raw parent-child correlations in education may reflect 1⁄6th genetic transmission and 5⁄6ths social inheritance. Second, conditional on a child’s genetic score, a parental genetic score has no statistically-significant relationship to the child’s educational attainment. Third, the effects of offspring genotype do not seem to be moderated by measured sociodemographic variables at the parental level (but parent-child genetic interaction effects are statistically-significant). These results are consistent with the existence of two separate systems of ascription: genetic inheritance (a random lottery within families) and social inheritance (across-family ascription). We caution, however, that at the presently attainable levels of explanatory power, these results are preliminary and may change when better-powered genetic risk scores are developed.

  118. http://journals.sagepub.com/doi/full/10.1177/2332858415599972

  119. https://journals.sagepub.com/doi/suppl/10.1177/2332858415599972/suppl_file/ERO599972_Online_Supplement.docx

  120. ⁠, Lencz, T. Knowles, E. Davies, G. Guha, S. Liewald, D. C Starr, J. M Djurovic, S. Melle, I. Sundet, K. Christoforou, A. Reinvang, I. Mukherjee, S. DeRosse, Pamela Lundervold, A. Steen, V. M John, M. Espeseth, T. Räikkönen, K. Widen, E. Palotie, A. Eriksson, J. G Giegling, I. Konte, B. Ikeda, M. Roussos, P. Giakoumaki, S. Burdick, K. E Payton, A. Ollier, W. Horan, M. Donohoe, G. Morris, D. Corvin, A. Gill, M. Pendleton, N. Iwata, N. Darvasi, A. Bitsios, P. Rujescu, D. Lahti, J. Hellard, S. L Keller, M. C Andreassen, O. A Deary, I. J Glahn, D. C Malhotra, A. K (2014):

    It has long been recognized that generalized deficits in cognitive ability represent a core component of schizophrenia (SCZ), evident before full illness onset and independent of medication. The possibility of genetic overlap between risk for SCZ and cognitive phenotypes has been suggested by the presence of cognitive deficits in first-degree relatives of patients with SCZ; however, until recently, molecular genetic approaches to test this overlap have been lacking. Within the last few years, large-scale genome-wide association studies (GWAS) of SCZ have demonstrated that a substantial proportion of the heritability of the disorder is explained by a polygenic component consisting of many common single-nucleotide polymorphisms (SNPs) of extremely small effect. Similar results have been reported in GWAS of general cognitive ability. The primary aim of the present study is to provide the first molecular genetic test of the classic endophenotype hypothesis, which states that alleles associated with reduced cognitive ability should also serve to increase risk for SCZ. We tested the endophenotype hypothesis by applying polygenic SNP scores derived from a large-scale cognitive GWAS meta-analysis (~5000 individuals from nine nonclinical cohorts comprising the Cognitive Genomics consorTium (COGENT)) to four SCZ case-control cohorts. As predicted, cases had significantly lower cognitive polygenic scores compared to controls. In parallel, polygenic risk scores for SCZ were associated with lower general cognitive ability. In addition, using our large cognitive meta-analytic data set, we identified nominally statistically-significant cognitive associations for several SNPs that have previously been robustly associated with SCZ susceptibility. Results provide molecular confirmation of the genetic overlap between SCZ and general cognitive ability, and may provide additional insight into pathophysiology of the disorder.

  121. https://www.nature.com/mp/journal/v21/n11/extref/mp2015225x1.doc

  122. https://www.nature.com/articles/mp201645/figures/2

  123. ⁠, 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.

  124. 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.

  125. https://media.nature.com/original/nature-assets/nature/journal/v533/n7604/extref/nature17671-s1.pdf

  126. https://media.nature.com/original/nature-assets/nature/journal/v533/n7604/extref/nature17671-s2.xlsx

  127. http://www.thessgac.org/data

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

  129. https://www.nature.com/article-assets/npg/mp/journal/v22/n2/extref/mp2016107x1.pdf

  130. 2018-elliott.pdf: ⁠, 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 (2018-01-01; iq):

    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 the development of larger brains. We tested this hypothesis using four large imaging genetics studies (combined n = 7965) with polygenic scores derived from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. 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 samples than in the convenience-based 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 to understand neurobiology linking genetics with cognitive performance.

  131. ⁠, 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.

  132. https://www.biorxiv.org/content/biorxiv/suppl/2017/11/14/219261.DC1/219261-1.pdf#page=2

  133. 2018-kong.pdf: “The nature of nurture: Effects of parental genotypes”⁠, Augustine Kong, Gudmar Thorleifsson, Michael L. Frigge, Bjarni J. Vilhjalmsson, Alexander I. Young, Thorgeir E. Thorgeirsson, Stefania Benonisdottir, Asmundur Oddsson, Bjarni V. Halldorsson, Gisli Masson, Daniel F. Gudbjartsson, Agnar Helgason, Gyda Bjornsdottir, Unnur Thorsteinsdottir, Kari Stefansson

  134. http://programme.exordo.com/isir2017/delegates/presentation/29/

  135. 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.

  136. 2018-bates.pdf: ⁠, Timothy C. Bates, Brion S. Maher, Sarah E. Medland, Kerrie McAloney, Margaret J. Wright, Narelle K. Hansell, Kenneth S. Kendler, Nicholas G. Martin, Nathan A. Gillespie (2018-03-13; genetics  /​ ​​ ​heritable):

    Research on environmental and genetic pathways to complex traits such as educational attainment (EA) is confounded by uncertainty over whether correlations reflect effects of transmitted parental genes, causal family environments, or some, possibly interactive, mixture of both. Thus, an aggregate of thousands of alleles associated with EA (a polygenic risk score; PRS) may tap parental behaviors and home environments promoting EA in the offspring. New methods for unpicking and determining these causal pathways are required. Here, we utilize the fact that parents pass, at random, 50% of their genome to a given offspring to create independent scores for the transmitted alleles (conventional EA PRS) and a parental score based on alleles not transmitted to the offspring (EA VP_PRS). The formal effect of non-transmitted alleles on offspring attainment was tested in 2,333 genotyped twins for whom high-quality measures of EA, assessed at age 17 years, were available, and whose parents were also genotyped. Four key findings were observed. First, the EA PRS and EA VP_PRS were empirically independent, validating the virtual-parent design. Second, in this family-based design, children’s own EA PRS statistically-significantly predicted their EA (β = 0.15), ruling out stratification confounds as a cause of the association of attainment with the EA PRS. Third, parental EA PRS predicted the SES environment parents provided to offspring (β = 0.20), and parental SES and offspring EA were statistically-significantly associated (β = 0.33). This would suggest that the EA PRS is at least as strongly linked to social competence as it is to EA, leading to higher attained SES in parents and, therefore, a higher experienced SES for children. In a full structural equation model taking account of family genetic relatedness across multiple siblings the non-transmitted allele effects were estimated at similar values; but, in this more complex model, confidence intervals included zero. A test using the forthcoming EA3 PRS may clarify this outcome. The virtual-parent method may be applied to clarify causality in other phenotypes where observational evidence suggests parenting may moderate expression of other outcomes, for instance in psychiatry.

  137. https://www.nature.com/articles/s41398-018-0222-7

  138. ⁠, 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.

  139. ⁠, 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.

  140. 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.

  141. ⁠, 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.

  142. ⁠, 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 ⁠, 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.

  143. ⁠, 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.

  144. ⁠, Robert M. Maier, Zhihong Zhu, Sang Hong Lee, Maciej Trzaskowski, Douglas M. Ruderfer, Eli A. Stahl, Stephan Ripke, Naomi R. Wray, Jian Yang, Peter M. Visscher, Matthew R. Robinson (2018-03-07):

    Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.

  145. ⁠, 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.

  146. 2018-lee.pdf: ⁠, James J. Lee, Robbee Wedow, Aysu Okbay, Edward Kong, Omeed Maghzian, Meghan Zacher, Tuan Anh Nguyen-Viet, Peter Bowers, Julia Sidorenko, Richard Karlsson Linnér, Mark Alan Fontana, Tushar Kundu, Chanwook Lee, Hui Li, Ruoxi Li, Rebecca Royer, Pascal N. Timshel, Raymond K. Walters, Emily A. Willoughby, Loïc Yengo, 23andMe Research Team, COGENT (Cognitive Genomics Consortium), Social Science Genetic Association Consortium, Maris Alver, Yanchun Bao, David W. Clark, Felix R. Day, Nicholas A. Furlotte, Peter K. Joshi, Kathryn E. Kemper, Aaron Kleinman, Claudia Langenberg, Reedik Mägi, Joey W. Trampush, Shefali Setia Verma, Yang Wu, Max Lam, Jing Hua Zhao, Zhili Zheng, Jason D. Boardman, Harry Campbell, Jeremy Freese, Kathleen Mullan Harris, Caroline Hayward, Pamela Herd, M. Kumari, Todd Lencz, Jian’an Luan, Anil K. Malhotra, Andres Metspalu, Lili Milani, Ken K. Ong, John R. B. Perry, David J. Porteous, Marylyn D. Ritchie, Melissa C. Smart, Blair H. Smith, Joyce Y. Tung, Nicholas J. Wareham, James F. Wilson, Jonathan P. Beauchamp, Dalton C. Conley, Tõnu Esko, Steven F. Lehrer, Patrik K. E. Magnusson, Sven Oskarsson, Tune H. Pers, Matthew R. Robinson, Kevin Thom, Chelsea Watson, Christopher F. Chabris, Michelle N. Meyer, David I. Laibson, Jian Yang, Magnus Johannesson, Philipp D. Koellinger, Patrick Turley, Peter M. Visscher, Daniel J. Benjamin, David Cesarini (2018-07-23; iq):

    Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

  147. https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0147-3/MediaObjects/41588_2018_147_MOESM1_ESM.pdf

  148. https://www.thessgac.org/data

  149. 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.

  150. 2020-barth.pdf: ⁠, Daniel Barth, Nicholas W. Papageorge, Kevin Thom (2020-04-01; economics):

    We show that genetic endowments linked to educational attainment strongly and robustly predict wealth at retirement. The estimated relationship is not fully explained by flexibly controlling for education and labor income. We therefore investigate a host of additional mechanisms that could account for the gene-wealth gradient, including inheritances, mortality, risk preferences, portfolio decisions, beliefs about the probabilities of macroeconomic events, and planning horizons. We provide evidence that genetic endowments related to human capital accumulation are associated with wealth not only through educational attainment and labor income but also through a facility with complex financial decision-making.

  151. 2018-papageorge.pdf: ⁠, Nicholas W. Papageorge, Kevin Thom (2018-09; genetics  /​ ​​ ​heritable):

    Recent advances have led to the discovery of specific genetic variants that predict educational attainment. We study how these variants, summarized as a linear index—known as a polygenic score—are associated with human capital accumulation and labor market outcomes in the Health and Retirement Study (HRS). We present two main sets of results. First, we find evidence that the genetic factors measured by this score interact strongly with childhood socioeconomic status in determining educational outcomes. In particular, while the polygenic score predicts higher rates of college graduation on average, this relationship is substantially stronger for individuals who grew up in households with higher socioeconomic status relative to those who grew up in poorer households. Second, the polygenic score predicts labor earnings even after adjusting for completed education, with larger returns in more recent decades. These patterns suggest that the genetic traits that promote education might allow workers to better accommodate ongoing skill biased technological change. Consistent with this interpretation, we find a positive association between the polygenic score and non-routine analytic tasks that have benefited from the introduction of new technologies. Nonetheless, the college premium remains the dominant determinant of earnings differences at all levels of the polygenic score. Given the role of childhood SES in predicting college attainment, this raises concerns about wasted potential arising from limited household resources.

  152. ⁠, Aldo Rustichini, William G. Iacono, James Lee, Matt McGue (2019-09-17):

    A Genome-wide association study (GWAS) estimates size and statistical-significance of the effect of common genetic variants on a phenotype of interest. A Polygenic Score (PGS) is a score, computed for each individual, summarizing the of a phenotype on the basis of the individual’s genotype. The PGS is computed as a weighted sum of the values of the individual’s genetic variants, using as weights the GWAS estimated coefficients from a training sample. Thus, PGS carries information on the genotype, and only on the genotype, of an individual. In our case phenotypes of interest are measures of educational achievement, such as having a college degree, or the education years, in a sample of approximately 2700 adult twins and their parents.

    We set up the analysis in a standard model of optimal parental investment and intergenerational mobility, extended to include a fully specified genetic analysis of skill transmission, and show that the model’s predictions on mobility differ substantially from those of the standard model. For instance, the coefficient of intergenerational income elasticity maybe larger, and may differ across countries because the distribution of the genotype is different, completely independently of any difference in institution, technology or preferences.

    We then study how much of the educational achievement is explained by the PGS for education, thus estimating how much of the variance of education can be explained by genetic factors alone. We find a substantial effect of PGS on performance in school, years of education and college.

    Finally we study the channels between PGS and the educational achievement, distinguishing how much is due to cognitive skills and to personality traits. We show that the effect of PGS is substantially stronger on Intelligence than on other traits, like Constraint, which seem natural explanatory factors of educational success. For educational achievement, both cognitive and non cognitive skills are important, although the larger fraction of success is channeled by Intelligence.

  153. ⁠, A. G. Allegrini, S. Selzam, K. Rimfeld, S. von Stumm, J. B. Pingault, R. Plomin (2018-09-17):

    Recent advances in genomics are producing powerful DNA predictors of complex traits, especially cognitive abilities. Here, we leveraged summary statistics from the most recent genome-wide association studies of intelligence and educational attainment to build prediction models of general cognitive ability and educational achievement. To this end, we compared the performances of multi-trait genomic and polygenic scoring methods. In a representative UK sample of 7,026 children at age 12 and 16, we show that we can now predict up to 11 percent of the variance in intelligence and 16 percent in educational achievement. We also show that predictive power increases from age 12 to age 16 and that genomic predictions do not differ for girls and boys. Multivariate genomic methods were effective in boosting predictive power and, even though prediction accuracy varied across polygenic scores approaches, results were similar using different multivariate and polygenic score methods. Polygenic scores for educational attainment and intelligence are the most powerful predictors in the behavioural sciences and exceed predictions that can be made from parental phenotypes such as educational attainment and occupational status.

  154. ⁠, Sophie von Stumm, Emily Smith-Woolley, Ziada Ayorech, Andrew McMillan, Kaili Rimfeld, Philip S. Dale, Robert Plomin (2019-02-04):

    The two best predictors of children’s educational achievement available from birth are parents’ socioeconomic status (SES) and, recently, children’s inherited DNA differences that can be aggregated in genome-wide polygenic scores (GPS). Here we chart for the first time the developmental interplay between these two predictors of educational achievement at ages 7, 11, 14 and 16 in a sample of almost 5,000 UK school children. We show that the prediction of educational achievement from both GPS and SES increases steadily throughout the school years. Using latent growth curve models, we find that GPS and SES not only predict educational achievement in the first grade but they also account for systematic changes in achievement across the school years. At the end of compulsory education at age 16, GPS and SES respectively predict 14% and 23% of the variance of educational achievement; controlling for genetic influence on SES reduces its predictive power to 16%. Analyses of the extremes of GPS and SES highlight their influence and interplay: In children who have high GPS and come from high SES families, 77% go to university, whereas 21% of children with low GPS and from low SES backgrounds attend university. We find that the effects of GPS and SES are primarily additive, suggesting that their joint impact is particularly dramatic for children at the extreme ends of the distribution.

  155. ⁠, Javier de la Fuente, Gail Davies, Andrew D. Grotzinger, Elliot M. Tucker-Drob, Ian J. Deary (2019-09-12):

    It has been known for 115 years that, in humans, diverse cognitive traits are positively intercorrelated; this forms the basis for the general factor of intelligence (g). We directly test for a genetic basis for g using data from seven different cognitive tests (n = 11,263 to n = 331,679) and genome-wide autosomal single nucleotide polymorphisms. A genetic g factor accounts for 58.4% (SE = 4.8%) of the genetic variance in the cognitive traits, with trait-specific genetic factors accounting for the remaining 41.6%. We distill genetic loci broadly relevant for many cognitive traits (g) from loci associated with only individual cognitive traits. These results elucidate the etiological basis for a long-known yet poorly-understood phenomenon, revealing a fundamental dimension of genetic sharing across diverse cognitive traits.

  156. https://www.biorxiv.org/content/biorxiv/early/2019/09/12/766600.1/DC1/embed/media-1.xlsx

  157. 2019-delafuente-s7-geneticgpgses.png

  158. 2018-rimfeld.pdf: “Genetic influence on social outcomes during and after the Soviet era in Estonia”⁠, Kaili Rimfeld, Eva Krapohl, Maciej Trzaskowski, Jonathan R. I. Coleman, Saskia Selzam, Philip S. Dale, Tonu Esko, Andres Metspalu, Robert Plomin

  159. ⁠, Péter P. Ujma, Nóra Eszlári, András Millinghoffer, Bence Bruncsics, Péter Petschner, Péter Antal, Bill Deakin, György Bagdy, Gabriella Juhász (2020-01-14):

    Educational attainment is a substantially heritable trait, and it has recently been linked to specific genetic variants by genome-wide association studies (GWASs). However, some variants may index social stratification, and polygenic score (PGS) heritability may differ across cohorts reflecting the changing relative influence of genetic and environmental influences on educational attainment over time. We used a Hungarian (n = 829) sample of healthy volunteers to assess the validity of the most recent educational attainment polygenic score in a population culturally and genetically different from the one used in GWAS discovery, as well as changes in PGS heritability over time. We used an English (n = 976) sample with identical measurement protocols as comparison.

    We found that the PGS is valid in Hungary, accounting for 2–6.5% of the variance in educational attainment. We also replicated previous Estonian findings about generally increased PGS heritability in those attaining higher education after the fall of Communism, with PGS heritability up to 6.5% in the youngest cohort. In a comparable English sample the same PGS accounted for 9–11% of educational attainment variance. Our results provide evidence that polygenic scores for educational attainment are valid in diverse European populations. Our findings also provide further evidence that the fall of Communism, possibly along with other historical changes in education policy, was the source of a gene-environment interaction through which genetic factors became more important for higher educational attainment in those who graduated high school after this event.

  160. ⁠, Margherita Malanchini, Kaili Rimfeld, Agnieszka Gidziela, Rosa Cheesman, Andrea G. Allegrini, Nicholas Shakeshaft, Kerry Schofield, Amy Packer, Rachel Ogden, Andrew McMillan, Stuart J. Ritchie, Philip S. Dale, Thalia C. Eley, Sophie von Stumm, Robert Plomin (2021-02-10):

    Genome-wide association (GWA) studies have uncovered DNA variants associated with individual differences in general cognitive ability (g), but these are far from capturing heritability estimates obtained from twin studies. A major barrier is measurement heterogeneity. In a series of four studies, we created a 15-minute, online, gamified measure of g that is highly reliable, psychometrically valid and scalable. In a fifth study, we administered this measure to 4,751 young adults from the Twins Early Development Study. This novel g measure, which also yields verbal and nonverbal scores, showed substantial twin heritability (57%) and SNP heritability (37%). A polygenic score computed from GWA studies of five cognitive and educational traits accounted for 12% of the variation in g, the strongest DNA-based prediction of g to date. Widespread use of this engaging new measure will advance research not only in genomics but throughout the biological, medical and behavioural sciences.

  161. ⁠, 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 statistical power 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.

  162. ⁠, Doug Speed, David J. Balding (2018-03-19):

    LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide statistically-significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13× enriched, and found a further twelve categories with above 2× enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6× (SD 0.06) enriched, and that no category has enrichment above 1.7×. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

  163. ⁠, Andrew D. Grotzinger, Mijke Rhemtulla, Ronald de Vlaming, Stuart J. Ritchie, Travis T. Mallard, W. David Hill, Hill F. Ip, Andrew M. McIntosh, Ian J. Deary, Philipp D. Koellinger, K. Paige Harden, Michel G. Nivard, Elliot M. Tucker-Drob (2018-04-21):

    Methods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced “atlases” of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide statistically-significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes. We demonstrate several applications of Genomic ⁠, including a joint analysis of GWAS summary statistics from five genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits. Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.

  164. https://gigascience.biomedcentral.com/articles/10.1186/2047-217X-3-10

  165. https://gigascience.biomedcentral.com/articles/10.1186/s13742-015-0081-6

  166. https://www.nature.com/articles/s41467-019-08535-0

  167. ⁠, Qianqian Zhang, Florian Privé, Bjarni Vilhjálmsson, Doug Speed (2020-08-24):

    At present, most tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a sub-optimal model for how heritability is distributed across the genome. Here we construct prediction models for 14 phenotypes from the UK Biobank (200,000 individuals per phenotype) using four of the most popular prediction tools: ⁠, ridge regression, Bolt-LMM and BayesR. When we improve the assumed heritability model, prediction accuracy always improves (i.e., for all four tools and for all 14 phenotypes). When we construct prediction models using individual-level data, the best-performing tool is Bolt-LMM; if we replace its default heritability model with the most realistic model currently available, the average proportion of phenotypic variance explained increases by 19% (s.d. 2), equivalent to increasing the sample size by about a quarter. When we construct prediction models using summary statistics, the best tool depends on the phenotype. Therefore, we develop MegaPRS, a summary statistic prediction tool for constructing lasso, ⁠, Bolt-LMM and BayesR prediction models, that allows the user to specify the heritability model.

  168. ⁠, Guiyan Ni, Jian Zeng, Joana A. Revez, Ying Wang, Zhili Zheng, Tian Ge, Restuadi Restuadi, Jacqueline Kiewa, Dale R. Nyholt, Jonathan R. I Coleman, Jordan W. Smoller, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Jian Yang, Peter M. Visscher, Naomi R. Wray (2021-05-05):

    Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies (GWASs). PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors.

    Methods: The Psychiatric Genomics Consortium working groups for schizophrenia (SCZ) and major depressive disorder (MDD) bring together many independently collected case-control cohorts. We used these resources (31K SCZ cases, 41K controls; 248K MDD cases, 563K controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and nine methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) are compared.

    Results: Compared to PC+T, the other nine methods give higher prediction statistics, MegaPRS, LDPred2 and SBayesR significantly so, up to 9.2% variance in liability for SCZ across 30 target cohorts, an increase of 44%. For MDD across 26 target cohorts these statistics were 3.5% and 59%, respectively.

    Conclusion: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparison and are recommended in applications to psychiatric disorders.

  169. ⁠, Pinar Demetci, Wei Cheng, Gregory Darnell, Xiang Zhou, Sohini Ramachandran, Lorin Crawford (2021-05-06):

    In this article, we present Biologically Annotated Neural Networks (BANNs), a nonlinear probabilistic framework for association mapping in genome-wide association (GWA) studies. BANNs are feedforward models with partially connected architectures that are based on biological annotations. This setup yields a fully interpretable neural network where the input layer encodes SNP-level effects, and the hidden layer models the aggregated effects among SNP-sets. We treat the weights and connections of the network as random variables with prior distributions that reflect how genetic effects manifest at different genomic scales. The BANNs software uses variational inference to provide posterior summaries which allow researchers to simultaneously perform (i) mapping with SNPs and (ii) enrichment analyses with SNP-sets on complex traits. Through simulations, we show that our method improves upon state-of-the-art association mapping and enrichment approaches across a wide range of genetic architectures. We then further illustrate the benefits of BANNs by analyzing real GWA data assayed in approximately 2,000 heterogenous stock of mice from the Wellcome Trust Centre for Human Genetics and approximately 7,000 individuals from the Framingham Heart Study. Lastly, using a random subset of individuals of European ancestry from the UK Biobank, we show that BANNs is able to replicate known associations in high and low-density lipoprotein cholesterol content.

    Author Summary

    A common goal in genome-wide association (GWA) studies is to characterize the relationship between genotypic and phenotypic variation. Linear models are widely used tools in GWA analyses, in part, because they provide significance measures which detail how individual single nucleotide polymorphisms (SNPs) are statistically associated with a trait or disease of interest. However, traditional linear regression largely ignores non-additive genetic variation, and the univariate SNP-level mapping approach has been shown to be underpowered and challenging to interpret for certain trait architectures. While nonlinear methods such as neural networks are well known to account for complex data structures, these same algorithms have also been criticized as “black box” since they do not naturally carry out statistical hypothesis testing like classic linear models. This limitation has prevented nonlinear regression approaches from being used for association mapping tasks in GWA applications. Here, we present Biologically Annotated Neural Networks (BANNs): a flexible class of feedforward models with partially connected architectures that are based on biological annotations. The BANN framework uses approximate Bayesian inference to provide interpretable probabilistic summaries which can be used for simultaneous (i) mapping with SNPs and (ii) enrichment analyses with SNP-sets (e.g., genes or signaling pathways). We illustrate the benefits of our method over state-of-the-art approaches using extensive simulations. We also demonstrate the ability of BANNs to recover novel and previously discovered genomic associations using quantitative traits from the Wellcome Trust Centre for Human Genetics, the Framingham Heart Study, and the UK Biobank.

  170. ⁠, Florian Privé, Bjarni J. Vilhjálmsson, Hugues Aschard, Michael G. B. Blum (2019-05-30):

    Polygenic prediction has the potential to contribute to precision medicine. Clumping and Thresholding (C+T) is a widely used method to derive polygenic scores. When using C+T, people usually test several p-value thresholds to maximize predictive ability of derived polygenic scores. Along with this p-value threshold, we propose to tune 3 other hyper-parameters for C+T. We implement an efficient way to derive C+T scores corresponding to many different sets of hyper-parameters. For example, you can now derive thousands of different C+T scores for 300K individuals and 1M variants in less than one day. We show that tuning 4 hyper-parameters of C+T consistently improves its predictive performance in both simulations and real data applications as compared to tuning only the p-value threshold.

    Using this grid of computed C+T scores, we further extend C+T with stacking. More precisely, instead of choosing one set of hyper-parameters that maximizes prediction in some training set, we propose to learn an optimal linear combination of all these C+T scores using an efficient penalized regression. We call this method Stacked Clumping and Thresholding (SCT) and show that this makes C+T more flexible. When the training set is large enough, SCT can provide much larger predictive performance as compared to any of the C+T scores individually.

  171. http://emilkirkegaard.dk/en/?p=5813

  172. https://www.nature.com/articles/s41467-018-05510-z

  173. ⁠, Chloe Fawns-Ritchie, Ian J. Deary (2019-07-15):

    UK Biobank is a health resource with data from over 500,000 adults. The participants have been assessed on cognitive function since baseline. The cognitive tests in UK Biobank are brief and bespoke, and are administered without supervision on a touchscreen computer. Psychometric information on the tests is limited. The present study examined their concurrent validity and short-term test-retest reliability. A sample of 160 participants (mean age = 62.59, SD = 10.24) completed the UK Biobank cognitive assessment and a range of well-validated cognitive tests (‘reference tests’). Fifty-two participants returned 4 weeks later to repeat the UK Biobank tests. Correlations were calculated between UK Biobank tests and the reference tests. Four-week test-retest correlations were calculated for UK Biobank tests. UK Biobank cognitive tests showed a range of correlations with their respective reference tests, i.e. those tests that are thought to assess the same underlying cognitive ability (mean Pearson r = 0.53, range = 0.22 to 0.83, p≤.005). Four-week test-retest reliability of the UK Biobank tests were moderate-to-high (mean Pearson r = 0.55, range = 0.40 to 0.89, p≤.003). Despite the brief, non-standard nature of the UK Biobank cognitive tests, some showed substantial concurrent validity and test-retest reliability. These psychometric results provide currently-lacking information on the validity of the UK Biobank cognitive tests.

  174. Research-criticism

  175. ⁠, 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.

  176. ⁠, James J. Lee, Carson C. Chow (2017-12-30):

    In order to infer that a single-nucleotide polymorphism (SNP) either affects a phenotype or is linkage disequilibrium with a causal site, we must have some assurance that any SNP-phenotype correlation is not the result of confounding with some environmental variable that also affects the trait. Here we provide a mathematical analysis of LD Score regression, a recently developed method for using summary statistics from genome-wide association studies (GWAS) to ensure that confounding does not inflate the number of false positives. We do not treat the effects of genetic variation as a random variable and thus are able to obtain results about the unbiasedness of this method. We demonstrate that LD Score regression can produce estimates of confounding at null SNPs that are nearly unbiased or overly conservative under fairly general conditions. This robustness can hold even in cases now thought to be unfavorable, such as a correlation over SNPs between LD Scores and the degree of confounding. LD Score regression is thus an even stronger technique for causal inference than foreseen by its developers. Additionally, we demonstrate that LD Score regression can produce unbiased estimates of the genetic correlation, even when its estimates of the genetic covariance and the two univariate heritabilities are substantially biased.

  177. 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]

  178. {#linkBibliography-palmer-pe’er-2017 .docMetadata doi=“10.1371/​​journal.pgen.1006916”}, Cameron Palmer, Itsik Pe’er (2017-07-10):

    Genome-wide association studies (GWAS) have identified hundreds of SNPs responsible for variation in human quantitative traits. However, genome-wide-significant associations often fail to replicate across independent cohorts, in apparent inconsistency with their apparent strong effects in discovery cohorts. This limited success of replication raises pervasive questions about the utility of the GWAS field.

    We identify all 332 studies of quantitative traits from the NHGRI-EBI GWAS Database with attempted replication. We find that the majority of studies provide insufficient data to evaluate replication rates. The remaining papers replicate statistically-significantly worse than expected (p < 10−14), even when adjusting for regression-to-the-mean of effect size between discovery-cohort and replication-cohorts termed the Winner’s Curse (p < 10−16). We show this is due in part to misreporting replication cohort-size as a maximum number, rather than per-locus one. In 39 studies accurately reporting per-locus cohort-size for attempted replication of 707 loci in samples with similar ancestry, replication rate matched expectation (predicted 458, observed 457, p = 0.94). In contrast, ancestry differences between replication and discovery (13 studies, 385 loci) cause the most highly-powered decile of loci to replicate worse than expected, due to difference in linkage disequilibrium.

    Author summary:

    The majority of associations between common genetic variation and human traits come from genome-wide association studies, which have analyzed millions of single-nucleotide polymorphisms in millions of samples. These kinds of studies pose serious statistical challenges to discovering new associations. Finite resources restrict the number of candidate associations that can brought forward into validation samples, introducing the need for a statistical-significance threshold. This threshold creates a phenomenon called the Winner’s Curse, in which candidate associations close to the discovery threshold are more likely to have biased overestimates of the variant’s true association in the sampled population.

    We survey all human quantitative trait association studies that validated at least one signal. We find the majority of these studies do not publish sufficient information to actually support their claims of replication. For studies that did, we computationally correct the Winner’s Curse and evaluate replication performance. While all variants combined replicate statistically-significantly less than expected, we find that the subset of studies that (1) perform both discovery and replication in samples of the same ancestry; and (2) report accurate per-variant sample sizes, replicate as expected.

    This study provides strong, rigorous evidence for the broad reliability of genome-wide association studies. We furthermore provide a model for more efficient selection of variants as candidates for replication, as selecting variants using cursed discovery data enriches for variants with little real evidence for trait association.

  179. https://academic.oup.com/ije/article/39/2/329/683156

  180. https://www.pnas.org/content/111/38/13790.long

  181. ⁠, 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.

  182. 2019-willoughby.pdf: “The role of parental genotype in predicting offspring years of education: evidence for genetic nurture”⁠, Emily A. Willoughby, Matt McGue, William G. Iacono, Aldo Rustichini, James J. Lee

  183. ⁠, Daniel W. Belsky, Benjamin W. Domingue, Robbee Wedow, Louise Arseneault, Jason D. Boardman, Avshalom Caspi, Dalton Conley, Jason M. Fletcher, Jeremy Freese, Pamela Herd, Terrie E. Moffitt, Richie Poulton, Kamil Sicinski, Jasmin Wertz, Kathleen Mullan Harris (2018-07-31):

    Genome-wide association study (GWAS) discoveries about educational attainment have raised questions about the meaning of the genetics of success. These discoveries could offer clues about biological mechanisms or, because children inherit genetics and social class from parents, education-linked genetics could be spurious correlates of socially transmitted advantages. To distinguish between these hypotheses, we studied social mobility in five cohorts from three countries. We found that people with more education-linked genetics were more successful compared with parents and siblings. We also found mothers’ education-linked genetics predicted their children’s attainment over and above the children’s own genetics, indicating an environmentally mediated genetic effect. Findings reject pure social-transmission explanations of education GWAS discoveries. Instead, genetics influences attainment directly through social mobility and indirectly through family environments.

    A summary genetic measure, called a “polygenic score”, derived from a genome-wide association study (GWAS) of education can modestly predict a person’s educational and economic success. This prediction could signal a biological mechanism: Education-linked genetics could encode characteristics that help people get ahead in life. Alternatively, prediction could reflect social history: People from well-off families might stay well-off for social reasons, and these families might also look alike genetically. A key test to distinguish biological mechanism from social history is if people with higher education polygenic scores tend to climb the social ladder beyond their parents’ position. Upward mobility would indicate education-linked genetics encodes characteristics that foster success. We tested if education-linked polygenic scores predicted social mobility in >20,000 individuals in five longitudinal studies in the United States, Britain, and New Zealand. Participants with higher polygenic scores achieved more education and career success and accumulated more wealth. However, they also tended to come from better-off families. In the key test, participants with higher polygenic scores tended to be upwardly mobile compared with their parents. Moreover, in sibling-difference analysis, the sibling with the higher polygenic score was more upwardly mobile. Thus, education GWAS discoveries are not mere correlates of privilege; they influence social mobility within a life. Additional analyses revealed that a mother’s polygenic score predicted her child’s attainment over and above the child’s own polygenic score, suggesting parents’ genetics can also affect their children’s attainment through environmental pathways. Education GWAS discoveries affect socioeconomic attainment through influence on individuals’ family-of-origin environments and their social mobility.

    [Keywords: genetics, social class, social mobility, sociogenomics, polygenic score]

  184. ⁠, Saskia Selzam, Stuart J. Ritchie, Jean-Baptiste Pingault, Chandra A. Reynolds, Paul F. O’Reilly, Robert Plomin (2019-04-10):

    Polygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative mating and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction.

    Here, we compared within-family to between-family GPS predictions of eight life outcomes (anthropometric, cognitive, personality and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modelling, simultaneously estimating within-family and between-family effects for target-trait and cross-trait GPS prediction of the outcomes.

    There were three main findings: (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) this within-family and between-family difference for cognitive traits disappeared after controlling for family socio-economic status (SES), suggesting that SES is a source of between-family prediction through rGE mechanisms.

    These results provide novel insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and mating.

  185. 2020-mcgue.pdf: ⁠, Matt McGue, Emily A. Willoughby, Aldo Rustichini, Wendy Johnson, William G. Iacono, James J. Lee (2020-06-30; iq):

    We investigated intergenerational educational and occupational mobility in a sample of 2,594 adult offspring and 2,530 of their parents. Participants completed assessments of general cognitive ability and five noncognitive factors related to social achievement; 88% were also genotyped, allowing computation of educational-attainment polygenic scores. Most offspring were socially mobile. Offspring who scored at least 1 standard deviation higher than their parents on both cognitive and noncognitive measures rarely moved down and frequently moved up. Polygenic scores were also associated with social mobility. Inheritance of a favorable subset of parent alleles was associated with moving up, and inheritance of an unfavorable subset was associated with moving down. Parents’ education did not moderate the association of offspring’s skill with mobility, suggesting that low-skilled offspring from advantaged homes were not protected from downward mobility. These data suggest that cognitive and noncognitive skills as well as genetic factors contribute to the reordering of social standing that takes place across generations.

  186. ⁠, Hyeokmoon Kweon, Casper A. P. Burik, Richard Karlsson Linnér, Ronald De Vlaming, Aysu Okbay, Daphne Martschenko, K. Paige Harden, Thomas A. Diprete, Philipp D. Koellinger (2020-09-01):

    We study the effects of genetic endowments on inequalities in education, income, and health. Specifically, we conduct the first genome-wide association study (GWAS) of individual income, using data from individuals of European ancestries.

    We find that ≈10% of the variance in occupational wages can be attributed to genetic similarities between individuals who are only very distantly related to each other. Our GWAS (N = 282,963) identifies 45 approximately independent genetic loci for occupational wages, each with a tiny effect size ( R2 < 0.04%). An aggregated genetic score constructed from these GWAS results accounts for ≈1% of the variance in self-reported income in two independent samples (N = 29,440) and improves upon the variance captured by a genetic score obtained from previous GWAS results for educational attainment. A one-standard-deviation increase in our genetic score for occupational wages is associated with a 6–8% increase in self-reported hourly wages.

    We exploit random genetic differences between ~35,000 biological siblings to show that:

    1. roughly half of the covariance between our genetic score and socioeconomic outcomes is causal
    2. genetic luck for higher income is linked with better health outcomes in late adulthood, and
    3. having a college degree partly mediates this relationship.

    We also demonstrate that the returns to schooling remain substantial even after controlling for genetic confounds, with an average of 8–11% higher hourly wages for each additional year of education obtained in a US sample.

    Thus, the implications of genetic endowments are malleable, for example, via policies targeting education.

    [Keywords: Income, education, health, inequality, heritability, genetics, polygenic score]

  187. ⁠, Jonas B. Nielsen, Rosa B. Thorolfsdottir, Lars G. Fritsche, Wei Zhou, Morten W. Skov, Sarah E. Graham, Todd J. Herron, Shane McCarthy, Ellen M. Schmidt, Gardar Sveinbjornsson, Ida Surakka, Michael R. Mathis, Masatoshi Yamazaki, Ryan D. Crawford, Maiken E. Gabrielsen, Anne Heidi Skogholt, Oddgeir L. Holmen, Maoxuan Lin, Brooke N. Wolford, Rounak Dey, Håvard Dalen, Patrick Sulem, Jonathan H. Chung, Joshua D. Backman, David O. Arnar, Unnur Thorsteinsdottir, Aris Baras, Colm O’Dushlaine, Anders G. Holst, Xiaoquan Wen, Whitney Hornsby, Frederick E. Dewey, Michael Boehnke, Sachin Kheterpal, Seunggeun Lee, Hyun M. Kang, Hilma Holm, Jacob Kitzman, Jordan A. Shavit, José Jalife, Chad M. Brummett, Tanya M. Teslovich, David J. Carey, Daniel F. Gudbjartsson, Kari Stefansson, Goncalo R. Abecasis, Kristian Hveem, Cristen J. Willer (2018-01-04):

    To understand the genetic variation underlying atrial fibrillation (AF), the most common cardiac arrhythmia, we performed a genome-wide association study (GWAS) of > 1 million people, including 60,620 AF cases and 970,216 controls. We identified 163 independent risk variants at 111 loci and prioritized 165 candidate genes likely to be involved in AF. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans or mice (MYH6, NKX2-5, PITX2, TBC1D32, TBX5),1,2 or near genes important for striated muscle function and integrity (e.g. MYH7, PKP2, SSPN, SGCA). Experiments in rabbits with heart failure and left atrial dilation identified a heterogeneous distributed molecular switch from MYH6 to MYH7 in the left atrium, which resulted in contractile and functional heterogeneity and may predispose to initiation and maintenance of atrial arrhythmia.

  188. https://www.sciencedirect.com/science/article/pii/S2211124717316480

  189. ⁠, Visscher, Peter M. Brown, Matthew A. McCarthy, Mark I. Yang, Jian (2012):

    The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section.

  190. 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.

  191. https://www.wsj.com/articles/the-fertility-clinic-that-cut-ivf-prices-in-half-1536589667

  192. http://koreajoongangdaily.joins.com/news/article/Article.aspx?aid=2953870

  193. http://www.koreaherald.com/view.php?ud=20160825000693

  194. http://www.china-embassy.org/eng/visas/fees/t1236036.htm

  195. https://www.lonelyplanet.com/china/money-costs

  196. https://www.npr.org/2011/07/01/137530286/in-china-an-ivf-clinic-grapples-with-huge-demand

  197. http://www.scientificamerican.com/article/china-s-bold-push-into-genetically-customized-animals/

  198. https://web.archive.org/web/20160224094858/https://www.bloomberg.com/news/articles/2016-02-23/china-bets-big-on-gene-editing-technology-in-race-against-u-s

  199. http://host.cals.wisc.edu/horticulturedepartment/wp-content/uploads/sites/96/2013/09/Wang-et-al.-2014.pdf

  200. https://www.nature.com/news/monkeys-genetically-modified-to-show-autism-symptoms-1.19228

  201. https://www.nature.com/articles/523013a

  202. http://www.genome.gov/sequencingcosts/

  203. http://www.genome.gov/pages/der/seqcost2015_4.xlsx

  204. https://www.dantelabs.com/collections/our-tests/products/whole-genome-sequencing

  205. https://caglab.org/index.php/for-researchers/pricing-and-services.html

  206. http://www.affymetrix.com/catalog/131533/AFFY/Genome-Wide+Human+SNP+Array+6.0

  207. http://www.illumina.com/content/dam/illumina-marketing/documents/products/brochures/datasheet_omni_whole-genome_arrays.pdf

  208. http://www.cidr.jhmi.edu/services/pricing.pdf

  209. http://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/UK-Biobank-Axiom-Array-Content-Summary-2014.pdf

  210. http://www.rockefeller.edu/genomics/pricing

  211. https://www.youtube.com/watch?v=8R_DICKkMfo

  212. http://www.ukbiobank.ac.uk/frontiers-meeting-london-2014/

  213. http://www.ukbiobank.ac.uk/wp-content/uploads/2011/03/2014-UK-Biobank-Limited-Signed-2014-Report-and-Financial-Statements.pdf

  214. https://fortune.com/2017/09/12/23andme-250-million-sequoia-drugs/

  215. https://www.nhsa.org/pr-update/nhsa-statement-fy-2018-president%E2%80%99s-budget

  216. https://www.cell.com/ajhg/fulltext/S0002-9297(19)30192-2

  217. https://www.gnxp.com/WordPress/2017/05/15/the-end-of-insurance-some-if-it/

  218. 2020-08-nhgri-sequencingcostpergenome.jpg

  219. http://www.fertility-docs.com/programs-and-services/pgd-screening/genetic-diagnosis-of-embryos-for-diseases-using-pgd.php

  220. 2010-turkaspa.pdf

  221. https://www.geneticalliance.org.uk/information/services-and-testing/how-can-i-access-preimplantation-genetic-diagnosis/#Question6

  222. https://web.archive.org/web/20160311150735/http://www.rhtp.org/fertility/pgd/

  223. http://www.alzforum.org/news/research-news/preimplantation-genetic-diagnosis-its-no-walk-park

  224. https://www.sdfertility.com/fertility-treatments/genetic-testing/pgd

  225. http://www.advancedfertility.com/pgd-costs.htm

  226. https://genomicprediction.com/epgt/

  227. Pipeline

  228. ⁠, Yueqiu Tan, Xuyang Yin, Shuoping Zhang, Hui Jiang, Ke Tan, Jian Li, Bo Xiong, Fei Gong, Chunlei Zhang, Xiaoyu Pan, Fang Chen, Shengpei Chen, Chun Gong, Changfu Lu, Keli Luo, Yifan Gu, Xiuqing Zhang, Wei Wang, Xun Xu, Gábor Vajta, Lars Bolund, Huanming Yang, Guangxiu Lu, Yutao Du, Ge Lin (2014-12-04):

    Background: Next generation sequencing (NGS) is now being used for detecting chromosomal abnormalities in blastocyst trophectoderm (TE) cells from in vitro fertilized embryos. However, few data are available regarding the clinical outcome, which provides vital reference for further application of the methodology. Here, we present a clinical evaluation of NGS-based preimplantation genetic diagnosis/​​​​screening (PGD/​​​​PGS) compared with single nucleotide polymorphism (SNP) array-based PGD/​​​​PGS as a control.

    Results: A total of 395 couples participated. They were carriers of either translocation or inversion mutations, or were patients with recurrent miscarriage and/​​​​or advanced maternal age. A total of 1,512 blastocysts were biopsied on D5 after fertilization, with 1,058 blastocysts set aside for SNP array testing and 454 blastocysts for NGS testing. In the NGS cycles group, the implantation, clinical pregnancy and miscarriage rates were 52.6% (60⁄114), 61.3% (49⁄80) and 14.3% (7⁄49), respectively. In the SNP array cycles group, the implantation, clinical pregnancy and miscarriage rates were 47.6% (139⁄292), 56.7% (115⁄203) and 14.8% (17⁄115), respectively. The outcome measures of both the NGS and SNP array cycles were the same with insignificant differences. There were 150 blastocysts that underwent both NGS and SNP array analysis, of which seven blastocysts were found with inconsistent signals. All other signals obtained from NGS analysis were confirmed to be accurate by validation with qPCR. The relative copy number of mitochondrial DNA (mtDNA) for each blastocyst that underwent NGS testing was evaluated, and a statistically-significant difference was found between the copy number of mtDNA for the euploid and the chromosomally abnormal blastocysts. So far, out of 42 ongoing pregnancies, 24 babies were born in NGS cycles; all of these babies are healthy and free of any developmental problems.

    Conclusions: This study provides the first evaluation of the clinical outcomes of NGS-based pre-implantation genetic diagnosis/​​​​screening, and shows the reliability of this method in a clinical and array-based laboratory setting. NGS provides an accurate approach to detect embryonic imbalanced segmental rearrangements, to avoid the potential risks of false signals from SNP array in this study.

    [Keywords: preimplantation genetic diagnosis/​​​​screening, next generation sequencing, blastocyst, embryo transfer, clinical outcome]

  229. 2015-murugappan.pdf: “Cost-effectiveness analysis of preimplantation genetic screening and in vitro fertilization versus expectant management in patients with unexplained recurrent pregnancy loss”⁠, Gayathree Murugappan, Mika S. Ohno, Ruth B. Lathi M. D.

  230. 2012-hodeswertz.pdf

  231. https://web.archive.org/web/20100706183112/http://www.resolve.org/family-building-options/insurance_coverage/the-costs-of-infertility-treatment.html

  232. 2015-dahdouh.pdf: ⁠, Elias M. Dahdouh, Jacques Balayla, François Audibert (2015-05; genetics  /​ ​​ ​selection):

    Objective: To update and review the techniques and indications of preimplantation genetic diagnosis (PGD) and preimplantation genetic screening (PGS).

    Options: Discussion about the genetic and technical aspects of preimplantation reproductive techniques, particularly those using new cytogenetic technologies and embryo-stage biopsy.

    Outcomes: Clinical outcomes of reproductive techniques following the use of PGD and PGS are included. This update does not discuss in detail the adverse outcomes that have been recorded in association with assisted reproductive technologies.

    Evidence: Published literature was retrieved through searches of The Cochrane Library and MEDLINE in April 2014 using appropriate controlled vocabulary (aneuploidy, blastocyst/​​​​physiology, genetic diseases, preimplantation diagnosis/​​​​methods, fertilization in vitro) and key words (eg., preimplantation genetic diagnosis, preimplantation genetic screening, comprehensive chromosome screening, aCGH, SNP microarray, qPCR, and embryo selection). Results were restricted to systematic reviews, ⁠/​​​​controlled clinical trials, and observational studies published from 1990 to April 2014. There were no language restrictions. Searches were updated on a regular basis and incorporated in the update to January 2015. Additional publications were identified from the bibliographies of retrieved articles. Grey (unpublished) literature was identified through searching the websites of health technology assessment and health technology-related agencies, clinical practice guideline collections, clinical trial registries, and national and international medical specialty societies.

    Values: The quality of evidence in this document was rated using the criteria described in the Report of the Canadian Task Force on Preventive Health Care. (Table 1)

    Benefits, harms, and costs: This update will educate readers about new preimplantation genetic concepts, directions, and technologies. The major harms and costs identified are those of assisted reproductive technologies.

    Summary: Preimplantation genetic diagnosis is an alternative to prenatal diagnosis for the detection of genetic disorders in couples at risk of transmitting a genetic condition to their offspring. Preimplantation genetic screening is being proposed to improve the effectiveness of in vitro fertilization by screening for embryonic aneuploidy. Though FISH-based PGS showed adverse effects on IVF success, emerging evidence from new studies using comprehensive chromosome screening technology appears promising.

    [Keywords: Preimplantation genetic diagnosis, preimplantation genetic screening, comprehensive chromosome screening, aCGH, SNP microarray, qPCR, embryo selection]

  233. http://humrep.oxfordjournals.org/content/26/7/1768.full

  234. 2011-sunkara-figure2-eggembryodistribution.jpg

  235. http://www.advancedfertility.com/bmi-ivf-eggs-research.htm

  236. http://www.cdc.gov/art/pdf/2013-report/art_2013_national_summary_report.pdf

  237. Tryon

  238. 1940-tryon-figure4-mazebrightdullrats-distributions.png

  239. 1940-tryon.pdf: “Genetic Differences in Maze-Learning Ability in Rats”⁠, Robert Choate Tryon

  240. 2019-coop-illinoislongtermselectionexperiment-responsetoselection.jpg

  241. 2019-coop-illinoislongtermselectionexperiment-responsetoselection-animation.mp4

  242. ⁠, Sergio Eliseo Palma-Vera, Henry Reyer, Martina Langhammer, Norbert Reinsch, Lorena Derezanin, Joerns Fickel, Saber Qanbari, Joachim Weitzel, Soeren Franzenburg, Georg Hemmrich-Stanisak, Jennifer Schoen (2021-05-29):

    A unique set of mouse outbred lines has been generated through selective breeding in the longest selection experiment ever conducted on mice. Over the course of >140 generations, selection on the control line has given rise to two extremely fertile lines (>20 pups per litter each), two giant growth lines (one lean, one obese) and one long-distance running line.

    Genomic analysis revealed line-specific patterns of genetic variation among lines and high levels of within lines as a result of long-term intensive selection, genetic drift and isolation. Detection of line-specific patterns of genetic differentiation and structural variation revealed multiple candidate genes behind the improvement of the selected traits.

    We conclude that the genomes of these lines are rich in beneficial alleles for the respective selected traits and represent an invaluable resource for unraveling the polygenic basis of fertility, obesity, muscle growth and endurance fitness.

    …The worldwide longest selection experiment on mice began in the early 1970’s at the former Forschungszentrum für Tierproduktion (FZT), nowadays called Leibniz Institute for Farm Animal Biology (FBN) located in Dummerstorf, Germany4,5. Starting from a single founder line, selection lines for different complex traits were bred with population sizes of 60–100 breeding pairs per line. An unselected control line from the same founder line was maintained over the entire selection period with a larger population size (125–200 breeding pairs)4,5. Over the course of >140 generations, selection has shaped the genomes of the Dummerstorf trait-selected mouse lines, leading to extreme phenotypes that include increased litter size (more than double the litter size of the unselected mouse line)6, body mass (approx. 90g body weight at 6 weeks of age)7 and endurance (up to 3× higher untrained running capacity)8

    Phenotypic impact of selection: Over the course of more than 140 generations, the selected traits have shown remarkable increments in each line (Figure 1). The span and number of generations makes the present study the longest selection experiment ever reported in mice. Relative to the unselected control line FZTDU (exposed to genetic drift only), reproductive performance has doubled in DUK and DUC (Figure 1A–B, F–G). Even though these 2 trait-selected lines have achieved comparable litter sizes at first delivery (>20 offspring)58, their reproductive lifespan differs, with 5.8 and 2.7 litters in average per lifetime for DUK and DUC, respectively58. A remarkable level of divergence has been achieved by the increased body size lines (Figure 1C–D). DU6 individuals have almost tripled their weight compared to FZTDU (Figure 1H), whereas mice of the protein line DU6P not only have become larger and heavier than FZTDU mice, but their level of muscularity is also considerably higher (Figure 1D, 1I). In terms of running distance capacity, DUhLB mice can on average cover distances 3 times as long as those covered by FZTDU (Figure 1J). With the exception of the obese line DU659, each one of the trait-selected mouse lines has developed an extreme phenotype without obvious detrimental effects on their general health, well-being and longevity.

    Figure 1: Phenotypic characteristics of the 5 trait-selected Dummerstorf mouse lines and the unselected control line FZTDU. Representative subjects showing the impressive litter size of DUK and DUC (A, B, F, G) and the considerable body size difference at 6 weeks of age between DU6 (C, H) or DU6P (D, H, I) relative to FZTDU. (E) Untrained mice undergoing a treadmill running endurance trial and the increased running performance of DUhLB due to selection (J). Stars signify differences (p < 0.05) after conducting a t-test between trait-selected lines and FZTDU. Sample sizes are indicated below tick labels (x-axis).
  243. 1926-cox-theearlymentaltraitsof300geniuses.pdf

  244. SMPY

  245. 2016-makel.pdf: ⁠, Matthew C. Makel, Harrison J. Kell, David Lubinski, Martha Putallaz, Camilla P. Benbow (2016-07-01; iq  /​ ​​ ​smpy):

    The educational, occupational, and creative accomplishments of the profoundly gifted participants (IQs ⩾ 160) in the Study of Mathematically Precocious Youth (SMPY) are astounding, but are they representative of equally able 12-year-olds? Duke University’s Talent Identification Program (TIP) identified 259 young adolescents who were equally gifted. By age 40, their life accomplishments also were extraordinary: Thirty-seven percent had earned doctorates, 7.5% had achieved academic tenure (4.3% at research-intensive universities), and 9% held patents; many were high-level leaders in major organizations. As was the case for the SMPY sample before them, differential ability strengths predicted their contrasting and eventual developmental trajectories—even though essentially all participants possessed both mathematical and verbal reasoning abilities far superior to those of typical Ph.D. recipients. Individuals, even profoundly gifted ones, primarily do what they are best at. Differences in ability patterns, like differences in interests, guide development along different paths, but ability level, coupled with commitment, determines whether and the extent to which noteworthy accomplishments are reached if opportunity presents itself.

    [Keywords: intelligence, creativity, giftedness, replication, blink comparator]

  246. Anne-Roe

  247. http://www.cdc.gov/nchs/births.htm

  248. https://academic.oup.com/hropen/article/2017/2/hox012/4096838

  249. 2017-calhazjorge.pdf

  250. https://fertilitetsselskab.dk/wp-content/uploads/2019/03/dfs2018-til-hjemmesiden.pdf

  251. https://npesu.unsw.edu.au/sites/default/files/npesu/surveillances/Assisted%20Reproductive%20Technology%20in%20Australia%20and%20New%20Zealand%202018.pdf#page=7

  252. https://blog.id.com.au/2020/population/demographic-trends/australias-birth-rate-lowest-on-record-but-more-babies-than-ever/

  253. https://www.stats.govt.nz/information-releases/births-and-deaths-year-ended-december-2018

  254. https://www.japantimes.co.jp/news/2017/09/12/national/social-issues/1-20-infants-born-vitro-fertilization-japan-survey/

  255. https://sundhedsdatastyrelsen.dk/da/tal-og-analyser/analyser-og-rapporter/andre-analyser-og-rapporter/assisteret-reproduktion

  256. https://humupd.oxfordjournals.org/content/19/3/244.full

  257. 2003-murray-humanaccomplishment.pdf: “Human Accomplishment”⁠, Charles Murray

  258. http://www.rationaloptimist.com/blog/gene-editing-and-eugenics/

  259. #the-genius-factory-plotz-2005

  260. https://www.bbc.com/future/story/20160518-the-moral-maze-of-using-a-dead-mans-sperm

  261. 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]

  262. ⁠, 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.

  263. 2012-choi.pdf

  264. 2000-kaback.pdf

  265. 2005-liao.pdf

  266. 2008-scotet.pdf

  267. 2015-ioannou.pdf

  268. 2006-sawyer.pdf

  269. https://www.nejm.org/doi/full/10.1056/NEJMc0707530

  270. ⁠, Cunningham, S. Marshall, T (1998):

    Background: Antenatal screening for cystic fibrosis has been endorsed by the US National Institutes of Health. Edinburgh is the only city in the UK with an established routine antenatal screening programme for cystic fibrosis.

    Aims: To report the change in numbers of infants diagnosed with cystic fibrosis born in Edinburgh after the introduction of antenatal screening for the disease.

    Population: Infants diagnosed as having cystic fibrosis (by sweat test or genotyping, or both) in the seven years before antenatal testing (1984-90) and the first five years of antenatal testing (1991-95). Children born in this region who had moved before diagnosis were identified from the UK cystic fibrosis survey database.

    Results: The incidence of cystic fibrosis decreased from an average of 4.6 to 1.6 children each year with antenatal screening. The reduction in the incidence (65%) was greater than that accounted for by prenatal diagnosis and termination (36%). Of the eight children born with cystic fibrosis during the period of antenatal screening, five had been subject to antenatal screening: three had only one mutation identified, one was missed due to a laboratory error, and one was identified as a one in four risk, but prenatal diagnosis was not performed.

    Conclusions: Antenatal testing for cystic fibrosis has successfully reduced the incidence of cystic fibrosis in this region. Although the numbers are small, it is possible that the reduction in numbers may have been greater than might be expected from antenatal screening alone.

  271. 2009-massie.pdf: “ajo_1045.fm”

  272. ⁠, Caroline Ghiossi, James D. Goldberg, Imran S. Haque, Gabriel A. Lazarin, Kenny K. Wong (2016-08-14):

    Purpose: Expanded carrier screening (ECS) analyzes dozens or hundreds of recessive genes for determining reproductive risk. Data on clinical utility of screening conditions beyond professional guidelines is scarce.

    Methods: Individuals underwent ECS for up to 110 genes. 537 at-risk couples (ARC), those in which both partners carry the same recessive disease, were invited to a retrospective IRB-approved survey of their reproductive decision making after receiving ECS results.

    Results: 64 eligible ARC completed the survey. Of 45 respondents screened preconceptionally, 62% (n = 28) planned IVF with PGD or prenatal diagnosis (PNDx) in future pregnancies. 29% (n = 13) were not planning to alter reproductive decisions. The remaining 9% (n = 4) of responses were unclear.

    Of 19 pregnant respondents, 42% (n = 8) elected PNDx, 11% (n = 2) planned amniocentesis but miscarried, and 47% (n = 9) considered the condition insufficiently severe to warrant invasive testing. Of the 8 pregnancies that underwent PNDx, 5 were unaffected and 3 were affected. 2 of 3 affected pregnancies were terminated.

    Disease severity was found to have statistically-significant association (p = 0.000145) with changes in decision making, whereas guideline status of diseases, controlled for severity, was not (p = 0.284).

    Conclusion: Most ARC altered reproductive planning, demonstrating the clinical utility of ECS. Severity of conditions factored into decision making.

  273. 2016-franasiak.pdf

  274. 2016-bouchghoul.pdf: ⁠, Hanane Bouchghoul, Stéphane-Françoise Clément, Danièle Vauthier, Cécile Cazeneuve, Sandrine Noel, Marc Dommergues, Delphine Héron, Jacky Nizard, Marcela Gargiulo, Alexandra Durr (2016-01-01; genetics  /​ ​​ ​selection):

    The objective of this study was (1) to determine the impact of prenatal diagnosis (PND) for (HD) on subsequent reproductive choices and family structure; and (2) to assess whether children born after PND were informed of their genetic status.

    Out of 354 presymptomatic carriers of HD gene mutation, aged 18–45 years, 61 couples requested 101 PNDs. 54 women, 29 female carriers and 25 spouses of male carriers, accepted to be interviewed (0.6–16.3 years after the last PND, median 6.5 years) on their obstetrical history and information given to children born after PND. Women were willing to undergo 2 or more PNDs with a final success rate of 75%.

    Reproductive decisions differed depending on the outcome of the first PND. If favourable, 62% couples decided against another pregnancy and 10% chose to have an untested child. If unfavourable, 83% decided for another pregnancy (p < 0.01), and the majority (87%) re-entered the PND procedure. In contrast, after a second PND, only 37% asked for a PND and 30% chose to have an untested child. 33% had both, tested and untested children. Among children born after PND, 10 years and older, 75% were informed of their genetic status.

    The decision to prevent transmission of the HD mutation is made anew with each pregnancy. Couples may need more psychological support after PND and pre-counselling sessions should take into account the effect of the outcome of a first PND on subsequent reproductive choices.

  275. ⁠, 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 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.

  276. ⁠, Jack W. Scannell, Jim Bosley (2016-02-10):

    A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a “reproducibility crisis”; inflation-adjusted industrial R&D costs per novel drug increased nearly 100× between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/​​​​or if many “gains” have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly.

    The main objectives of the primary research in this paper are: (a) to provide quantitatively and historically plausible explanations of the contrast; and (b) identify factors to which R&D efficiency is sensitive.

    We present a quantitative decision-theoretic model of the R&D process [a ‘leaky pipeline’⁠; cf the log-normal]. The model represents therapeutic candidates (eg., putative drug targets, molecules in a screening library, etc.) within a “measurement space”, with candidates’ positions determined by their performance on a variety of assays (eg., binding affinity, toxicity, in vivo efficacy, etc.) whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions.

    We find that when searching for rare positives (eg., candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/​​​​or unknowable (ie., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (eg., 10×, even 100×) changes in models’ brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated screening and disease models.

    We hypothesize that screening and disease models with high predictive validity are more likely to yield good answers and good treatments, so tend to render themselves and their diseases academically and commercially redundant. Perhaps there has also been too much enthusiasm for reductionist molecular models which have insufficient predictive validity. Thus we hypothesize that the average predictive validity of the stock of academically and industrially “interesting” screening and disease models has declined over time, with even small falls able to offset large gains in scientific knowledge and brute-force efficiency. The rate of creation of valid screening and disease models may be the major constraint on R&D productivity.

  277. https://www.wired.com/story/reboot-reproduction-modern-fertility

  278. https://www.technologyreview.com/s/535661/engineering-the-perfect-baby/

  279. http://www2.technologyreview.com/article/427672/egg-stem-cells/

  280. Tool-AI

  281. https://www.math.upenn.edu/~ted/210F10/References/Expectations.pdf

  282. https://archive.org/stream/introductiontoq00falc#page/312/mode/2up

  283. 1998-lynchwalsh-geneticsquantitativetraits-ch21-geneticcorrelations.pdf: “Chapter 21: Correlations Between Characters”⁠, Michael Lynch, Bruce Walsh

  284. https://nickbostrom.com/ethics/statusquo.pdf

  285. ⁠, Bruce Walsh, Michael Lynch (1997-08-04):

    While Chapters 28 and 29 present the basic theory for multivariate response, how, in practice, does one perform artificial selection on multiple traits? One of the commonest schemes is to construct some sort of index, wherein the investigator assigns (either explicitly or implicitly) a weighting scheme to each trait, creating a univariate character that becomes the target of selection. For example, if z is the vector of character values measured in an individual, the most common index is a linear combination Pbizi = bT z and most of our discussion focuses on such linear indices. We start with a general review of the theory of selection on a linear index and then cover in great detail the Smith-Hazel index (the index giving the largest expected response in a specified linear combination of characters) and its extensions. We also discuss a number of other indices for different purposes, such as restricted (constraining changes in specified traits) and desired-gains (specifying how the components, rather than the index, will evolve) indices. We conclude our discussion of index selection by considering how to best handle nonlinear indices. We finish the chapter by examining the other approach for selecting on multiple traits, namely choosing traits sequentially. Tandem selection, focusing on a single trait each generation (where the focal trait changes over generations) is one such approach, while the other is to select different traits at different times within the life span of single individuals (independent culling and multistage index selection).

  286. ⁠, Bruce Walsh, Michael Lynch (1997-08-04):

    The first topic, which consists of the bulk of this chapter, is using index selection to improve a single trait. One can have a number of measures of the same trait in either relatives of a focal individual or as multiple measures of the same trait in a single individual, or both. How does one best use this information? We start by developing the general theory for using an index to improve the response in a single trait (which follows as a simplification of the Smith-Hazel index). We then apply these results to several important cases—a general analysis when either phenotypic or genotypic correlations are zero, improving response using repeated measurements of a characters over time, and using information from relatives to improve response with a special focus on combined selection (the optimal weighting of individual and family information, proving many of the details first presented in Chapter 17). As we will see in Chapter 35, the mixed-model power of BLUP provides a better solution to many of these problems, but index selection is both historically important as well as providing clean analytic results. In contrast to the first topic, the final three are essentially independent of each other and we try to present them as such (so that the reader can simply turn to the section of interest without regard to previous material in this chapter). They include selection on a ratio, selection on sex-specific and sexually-dimorphic traits, and finally selection on the environmental variance σ2E when it shows heritable variation (expanding upon results from Chapter 13).

  287. http://www.nealelab.is/uk-biobank

  288. 1943-hazel.pdf

  289. https://archive.org/details/animalbreedingpl032391mbp/page/n167/mode/2up

  290. ⁠, 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.

  291. http://vizhub.healthdata.org/gbd-compare/

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

  293. ⁠, Locke, Adam E. Kahali, Bratati Berndt, Sonja I. Justice, Anne E. Pers, Tune H. Day, Felix R. Powell, Corey Vedantam, Sailaja Buchkovich, Martin L. Yang, Jian Croteau-Chonka, Damien C. Esko, Tonu Fall, Tove Ferreira, Teresa Gustafsson, Stefan Kutalik, Zoltán Luan, Jian'an Mägi, Reedik Randall, Joshua C. Winkler, Thomas W. Wood, Andrew R. Workalemahu, Tsegaselassie Faul, Jessica D. Smith, Jennifer A. Zhao, Jing Hua Zhao, Wei Chen, Jin Fehrmann, Rudolf Hedman, Åsa K. Karjalainen, Juha Schmidt, Ellen M. Absher, Devin Amin, Najaf Anderson, Denise Beekman, Marian Bolton, Jennifer L. Bragg-Gresham, Jennifer L. Buyske, Steven Demirkan, Ayse Deng, Guohong Ehret, Georg B. Feenstra, Bjarke Feitosa, Mary F. Fischer, Krista Goel, Anuj Gong, Jian Jackson, Anne U. Kanoni, Stavroula Kleber, Marcus E. Kristiansson, Kati Lim, Unhee Lotay, Vaneet Mangino, Massimo Leach, Irene Mateo Medina-Gomez, Carolina Medland, Sarah E. Nalls, Michael A. Palmer, Cameron D. Pasko, Dorota Pechlivanis, Sonali Peters, Marjolein J. Prokopenko, Inga Shungin, Dmitry Stančáková, Alena Strawbridge, Rona J. Sung, Yun Ju Tanaka, Toshiko Teumer, Alexander Trompet, Stella van der Laan, Sander W. van Setten, Jessica Van Vliet-Ostaptchouk, Jana V. Wang, Zhaoming Yengo, Loïc Zhang, Weihua Isaacs, Aaron Albrecht, Eva Ärnlöv, Johan Arscott, Gillian M. Attwood, Antony P. Bandinelli, Stefania Barrett, Amy Bas, Isabelita N. Bellis, Claire Bennett, Amanda J. Berne, Christian Blagieva, Roza Blüher, Matthias Böhringer, Stefan Bonnycastle, Lori L. Böttcher, Yvonne Boyd, Heather A. Bruinenberg, Marcel Caspersen, Ida H. Chen, Yii-Der Ida Clarke, Robert Daw, E. Warwick de Craen, Anton J. M Delgado, Graciela Dimitriou, Maria Doney, Alex S. F Eklund, Niina Estrada, Karol Eury, Elodie Folkersen, Lasse Fraser, Ross M. Garcia, Melissa E. Geller, Frank Giedraitis, Vilmantas Gigante, Bruna Go, Alan S. Golay, Alain Goodall, Alison H. Gordon, Scott D. Gorski, Mathias Grabe, Hans-Jörgen Grallert, Harald Grammer, Tanja B. Gräßler, Jürgen Grönberg, Henrik Groves, Christopher J. Gusto, Gaëlle Haessler, Jeffrey Hall, Per Haller, Toomas Hallmans, Goran Hartman, Catharina A. Hassinen, Maija Hayward, Caroline Heard-Costa, Nancy L. Helmer, Quinta Hengstenberg, Christian Holmen, Oddgeir Hottenga, Jouke-Jan James, Alan L. Jeff, Janina M. Johansson, Åsa Jolley, Jennifer Juliusdottir, Thorhildur Kinnunen, Leena Koenig, Wolfgang Koskenvuo, Markku Kratzer, Wolfgang Laitinen, Jaana Lamina, Claudia Leander, Karin Lee, Nanette R. Lichtner, Peter Lind, Lars Lindström, Jaana Lo, Ken Sin Lobbens, Stéphane Lorbeer, Roberto Lu, Yingchang Mach, François Magnusson, Patrik K. E Mahajan, Anubha McArdle, Wendy L. McLachlan, Stela Menni, Cristina Merger, Sigrun Mihailov, Evelin Milani, Lili Moayyeri, Alireza Monda, Keri L. Morken, Mario A. Mulas, Antonella Müller, Gabriele Müller-Nurasyid, Martina Musk, Arthur W. Nagaraja, Ramaiah Nöthen, Markus M. Nolte, Ilja M. Pilz, Stefan Rayner, Nigel W. Renstrom, Frida Rettig, Rainer Ried, Janina S. Ripke, Stephan Robertson, Neil R. Rose, Lynda M. Sanna, Serena Scharnagl, Hubert Scholtens, Salome Schumacher, Fredrick R. Scott, William R. Seufferlein, Thomas Shi, Jianxin Smith, Albert Vernon Smolonska, Joanna Stanton, Alice V. Steinthorsdottir, Valgerdur Stirrups, Kathleen Stringham, Heather M. Sundström, Johan Swertz, Morris A. Swift, Amy J. Syvänen, Ann-Christine Tan, Sian-Tsung Tayo, Bamidele O. Thorand, Barbara Thorleifsson, Gudmar Tyrer, Jonathan P. Uh, Hae-Won Vandenput, Liesbeth Verhulst, Frank C. Vermeulen, Sita H. Verweij, Niek Vonk, Judith M. Waite, Lindsay L. Warren, Helen R. Waterworth, Dawn Weedon, Michael N. Wilkens, Lynne R. Willenborg, Christina Wilsgaard, Tom Wojczynski, Mary K. Wong, Andrew Wright, Alan F. Zhang, Qunyuan Brennan, Eoin P. Choi, Murim Dastani, Zari Drong, Alexander W. Eriksson, Per Franco-Cereceda, Anders Gådin, Jesper R. Gharavi, Ali G. Goddard, Michael E. Handsaker, Robert E. Huang, Jinyan Karpe, Fredrik Kathiresan, Sekar Keildson, Sarah Kiryluk, Krzysztof Kubo, Michiaki Lee, Jong-Young Liang, Liming Lifton, Richard P. Ma, Baoshan McCarroll, Steven A. McKnight, Amy J. Min, Josine L. Moffatt, Miriam F. Montgomery, Grant W. Murabito, Joanne M. Nicholson, George Nyholt, Dale R. Okada, Yukinori Perry, John R. B Dorajoo, Rajkumar Reinmaa, Eva Salem, Rany M. Sandholm, Niina Scott, Robert A. Stolk, Lisette Takahashi, Atsushi Tanaka, Toshihiro van 't Hooft, Ferdin, M. Vinkhuyzen, Anna A. E Westra, Harm-Jan Zheng, Wei Zondervan, Krina T. Heath, Andrew C. Arveiler, Dominique Bakker, Stephan J. L Beilby, John Bergman, Richard N. Blangero, John Bovet, Pascal Campbell, Harry Caulfield, Mark J. Cesana, Giancarlo Chakravarti, Aravinda Chasman, Daniel I. Chines, Peter S. Collins, Francis S. Crawford, Dana C. Cupples, L. Adrienne Cusi, Daniele Danesh, John de Faire, Ulf den Ruijter, Hester M. Dominiczak, Anna F. Erbel, Raimund Erdmann, Jeanette Eriksson, Johan G. Farrall, Martin Felix, Stephan B. Ferrannini, Ele Ferrières, Jean Ford, Ian Forouhi, Nita G. Forrester, Terrence Franco, Oscar H. Gansevoort, Ron T. Gejman, Pablo V. Gieger, Christian Gottesman, Omri Gudnason, Vilmundur Gyllensten, Ulf Hall, Alistair S. Harris, Tamara B. Hattersley, Andrew T. Hicks, Andrew A. Hindorff, Lucia A. Hingorani, Aroon D. Hofman, Albert Homuth, Georg Hovingh, G. Kees Humphries, Steve E. Hunt, Steven C. Hyppönen, Elina Illig, Thomas Jacobs, Kevin B. Jarvelin, Marjo-Riitta Jöckel, Karl-Heinz Johansen, Berit Jousilahti, Pekka Jukema, J. Wouter Jula, Antti M. Kaprio, Jaakko Kastelein, John J. P Keinanen-Kiukaanniemi, Sirkka M. Kiemeney, Lambertus A. Knekt, Paul Kooner, Jaspal S. Kooperberg, Charles Kovacs, Peter Kraja, Aldi T. Kumari, M. Kuusisto, Johanna Lakka, Timo A. Langenberg, Claudia Marchand, Loic Le Lehtimäki, Terho Lyssenko, Valeriya Männistö, Satu Marette, André Matise, Tara C. McKenzie, Colin A. McKnight, Barbara Moll, Frans L. Morris, Andrew D. Morris, Andrew P. Murray, Jeffrey C. Nelis, Mari Ohlsson, Claes Oldehinkel, Albertine J. Ong, Ken K. Madden, Pamela A. F Pasterkamp, Gerard Peden, John F. Peters, Annette Postma, Dirkje S. Pramstaller, Peter P. Price, Jackie F. Qi, Lu Raitakari, Olli T. Rankinen, Tuomo Rao, D. C Rice, Treva K. Ridker, Paul M. Rioux, John D. Ritchie, Marylyn D. Rudan, Igor Salomaa, Veikko Samani, Nilesh J. Saramies, Jouko Sarzynski, Mark A. Schunkert, Heribert Schwarz, Peter E. H Sever, Peter Shuldiner, Alan R. Sinisalo, Juha Stolk, Ronald P. Strauch, Konstantin Tönjes, Anke Trégouët, David-Alexandre Tremblay, Angelo Tremoli, Elena Virtamo, Jarmo Vohl, Marie-Claude Völker, Uwe Waeber, Gérard Willemsen, Gonneke Witteman, Jacqueline C. Zillikens, M. Carola Adair, Linda S. Amouyel, Philippe Asselbergs, Folkert W. Assimes, Themistocles L. Bochud, Murielle Boehm, Bernhard O. Boerwinkle, Eric Bornstein, Stefan R. Bottinger, Erwin P. Bouchard, Claude Cauchi, Stéphane Chambers, John C. Chanock, Stephen J. Cooper, Richard S. de Bakker, Paul I. W Dedoussis, George Ferrucci, Luigi Franks, Paul W. Froguel, Philippe Groop, Leif C. Haiman, Christopher A. Hamsten, Anders Hui, Jennie Hunter, David J. Hveem, Kristian Kaplan, Robert C. Kivimaki, Mika Kuh, Diana Laakso, Markku Liu, Yongmei Martin, Nicholas G. März, Winfried Melbye, Mads Metspalu, Andres Moebus, Susanne Munroe, Patricia B. Njølstad, Inger Oostra, Ben A. Palmer, Colin N. A Pedersen, Nancy L. Perola, Markus Pérusse, Louis Peters, Ulrike Power, Chris Quertermous, Thomas Rauramaa, Rainer Rivadeneira, Fernando Saaristo, Timo E. Saleheen, Danish Sattar, Naveed Schadt, Eric E. Schlessinger, David Slagboom, P. Eline Snieder, Harold Spector, Tim D. Thorsteinsdottir, Unnur Stumvoll, Michael Tuomilehto, Jaakko Uitterlinden, André G. Uusitupa, Matti van der Harst, Pim Walker, Mark Wallaschofski, Henri Wareham, Nicholas J. Watkins, Hugh Weir, David R. Wichmann, H-Erich Wilson, James F. Zanen, Pieter Borecki, Ingrid B. Deloukas, Panos Fox, Caroline S. Heid, Iris M. O'Connell, Jeffrey R. Strachan, David P. Stefansson, Kari van Duijn, Cornelia M. Abecasis, Gonçalo R. Franke, Lude Frayling, Timothy M. McCarthy, Mark I. Visscher, Peter M. Scherag, André Willer, Cristen J. Boehnke, Michael Mohlke, Karen L. Lindgren, Cecilia M. Beckmann, Jacques S. Barroso, Inês North, Kari E. Ingelsson, Erik Hirschhorn, Joel N. Loos, Ruth J. F Speliotes, Elizabeth K (2015):

    Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10−8), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have statistically-significant effects on other metabolic phenotypes. The 97 loci account for ~2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/​​​​action, energy metabolism, lipid biology and adipogenesis.

  294. https://www.nature.com/mp/journal/vaop/ncurrent/full/mp2015205a.html

  295. ⁠, Berndt, Sonja I. Gustafsson, Stefan Mägi, Reedik Ganna, Andrea Wheeler, Eleanor Feitosa, Mary F. Justice, Anne E. Monda, Keri L. Croteau-Chonka, Damien C. Day, Felix R. Esko, Tõnu Fall, Tove Ferreira, Teresa Gentilini, Davide Jackson, Anne U. Luan, Jian'an Randall, Joshua C. Vedantam, Sailaja Willer, Cristen J. Winkler, Thomas W. Wood, Andrew R. Workalemahu, Tsegaselassie Hu, Yi-Juan Lee, Sang Hong Liang, Liming Lin, Dan-Yu Min, Josine L. Neale, Benjamin M. Thorleifsson, Gudmar Yang, Jian Albrecht, Eva Amin, Najaf Bragg-Gresham, Jennifer L. Cadby, Gemma den Heijer, Martin Eklund, Niina Fischer, Krista Goel, Anuj Hottenga, Jouke-Jan Huffman, Jennifer E. Jarick, Ivonne Johansson, Åsa Johnson, Toby Kanoni, Stavroula Kleber, Marcus E. König, Inke R. Kristiansson, Kati Kutalik, Zoltán Lamina, Claudia Lecoeur, Cecile Li, Guo Mangino, Massimo McArdle, Wendy L. Medina-Gomez, Carolina Müller-Nurasyid, Martina Ngwa, Julius S. Nolte, Ilja M. Paternoster, Lavinia Pechlivanis, Sonali Perola, Markus Peters, Marjolein J. Preuss, Michael Rose, Lynda M. Shi, Jianxin Shungin, Dmitry Smith, Albert Vernon Strawbridge, Rona J. Surakka, Ida Teumer, Alexander Trip, Mieke D. Tyrer, Jonathan Van Vliet-Ostaptchouk, Jana V. Vandenput, Liesbeth Waite, Lindsay L. Zhao, Jing Hua Absher, Devin Asselbergs, Folkert W. Atalay, Mustafa Attwood, Antony P. Balmforth, Anthony J. Basart, Hanneke Beilby, John Bonnycastle, Lori L. Brambilla, Paolo Bruinenberg, Marcel Campbell, Harry Chasman, Daniel I. Chines, Peter S. Collins, Francis S. Connell, John M. Cookson, William O. de Faire, Ulf de Vegt, Femmie Dei, Mariano Dimitriou, Maria Edkins, Sarah Estrada, Karol Evans, David M. Farrall, Martin Ferrario, Marco M. Ferrières, Jean Franke, Lude Frau, Francesca Gejman, Pablo V. Grallert, Harald Grönberg, Henrik Gudnason, Vilmundur Hall, Alistair S. Hall, Per Hartikainen, Anna-Liisa Hayward, Caroline Heard-Costa, Nancy L. Heath, Andrew C. Hebebrand, Johannes Homuth, Georg Hu, Frank B. Hunt, Sarah E. Hyppönen, Elina Iribarren, Carlos Jacobs, Kevin B. Jansson, John-Olov Jula, Antti Kähönen, Mika Kathiresan, Sekar Kee, Frank Khaw, Kay-Tee Kivimäki, Mika Koenig, Wolfgang Kraja, Aldi T. Kumari, M. Kuulasmaa, Kari Kuusisto, Johanna Laitinen, Jaana H. Lakka, Timo A. Langenberg, Claudia Launer, Lenore J. Lind, Lars Lindström, Jaana Liu, Jianjun Liuzzi, Antonio Lokki, Marja-Liisa Lorentzon, Mattias Madden, Pamela A. Magnusson, Patrik K. Manunta, Paolo Marek, Diana März, Winfried Mateo Leach, Irene McKnight, Barbara Medland, Sarah E. Mihailov, Evelin Milani, Lili Montgomery, Grant W. Mooser, Vincent Mühleisen, Thomas W. Munroe, Patricia B. Musk, Arthur W. Narisu, Narisu Navis, Gerjan Nicholson, George Nohr, Ellen A. Ong, Ken K. Oostra, Ben A. Palmer, Colin N. A Palotie, Aarno Peden, John F. Pedersen, Nancy Peters, Annette Polasek, Ozren Pouta, Anneli Pramstaller, Peter P. Prokopenko, Inga Pütter, Carolin Radhakrishnan, Aparna Raitakari, Olli Rendon, Augusto Rivadeneira, Fernando Rudan, Igor Saaristo, Timo E. Sambrook, Jennifer G. Sanders, Alan R. Sanna, Serena Saramies, Jouko Schipf, Sabine Schreiber, Stefan Schunkert, Heribert Shin, So-Youn Signorini, Stefano Sinisalo, Juha Skrobek, Boris Soranzo, Nicole Stančáková, Alena Stark, Klaus Stephens, Jonathan C. Stirrups, Kathleen Stolk, Ronald P. Stumvoll, Michael Swift, Amy J. Theodoraki, Eirini V. Thorand, Barbara Tregouet, David-Alexandre Tremoli, Elena Van der Klauw, Melanie M. van Meurs, Joyce B. J Vermeulen, Sita H. Viikari, Jorma Virtamo, Jarmo Vitart, Veronique Waeber, Gérard Wang, Zhaoming Widén, Elisabeth Wild, Sarah H. Willemsen, Gonneke Winkelmann, Bernhard R. Witteman, Jacqueline C. M Wolffenbuttel, Bruce H. R Wong, Andrew Wright, Alan F. Zillikens, M. Carola Amouyel, Philippe Boehm, Bernhard O. Boerwinkle, Eric Boomsma, Dorret I. Caulfield, Mark J. Chanock, Stephen J. Cupples, L. Adrienne Cusi, Daniele Dedoussis, George V. Erdmann, Jeanette Eriksson, Johan G. Franks, Paul W. Froguel, Philippe Gieger, Christian Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Hengstenberg, Christian Hicks, Andrew A. Hingorani, Aroon Hinney, Anke Hofman, Albert Hovingh, Kees G. Hveem, Kristian Illig, Thomas Jarvelin, Marjo-Riitta Jöckel, Karl-Heinz Keinanen-Kiukaanniemi, Sirkka M. Kiemeney, Lambertus A. Kuh, Diana Laakso, Markku Lehtimäki, Terho Levinson, Douglas F. Martin, Nicholas G. Metspalu, Andres Morris, Andrew D. Nieminen, Markku S. Njølstad, Inger Ohlsson, Claes Oldehinkel, Albertine J. Ouwehand, Willem H. Palmer, Lyle J. Penninx, Brenda Power, Chris Province, Michael A. Psaty, Bruce M. Qi, Lu Rauramaa, Rainer Ridker, Paul M. Ripatti, Samuli Salomaa, Veikko Samani, Nilesh J. Snieder, Harold Sørensen, Thorkild I. A Spector, Timothy D. Stefansson, Kari Tönjes, Anke Tuomilehto, Jaakko Uitterlinden, André G. Uusitupa, Matti van der Harst, Pim Vollenweider, Peter Wallaschofski, Henri Wareham, Nicholas J. Watkins, Hugh Wichmann, H-Erich Wilson, James F. Abecasis, Goncalo R. Assimes, Themistocles L. Barroso, Inês Boehnke, Michael Borecki, Ingrid B. Deloukas, Panos Fox, Caroline S. Frayling, Timothy Groop, Leif C. Haritunian, Talin Heid, Iris M. Hunter, David Kaplan, Robert C. Karpe, Fredrik Moffatt, Miriam F. Mohlke, Karen L. O'Connell, Jeffrey R. Pawitan, Yudi Schadt, Eric E. Schlessinger, David Steinthorsdottir, Valgerdur Strachan, David P. Thorsteinsdottir, Unnur van Duijn, Cornelia M. Visscher, Peter M. Di Blasio, Anna Maria Hirschhorn, Joel N. Lindgren, Cecilia M. Morris, Andrew P. Meyre, David Scherag, André McCarthy, Mark I. Speliotes, Elizabeth K. North, Kari E. Loos, Ruth J. F Ingelsson, Erik (2013):

    Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.

  296. ⁠, Block, Jason P. Subramanian, S. V Christakis, Nicholas A. O'Malley, A. James (2013):

    Objective: We examined body mass index (BMI) across place and time to determine the pattern of BMI mean and standard deviation trajectories.

    Methods: We included participants in the Framingham Heart Study (FHS) Offspring Cohort over eight waves of follow-up, from 1971 to 2008. After exclusions, the final sample size was 4569 subjects with 28,625 observations. We used multi-level models to examine population means and variation at the individual and neighborhood (census tracts) levels across time with measured BMI as the outcome, controlling for individual demographics and behaviors and neighborhood poverty. Because neighborhoods accounted for limited BMI variance, we removed this level as a source of variation in final models. We examined sex-stratified models with all subjects and models stratified by sex and baseline weight classification.

    Results: Mean BMI increased from 24.0 kg/​​​​m(2) at Wave 1 to 27.7 at Wave 8 for women and from 26.6 kg/​​​​m(2) to 29.0 for men. In final models, BMI variation also increased from Waves 1 to 8, with the standard deviation increasing from 4.18 kg/​​​​m(2) to 6.15 for women and 3.31 kg/​​​​m(2) to 4.73 for men. BMI means increased in parallel across most baseline BMI weight classifications, except for more rapid increases through middle-age for obese women followed by declines in the last wave. BMI standard deviations also increased in parallel across baseline BMI classifications for women, with greater divergence of BMI variance for obese men compared to other weight classifications.

    Conclusion: Over nearly 40 years, BMI mean and variation increased in parallel across most baseline weight classifications in our sample. Individual-level characteristics, especially baseline BMI, were the primary factors in rising BMI. These findings have important implications not only for understanding the sources of the obesity epidemic in the United States but also for the targeting of interventions to address the epidemic.

  297. http://vinecon.ucdavis.edu/publications/cwe1201.pdf

  298. 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.

  299. ⁠, Morris, Andrew P. Voight, Benjamin F. Teslovich, Tanya M. Ferreira, Teresa Segrè, Ayellet V. Steinthorsdottir, Valgerdur Strawbridge, Rona J. Khan, Hassan Grallert, Harald Mahajan, Anubha Prokopenko, Inga Kang, Hyun Min Dina, Christian Esko, Tonu Fraser, Ross M. Kanoni, Stavroula Kumar, Ashish Lagou, Vasiliki Langenberg, Claudia Luan, Jian'an Lindgren, Cecilia M. Müller-Nurasyid, Martina Pechlivanis, Sonali Rayner, N. William Scott, Laura J. Wiltshire, Steven Yengo, Loic Kinnunen, Leena Rossin, Elizabeth J. Raychaudhuri, Soumya Johnson, Andrew D. Dimas, Antigone S. Loos, Ruth J. F Vedantam, Sailaja Chen, Han Florez, Jose C. Fox, Caroline Liu, Ching-Ti Rybin, Denis Couper, David J. Kao, Wen Hong L. Li, Man Cornelis, Marilyn C. Kraft, Peter Sun, Qi van Dam, Rob M. Stringham, Heather M. Chines, Peter S. Fischer, Krista Fontanillas, Pierre Holmen, Oddgeir L. Hunt, Sarah E. Jackson, Anne U. Kong, Augustine Lawrence, Robert Meyer, Julia Perry, John R. B Platou, Carl G. P Potter, Simon Rehnberg, Emil Robertson, Neil Sivapalaratnam, Suthesh Stančáková, Alena Stirrups, Kathleen Thorleifsson, Gudmar Tikkanen, Emmi Wood, Andrew R. Almgren, Peter Atalay, Mustafa Benediktsson, Rafn Bonnycastle, Lori L. Burtt, Noël Carey, Jason Charpentier, Guillaume Crenshaw, Andrew T. Doney, Alex S. F Dorkhan, Mozhgan Edkins, Sarah Emilsson, Valur Eury, Elodie Forsen, Tom Gertow, Karl Gigante, Bruna Grant, George B. Groves, Christopher J. Guiducci, Candace Herder, Christian Hreidarsson, Astradur B. Hui, Jennie James, Alan Jonsson, Anna Rathmann, Wolfgang Klopp, Norman Kravic, Jasmina Krjutškov, Kaarel Langford, Cordelia Leander, Karin Lindholm, Eero Lobbens, Stéphane Männistö, Satu Mirza, Ghazala Mühleisen, Thomas W. Musk, Bill Parkin, Melissa Rallidis, Loukianos Saramies, Jouko Sennblad, Bengt Shah, Sonia Sigurðsson, Gunnar Silveira, Angela Steinbach, Gerald Thorand, Barbara Trakalo, Joseph Veglia, Fabrizio Wennauer, Roman Winckler, Wendy Zabaneh, Delilah Campbell, Harry van Duijn, Cornelia Uitterlinden, Andre G. Hofman, Albert Sijbrands, Eric Abecasis, Goncalo R. Owen, Katharine R. Zeggini, Eleftheria Trip, Mieke D. Forouhi, Nita G. Syvänen, Ann-Christine Eriksson, Johan G. Peltonen, Leena Nöthen, Markus M. Balkau, Beverley Palmer, Colin N. A Lyssenko, Valeriya Tuomi, Tiinamaija Isomaa, Bo Hunter, David J. Qi, Lu Shuldiner, Alan R. Roden, Michael Barroso, Ines Wilsgaard, Tom Beilby, John Hovingh, Kees Price, Jackie F. Wilson, James F. Rauramaa, Rainer Lakka, Timo A. Lind, Lars Dedoussis, George Njølstad, Inger Pedersen, Nancy L. Khaw, Kay-Tee Wareham, Nicholas J. Keinanen-Kiukaanniemi, Sirkka M. Saaristo, Timo E. Korpi-Hyövälti, Eeva Saltevo, Juha Laakso, Markku Kuusisto, Johanna Metspalu, Andres Collins, Francis S. Mohlke, Karen L. Bergman, Richard N. Tuomilehto, Jaakko Boehm, Bernhard O. Gieger, Christian Hveem, Kristian Cauchi, Stephane Froguel, Philippe Baldassarre, Damiano Tremoli, Elena Humphries, Steve E. Saleheen, Danish Danesh, John Ingelsson, Erik Ripatti, Samuli Salomaa, Veikko Erbel, Raimund Jöckel, Karl-Heinz Moebus, Susanne Peters, Annette Illig, Thomas de Faire, Ulf Hamsten, Anders Morris, Andrew D. Donnelly, Peter J. Frayling, Timothy M. Hattersley, Andrew T. Boerwinkle, Eric Melander, Olle Kathiresan, Sekar Nilsson, Peter M. Deloukas, Panos Thorsteinsdottir, Unnur Groop, Leif C. Stefansson, Kari Hu, Frank Pankow, James S. Dupuis, Josée Meigs, James B. Altshuler, David Boehnke, Michael McCarthy, Mark I (2012):

    To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of additional common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in diabetes pathogenesis.

  300. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3442244/bin/NIHMS393294-supplement-2.pdf

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  302. http://care.diabetesjournals.org/content/37/9/2557.full

  303. http://jama.jamanetwork.com/article.aspx?articleid=197439

  304. 2013-hamshere.pdf: ⁠, Marian L. Hamshere, Kate Langley, Joanna Martin, Sharifah Shameem Agha, Evangelia Stergiakouli, Richard J. L. Anney, Jan Buitelaar, Stephen V. Faraone, Klaus-Peter Lesch, Benjamin M. Neale, Barbara Franke, Edmund Sonuga-Barke, Philip Asherson, Andrew Merwood, Jonna Kuntsi, Sarah E. Medland, Stephan Ripke, Hans-Christoph Steinhausen, Christine Freitag, Andreas Reif, Tobias J. Renner, Marcel Romanos, Jasmin Romanos, Andreas Warnke, Jobst Meyer, Haukur Palmason, Alejandro Arias Vasquez, Nanda Lambregts-Rommelse, Herbert Roeyers, Joseph Biederman, Alysa E. Doyle, Hakon Hakonarson, Aribert Rothenberger, Tobias Banaschewski, Robert D. Oades, James J. McGough, Lindsey Kent, Nigel Williams, Michael J. Owen, Peter Holmans, Michael C. O’Donovan, Anita Thapar (2013-08-01; genetics  /​ ​​ ​correlation):

    Objective: Although attention deficit hyperactivity disorder (ADHD) is highly heritable, genome-wide association studies (GWAS) have not yet identified any common genetic variants that contribute to risk. There is evidence that aggression or conduct disorder in children with ADHD indexes higher genetic loading and clinical severity. The authors examine whether common genetic variants considered en masse as polygenic scores for ADHD are especially enriched in children with comorbid conduct disorder.

    Method: Polygenic scores derived from an ADHD GWAS meta-analysis were calculated in an independent ADHD sample (452 case subjects, 5,081 comparison subjects). Multivariate logistic regression analyses were employed to compare polygenic scores in the ADHD and comparison groups and test for higher scores in ADHD case subjects with comorbid conduct disorder relative to comparison subjects and relative to those without comorbid conduct disorder. Association with symptom scores was tested using linear regression.

    Results: Polygenic risk for ADHD, derived from the meta-analysis, was higher in the independent ADHD group than in the comparison group. Polygenic score was statistically-significantly higher in ADHD case subjects with conduct disorder relative to ADHD case subjects without conduct disorder. ADHD polygenic score showed statistically-significant association with comorbid conduct disorder symptoms. This relationship was explained by the aggression items.

    Conclusions: Common genetic variation is relevant to ADHD, especially in individuals with comorbid aggression. The findings suggest that the previously published ADHD GWAS meta-analysis contains weak but true associations with common variants, support for which falls below genome-wide statistical-significance levels. The findings also highlight the fact that aggression in ADHD indexes genetic as well as clinical severity.

  305. https://www.sciencedirect.com/science/article/pii/S0890856715000404

  306. 2014-groenblokhuis.pdf: ⁠, Maria M. Groen-Blokhuis, Christel M. Middeldorp, Kees-Jan Kan, Abdel Abdellaoui, Catharina E. M. van Beijsterveldt, Erik A. Ehli, Gareth E. Davies, Paul A. Scheet, Xiangjun Xiao, James J. Hudziak, Jouke-Jan Hottenga, Ben M. Neale, Dorret I. Boomsma, Psychiatric Genomics Consortium ADHD Working Group (2014-10-01; genetics  /​ ​​ ​heritable):

    Objective: Clinically, attention-deficit/​​​​hyperactivity disorder (ADHD) is characterized by hyperactivity, impulsivity, and inattention and is among the most common childhood disorders. These same traits that define ADHD are variable in the general population, and the clinical diagnosis may represent the extreme end of a continuous distribution of inattentive and hyperactive behaviors. This hypothesis can be tested by assessing the predictive value of polygenic risk scores derived from a discovery sample of ADHD patients in a target sample from the general population with continuous scores of inattention and hyperactivity. In addition, the genetic overlap between ADHD and continuous ADHD scores can be tested across rater and age.

    Method: The Psychiatric Genomics Consortium has performed the largest genome-wide analysis (GWA) study of ADHD so far, including 5,621 clinical patients and 13,589 controls. The effects sizes of single nucleotide polymorphisms (SNPs) estimated in this meta-analysis were used to obtain individual polygenic risk scores in an independent population-based cohort of 2,437 children from the Netherlands Twin Register. The variance explained in Attention Problems (AP) scale scores by the polygenic risk scores was estimated by linear mixed modeling.

    Results: The ADHD polygenic risk scores statistically-significantly predicted both parent and teacher ratings of AP in preschool-aged and school-aged children.

    Conclusion: These results indicate genetic overlap between a diagnosis of ADHD and AP scale scores across raters and age groups and provides evidence for a dimensional model of ADHD. Future GWA studies on ADHD can likely benefit from the inclusion of population-based cohorts and the analysis of continuous scores.

    [Keywords: ADHD, attention problems, polygenic scores, genetics, dimensional models]

  307. ⁠, Hamshere, Marian L. Stergiakouli, Evangelia Langley, Kate Martin, Joanna Holmans, Peter Kent, Lindsey Owen, Michael J. Gill, Michael Thapar, Anita O'Donovan, Mick Craddock, Nick (2013):

    Background: There is recent evidence of some degree of shared genetic susceptibility between adult schizophrenia and childhood attention-deficit hyperactivity disorder (ADHD) for rare chromosomal variants.

    Aims: To determine whether there is overlap between common alleles conferring risk of schizophrenia in adults with those that do so for ADHD in children.

    Method: We used recently published Psychiatric Genome-wide Association Study (GWAS) Consortium (PGC) adult schizophrenia data to define alleles over-represented in people with schizophrenia and tested whether those alleles were more common in 727 children with ADHD than in 2067 controls.

    Results: Schizophrenia risk alleles discriminated ADHD cases from controls (p = 1.04 × 10−4, R(2) = 0.45%); stronger discrimination was given by alleles that were risk alleles for both adult schizophrenia and adult bipolar disorder (also derived from a PGC data-set) (p = 9.98 × 10−6, R(2) = 0.59%).

    Conclusions: This increasing evidence for a small, but significant, shared genetic susceptibility between adult schizophrenia and childhood ADHD highlights the importance of research work across traditional diagnostic boundaries.

  308. https://www.nature.com/tp/journal/v5/n2/full/tp20155a.html

  309. ⁠, Biederman, Joseph Faraone, Stephen V (2006):

    Many children with attention-deficit/​​​​hyperactivity disorder (ADHD) continue to exhibit symptoms of the disorder into adolescence and adulthood. Although ADHD may have a profound impact on activities of daily living, including educational achievement and work performance, limited research exists on ADHD’s impact on individual income loss and overall economic effect. Evaluate ADHD’s impact on individual employment and income, and quantify costs of ADHD on workforce productivity for the US population.

    Two telephone surveys were conducted between April 18, 2003, and May 11, 2003, to collect demographic, educational, employment, and income information. Two groups of adults aged 18–64 years were interviewed: those diagnosed with ADHD (n = 500) derived from a national list of mail-paneled members who identified themselves or a household member as having been diagnosed with ADHD, and an age-matched and gender-matched control group (n = 501) derived from a random digital-dialing sample of a national cross-section not diagnosed with ADHD. Statistically fewer subjects in the ADHD group achieved academic milestones beyond some high school (p < 0.05). In addition, fewer subjects with ADHD were employed full time (34%) compared with controls (59%; p < 0.001). Except for the subgroup of subjects aged 18–24 years, average household incomes were statistically-significantly lower among individuals with ADHD compared with controls, regardless of academic achievement or personal characteristics.

    On the basis of these findings, loss of workforce productivity associated with ADHD was estimated between $91$672006 billion and $158$1162006 billion. Decreased individual income among adults with ADHD contributes to substantial loss in US workforce productivity.

  310. 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.

  311. 2009-schizophreniaconsortium.pdf

  312. ⁠, (2011):

    We conducted a combined genome-wide association study (GWAS) of 7,481 individuals with bipolar disorder (cases) and 9,250 controls as part of the Psychiatric GWAS Consortium. Our replication study tested 34 SNPs in 4,496 independent cases with bipolar disorder and 42,422 independent controls and found that 18 of 34 SNPs had P < 0.05, with 31 of 34 SNPs having signals with the same direction of effect (p = 3.8 × 10−7). An analysis of all 11,974 bipolar disorder cases and 51,792 controls confirmed genome-wide statistically-significant evidence of association for CACNA1C and identified a new intronic variant in ODZ4. We identified a pathway comprised of subunits of calcium channels enriched in bipolar disorder association intervals. Finally, a combined GWAS analysis of schizophrenia and bipolar disorder yielded strong association evidence for SNPs in CACNA1C and in the region of NEK4-ITIH1-ITIH3-ITIH4. Our replication results imply that increasing sample sizes in bipolar disorder will confirm many additional loci.

  313. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3637176/bin/halms634944-supplement_1.pdf

  314. ⁠, Niamh Mullins, Andreas J. Forstner, Kevin S. O'Connell, Brandon Coombes, Jonathan R. I. Coleman, Zhen Qiao, Thomas D. Als, Tim B. Bigdeli, Sigrid Børte, Julien Bryois, Alexander W. Charney, Ole Kristian Drange, Michael J. Gandal, Saskia P. Hagenaars, Masashi Ikeda, Nolan Kamitaki, Minsoo Kim, Kristi Krebs, Georgia Panagiotaropoulou, Brian M. Schilder, Laura G. Sloofman, Stacy Steinberg, Vassily Trubetskoy, Bendik S. Winsvold, Hong-Hee Won, Liliya Abramova, Kristina Adorjan, Esben Agerbo, Mariam Al Eissa, Diego Albani, Ney Alliey-Rodriguez, Adebayo Anjorin, Verneri Antilla, Anastasia Antoniou, Swapnil Awasthi, Ji Hyun Baek, Marie Bækvad-Hansen, Nicholas Bass, Michael Bauer, Eva C. Beins, Sarah E. Bergen, Armin Birner, Carsten Bøcker Pedersen, Erlend Bøen, Marco P. Boks, Rosa Bosch, Murielle Brum, Ben M. Brumpton, Nathalie Brunkhorst-Kanaan, Monika Budde, Jonas Bybjerg-Grauholm, William Byerley, Murray Cairns, Miquel Casas, Pablo Cervantes, Toni-Kim Clarke, Cristiana Cruceanu, Alfredo Cuellar-Barboza, Julie Cunningham, David Curtis, Piotr M. Czerski, Anders M. Dale, Nina Dalkner, Friederike S. David, Franziska Degenhardt, Srdjan Djurovic, Amanda L. Dobbyn, Athanassios Douzenis, Torbjørn Elvsåshagen, Valentina Escott-Price, I. Nicol Ferrier, Alessia Fiorentino, Tatiana M. Foroud, Liz Forty, Josef Frank, Oleksandr Frei, Nelson B. Freimer, Louise Frisén, Katrin Gade, Julie Garnham, Joel Gelernter, Marianne Giørtz Pedersen, Ian R. Gizer, Scott D. Gordon, Katherine Gordon-Smith, Tiffany A. Greenwood, Jakob Grove, José Guzman-Parra, Kyooseob Ha, Magnus Haraldsson, Martin Hautzinger, Urs Heilbronner, Dennis Hellgren, Stefan Herms, Per Hoffmann, Peter A. Holmans, Laura Huckins, Stéphane Jamain, Jessica S. Johnson, Janos L. Kalman, Yoichiro Kamatani, James L. Kennedy, Sarah Kittel-Schneider, James A. Knowles, Manolis Kogevinas, Maria Koromina, Thorsten M. Kranz, Henry R. Kranzler, Michiaki Kubo, Ralph Kupka, Steven A. Kushner, Catharina Lavebratt, Jacob Lawrence, Markus Leber, Heon-Jeong Lee, Phil H. Lee, Shawn E. Levy, Catrin Lewis, Calwing Liao, Susanne Lucae, Martin Lundberg, Donald J. MacIntyre, Wolfgang Maier, Adam Maihofer, Dolores Malaspina, Eirini Maratou, Lina Martinsson, Manuel Mattheisen, Nathaniel W. McGregor, Peter McGuffin, James D. McKay, Helena Medeiros, Sarah E. Medland, Vincent Millischer, Grant W. Montgomery, Jennifer L. Moran, Derek W. Morris, Thomas W. Mühleisen, Niamh O'Brien, Claire O'Donovan, Loes M. Olde Loohuis, Lilijana Oruc, Sergi Papiol, Antonio F. Pardiñas, Amy Perry, Andrea Pfennig, Evgenia Porichi, James B. Potash, Digby Quested, Towfique Raj, Mark H. Rapaport, J. Raymond DePaulo, Eline J. Regeer, John P. Rice, Fabio Rivas, Margarita Rivera, Julian Roth, Panos Roussos, Douglas M. Ruderfer, Cristina Sánchez-Mora, Eva C. Schulte, Fanny Senner, Sally Sharp, Paul D. Shilling, Engilbert Sigurdsson, Lea Sirignano, Claire Slaney, Olav B. Smeland, Janet L. Sobell, Christine Søholm Hansen, Maria Soler Artigas, Anne T. Spijker, Dan J. Stein, John S. Strauss, Beata Beata Świątkowska, Chikashi Terao, Thorgeir E. Thorgeirsson, Claudio Toma, Paul Tooney, Evangelia-Eirini Tsermpini, Marquis P. Vawter, Helmut Vedder, James T. R. Walters, Stephanie H. Witt, Simon Xi, Wei Xu, Hannah Young, Allan H. Young, Peter P. Zandi, Hang Zhou, Lea Zillich, HUNT All-In Psychiatry, Rolf Adolfsson, Ingrid Agartz, Martin Alda, Lars Alfredsson, Gulja Babadjanova, Lena Backlund, Bernhard T. Baune, Frank Bellivier, Susanne Bengesser, Wade H. Berrettini, Douglas H. R. Blackwood, Michael Boehnke, Anders D. Børglum, Gerome Breen, Vaughan J. Carr, Stanley Catts, Aiden Corvin, Nicholas Craddock, Udo Dannlowski, Dimitris Dikeos, Tõnu Esko, Bruno Etain, Panagiotis Ferentinos, Mark Frye, Janice M. Fullerton, Micha Gawlik, Elliot S. Gershon, Fernando Goes, Melissa J. Green, Maria Grigoroiu-Serbanescu, Joanna Hauser, Frans Henskens, Jan Hillert, Kyung Sue Hong, David M. Hougaard, Christina M. Hultman, Kristian Hveem, Nakao Iwata, Assen V. Jablensky, Ian Jones, Lisa A. Jones, René S. Kahn, John R. Kelsoe, George Kirov, Mikael Mikael Landén, Marion Leboyer, Cathryn M. Lewis, Qingqin S. Li, Jolanta Lissowska, Christine Lochner, Carmel Loughland, Nicholas G. Martin, Carol A. Mathews, Fermin Mayoral, Susan L. McElroy, Andrew M. McIntosh, Francis J. McMahon, Ingrid Melle, Patricia Michie, Lili Milani, Philip B. Mitchell, Gunnar Morken, Ole Mors, Preben Bo Mortensen, Bryan Mowry, Bertram Müller-Myhsok, Richard M. Myers, Benjamin M. Neale, Caroline M. Nievergelt, Merete Nordentoft, Markus M. Nöthen, Michael C. O'Donovan, Ketil J. Oedegaard, Tomas Olsson, Michael J. Owen, Sara A. Paciga, Chris Pantelis, Carlos Pato, Michele T. Pato, George P. Patrinos, Roy H. Perlis, Danielle Posthuma, Josep Antoni Ramos-Quiroga, Andreas Reif, Eva Z. Reininghaus, Marta Ribasés, Marcella Rietschel, Stephan Ripke, Guy A. Rouleau, Takeo Saito, Ulrich Schall, Martin Schalling, Peter R. Schofield, Thomas G. Schulze, Rodney J. Scott, Laura J. Scott, Alessandro Serretti, Cynthia Shannon Weickert, Jordan W. Smoller, Hreinn Stefansson, Kari Stefansson, Eystein Stordal, Fabian Streit, Patrick F. Sullivan, Gustavo Turecki, Arne E. Vaaler, Eduard Vieta, John B. Vincent, Irwin D. Waldman, Thomas W. Weickert, Thomas Werge, Naomi R. Wray, John-Anker Zwart, Joanna M. Biernacka, John I. Nurnberger, Sven Cichon, Howard J. Edenberg, Eli A. Stahl, Andrew McQuillin, Arianna Di Florio, Roel A. Ophoff, Ole A. Andreassen (2020-09-18):

    Bipolar disorder (BD) is a heritable mental illness with complex etiology. We performed a genome-wide association study (GWAS) of 41,917 BD cases and 371,549 controls, which identified 64 associated genomic loci. BD risk alleles were enriched in genes in synaptic and calcium signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. statistically-significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers and antiepileptics. Integrating eQTL data implicated 15 genes robustly linked to BD via gene expression, including druggable genes such as HTR6, MCHR1, DCLK3 and FURIN. This GWAS provides the best-powered BD polygenic scores to date, when applied in both European and diverse ancestry samples. Together, these results advance our understanding of the biological etiology of BD, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

    …PRS explained ~4.75% of phenotypic variance in BD on the liability scale (at GWAS p value threshold (pT) < 0.1, BD population prevalence 2%), based on the weighted mean R2R across cohorts (Figure 2, Table S12). This corresponds to a weighted mean area under the curve (AUC) of 66%. Results per cohort and per wave of recruitment to the PGC are in Tables S12–S13 and Supplementary Figure 7: At pT < 0.1, individuals in the top 10% of BD PRS had an odds ratio of 3.62 (95% CI 1.7–7.9) of being affected with the disorder compared with individuals in the middle decile (based on the weighted mean OR across PGC cohorts), and an odds ratio of 9.5 (95% CI 5.4–20) compared with individuals in the lowest decile. The generalizability of PRS from this meta-analysis was examined in several non-European cohorts. PRS explained up to 2.3% and 1.9% of variance in BD in two East Asian samples, and 1.2% and 0.4% in two admixed African American samples (Figure 2, Table S14). The variance explained by the PRS increased in every cohort with increasing sample size of the PGC BD European discovery sample (Supplementary Figure 8, Table S14)

    …Polygenic risk scores (PRS) for BD explained on average 4.75% of phenotypic variance (liability scale) across European cohorts, although this varied in different waves of the BD GWAS, ranging from 6.6% in the PGC1 cohorts to 2.9% in the External biobank studies (Supplementary Figure 7, Table S12). These results are in line with the h2SNP of BD per wave, which ranged from 24.6% (SE = 0.01) in PGC1 to 11.9% (SE = 0.01) in External studies (Table S3). Some variability in h2SNP estimates may arise from the inclusion of cases from population biobanks ascertained using ICD codes, who may have more heterogeneous clinical presentations or less severe illness than BD patients ascertained via inpatient or outpatient psychiatric clinics. Across the waves of clinically ascertained samples within the PGC, h2SNP and the R2R of PRS also varied, likely reflecting clinical and genetic heterogeneity in the type of BD cases ascertained; the PGC1 cohorts consisted mostly of BD I cases9, known to be the most heritable of the BD subtypes11,24, while later waves included more individuals with BD II24. Overall, the h2SNP of BD calculated from the meta-analysis summary statistics was 18% on the liability scale, a decrease of ~2% compared with the PGC2 GWAS24, which was driven by the addition of cohorts with lower h2SNP estimates (Table S3). However, despite differences in h2SNP and R2R of PRS per wave, the genetic correlation of BD between all waves was high (weighted mean rg = 0.94, SE = 0.03), supporting our rationale for combining cases with different BD subtypes or ascertainment to increase power for discovery of risk variants. In Europeans, individuals in the top 10% of PRS had an OR of 3.6 for BD, compared with individuals with average PRS (middle decile), which translates into a modest absolute lifetime risk of the disorder (7.2% based on PRS alone).

  315. 2005-hirschfeld.pdf: “A126_jun05Hirschfeld_S85toS90”⁠, Charlie

  316. ⁠, Wyatt, R. J Henter, I (1995):

    In 1991, the costs for manic-depressive illness, which has a lifetime prevalence of 1.3% among adult Americans, totaled $45 billion. Costs were broken down into their direct and indirect components. Direct costs totaling $7 billion consist of expenditures for inpatient and outpatient care, which are treatment related, as well as nontreatment-related expenditures such as those for the criminal justice system used by individuals with manic-depressive illness. Indirect costs, which were $38 billion, include the lost productivity of both wage-earners ($17 billion) and homemakers ($3 billion), individuals who are in institutions ($3 billion) or who have committed suicide ($8 billion), and caregivers who take care of manic-depressive family members ($6 billion). The method for determining each expenditure is provided, and the implications of these staggering costs are discussed. These calculations rely heavily on methods and data bases that were developed for the accompanying paper on the costs of schizophrenia.

  317. 2015-agerbo.pdf

  318. ⁠, Fanous, Ayman H. Zhou, Baiyu Aggen, Steven H. Bergen, Sarah E. Amdur, Richard L. Duan, Jubao Sanders, Alan R. Shi, Jianxin Mowry, Bryan J. Olincy, Ann Amin, Farooq Cloninger, C. Robert Silverman, Jeremy M. Buccola, Nancy G. Byerley, William F. Black, Donald W. Freedman, Robert Dudbridge, Frank Holmans, Peter A. Ripke, Stephan Gejman, Pablo V. Kendler, Kenneth S. Levinson, Douglas F (2012):

    Objective: Multiple sources of evidence suggest that genetic factors influence variation in clinical features of schizophrenia. The authors present the first genome-wide association study (GWAS) of dimensional symptom scores among individuals with schizophrenia.

    Method: Based on the Lifetime Dimensions of Psychosis Scale ratings of 2,454 case subjects of European ancestry from the Molecular Genetics of Schizophrenia (MGS) sample, three symptom factors (positive, negative/​​​​disorganized, and mood) were identified with exploratory ⁠. Quantitative scores for each factor from a confirmatory factor analysis were analyzed for association with 696,491 single-nucleotide polymorphisms (SNPs) using linear regression, with correction for age, sex, clinical site, and ancestry. Polygenic score analysis was carried out to determine whether case and comparison subjects in 16 Psychiatric GWAS Consortium (PGC) schizophrenia samples (excluding MGS samples) differed in scores computed by weighting their genotypes by MGS association test results for each symptom factor.

    Results: No genome-wide statistically-significant associations were observed between SNPs and factor scores. Most of the SNPs producing the strongest evidence for association were in or near genes involved in neurodevelopment, neuroprotection, or neurotransmission, including genes playing a role in Mendelian CNS diseases, but no statistically-significant effect was observed for any defined gene pathway. Finally, polygenic scores based on MGS GWAS results for the negative/​​​​disorganized factor were significantly different between case and comparison subjects in the PGC data set; for MGS subjects, negative/​​​​disorganized factor scores were correlated with polygenic scores generated using case-control GWAS results from the other PGC samples.

    Conclusions: The polygenic signal that has been observed in cross-sample analyses of schizophrenia GWAS data sets could be in part related to genetic effects on negative and disorganized symptoms (i.e., core features of chronic schizophrenia).

  319. ⁠, (2014):

    Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide statistical-significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.

  320. 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.

  321. {#linkBibliography-(pgc)-et-al-2020 .docMetadata doi=“10.1101/​​2020.09.12.20192922”}, Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC), Stephan Ripke, James T. R. Walters, Michael C. O'Donovan (2020-09-13):

    Extended Data Figure 2: GWAS progress over time. The relationship of GWAS associations to sample-size is shown in this plot with selected SCZ GWAS meta-analyses of the past 11 years. The x-axis shows number of cases. The y-axis shows the number of independent loci discovered with at least one genome-wide statistically-significant index SNP in the discovery meta-analysis (eg without replication data)…The slope of ~4 newly discovered loci per 1000 cases between 2013 and 2019 increased to a slope of ~6 with the latest sample-size increase.

    Schizophrenia is a psychiatric disorder whose pathophysiology is largely unknown. It has a heritability of 60–80%, much of which is attributable to common risk alleles, suggesting genome-wide association studies can inform our understanding of aetiology. Here, in 69,369 people with schizophrenia and 236,642 controls, we report common variant associations at 270 distinct loci. Using fine-mapping and functional genomic data, we prioritise 19 genes based on protein-coding or UTR variation, and 130 genes in total as likely to explain these associations. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in autism and developmental disorder. Associations were concentrated in genes expressed in CNS neurons, both excitatory and inhibitory, but not other tissues or cell types, and implicated fundamental processes related to neuronal function, particularly synaptic organisation, differentiation and transmission. We identify biological processes of pathophysiological relevance to schizophrenia, show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders, and provide a rich resource of priority genes and variants to advance mechanistic studies.

  322. http://schizophreniabulletin.oxfordjournals.org/content/30/2/279.full.pdf

  323. 2005-wu.pdf: “The Economic Burden of Schizophrenia in the United States in 2002”⁠, Wu, et al.

  324. 2017-hjorthoj.pdf: “Years of potential life lost and life expectancy in schizophrenia: a systematic review and meta-analysis”⁠, Carsten Hjorthøj, Anne Emilie Stürup, John J. McGrath, Merete Nordentoft DMSc

  325. Everything

  326. http://ldsc.broadinstitute.org/

  327. ⁠, 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):

    Motivation

    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/​​​​

  328. http://ldsc.broadinstitute.org/static/media/LD-Hub_genetic_correlation_49x49.xlsx

  329. http://ldsc.broadinstitute.org/static/media/LD-Hub_study_information_and_SNP_heritability.xlsx

  330. http://ije.oxfordjournals.org/content/45/2/417.full

  331. ⁠, Perry, John Rb Day, Felix Elks, Cathy E. Sulem, Patrick Thompson, Deborah J. Ferreira, Teresa He, Chunyan Chasman, Daniel I. Esko, Tõnu Thorleifsson, Gudmar Albrecht, Eva Ang, Wei Q. Corre, Tanguy Cousminer, Diana L. Feenstra, Bjarke Franceschini, Nora Ganna, Andrea Johnson, Andrew D. Kjellqvist, Sanela Lunetta, Kathryn L. McMahon, George Nolte, Ilja M. Paternoster, Lavinia Porcu, Eleonora Smith, Albert V. Stolk, Lisette Teumer, Alexander Tšernikova, Natalia Tikkanen, Emmi Ulivi, Sheila Wagner, Erin K. Amin, Najaf Bierut, Laura J. Byrne, Enda M. Hottenga, Jouke-Jan Koller, Daniel L. Mangino, Massimo Pers, Tune H. Yerges-Armstrong, Laura M. Zhao, Jing Hua Andrulis, Irene L. Anton-Culver, Hoda Atsma, Femke Bandinelli, Stefania Beckmann, Matthias W. Benitez, Javier Blomqvist, Carl Bojesen, Stig E. Bolla, Manjeet K. Bonanni, Bernardo Brauch, Hiltrud Brenner, Hermann Buring, Julie E. Chang-Claude, Jenny Chanock, Stephen Chen, Jinhui Chenevix-Trench, Georgia Collée, J. Margriet Couch, Fergus J. Couper, David Coveillo, Andrea D. Cox, Angela Czene, Kamila D'adamo, Adamo Pio Smith, George Davey De Vivo, Immaculata Demerath, Ellen W. Dennis, Joe Devilee, Peter Dieffenbach, Aida K. Dunning, Alison M. Eiriksdottir, Gudny Eriksson, Johan G. Fasching, Peter A. Ferrucci, Luigi Flesch-Janys, Dieter Flyger, Henrik Foroud, Tatiana Franke, Lude Garcia, Melissa E. García-Closas, Montserrat Geller, Frank de Geus, Eco Ej Giles, Graham G. Gudbjartsson, Daniel F. Gudnason, Vilmundur Guénel, Pascal Guo, Suiqun Hall, Per Hamann, Ute Haring, Robin Hartman, Catharina A. Heath, Andrew C. Hofman, Albert Hooning, Maartje J. Hopper, John L. Hu, Frank B. Hunter, David J. Karasik, David Kiel, Douglas P. Knight, Julia A. Kosma, Veli-Matti Kutalik, Zoltan Lai, Sandra Lambrechts, Diether Lindblom, Annika Mägi, Reedik Magnusson, Patrik K. Mannermaa, Arto Martin, Nicholas G. Masson, Gisli McArdle, Patrick F. McArdle, Wendy L. Melbye, Mads Michailidou, Kyriaki Mihailov, Evelin Milani, Lili Milne, Roger L. Nevanlinna, Heli Neven, Patrick Nohr, Ellen A. Oldehinkel, Albertine J. Oostra, Ben A. Palotie, Aarno Peacock, Munro Pedersen, Nancy L. Peterlongo, Paolo Peto, Julian Pharoah, Paul Dp Postma, Dirkje S. Pouta, Anneli Pylkäs, Katri Radice, Paolo Ring, Susan Rivadeneira, Fernando Robino, Antonietta Rose, Lynda M. Rudolph, Anja Salomaa, Veikko Sanna, Serena Schlessinger, David Schmidt, Marjanka K. Southey, Mellissa C. Sovio, Ulla Stampfer, Meir J. Stöckl, Doris Storniolo, Anna M. Timpson, Nicholas J. Tyrer, Jonathan Visser, Jenny A. Vollenweider, Peter Völzke, Henry Waeber, Gerard Waldenberger, Melanie Wallaschofski, Henri Wang, Qin Willemsen, Gonneke Winqvist, Robert Wolffenbuttel, Bruce Hr Wright, Margaret J. Boomsma, Dorret I. Econs, Michael J. Khaw, Kay-Tee Loos, Ruth Jf McCarthy, Mark I. Montgomery, Grant W. Rice, John P. Streeten, Elizabeth A. Thorsteinsdottir, Unnur van Duijn, Cornelia M. Alizadeh, Behrooz Z. Bergmann, Sven Boerwinkle, Eric Boyd, Heather A. Crisponi, Laura Gasparini, Paolo Gieger, Christian Harris, Tamara B. Ingelsson, Erik Järvelin, Marjo-Riitta Kraft, Peter Lawlor, Debbie Metspalu, Andres Pennell, Craig E. Ridker, Paul M. Snieder, Harold Sørensen, Thorkild Ia Spector, Tim D. Strachan, David P. Uitterlinden, André G. Wareham, Nicholas J. Widen, Elisabeth Zygmunt, Marek Murray, Anna Easton, Douglas F. Stefansson, Kari Murabito, Joanne M. Ong, Ken K (2014):

    Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10−8) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition.

  332. 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.

  333. https://www.theatlantic.com/health/archive/2016/06/the-long-term-risks-of-early-puberty/488834/

  334. ⁠, Escott-Price, Valentina Sims, Rebecca Bannister, Christian Harold, Denise Vronskaya, Maria Majounie, Elisa Badarinarayan, Nandini Morgan, Kevin Passmore, Peter Holmes, Clive Powell, John Brayne, Carol Gill, Michael Mead, Simon Goate, Alison Cruchaga, Carlos Lambert, Jean-Charles van Duijn, Cornelia Maier, Wolfgang Ramirez, Alfredo Holmans, Peter Jones, Lesley Hardy, John Seshadri, Sudha Schellenberg, Gerard D. Amouyel, Philippe Williams, Julie (2015):

    The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were statistically-significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (p = 4.9 × 10−26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (p = 3.4 × 10−19). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer’s disease has a significant polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/​​​​low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.

  335. ⁠, Kelley, Amy S. McGarry, Kathleen Gorges, Rebecca Skinner, Jonathan S (2015):

    Background: Common diseases, particularly dementia, have large social costs for the U.S. population. However, less is known about the end-of-life costs of specific diseases and the associated financial risk for individual households.

    Objective: To examine social costs and financial risks faced by Medicare beneficiaries 5 years before death.

    Design: Retrospective cohort.

    Setting: The HRS (Health and Retirement Study).

    Participants: Medicare fee-for-service beneficiaries, aged 70 years or older, who died between 2005 and 2010 (n = 1702), stratified into 4 groups: persons with a high probability of dementia or those who died because of heart disease, cancer, or other causes.

    Measurements: Total social costs and their components, including Medicare, Medicaid, private insurance, out-of-pocket spending, and informal care, measured over the last 5 years of life; and out-of-pocket spending as a proportion of household wealth.

    Results: Average total cost per decedent with dementia ($287 038) was significantly greater than that of those who died of heart disease ($175 136), cancer ($173 383), or other causes ($197 286) (P < 0.001). Although Medicare expenditures were similar across groups, average out-of-pocket spending for patients with dementia ($61 522) was 81% higher than that for patients without dementia ($34 068); a similar pattern held for informal care. Out-of-pocket spending for the dementia group (median, $36 919) represented 32% of wealth measured 5 years before death compared with 11% for the nondementia group (P < 0.001). This proportion was greater for black persons (84%), persons with less than a high school education (48%), and unmarried or widowed women (58%).

    Limitation: Imputed Medicaid, private insurance, and informal care costs.

    Conclusion: Health care expenditures among persons with dementia were substantially larger than those for other diseases, and many of the expenses were uncovered (uninsured). This places a large financial burden on families, and these burdens are particularly pronounced among the demographic groups that are least prepared for financial risk.

    Primary Funding Source: National Institute on Aging.

  336. ⁠, Boraska, V. Franklin, C. S Floyd, J. A B. Thornton, L. M Huckins, L. M Southam, L. Rayner, N. W Tachmazidou, I. Klump, K. L Treasure, J. Lewis, C. M Schmidt, U. Tozzi, F. Kiezebrink, K. Hebebrand, J. Gorwood, P. Adan, R. A H. Kas, M. J H. Favaro, A. Santonastaso, P. Fernández-Aranda, F. Gratacos, M. Rybakowski, F. Dmitrzak-Weglarz, M. Kaprio, J. Keski-Rahkonen, A. Raevuori, A. Van Furth, E. F Slof-Op 't Landt, M. C T. Hudson, J. I Reichborn-Kjennerud, T. Knudsen, G. P S. Monteleone, P. Kaplan, A. S Karwautz, A. Hakonarson, H. Berrettini, W. H Guo, Y. Li, D. Schork, N. J Komaki, G. Ando, T. Inoko, H. Esko, T. Fischer, K. Männik, K. Metspalu, A. Baker, J. H Cone, R. D Dackor, J. DeSocio, J. E Hilliard, C. E O'Toole, J. K Pantel, J. Szatkiewicz, J. P Taico, C. Zerwas, S. Trace, S. E Davis, O. S P. Helder, S. Bühren, K. Burghardt, R. de Zwaan, M. Egberts, K. Ehrlich, S. Herpertz-Dahlmann, B. Herzog, W. Imgart, H. Scherag, A. Scherag, S. Zipfel, S. Boni, C. Ramoz, N. Versini, A. Brandys, M. K Danner, U. N de Kovel, C. Hendriks, J. Koeleman, B. P C. Ophoff, R. A Strengman, E. van Elburg, A. A Bruson, A. Clementi, M. Degortes, D. Forzan, M. Tenconi, E. Docampo, E. Escaramís, G. Jiménez-Murcia, S. Lissowska, J. Rajewski, A. Szeszenia-Dabrowska, N. Slopien, A. Hauser, J. Karhunen, L. Meulenbelt, I. Slagboom, P. E Tortorella, A. Maj, M. Dedoussis, G. Dikeos, D. Gonidakis, F. Tziouvas, K. Tsitsika, A. Papezova, H. Slachtova, L. Martaskova, D. Kennedy, J. L Levitan, R. D Yilmaz, Z. Huemer, J. Koubek, D. Merl, E. Wagner, G. Lichtenstein, P. Breen, G. Cohen-Woods, S. Farmer, A. McGuffin, P. Cichon, S. Giegling, I. Herms, S. Rujescu, D. Schreiber, S. Wichmann, H-E Dina, C. Sladek, R. Gambaro, G. Soranzo, N. Julia, A. Marsal, S. Rabionet, R. Gaborieau, V. Dick, D. M Palotie, A. Ripatti, S. Widén, E. Andreassen, O. A Espeseth, T. Lundervold, A. Reinvang, I. Steen, V. M Le Hellard, S. Mattingsdal, M. Ntalla, I. Bencko, V. Foretova, L. Janout, V. Navratilova, M. Gallinger, S. Pinto, D. Scherer, S. W Aschauer, H. Carlberg, L. Schosser, A. Alfredsson, L. Ding, B. Klareskog, L. Padyukov, L. Courtet, P. Guillaume, S. Jaussent, I. Finan, C. Kalsi, G. Roberts, M. Logan, D. W Peltonen, L. Ritchie, G. R S. Barrett, J. C Estivill, X. Hinney, A. Sullivan, P. F Collier, D. A Zeggini, E. Bulik, C. M (2014):

    Anorexia nervosa (AN) is a complex and heritable eating disorder characterized by dangerously low body weight. Neither candidate gene studies nor an initial genome-wide association study (GWAS) have yielded significant and replicated results. We performed a GWAS in 2907 cases with AN from 14 countries (15 sites) and 14 860 ancestrally matched controls as part of the Genetic Consortium for AN (GCAN) and the Wellcome Trust Case Control Consortium 3 (WTCCC3). Individual association analyses were conducted in each stratum and meta-analyzed across all 15 discovery data sets. Seventy-six (72 independent) single nucleotide polymorphisms were taken forward for in silico (two data sets) or de novo (13 data sets) replication genotyping in 2677 independent AN cases and 8629 European ancestry controls along with 458 AN cases and 421 controls from Japan. The final global meta-analysis across discovery and replication data sets comprised 5551 AN cases and 21 080 controls. AN subtype analyses (1606 AN restricting; 1445 AN binge-purge) were performed. No findings reached genome-wide statistical-significance. Two intronic variants were suggestively associated: rs9839776 (p = 3.01 × 10−7) in SOX2OT and rs17030795 (p = 5.84 × 10−6) in PPP3CA. Two additional signals were specific to Europeans: rs1523921 (p = 5.76 × 10−6) between CUL3 and FAM124B and rs1886797 (p = 8.05 × 10−6) near SPATA13. Comparing discovery with replication results, 76% of the effects were in the same direction, an observation highly unlikely to be due to chance (p = 4 × 10−6), strongly suggesting that true findings exist but our sample, the largest yet reported, was underpowered for their detection. The accrual of large genotyped AN case-control samples should be an immediate priority for the field.

  337. ⁠, (2013):

    Background: Findings from family and twin studies suggest that genetic contributions to psychiatric disorders do not in all cases map to present diagnostic categories. We aimed to identify specific variants underlying genetic effects shared between the five disorders in the Psychiatric Genomics Consortium: autism spectrum disorder, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder, and schizophrenia.

    Methods: We analysed genome-wide single-nucleotide polymorphism (SNP) data for the five disorders in 33,332 cases and 27,888 controls of European ancestory. To characterise allelic effects on each disorder, we applied a multinomial logistic regression procedure with model selection to identify the best-fitting model of relations between genotype and phenotype. We examined cross-disorder effects of genome-wide statistically-significant loci previously identified for bipolar disorder and schizophrenia, and used polygenic risk-score analysis to examine such effects from a broader set of common variants. We undertook pathway analyses to establish the biological associations underlying genetic overlap for the five disorders. We used enrichment analysis of expression quantitative trait loci (eQTL) data to assess whether SNPs with cross-disorder association were enriched for regulatory SNPs in post-mortem brain-tissue samples.

    Findings: SNPs at four loci surpassed the cutoff for genome-wide statistical-significance (p < 5×10−8) in the primary analysis: regions on chromosomes 3p21 and 10q24, and SNPs within two L-type voltage-gated calcium channel subunits, CACNA1C and CACNB2. Model selection analysis supported effects of these loci for several disorders. Loci previously associated with bipolar disorder or schizophrenia had variable diagnostic specificity. Polygenic risk scores showed cross-disorder associations, notably between adult-onset disorders. Pathway analysis supported a role for calcium channel signalling genes for all five disorders. Finally, SNPs with evidence of cross-disorder association were enriched for brain eQTL markers.

    Interpretation: Our findings show that specific SNPs are associated with a range of psychiatric disorders of childhood onset or adult onset. In particular, variation in calcium-channel activity genes seems to have pleiotropic effects on psychopathology. These results provide evidence relevant to the goal of moving beyond descriptive syndromes in psychiatry, and towards a nosology informed by disease cause.

    Funding: National Institute of Mental Health.

  338. https://www.brookings.edu/research/fourteen-economic-facts-on-education-and-economic-opportunity-2/

  339. ⁠, Deloukas, Panos Kanoni, Stavroula Willenborg, Christina Farrall, Martin Assimes, Themistocles L. Thompson, John R. Ingelsson, Erik Saleheen, Danish Erdmann, Jeanette Goldstein, Benjamin A. Stirrups, Kathleen König, Inke R. Cazier, Jean-Baptiste Johansson, Asa Hall, Alistair S. Lee, Jong-Young Willer, Cristen J. Chambers, John C. Esko, Tõnu Folkersen, Lasse Goel, Anuj Grundberg, Elin Havulinna, Aki S. Ho, Weang K. Hopewell, Jemma C. Eriksson, Niclas Kleber, Marcus E. Kristiansson, Kati Lundmark, Per Lyytikäinen, Leo-Pekka Rafelt, Suzanne Shungin, Dmitry Strawbridge, Rona J. Thorleifsson, Gudmar Tikkanen, Emmi Van Zuydam, Natalie Voight, Benjamin F. Waite, Lindsay L. Zhang, Weihua Ziegler, Andreas Absher, Devin Altshuler, David Balmforth, Anthony J. Barroso, Inês Braund, Peter S. Burgdorf, Christof Claudi-Boehm, Simone Cox, David Dimitriou, Maria Do, Ron Doney, Alex S. F El Mokhtari, NourEddine Eriksson, Per Fischer, Krista Fontanillas, Pierre Franco-Cereceda, Anders Gigante, Bruna Groop, Leif Gustafsson, Stefan Hager, Jörg Hallmans, Göran Han, Bok-Ghee Hunt, Sarah E. Kang, Hyun M. Illig, Thomas Kessler, Thorsten Knowles, Joshua W. Kolovou, Genovefa Kuusisto, Johanna Langenberg, Claudia Langford, Cordelia Leander, Karin Lokki, Marja-Liisa Lundmark, Anders McCarthy, Mark I. Meisinger, Christa Melander, Olle Mihailov, Evelin Maouche, Seraya Morris, Andrew D. Müller-Nurasyid, Martina Nikus, Kjell Peden, John F. Rayner, N. William Rasheed, Asif Rosinger, Silke Rubin, Diana Rumpf, Moritz P. Schäfer, Arne Sivananthan, Mohan Song, Ci Stewart, Alexandre F. R Tan, Sian-Tsung Thorgeirsson, Gudmundur van der Schoot, C. Ellen Wagner, Peter J. Wells, George A. Wild, Philipp S. Yang, Tsun-Po Amouyel, Philippe Arveiler, Dominique Basart, Hanneke Boehnke, Michael Boerwinkle, Eric Brambilla, Paolo Cambien, Francois Cupples, Adrienne L. de Faire, Ulf Dehghan, Abbas Diemert, Patrick Epstein, Stephen E. Evans, Alun Ferrario, Marco M. Ferrières, Jean Gauguier, Dominique Go, Alan S. Goodall, Alison H. Gudnason, Villi Hazen, Stanley L. Holm, Hilma Iribarren, Carlos Jang, Yangsoo Kähönen, Mika Kee, Frank Kim, Hyo-Soo Klopp, Norman Koenig, Wolfgang Kratzer, Wolfgang Kuulasmaa, Kari Laakso, Markku Laaksonen, Reijo Lee, Ji-Young Lind, Lars Ouwehand, Willem H. Parish, Sarah Park, Jeong E. Pedersen, Nancy L. Peters, Annette Quertermous, Thomas Rader, Daniel J. Salomaa, Veikko Schadt, Eric Shah, Svati H. Sinisalo, Juha Stark, Klaus Stefansson, Kari Trégouët, David-Alexandre Virtamo, Jarmo Wallentin, Lars Wareham, Nicholas Zimmermann, Martina E. Nieminen, Markku S. Hengstenberg, Christian Sandhu, Manjinder S. Pastinen, Tomi Syvänen, Ann-Christine Hovingh, G. Kees Dedoussis, George Franks, Paul W. Lehtimäki, Terho Metspalu, Andres Zalloua, Pierre A. Siegbahn, Agneta Schreiber, Stefan Ripatti, Samuli Blankenberg, Stefan S. Perola, Markus Clarke, Robert Boehm, Bernhard O. O'Donnell, Christopher Reilly, Muredach P. März, Winfried Collins, Rory Kathiresan, Sekar Hamsten, Anders Kooner, Jaspal S. Thorsteinsdottir, Unnur Danesh, John Palmer, Colin N. A Roberts, Robert Watkins, Hugh Schunkert, Heribert Samani, Nilesh J (2013):

    Coronary artery disease (CAD) is the commonest cause of death. Here, we report an association analysis in 63,746 CAD cases and 130,681 controls identifying 15 loci reaching genome-wide statistical-significance, taking the number of susceptibility loci for CAD to 46, and a further 104 independent variants (r(2) < 0.2) strongly associated with CAD at a 5% false discovery rate (FDR). Together, these variants explain approximately 10.6% of CAD heritability. Of the 46 genome-wide statistically-significant lead SNPs, 12 show a statistically-significant association with a lipid trait, and 5 show a statistically-significant association with blood pressure, but none is statistically-significantly associated with diabetes. Network analysis with 233 candidate genes (loci at 10% FDR) generated 5 interaction networks comprising 85% of these putative genes involved in CAD. The four most significant pathways mapping to these networks are linked to lipid metabolism and inflammation, underscoring the causal role of these activities in the genetic etiology of CAD. Our study provides insights into the genetic basis of CAD and identifies key biological pathways.

  340. 2003-birnbaum.pdf

  341. ⁠, Jostins, Luke Ripke, Stephan Weersma, Rinse K. Duerr, Richard H. McGovern, Dermot P. Hui, Ken Y. Lee, James C. Schumm, L. Philip Sharma, Yashoda Anderson, Carl A. Essers, Jonah Mitrovic, Mitja Ning, Kaida Cleynen, Isabelle Theatre, Emilie Spain, Sarah L. Raychaudhuri, Soumya Goyette, Philippe Wei, Zhi Abraham, Clara Achkar, Jean-Paul Ahmad, Tariq Amininejad, Leila Ananthakrishnan, Ashwin N. Andersen, Vibeke Andrews, Jane M. Baidoo, Leonard Balschun, Tobias Bampton, Peter A. Bitton, Alain Boucher, Gabrielle Brand, Stephan Büning, Carsten Cohain, Ariella Cichon, Sven D'Amato, Mauro De Jong, Dirk Devaney, Kathy L. Dubinsky, Marla Edwards, Cathryn Ellinghaus, David Ferguson, Lynnette R. Franchimont, Denis Fransen, Karin Gearry, Richard Georges, Michel Gieger, Christian Glas, Jürgen Haritunians, Talin Hart, Ailsa Hawkey, Chris Hedl, Matija Hu, Xinli Karlsen, Tom H. Kupcinskas, Limas Kugathasan, Subra Latiano, Anna Laukens, Debby Lawrance, Ian C. Lees, Charlie W. Louis, Edouard Mahy, Gillian Mansfield, John Morgan, Angharad R. Mowat, Craig Newman, William Palmieri, Orazio Ponsioen, Cyriel Y. Potocnik, Uros Prescott, Natalie J. Regueiro, Miguel Rotter, Jerome I. Russell, Richard K. Sanderson, Jeremy D. Sans, Miquel Satsangi, Jack Schreiber, Stefan Simms, Lisa A. Sventoraityte, Jurgita Targan, Stephan R. Taylor, Kent D. Tremelling, Mark Verspaget, Hein W. De Vos, Martine Wijmenga, Cisca Wilson, David C. Winkelmann, Juliane Xavier, Ramnik J. Zeissig, Sebastian Zhang, Bin Zhang, Clarence K. Zhao, Hongyu Silverberg, Mark S. Annese, Vito Hakonarson, Hakon Brant, Steven R. Radford-Smith, Graham Mathew, Christopher G. Rioux, John D. Schadt, Eric E. Daly, Mark J. Franke, Andre Parkes, Miles Vermeire, Severine Barrett, Jeffrey C. Cho, Judy H (2012):

    Crohn’s disease and ulcerative colitis, the two common forms of inflammatory bowel disease (IBD), affect over 2.5 million people of European ancestry, with rising prevalence in other populations. Genome-wide association studies and subsequent meta-analyses of these two diseases as separate phenotypes have implicated previously unsuspected mechanisms, such as autophagy, in their pathogenesis and showed that some IBD loci are shared with other inflammatory diseases. Here we expand on the knowledge of relevant pathways by undertaking a meta-analysis of Crohn’s disease and ulcerative colitis genome-wide association scans, followed by extensive validation of significant findings, with a combined total of more than 75,000 cases and controls. We identify 71 new associations, for a total of 163 IBD loci, that meet genome-wide statistical-significance thresholds. Most loci contribute to both phenotypes, and both directional (consistently favouring one allele over the course of human history) and balancing (favouring the retention of both alleles within populations) selection effects are evident. Many IBD loci are also implicated in other immune-mediated disorders, most notably with ankylosing spondylitis and psoriasis. We also observe considerable overlap between susceptibility loci for IBD and mycobacterial infection. Gene co-expression network analysis emphasizes this relationship, with pathways shared between host responses to mycobacteria and those predisposing to IBD.

  342. https://web.archive.org/web/20170721085617/http://www.crohns.org/congress/submission00/appendix2.htm

  343. http://neurogenetics.qimrberghofer.edu.au/papers/Ripke2013MolPsychiatry.pdf

  344. 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.

  345. ⁠, Smith, James Patrick Smith, Gillian C (2010):

    Childhood psychological conditions including depression and substance abuse are a growing concern among American children, but their long-term economic costs are unknown. This paper uses unique data from the US Panel Study of Income Dynamics (PSID) following groups of siblings and their parents for up to 40 years prospectively collecting information on education, income, work, and marriage. Following siblings offers an opportunity to control for unobserved family and neighborhood effects. A retrospective child health history designed by the author was placed into the 2007 PSID wave measuring whether respondents had any of 14 childhood physical illnesses or suffered from depression, substance abuse, or other psychological conditions. Large effects are found on the ability of affected children to work and earn as adults. Educational accomplishments are diminished, and adult family incomes are reduced by 20% or $10,400 per year with $18,000 less family household assets. Lost income is partly a consequence of seven fewer weeks worked per year. There is also an 11% point lower probability of being married. Controlling for physical childhood diseases shows that these effects are not due to the co-existence of psychological and physical diseases, and estimates controlling for within-sibling differences demonstrate that these effects are not due to unobserved common family differences. The long-term economic damages of childhood psychological problems are large-a lifetime cost in lost family income of approximately $300,000, and total lifetime economic cost for all those affected of 2.1 trillion dollars.

  346. http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5542a1.htm

  347. ⁠, Vink, Jacqueline M. Hottenga, Jouke Jan de Geus, Eco J. C Willemsen, Gonneke Neale, Michael C. Furberg, Helena Boomsma, Dorret I (2014):

    Background and Aims: A strong correlation exists between smoking and the use of alcohol and cannabis. This paper uses polygenic risk scores to explore the possibility of overlapping genetic factors. Those scores reflect a combined effect of selected risk alleles for smoking.

    Methods: Summary-level p-values were available for smoking initiation, age at onset of smoking, cigarettes per day and smoking cessation from the Tobacco and Genetics Consortium (n between 22,000 and 70,000 subjects). Using different p-value thresholds (0.1, 0.2 and 0.5) from the meta-analysis, sets of ‘risk alleles’ were defined and used to generate a polygenic risk score (weighted sum of the alleles) for each subject in an independent target sample from the Netherlands Twin Register (n = 1583). The association between polygenic smoking scores and alcohol/​​​​cannabis use was investigated with regression analysis.

    Results: The polygenic scores for ‘cigarettes per day’ were associated significantly with the number of glasses alcohol per week (p = 0.005, R2 = 0.4-0.5%) and cannabis initiation (p = 0.004, R2 = 0.6-0.9%). The polygenic scores for ‘age at onset of smoking’ were associated significantly with ‘age at regular drinking’ (p = 0.001, R2 = 1.1-1.5%), while the scores for ‘smoking initiation’ and ‘smoking cessation’ did not significantly predict alcohol or cannabis use.

    Conclusions: Smoking, alcohol and cannabis use are influenced by aggregated genetic risk factors shared between these substances. The many common genetic variants each have a very small individual effect size.

  348. ⁠, Crowson, Cynthia S. Matteson, Eric L. Myasoedova, Elena Michet, Clement J. Ernste, Floranne C. Warrington, Kenneth J. Davis, John M. Hunder, Gene G. Therneau, Terry M. Gabriel, Sherine E (2011):

    Objective: Understanding of the personal risks for rheumatoid arthritis (RA) and other rheumatic diseases remains poor, despite advances in knowledge with regard to their pathogenesis, therapeutics, and clinical impact, in part because the personal lifetime risk of developing these diseases is unknown. This study was undertaken to estimate the lifetime risk of RA, as well as other inflammatory autoimmune rheumatic diseases, including systemic lupus erythematosus, psoriatic arthritis, polymyalgia rheumatica (PMR), giant cell arteritis, ankylosing spondylitis, and Sjögren’s syndrome, and to provide an overall estimate of the risk of developing inflammatory autoimmune rheumatic disease over a lifetime.

    Methods: Using the incidence rates obtained from our population-based studies of rheumatic diseases among residents of Olmsted County, Minnesota, and mortality rates from life tables for the general population, we estimated the sex-specific lifetime risk of rheumatic disease.

    Results: The lifetime risk of RA developing in US adults was 3.6% for women and 1.7% for men, and the lifetime risk of rheumatoid factor-positive RA was 2.4% for women and 1.1% for men. The second most common inflammatory autoimmune rheumatic disease was PMR, with a lifetime risk of 2.4% for women and 1.7% for men. The overall lifetime risk of inflammatory autoimmune rheumatic disease was 8.4% for women and 5.1% for men.

    Conclusion: One in 12 women and 1 in 20 men will develop an inflammatory autoimmune rheumatic disease during their lifetime. These results can serve as useful guides in counseling patients regarding their lifetime risk of these conditions and have important implications regarding disease awareness campaigns.

  349. ⁠, Okada, Yukinori Wu, Di Trynka, Gosia Raj, Towfique Terao, Chikashi Ikari, Katsunori Kochi, Yuta Ohmura, Koichiro Suzuki, Akari Yoshida, Shinji Graham, Robert R. Manoharan, Arun Ortmann, Ward Bhangale, Tushar Denny, Joshua C. Carroll, Robert J. Eyler, Anne E. Greenberg, Jeffrey D. Kremer, Joel M. Pappas, Dimitrios A. Jiang, Lei Yin, Jian Ye, Lingying Su, Ding-Feng Yang, Jian Xie, Gang Keystone, Ed Westra, Harm-Jan Esko, Tõnu Metspalu, Andres Zhou, Xuezhong Gupta, Namrata Mirel, Daniel Stahl, Eli A. Diogo, Dorothée Cui, Jing Liao, Katherine Guo, Michael H. Myouzen, Keiko Kawaguchi, Takahisa Coenen, Marieke J. H van Riel, Piet L. C M. van de Laar, Mart A. F J. Guchelaar, Henk-Jan Huizinga, Tom W. J Dieudé, Philippe Mariette, Xavier Bridges, S. Louis Zhernakova, Alexandra Toes, Rene E. M Tak, Paul P. Miceli-Richard, Corinne Bang, So-Young Lee, Hye-Soon Martin, Javier Gonzalez-Gay, Miguel A. Rodriguez-Rodriguez, Luis Rantapää-Dahlqvist, Solbritt Arlestig, Lisbeth Choi, Hyon K. Kamatani, Yoichiro Galan, Pilar Lathrop, Mark Eyre, Steve Bowes, John Barton, Anne de Vries, Niek Moreland, Larry W. Criswell, Lindsey A. Karlson, Elizabeth W. Taniguchi, Atsuo Yamada, Ryo Kubo, Michiaki Liu, Jun S. Bae, Sang-Cheol Worthington, Jane Padyukov, Leonid Klareskog, Lars Gregersen, Peter K. Raychaudhuri, Soumya Stranger, Barbara E. De Jager, Philip L. Franke, Lude Visscher, Peter M. Brown, Matthew A. Yamanaka, Hisashi Mimori, Tsuneyo Takahashi, Atsushi Xu, Huji Behrens, Timothy W. Siminovitch, Katherine A. Momohara, Shigeki Matsuda, Fumihiko Yamamoto, Kazuhiko Plenge, Robert M (2014):

    A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2—4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression and pathway analyses–as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes–to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

  350. http://rheumatology.oxfordjournals.org/content/39/1/28.long

  351. 2010-cohen.pdf

  352. 2011-park.pdf

  353. ⁠, da Silva, Bruno César Lyra, Andre Castro Rocha, Raquel Santana, Genoile Oliveira (2014):

    Ulcerative colitis (UC) is a chronic disease characterized by diffuse inflammation of the mucosa of the colon and rectum. The hallmark clinical symptom of UC is bloody diarrhea. The clinical course is marked by exacerbations and remissions, which may occur spontaneously or in response to treatment changes or intercurrent illnesses. UC is most commonly diagnosed in late adolescence or early adulthood, but it can occur at any age. The incidence of UC has increased worldwide over recent decades, especially in developing nations. In contrast, during this period, therapeutic advances have improved the life expectancy of patients, and there has been a decrease in the mortality rate over time. It is important to emphasize that there is considerable variability in the phenotypic presentation of UC. Within this context, certain clinical and demographic characteristics are useful in identifying patients who tend to have more severe evolution of the disease and a poor prognosis. In this group of patients, better clinical surveillance and more intensive therapy may change the natural course of the disease. The aim of this article was to review the epidemiology and demographic characteristics of UC and the factors that may be associated with its clinical prognosis.

  354. https://academic.oup.com/ije/article/47/1/89/4085882

  355. 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.

  356. ⁠, Paul RHJ Timmers, Ninon Mounier, Kristi Läll, Krista Fischer, Zheng Ning, Xiao Feng, Andrew Bretherick, David W. Clark, eQTLGen Consortium, Xia Shen, Tōnu Esko, Zoltán Kutalik, James F. Wilson, Peter K. Joshi (2018-07-06):

    We use a multi-stage genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/​​​​BRAP, FURIN/​​​​FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near GADD45G, KCNK3, LDLR, POM121C, ZC3HC1, and ABO. We also validate previous findings near 5q33.3/​​​​EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and tissue-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer –but not other cancers-explain the most variance, possibly reflecting modern susceptibilities, whilst cancer may act through many rare variants, or the environment. Resultant polygenic scores predict a mean lifespan difference of around five years of life across the deciles.

  357. https://www.chrisstucchio.com/blog/2014/equal_weights.html

  358. https://simplystatistics.org/2015/04/09/a-blessing-of-dimensionality-often-observed-in-high-dimensional-data-sets/

  359. ⁠, Hannah Bourne, Thomas Douglas, Julian Savulescu (2012-09-01):

    The Principle of Procreative Beneficence (PB) holds that when a couple plans to have a child, they have substantial moral reason to select, of the possible children they could have, the child who is most likely to experience the greatest wellbeing—that is, the most advantaged child, the child with the best chance at the best life…In this paper we wish address a different and more practical objection: the objection that parents will be heavily restricted in the number of traits that they can select, since they will have to choose among a very limited number of embryos. Recent advances in stem cell research may provide a solution to this problem. Recent research suggests that it may become possible to derive gametes (eggs and sperm) from human stem cells in vitro, a process which we will term in vitro gametogenesis (IVG). IVG would allow the creation of stems cells from a patient’s somatic (body) cells, and these stems cells could then be used to generate a plentiful supply of eggs or sperm in the laboratory…The ability to create large numbers of eggs or sperm through IVG greatly increases our capacity to select the best child possible. Selection could occur in two ways: (1) the most genetically desirable of this massive number of gametes could be selected and then used to create an embryo, or alternatively, (2) large numbers of embryos could be produced from these gametes and then the best embryo selected. Whatever the method, the advent of IVG could allow us to select for a much larger number of traits than is currently conceivable.

    …Suppose that a couple would like to select for 20 single gene traits which are carried on 20 different and unlinked autosomal loci. Suppose further that at ten of these loci, alleles contribute recessively to the desired trait…The chance of the couple having such a child would be just over 1% with traditional IVF plus selection, but would increased to over 99.99% if 10,000 embryos could be created using IVG…By enabling the creation of large numbers of gametes and embryos, IVG may allow the selection of traits in future children to a degree that has previously been inconceivable.

  360. ⁠, Auger, Jacques (2010):

    The assessment of the percentage of spermatozoa having an ‘ideal’ morphology using so-called strict method is the method recommended in the latest edition of the World Health Organization (WHO) laboratory manual for semen analysis. This recommendation is a result of the statistical association between ‘ideal’ sperm morphology and fertility, and of the current general belief that sperm morphology assessment should be used primarily as a fertility tool. The notion of an ‘ideal’ sperm morphology has persisted despite the very low percentage of such spermatozoa in the semen of fertile men, a subject of intense controversy. The detailed categorization of each abnormal spermatozoon has thus, for a long time, been considered optional and partially redundant, an idea which is reflected in the earlier editions of the WHO manual. However, several recent studies have shown the importance of carefully assessing abnormal sperm morphology for use in the diagnosis of infertility, to determine fertility prognosis, and for basic or public health studies. One approach, which combines videomicroscopy and computer vision, and is the only approach able to assess the continuum of sperm biometrics, has been used successfully in several recent clinical, basic and toxicology studies. In summary, the visual assessment of detailed sperm morphology-including the categorization of anomalies allowing arithmetically derived indices of teratozoospermia-and the more modern computer-based approaches, although often considered to be redundant, are in fact complementary. The choice of the most appropriate method depends on the field of investigation (clinical, research, toxicology) and the problem being addressed. Each approach has advantages as well as certain limitations, which will be discussed briefly herein.

  361. ⁠, Takashi Umehara, Natsumi Tsujita, Masayuki Shimada (2019-07-08):

    In most mammals, the male to female sex ratio of offspring is about 50% because half of the sperm contain either the Y chromosome or X chromosome. In mice, the Y chromosome encodes fewer than 700 genes, whereas the X chromosome encodes over 3,000 genes. Although overall gene expression is lower in sperm than in somatic cells, transcription is activated selectively in round spermatids. By regulating the expression of specific genes, we hypothesized that the X chromosome might exert functional differences in sperm that are usually masked during fertilization. In this study, we found that Toll-like receptors 7/​​​​8 (TLR7/​​​​8) coding the X chromosome were expressed by approximately 50% of the round spermatids in testis and in approximately 50% of the epididymal sperm. Especially, TLR7 was localized to the tail, and TLR8 was localized to the midpiece. Ligand activation of TLR7/​​​​8 selectively suppressed the mobility of the X chromosome-bearing sperm (X-sperm) but not the Y-sperm without altering sperm viability or acrosome formation. The difference in sperm motility allowed for the separation of Y-sperm from X-sperm. Following in vitro fertilization using the ligand-selected high-mobility sperm, 90% of the embryos were XY male. Likewise, 83% of the pups obtained following embryo transfer were XY males. Conversely, the TLR7/​​​​8-activated, slow mobility sperm produced embryos and pups that were 81% XX females. Therefore, the functional differences between Y-sperm and X-sperm motility were revealed and related to different gene expression patterns, specifically TLR7/​​​​8 on X-sperm.

  362. ⁠, Samuel, Raheel Feng, Haidong Jafek, Alex Despain, Dillon Jenkins, Timothy Gale, Bruce (2018):

    Microfluidics technology has emerged as an enabling technology for different fields of medicine and life sciences. One such field is male infertility where microfluidic technologies are enabling optimization of sperm sample preparation and analysis. In this chapter we review how microfluidic technology has been used for sperm quantification, sperm quality analysis, and sperm manipulation and isolation with subsequent use of the purified sperm population for treatment of male infertility. As we discuss demonstrations of microfluidic sperm sorting/​​​​manipulation/​​​​analysis, we highlight systems that have demonstrated feasibility towards clinical adoption or have reached commercialization in the male infertility market. We then review microfluidic-based systems that facilitate non-invasive identification and sorting of viable sperm for in vitro fertilization. Finally, we explore commercialization challenges associated with microfluidic sperm sorting systems and provide suggestions and future directions to best overcome them.

  363. 2018-alavioon.pdf: ⁠, Ghazal Alavioon (2018-08-24; genetics  /​ ​​ ​selection):

    A consequence of sexual reproduction in eukaryotes is the evolution of a biphasic life cycle with alternating diploid and haploid gametic phases. While our focus in evolutionary biology is on selection during the diploid phase, we know relatively little about selection occurring during the haploid gametic stage. This is particularly true in predominantly diploid animals, where gene expression and hence selection have long been thought to be absent in haploid cells like gametes and particularly sperm.

    During my PhD, I tested the idea of selection during the haploid gametic phase using zebrafish as a study species. I combined a large-scale selection experiment over 3 generations with fitness assays and next-generation sequencing to assess the importance of haploid selection. We measured offspring fitness in all 3 generations. In addition, we compared gene expression in brain and testes of F1 and F3 adult male from each treatment by RNA sequencing.

    We found that offspring sired by longer-lived sperm showed higher survival rate and higher early-life and late-life reproductive fitness compared to offspring sired by shorter-lived sperm. We also found differentially expressed genes between the 2 treatments with functions in metabolic and developmental pathways.

    These findings suggest that the observed fitness differences to be caused by small expression changes in many basic genes. We also tested for a genetic underpinning of the selected sperm phenotypes and identified allelic differences across the entire genome. Finally, we investigated the additive genetic component and parental effect of different sperm phenotypes. We found generally low additive genetic variation and high parental effects on sperm performance traits.

    In conclusion, this thesis provides evidence that the phenotypic variation among intact fertile sperm within an ejaculate affects offspring fitness throughout life and provides a clear link between sperm phenotype and offspring fitness and between sperm phenotype and sperm genotype.

    [Keywords: sperm, evolution, haploid selection, reproductive aging, fitness]

    …List of Papers: This thesis is based on the following papers, which are referred to in the text by their Roman numerals.

    I. Alavioon et al 2017, “Sperm selection within a single ejaculate increases offspring fitness” II. Alavioon et al, “Within-ejaculate selection for sperm longevity reduces male reproductive ageing”. Manuscript III. Alavioon et al, “The fitness consequences of selection in haploid sperm across generations”. Manuscript IV. Alavioon et al, “Sperm performance traits exhibit low heritability and strong parental effects in external fertilizer”. Manuscript.

    Additional Papers:

    The following papers were published/​​​​in publishing process during the course of my doctoral studies but are not part of the thesis.

    • Immler et al 2014,
    • Berg et al 2014, “Evolution of differential maternal age effects on male and female offspring development and longevity”
    • Promerova et al 2017, “No evidence for MHC class II-based disassortative mating at the gamete level in Atlantic salmon”
    • Silva et al, “Perceived sperm competition intensity in zebrafish males affects gene expression in early offspring”. Manuscript

    …The mechanisms and outcomes of selection occurring during diploid and haploid phases differ substantially (Crow & Kimura 1965). Because diploids have 2 copies of each allele, they can mask recessive mutations, which are therefore less exposed to selection. In contrast, when an allele is expressed in a haploid state, it is entirely exposed to selection since there is no masking effect of a sister allele. This can in fact facilitate the rate of spreading and fixation of beneficial alleles while reducing the accumulation of deleterious mutations in a population by efficiently eliminating deleterious mutations (Haldane 1924; Crow & Kimura 1965; Mable & Otto 1998). …Haploid selection is a situation in which a phenotype under selection is determined by a haploid allele (Joseph & Kirkpatrick 2004).

    …in animals, haploid selection is understudied due to an existing dogma that gene expression at the post-meiotic haploid phase is largely absent. Many researchers dismissed the possibility of haploid selection in animals for several reasons. The first reason was the fact that most animals spend the majority of their life cycle as diploids followed by a very short haploid stage. The other reason was that the DNA in sperm/​​​​gametes is densely packed and almost entirely lacking a cytoplasm, therefore haploid gene expression and translation (Kettaneh & Hartl 1976) are impossible and sperm in basically transcriptionally silent (Steger 1999; Joseph & Kirkpatrick 2004). Although later on, researchers found evidence of post-meiotic DNA transcription in sperm, they believed that the newly made transcriptomic products and other molecules could be shared between sperm cells through cytoplasmic bridges (Jeon 2004), therefore, all sperm cells benefit from the similarly defined sperm traits and none of the sperm develops advantages over others. Later on researchers found more proofs of DNA transcription (Erickson et al 1981) and even small amounts of protein translation in sperm cells (Gur & Breitbart 2006; Gur & Breitbart 2007; Gur & Breitbart 2008), and evidence that showed not all of the transcriptomes and proteins can be passed through cytoplasmic bridges (Erickson et al 1981). They also found that the alterations of the epigenome of sperm after meiosis (Teperek et al 2016) cause individual sperm to vary and to affect the next generation offspring differently. All these post-meiotic changes may form a basis for differences between individual sperm and create a potential for haploid selection to occur. In 2004 a review on a few studies showed several loci in animals’ genome experience haploid selection and it emphasized that such selection might potentially affect several evolutionary processes. Antagonistic adaptation between haploid and diploid phases, sex specific rates and genome imprinting, loads of deleterious mutations and extent of inbreeding depression are a few, among the many ways that haploid selection can affect evolutionary processes (Charlesworth & Charlesworth 1987; Charlesworth et al 1993; Joseph & Kirkpatrick 2004; Wyman & Wyman 2013).

  364. ⁠, Rosalind Arden, Linda S. Gottfredson, Geoffrey Miller, Arand Pierce (2008):

    Human cognitive abilities intercorrelate to form a positive matrix, from which a large first factor, called ‘Spearman’s g’ or general intelligence, can be extracted. General intelligence itself is correlated with many important health outcomes including cardiovascular function and longevity. However, the important evolutionary question of whether intelligence is a fitness-related trait has not been tested directly, let alone answered. If the correlations among cognitive abilities are part of a larger matrix of positive associations among fitness-related traits, then intelligence ought to correlate with seemingly unrelated traits that affect fitness—such as semen quality. We found statistically-significant positive correlations between intelligence and 3 key indices of semen quality: log sperm concentration (r = 0.15, p = 0.002), log sperm count (r = 0.19, p < 0.001), and sperm motility (r = 0.14, p = 0.002) in a large sample of US Army Veterans. None was mediated by age, body mass index, days of sexual abstinence, service in Vietnam, or use of alcohol, tobacco, marijuana, or hard drugs. These results suggest that a phenotype-wide fitness factor may contribute to the association between intelligence and health. Clarifying whether a fitness factor exists is important theoretically for understanding the genomic architecture of fitness-related traits, and practically for understanding patterns of human physical and psychological health.

  365. ⁠, Pierce, Ar, Miller, Geoffrey Arden, Rosalind Gottfredson, Linda S (2009):

    We recently found positive correlations between human general intelligence and three key indices of semen quality, and hypothesized that these correlations arise through a phenotype-wide ‘general fitness factor’ reflecting overall mutation load. In this addendum we consider some of the biochemical pathways that may act as targets for pleiotropic mutations that disrupt both neuron function and sperm function in parallel. We focus especially on the inter-related roles of polyunsaturated fatty acids, exocytosis and receptor signaling.

  366. ⁠, Tina Kold Jensen, Rune Jacobsen, Kaare Christensen, Niels Christian Nielsen, Erik Bostofte (2009-09):

    Fertility status may predict later mortality, but no studies have examined the effect of semen quality on subsequent mortality. Men referred to the Copenhagen Sperm Analysis Laboratory by general practitioners and urologists from 1963 to 2001 were, through a unique personal identification number, linked to the Danish central registers that hold information on all cases of cancer, causes of death, and number of children in the Danish population. The men were followed until December 31, 2001, death, or censoring, whichever occurred first, and the total mortality and cause-specific mortality of the cohort were compared with those of all age-standardized Danish men or according to semen characteristics. Among 43,277 men without azospermia referred for infertility problems, mortality decreased as the sperm concentration increased up to a threshold of 40 million/​​​​mL. As the percentages of motile and morphologically normal spermatozoa and semen volume increased, mortality decreased in a dose-response manner (ptrend  <  0.05). The decrease in mortality among men with good semen quality was due to a decrease in a wide range of diseases and was found among men both with and without children; therefore, the decrease in mortality could not be attributed solely to lifestyle and/​​​​or social factors. Semen quality may therefore be a fundamental biomarker of overall male health.

  367. ⁠, Juan J. Tarín, Miguel A. García-Pérez, Toshio Hamatani, Antonio Cano (2015-05-15; genetics  /​ ​​ ​correlation⁠, genetics  /​ ​​ ​selection  /​ ​​ ​dysgenics):

    The present review aims to ascertain whether different infertility etiologies share particular genes and/​​​​or molecular pathways with other pathologies and are associated with distinct and particular risks of later-life morbidity and mortality. In order to reach this aim, we use two different sources of information: (1) a public web server named DiseaseConnect focused on the analysis of common genes and molecular mechanisms shared by diseases by integrating comprehensive omics and literature data; and (2) a literature search directed to find clinical comorbid relationships of infertility etiologies with only those diseases appearing after infertility is manifested. This literature search is performed because DiseaseConnect web server does not discriminate between pathologies emerging before, concomitantly or after infertility is manifested. Data show that different infertility etiologies not only share particular genes and/​​​​or molecular pathways with other pathologies but they have distinct clinical relationships with other diseases appearing after infertility is manifested. In particular, (1) testicular and high-grade prostate cancer in male infertility; (2) non-fatal stroke and endometrial cancer, and likely non-fatal coronary heart disease and ovarian cancer in polycystic ovary syndrome; (3) osteoporosis, psychosexual dysfunction, mood disorders and dementia in premature ovarian failure; (4) breast and ovarian cancer in carriers of BRCA1/​​​​2 mutations in diminished ovarian reserve; (5) clear cell and endometrioid histologic subtypes of invasive ovarian cancer, and likely low-grade serous invasive ovarian cancer, melanoma and non-Hodgkin lymphoma in endometriosis; and (6) endometrial and ovarian cancer in idiopathic infertility. The present data endorse the principle that the occurrence of a disease (in our case infertility) is non-random in the population and suggest that different infertility etiologies are genetically and clinically linked with other diseases in single meta-diseases. This finding opens new insights for clinicians and reproductive biologists to treat infertility problems using a phenomic approach instead of considering infertility as an isolated and exclusive disease of the reproductive system/​​​​hypothalamic-pituitary-gonadal axis. In agreement with a previous validation analysis of the utility of DiseaseConnect web server, the present study does not show a univocal correspondence between common gene expression and clinical comorbid relationship. Further work is needed to untangle the potential genetic, epigenetic and phenotypic relationships that may be present among different infertility etiologies, morbid conditions and physical/​​​​cognitive traits.

  368. ⁠, Austin John Jeffery, Michael N. Pham, Todd K. Shackelford, Bernhard Fink (2016):

    A given man’s phenotype embodies cues of his ancestral ability to effectively defend himself and his kin from harm, to survive adverse conditions, and to acquire status and mating opportunities. In this review, we explore the hypothesis that a man’s phenotype also embodies cues to fertility or the probability that an ejaculate will fertilize ova. Female mate choice depends on the ability to discern the quality of a male reproductive partner through his phenotype, and male fertility may be among the traits that females have evolved to detect. A female who selects as mates males that deliver higher quality ejaculates will, on average, be more fecund than her competitors. Data on several non-human species demonstrate correlations between ejaculate quality and secondary sexual characteristics that inform female mate choice, suggesting that females may select mates in part on the basis of fertility. While the non-human literature on this topic has advanced, the human literature remains limited in scope and there is no clear consensus on appropriate methodologies or theoretical positions. We provide a comprehensive review and meta-analysis of this literature, and conclude by proposing solutions to the many issues that impede progress in the field. In the process, we hope to encourage interest and insight from investigators in other areas of human mating and reproductive biology.

  369. ⁠, Tara DeLecce, Bernhard Fink, Todd Shackelford, Mohaned G. Abed (2020-09-18):

    Genetic quality may be expressed through many traits simultaneously, and this would suggest a phenotype-wide fitness factor. In humans, intelligence has been positively associated with several potential indicators of genetic quality, including ejaculate quality. We conducted a conceptual replication of one such study by investigating the relationship between intelligence (assessed by the Raven Advanced Progressive Matrices Test-Short Form) and ejaculate quality (indexed by sperm count, sperm concentration, and sperm motility) in a sample of 41 men (ages ranging 18 to 33 years; M = 23.33; SD = 3.60). By self-report, participants had not had a vasectomy, and had never sought infertility treatment. We controlled for several covariates known to affect ejaculate quality (eg., abstinence duration before providing an ejaculate) and found no statistically-significant relationship between intelligence and ejaculate quality; our findings, therefore, do not match those of Arden, Gottfredson, Miller et al. or those of previous studies. We discuss limitations of this study and the general research area and highlight the need for future research in this area, especially the need for larger data sets to address questions around phenotypic quality and ejaculate quality.

    [Keywords: phenotype-wide fitness factor, ejaculate quality, intelligence, fertility, Raven Advanced Progressive Matrices test]

    …An important limitation of the current research is the small sample of 41 men, as small sample sizes increase the risk of both Type I and Type II errors. Our analyses, therefore, may have lacked sufficient power to detect the small effect sizes, r = 0.14 to 0.19, reported by Arden, Gottfredson, Miller, and Pierce (2009). Small sample sizes are a recurrent limitation of psychological research investigating ejaculate quality (eg., Baker & Bellis, 1989; Pook et al 2005), perhaps due to difficulties recruiting participants outside a clinical setting. Arden, Gottfredson, Miller, and Pierce analyzed data from a sample of 425 men, which afforded the analyses over 80% power to detect small effects. However, it is important to note that the correlation coefficients we obtained were similar in magnitude to those reported by Arden, Gottfredson, Miller, and Pierce, ranging from −0.18 to 0.30, and the repeated-measures nature of our study gave it greater power despite the small sample size.

  370. 2019-immler.pdf: ⁠, Simone Immler (2019-01-01; genetics  /​ ​​ ​selection):

    Evolutionary rates and strength of selection differ markedly between haploid and diploid genomes. Any genes expressed in a haploid state will be directly exposed to selection, whereas alleles in a diploid state may be partially or fully masked by a homologous allele. This difference may shape key evolutionary processes, including rates of adaptation and inbreeding depression, but also the evolution of sex chromosomes, heterochiasmy, and stable sex ratio biases. All diploid organisms carry haploid genomes, most notably the haploid genomes in gametes produced by every sexually reproducing eukaryote. Furthermore, haploid expression occurs in genes with monoallelic expression, in sex chromosomes, and in organelles, such as mitochondria and plastids. A comparison of evolutionary rates among these haploid genomes reveals striking parallels. Evidence suggests that haploid selection has the potential to shape evolution in predominantly diploid organisms, and taking advantage of the rapidly developing technologies, we are now in the position to quantify the importance of such selection on haploid genomes.

  371. ⁠, Avery Davis Bell, Curtis J. Mello, James Nemesh, Sara A. Brumbaugh, Alec Wysoker, Steven A. McCarroll (2019-05-02):

    Meiosis, while critical for reproduction, is also highly variable and error prone: crossover rates vary among humans and individual gametes, and chromosome nondisjunction leads to aneuploidy, a leading cause of miscarriage. To study variation in meiotic outcomes within and across individuals, we developed a way to sequence many individual sperm genomes at once. We used this method to sequence the genomes of 31,228 gametes from 20 sperm donors, identifying 813,122 crossovers, 787 aneuploid chromosomes, and unexpected genomic anomalies. Different sperm donors varied four-fold in the frequency of aneuploid sperm, and aneuploid chromosomes gained in meiosis I had 36% fewer crossovers than corresponding non-aneuploid chromosomes. Diverse recombination phenotypes were surprisingly coordinated: donors with high average crossover rates also made a larger fraction of their crossovers in centromere-proximal regions and placed their crossovers closer together. These same relationships were also evident in the variation among individual gametes from the same donor: sperm with more crossovers tended to have made crossovers closer together and in centromere-proximal regions. Variation in the physical compaction of chromosomes could help explain this coordination of meiotic variation across chromosomes, gametes, and individuals.

  372. {#linkBibliography-(nyt)-2019 .docMetadata}, Randi Hutter Epstein (NYT) (2019-07-18):

    Fertility treatments have gone so high-tech, it’s logical to assume there’s an exact formula for each procedure. Embryos are frozen and warmed at precise temperatures, hormones are measured to the billionth of a gram, and women inject themselves with strictly-calibrated doses of drugs. But sperm selection remains more art than science. Though fertility specialists generally agree that an “ideal” human sperm has a smooth, olive-shaped head and a long, undulating tail, the degree to which the appearance of sperm cells correlates with their fertilizing potential is a subject of much controversy. It isn’t always possible to find sperm with this ideal physique in a given sample, Lo noted, and even homely, misshapen sperm can produce healthy babies. Sometimes, Lo said, “You pick the least ugly of the sample you have.”

    …These days, many leading fertility centers use techniques that allow them to bypass all these steps. Instead, they pick a single sperm and inject it into the egg, a technique called intracytoplasmic sperm injection or ICSI (pronounced ICK-see). ICSI was designed to help men with few or defective sperm, but has become so common that it’s used in more than half of all I.V.F. procedures. (Despite its widespread use, studies have not proven that ICSI boosts pregnancy rates when men have sufficient numbers of healthy sperm.)

    …Techniques to sort sperm by putting them through fine mesh filters and by having them swim through specially-engineered pathways called microchannels have also failed to yield better results than simply choosing by appearance. Research efforts continue but, for now, sperm selection is generally left up to the aesthetic judgement of the individual embryologist.

    …Since it’s impossible to individually examine each of the thousands of sperm in a typical sample, embryologists acknowledge that the quest for the best possible sperm involves an element of fate. “If I look in my scope and say, ‘That one looks really great’, I’ll choose it”, Lo explained. But if an especially strong swimmer darts across his field of vision, he sometimes changes course at the last minute. When this happens, he said, he wonders, “Did I choose that sperm? Did the sperm choose me?”

  373. 2014-mcdowell.pdf: ⁠, S. McDowell, B. Kroon, E. Ford, Y. Hook, D. Glujovsky, A. Yazdani (2014; genetics  /​ ​​ ​selection):

    Background: Assisted reproductive technologies (ART) such as in vitro fertilisation (IVF) and intracytoplasmic sperm injection (ICSI) bring together gametes outside of the body to enhance the probability of fertilisation and pregnancy. Advanced sperm selection techniques are increasingly being employed in ART, most commonly in cycles utilising ICSI. Advanced sperm selection techniques are thought to improve the chance that structurally intact and mature sperm with high DNA integrity are selected for fertilisation. Advanced sperm selection strategies include selection according to surface charge; sperm apoptosis; sperm birefringence; ability to bind to hyaluronic acid; and sperm morphology under ultra-high magnification. These techniques theoretically improve ART outcomes.

    Objectives: To evaluate the impact of advanced sperm selection techniques on ART outcomes.

    Search methods: Systematic search of electronic databases (Cochrane Menstrual Disorders and Subfertility Group Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, PsycINFO, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Latin American and Caribbean Health Science Information Database (LILACS)), trials registers (ClinicalTrials.gov, Current Controlled Trials, World Health Organization International Clinical Trials Registry Platform), conference abstracts (Web of Knowledge) and grey literature (OpenGrey) for relevant randomised controlled trials. We hand-searched the reference lists of included studies and similar reviews. The search was conducted in May 2014.

    Selection criteria: We included randomised controlled trials (RCTs) comparing an advanced sperm selection technique versus standard IVF or ICSI or versus another advanced sperm selection technique. We excluded studies of sperm selection using ultra-high magnification (intracytoplasmic morphologically selected sperm injection, or IMSI), as they are the subject of a separate Cochrane review. Quasi-randomised and pseudo-randomised trials were excluded. Our primary outcome measure was live birth rate per woman randomly assigned. Secondary outcome measures included clinical pregnancy per woman randomly assigned, miscarriage per clinical pregnancy and fetal abnormality per clinical pregnancy.

    Data collection and analysis: Two review authors independently assessed eligibility of studies and risk of bias, and performed data extraction. Disagreements were resolved by consultation with a third review author. Study investigators were consulted to resolve other queries that arose. Risk ratios (RRs) were calculated with 95% confidence intervals (CIs). We planned to combine studies using a fixed-effect model, if sufficient data were available. The quality of the evidence was evaluated using Grades of Recommendation, Assessment, Development and Evaluation (GRADE) methods.

    Main results: Two RCTs were included in the review. Both evaluated sperm selection by hyaluronanic acid binding for ICSI, but only one reported live births. No studies were identified that were related to surface charge selection, sperm apoptosis or sperm birefringence.

    One RCT compared hyaluronanic acid binding versus conventional ICSI. Live birth was not reported. Evidence was insufficient to show whether there was a difference between groups in clinical pregnancy rates (RR 1.01, 95% CI 0.84 to 1.22, one RCT, 482 women). This evidence was deemed to be of low quality, mainly as the result of poor reporting of methods and findings. Miscarriage data were unclear, and fetal abnormality rates were not reported.

    The other RCT compared two different hyaluronanic acid binding techniques, SpermSlow and physiological intracytoplasmic sperm injection (PISCI). Evidence was insufficient to indicate whether there was a difference between groups in rates of live birth (RR 1.16, 95% CI 0.65 to 2.05, one RCT, 99 women), clinical pregnancy (RR 1.07, 95% CI 0.67 to 1.71, one RCT, 99 women) or miscarriage (RR 0.76, 95% CI 0.24 to 2.44, one RCT, 41 women). The evidence for these comparisons was deemed to be of low quality, as it was limited by imprecision and poor reporting of study methods. Fetal abnormality rates were not reported.

    Authors’ conclusions: Evidence was insufficient to allow review authors to determine whether sperm selected by hyaluronanic acid binding improve live birth or pregnancy outcomes in ART, and no clear data on adverse effects were available. Evidence was also insufficient to show whether there is a difference in efficacy between the hyaluronic acid binding methods SpermSlow and PICSI. No randomised evidence evaluating sperm selection by sperm apoptosis, sperm birefringence or surface charge was found. Further studies of suitable quality are required to evaluate whether any of these advanced sperm selection techniques can be recommended for use in clinical practice.

  374. https://old.reddit.com/r/slatestarcodex/comments/8p91kt/crazy_ideas_thread_part_ii/e0b6rzm/?context=3

  375. ⁠, Paulis, Marianna Castelli, Alessandra Susani, Lucia Lizier, Michela Lagutina, Irina Focarelli, Maria Luisa Recordati, Camilla Uva, Paolo Faggioli, Francesca Neri, Tui Scanziani, Eugenio Galli, Cesare Lucchini, Franco Villa, Anna Vezzoni, Paolo (2015):

    Genomic disorders resulting from large rearrangements of the genome remain an important unsolved issue in gene therapy. Chromosome transplantation, defined as the perfect replacement of an endogenous chromosome with a homologous one, has the potential of curing this kind of disorders. Here we report the first successful case of chromosome transplantation by replacement of an endogenous X chromosome carrying a mutation in the Hprt genewith a normal one in mouse embryonic stem cells (ESCs), correcting the genetic defect. The defect was also corrected by replacing the Y chromosome with an X chromosome. Chromosome transplanted clones maintained in vitro and in vivo features of stemness and contributed to chimera formation. Genome integrity was confirmed by cytogenetic and molecular genome analysis. The approach here proposed, with some modifications, might be used to cure various disorders due to other X chromosome aberrations in induced pluripotent stem (iPS) cells derived from affected patients.

  376. ⁠, Delphine Mieulet, Gregoire Aubert, Cecile Bres, Anthony Klein, Gaëtan Droc, Emilie Vieille, Celine Rond-Coissieux, Myriam Sanchez, Marion Dalmais, Jean-Philippe Mauxion, Christophe Rothan, Emmanuel Guiderdoni, Raphael Mercier (2018-06-11):

    Improved plant varieties are important in our attempts to face the challenges of a growing human population and limited planet resources. Plant breeding relies on meiotic crossovers to combine favourable alleles into elite varieties. However, meiotic crossovers are relatively rare, typically one to three per chromosome, limiting the efficiency of the breeding process and related activities such as genetic mapping. Several genes that limit meiotic recombination were identified in the model species Arabidopsis thaliana. Mutation of these genes in Arabidopsis induces a large increase in crossover frequency. However, it remained to be demonstrated whether crossovers could also be increased in crop species hybrids. We explored the effects of mutating the orthologues of FANCM, RECQ4 or FIGL1 on recombination in three distant crop species, rice (Oryza sativa), pea (Pisum sativum) and tomato (Solanum lycopersicum). We found that the single recq4 mutation increases crossovers about three-fold in these crops, suggesting that manipulating RECQ4 may be a universal tool for increasing recombination in plants. Enhanced recombination could be used with other state-of-the-art technologies such as genomic selection, genome editing or speed breeding6 to enhance the pace and efficiency of plant improvement.

  377. 2008-wijnker.pdf

  378. ⁠, E. Tourrette, R. Bernardo, M. Falque, O. Martin (2019-07-17):

    Recombination generates genetic diversity but the number of crossovers per meiosis is limited in most species. Previous studies showed that increasing recombination can enhance response to selection. However, such studies did not assume a specific method of modifying recombination. Our objective was to test whether two methods used to increase recombination in plants could increase the genetic gain in a population undergoing genomic selection. The first method, in Oryza sativa, used a mutant of anti-crossover genes to increase global recombination without affecting the recombination landscape. The second one uses the ploidy level of a cross between Brassica rapa and Brassica napus to increase the recombination particularly in pericentromeric regions. These recombination landscapes were used to model recombination while quantitative trait loci positions were based on the actual gene distribution. We simulated selection programs with initially a cross between two inbred lines, for two species. Increased recombination enhanced the response to selection. The amount of enhancement in the cumulative gain largely depended on the species and the number of quantitative trait loci (2, 10, 20, 50, 200 or 1000 per chromosome). Genetic gains were increased up to 30% after 20 generations. Furthermore, modifying the recombination landscape was the most effective: the gain was larger by 25% with the first method and 33% with the second one in B. rapa, and 15% compared to 11% in O. sativa. Thus, increased recombination enhances the genetic gain in for long-term selection programs, with visible effects after four to five generations.

  379. 2013-walsh-book2-ch14-draft.pdf: ⁠, Michael Lynch, Bruce Walsh (2013; genetics  /​ ​​ ​selection):

    This brief chapter first considers the theory of truncation selection on the mean, which is of general interest, and then examines a number of more specialized topics that may be skipped by the casual reader. Truncation selection (Figure 14.1) occurs when all individuals on one side of a threshold are chosen, and is by far the commonest form of artificial selection in breeding and laboratory experiments. One key result is that for a trait, the selection intensity is fully determined by the fraction p saved (Equation 14.3a), provided that the chosen number of adults is large. This allows a breeder or experimentalist to predict the expected response given their choice of p.

    The remaining topics are loosely organized around the theme of selection intensity and threshold selection. First, when a small number of adults are chosen to form the next generation, Equation 14.3a overestimates the expected , and we discuss how to correct for this small sample effect. This correction is important when only a few individuals form the next generation, but is otherwise relatively minor. The rest of the chapter considers the response in discrete traits. We start with a binary (present/​​​​absence) trait, and show how an underlying liability model can be used to predict response. We also examine binary trait response in a framework (estimating the probability of showing the trait given some underlying liability scores) and the evolution of both the mean value on the liability scale and the threshold value. We conclude with a few brief comments on response when a trait is better modeled as Poisson, rather than normally, distributed…In addition to being the commonest form of artificial selection, truncation selection is also the most efficient, giving the largest selection intensity of any scheme culling the same fraction of individuals from a population (Kimura & Crow 1978, Crow and Kimura 1979).

    [Preprint chapter of Evolution and Selection of Quantitative Traits⁠, Lynch & Walsh 2018]

  380. ⁠, Robert A. Power, Simon Kyaga, Rudolf Uher, James H. MacCabe, Niklas Långström, Mikael Landen, Peter McGuffin, Cathryn M. Lewis, Paul Lichtenstein, Anna C. Svensson (2013-01):

    Context: It is unknown how genetic variants conferring liability to psychiatric disorders survive in the population despite strong ⁠. However, this is key to understanding their etiology and designing studies to identify risk variants.

    Objectives: To examine the reproductive fitness of patients with schizophrenia and other psychiatric disorders vs their unaffected siblings and to evaluate the level of selection on causal genetic variants.

    Design: We measured the fecundity of patients with schizophrenia, autism, bipolar disorder, depression, anorexia nervosa, or substance abuse and their unaffected siblings compared with the general population.

    Setting: Population databases in Sweden, including the Multi-Generation Register and the Swedish Hospital Discharge Register.

    Participants: In total, 2.3 million individuals among the 1950 to 1970 birth cohort in Sweden.

    Main Outcome Measures: Fertility ratio (FR), reflecting the mean number of children compared with that of the general population, accounting for age, sex, family size, and affected status.

    Results: Except for women with depression, affected patients had statistically-significantly fewer children (FR range for those with psychiatric disorder, 0.23–0.93; p < 10−10). This reduction was consistently greater among men than women, suggesting that male fitness was particularly sensitive. Although sisters of patients with schizophrenia and bipolar disorder had increased fecundity (FR range, 1.02–1.03; p < 0.01), this was too small on its own to counterbalance the reduced fitness of affected patients. Brothers of patients with schizophrenia and autism showed reduced fecundity (FR range, 0.94–0.97; p < 0.001). Siblings of patients with depression and substance abuse had statistically-significantly increased fecundity (FR range, 1.01–1.05; p < 10−10). In the case of depression, this more than compensated for the lower fecundity of affected individuals.

    Conclusions: Our results suggest that strong selection exists against schizophrenia, autism, and anorexia nervosa and that these variants may be maintained by new mutations or an as-yet unknown mechanism. Bipolar disorder did not seem to be under strong negative selection. Vulnerability to depression, and perhaps substance abuse, may be preserved by balancing selection, suggesting the involvement of common genetic variants in ways that depend on other genes and on environment.

  381. 2018-keller.pdf: ⁠, Matthew C. Keller (2018-05; genetics  /​ ​​ ​selection):

    Evolutionary medicine uses evolutionary theory to help elucidate why humans are vulnerable to disease and disorders. I discuss two different types of evolutionary explanations that have been used to help understand human psychiatric disorders.

    First, a consistent finding is that psychiatric disorders are moderately to highly heritable, and many, such as schizophrenia, are also highly disabling and appear to decrease Darwinian fitness. Models used in evolutionary genetics to understand why genetic variation exists in fitness-related traits can be used to understand why risk alleles for psychiatric disorders persist in the population. The usual explanation for species-typical adaptations—natural selection—is less useful for understanding individual differences in genetic risk to disorders. Rather, two other types of models, mutation-selection-drift and balancing selection, offer frameworks for understanding why genetic variation in risk to psychiatric (and other) disorders exists, and each makes predictions that are now testable using whole-genome data.

    Second, species-typical capacities to mount reactions to negative events are likely to have been crafted by natural selection to minimize fitness loss. The pain reaction to tissue damage is almost certainly such an example, but it has been argued that the capacity to experience depressive symptoms such as sadness, anhedonia, crying, and fatigue in the face of adverse life situations may have been crafted by natural selection as well. I review the rationale and strength of evidence for this hypothesis.

    Evolutionary hypotheses of psychiatric disorders are important not only for offering explanations for why psychiatric disorders exist, but also for generating new, testable hypotheses and understanding how best to design studies and analyze data.

    [Keywords: evolution, psychiatric disorders, genetics, schizophrenia, depression],/​​​​p>

  382. index.html: ⁠, Rafe Kennedy, Gwern Branwen (2018-12-17; statistics  /​ ​​ ​order  /​ ​​ ​beanmachine-multistage):

    An interactive JavaScript of order statistics visualized as a Galton bean machine, showing difference in means & maxima between single stage of selection and multiple stages.

    This is an interactive JS-based visualization of the difference in optimization potentials of a single-stage pipeline vs a multi-stage pipeline, in which new samples/​​​​measurements can be generated at each step (such as in evolutionary processes).

    Because it optimizes over multiple steps, the multi-stage pipeline “ratchets upward” and can attain far more extreme maxima than a single-stage pipeline, even with the same total number of samples—the single-stage process quickly hits “diminishing returns”, where large increases in sample count result in only small increases in the expected maximum. This means that small gains per stage, or a few stages, or a few generations of evolution, can result in large increases of sample means, compared to a single-stage process. Due to ⁠, the increases in means can cause larger increases in the probability of samples passing thresholds such as “top 1%”/​​​​≥2.32σ, or yielding extremes. And the more stages, the greater differences can be (single-stage selection increases logarithmically, while multi-stage increases linearly).

    These increases can be counterintuitively large, but the gains/​​​​losses are relevant to understanding many processes, such as the clinical drug discovery pipeline (eg ).

    The visualization metaphor here is : a ball (sample) falls from the top (zero-mean), and is affected by sets of pins (stochastic variables) which bounce the ball left/​​​​right with 50-50 probability (increase/​​​​decrease it) as it falls to the bottom (final total). The resulting binomial distribution approximates a normal distribution. The bean machine visually & concretely illustrates the sampling distribution of how a normally-distributed final variable can emerge out of the sum of many individual small discrete effects, without requiring any mathematics.

    In this visualization, we generalize Galton’s “bean machine” by allowing stacking of bean machines. To stack bean machines, we select the ball which is the maximum within each sample. How large is it? In the single-stage bean machine, selection stops there. In the multi-stage bean machine, another bean machine begins with the maximum serving as the seed & new average, and another round of generation & selection begins, and so on, until a final sample is selected, and we can see how large it is. The gains turn out to be larger the more samples we use total, unsurprisingly, but also the more stages we specify; the maximum possible maximum turns out to be when we have so many stages that there are just 2 samples per stage.

    Screenshot of the multi-stage bean machine, showing selection in progress in a 3×3 pipeline
  383. ⁠, Andreas Pavlogiannis, Josef Tkadlec, Krishnendu Chatterjee, Martin A. Nowak (2018-06-14):

    [] Because of the intrinsic randomness of the evolutionary process, a mutant with a fitness advantage has some chance to be selected but no certainty. Any experiment that searches for advantageous mutants will lose many of them due to random drift. It is therefore of great interest to find population structures that improve the odds of advantageous mutants. Such structures are called amplifiers of natural selection: they increase the probability that advantageous mutants are selected. Arbitrarily strong amplifiers guarantee the selection of advantageous mutants, even for very small fitness advantage. Despite intensive research over the past decade, arbitrarily strong amplifiers have remained rare. Here we show how to construct a large variety of them. Our amplifiers are so simple that they could be useful in biotechnology, when optimizing biological molecules, or as a diagnostic tool, when searching for faster dividing cells or viruses. They could also occur in natural population structures.

    In the evolutionary process, mutation generates new variants, while selection chooses between mutants that have different reproductive rates. Any new mutant is initially present at very low frequency and can easily be eliminated by ⁠. The probability that the lineage of a new mutant eventually takes over the entire population is called the ⁠. It is a key quantity of evolutionary dynamics and characterizes the rate of evolution.

    …In this work we resolve several open questions regarding strong amplification under uniform and temperature initialization. First, we show that there exists a vast variety of graphs with self-loops and weighted edges that are arbitrarily strong amplifiers for both uniform and temperature initialization. Moreover, many of those strong amplifiers are structurally simple, therefore they might be realizable in natural or laboratory setting. Second, we show that both self-loops and weighted edges are key features of strong amplification. Namely, we show that without either self-loops or weighted edges, no graph is a strong amplifier under temperature initialization, and no simple graph is a strong amplifier under uniform initialization.

    …In general, the fixation probability depends not only on the graph, but also on the initial placement of the invading mutants…For a wide class of population structures17, which include symmetric ones28, the fixation probability is the same as for the well-mixed population.

    … A population structure is an arbitrarily strong amplifier (for brevity hereafter also called “strong amplifier”) if it ensures a fixation probability arbitrarily close to one for any advantageous mutant, r > 1. Strong amplifiers can only exist in the limit of large population size.

    Numerical studies30 suggest that for spontaneously arising mutants and small population size, many unweighted graphs amplify for some values of r. But for a large population size, randomly constructed, unweighted graphs do not amplify31. Moreover, proven amplifiers for all values of r are rare. For spontaneously arising mutants (uniform initialization): (1) the Star has fixation probability of ~1 − 1⁄r2 in the limit of large N, and is thus an amplifier17, 32, 33; (2) the Superstar (introduced in ref. 17, see also ref. 34) and the Incubator (introduced in refs. 35, 36), which are graphs with unbounded degree, are strong amplifiers.

    …In this work we resolve several open questions regarding strong amplification under uniform and temperature initialization. First, we show that there exists a vast variety of graphs with self-loops and weighted edges that are arbitrarily strong amplifiers for both uniform and temperature initialization. Moreover, many of those strong amplifiers are structurally simple, therefore they might be realizable in natural or laboratory setting. Second, we show that both self-loops and weighted edges are key features of strong amplification. Namely, we show that without either self-loops or weighted edges, no graph is a strong amplifier under temperature initialization, and no simple graph is a strong amplifier under uniform initialization.

    Figure 1: Evolutionary dynamics in structured populations. Residents (yellow) and mutants (purple) differ in their reproductive rate. (a) A single mutant appears. The lineage of the mutant becomes extinct or reaches fixation. The probability that the mutant takes over the population is called “fixation probability”. (b) The classical, well-mixed population is described by a complete graph with self-loops. (Self-loops are not shown here.) (c) Isothermal structures do not change the fixation probability compared to the well-mixed population. (d) The Star is an amplifier for uniform initialization. (e) A self-loop means the offspring can replace the parent. Self-loops are a mathematical tool to assign different reproduction rates to different places. (f) The Superstar, which has unbounded degree in the limit of large population size, is a strong amplifier for uniform initialization. Its edges (shown as arrows) are directed which means that the connections are one-way.
    Figure 4: Infinite variety of strong amplifiers. Many topologies can be turned into arbitrarily strong amplifiers (Wheel (a), Triangular grid (b), Concentric circles (c), and Tree (d)). Each graph is partitioned into hub (orange) and branches (blue). The weights can be then assigned to the edges so that we obtain arbitrarily strong amplifiers. Thick edges receive large weights, whereas thin edges receive small (or zero) weights

    …Intuitively, the weight assignment creates a sense of global flow in the branches, directed toward the hub. This guarantees that the first 2 steps happen with high probability. For the third step, we show that once the mutants fixate in the hub, they are extremely likely to resist all resident invasion attempts and instead they will invade and take over the branches one by one thereby fixating on the whole graph. For more detailed description, see “Methods” section “Construction of strong amplifiers”.

    Necessary conditions for amplification: Our main result shows that a large variety of population structures can provide strong amplification. A natural follow-up question concerns the features of population structures under which amplification can emerge. We complement our main result by proving that both weights and self-loops are essential for strong amplification. Thus, we establish a strong dichotomy. Without either weights or self-loops, no graph can be a strong amplifier under temperature initialization, and no simple graph can be a strong amplifier under uniform initialization. On the other hand, if we allow both weights and self-loops, strong amplification is ubiquitous.

    …Some naturally occurring population structures could be amplifiers of natural selection. For example, the germinal centers of the immune system might constitute amplifiers for the affinity maturation process of adaptive immunity46. Habitats of animals that are divided into multiple islands with a central breeding location could potentially also act as amplifiers of selection. Our theory helps to identify those structures in natural settings.

  384. #history-of-ies

  385. #haley-visscher-1998

  386. ⁠, 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.

  387. https://web.archive.org/web/20091214014113/http://theuncertainfuture.com/faq.html#7

  388. https://jlb.oxfordjournals.org/content/early/2015/12/16/jlb.lsv057.full

  389. http://www.hinxtongroup.org/Consensus_HG08_FINAL.pdf

  390. http://fatstemserbia.brinkster.net/Library/Newspapers/Egg%20Engineers.pdf

  391. 1989-betteridge.pdf: “Potential genetic improvement of cattle by fertilization of fetal oocytes in vitro”⁠, K. J. Betteridge, C. Smith, R. B. Stubbings, K. P. Xu, W. A. King

  392. 2018-goszczynski.pdf: “In vitro breeding: application of embryonic stem cells to animal production”⁠, Daniel E. Goszczynski, Hao Cheng, Sebastian Demyda-Peyrás, Juan F. Medrano, Jun Wu, Pablo J. Ross

  393. 2019-goszczynski.pdf: ⁠, Daniel E. Goszczynski, Anna C. Denicol, Pablo J. Ross (2019-07-03; genetics  /​ ​​ ​selection):

    In vitro gamete differentiation could revolutionize animal production by decreasing generation intervals, increasing the number of gametes per animal and facilitating the dissemination of elite genetics. In addition, it could help to develop new strategies for the conservation of endangered species. The recent in vitro reconstitution of germ cell development in mice has inspired researchers to invest their best efforts into reproducing this achievement in livestock species.

    With this goal in mind, multiple differentiation approaches and cell sources have been evaluated. The degree of success in these evaluations varies according to the species and the stage of development studied, but, in general, partially positive results have been obtained. Evidence suggests that although functional gametes with true reproductive potential are still to be obtained, it is a matter of time before this goal is achieved. [Previously: “In vitro breeding: application of embryonic stem cells to animal production”⁠, Goszczynski et al 2018]

  394. 2021-hayashi.pdf: ⁠, Katsuhiko Hayashi, Cesare Galli, Sebastian Diecke, Thomas B. Hildebrandt (2021-01-08; genetics  /​ ​​ ​selection):

    The production of gametes from pluripotent stem cells in culture, also known as invitro gametogenesis, will make an important contribution to reproductive biology and regenerative medicine, both as a unique tool for understanding germ cell development and as an alternative source of gametes for reproduction. Invitro gametogenesis was developed using mouse pluripotent stem cells but is increasingly being applied in other mammalian species, including humans. In principle, the entire process of germ cell development is nearly reconstitutable in culture using mouse pluripotent stem cells, although the fidelity of differentiation processes and the quality of resultant gametes remain to be refined. The methodology in the mouse system is only partially applicable to other species, and thus it must be optimised for each species. In this review, we update the current status of invitro gametogenesis in mice, humans and other animals, and discuss challenges for further development of this technology.

  395. https://jasbsci.biomedcentral.com/articles/10.1186/s40104-018-0304-7

  396. ⁠, Hayashi, Katsuhiko Saitou, Mitinori (2014):

    Pluripotent stem cells, such as embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) are able to differentiate into all cell lineages of the embryo proper, including germ cells. This pluripotent property has a huge impact on the fields of regenerative medicine, developmental biology and reproductive engineering. Establishing the germ cell lineage from ESCs/​​​​iPSCs is the key biological subject, since it would contribute not only to dissection of the biological processes of germ cell development but also to production of unlimited numbers of functional gametes in vitro. Toward this goal, we recently established a culture system that induces functional mouse primordial germ cells (PGCs), precursors of all germ cells, from mouse ESCs/​​​​iPSCs. The successful in vitro production of PGCs arose from the study of pluripotent cell state, the signals inducing PGCs and the technology of transplantation. However, there are many obstacles to be overcome for the robust generation of mature gametes or for application of the culture system to other species, including humans and livestock. In this review, we discuss the requirements for a culture system to generate the germ cell lineage from ESCs/​​​​iPSCs.

  397. https://www.sciencedirect.com/science/article/pii/S0092867414015839

  398. https://www.cell.com/cell-stem-cell/fulltext/S1934-5909%2816%2900018-7

  399. 2016-hikabe.pdf

  400. 2016-zhang.pdf: “MLL1 Inhibition Reprograms Epiblast Stem Cells to Naive Pluripotency⁠, Hui Zhang, Srimonta Gayen, Jie Xiong, Bo Zhou, Avinash K. Shanmugam, Yuqing Sun, Hacer Karatas, Liu Liu, Rajesh C. Rao, Shaomeng Wang, Alexey I. Nesvizhskii, Sundeep Kalantry, Yali Dou

  401. https://www.pnas.org/content/pnas/115/9/2090.full.pdf

  402. ⁠, Chenglei Tian, Linlin Liu, Xiaoying Ye, Haifeng Fu, Xiaoyan Sheng, Lingling Wang, Huasong Wang, Dai Heng, Lin Liu (2019-12-24):

    • Granulosa cells can be reprogrammed to form oocytes by chemical reprogramming
    • Rock inhibition and crotonic acid facilitate the chemical induction of gPSCs from GCs
    • PGCLCs derived from gPSCs exhibit longer telomeres and high genomic stability

    The generation of genomically stable and functional oocytes has great potential for preserving fertility and restoring ovarian function. It remains elusive whether functional oocytes can be generated from adult female somatic cells through reprogramming to germline-competent pluripotent stem cells (gPSCs) by chemical treatment alone. Here, we show that somatic granulosa cells isolated from adult mouse ovaries can be robustly induced to generate gPSCs by a purely chemical approach, with additional Rock inhibition and critical reprogramming facilitated by crotonic sodium or acid. These gPSCs acquired high germline competency and could consistently be directed to differentiate into primordial-germ-cell-like cells and form functional oocytes that produce fertile mice. Moreover, gPSCs promoted by crotonylation and the derived germ cells exhibited longer telomeres and high genomic stability like PGCs in vivo, providing additional evidence supporting the safety and effectiveness of chemical induction, which is particularly important for germ cells in genetic inheritance.

    [Keywords: chemical reprogramming, pluripotent stem cell, oocyte, granulosa cell]

  403. 2021-yoshino.pdf: ⁠, Takashi Yoshino, Takahiro Suzuki, Go Nagamatsu, Haruka Yabukami, Mika Ikegaya, Mami Kishima, Haruka Kita, Takuya Imamura, Kinichi Nakashima, Ryuichi Nishinakamura, Makoto Tachibana, Miki Inoue, Yuichi Shima, Ken-ichirou Morohashi, Katsuhiko Hayashi (2021-07-16; genetics  /​ ​​ ​editing):

    Oocytes mature in a specialized fluid-filled sac, the ovarian follicle, which provides signals needed for meiosis and germ cell growth. Methods have been developed to generate functional oocytes from pluripotent stem cell–derived primordial germ cell–like cells (PGCLCs) when placed in culture with embryonic ovarian somatic cells. In this study, we developed culture conditions to recreate the stepwise differentiation process from pluripotent cells to fetal ovarian somatic cell–like cells (FOSLCs). When FOSLCs were aggregated with PGCLCs derived from mouse embryonic stem cells, the PGCLCs entered meiosis to generate functional oocytes capable of fertilization and development to live offspring. Generating functional mouse oocytes in a reconstituted ovarian environment provides a method for in vitro oocyte production and follicle generation for a better understanding of mammalian reproduction.

  404. 2019-zheng.pdf: ⁠, Yi Zheng, Xufeng Xue, Yue Shao, Sicong Wang, Sajedeh Nasr Esfahani, Zida Li, Jonathon M. Muncie, Johnathon N. Lakins, Valerie M. Weaver, Deborah L. Gumucio, Jianping Fu (2019-09-11; genetics  /​ ​​ ​editing):

    Early human embryonic development involves extensive lineage diversification, cell-fate specification and tissue patterning1. Despite its basic and clinical importance, early human embryonic development remains relatively unexplained owing to interspecies divergence2,3 and limited accessibility to human embryo samples. Here we report that human pluripotent stem cells (hPSCs) in a microfluidic device recapitulate, in a highly controllable and scalable fashion, landmarks of the development of the epiblast and amniotic ectoderm parts of the conceptus, including lumenogenesis of the epiblast and the resultant pro-amniotic cavity, formation of a bipolar embryonic sac, and specification of primordial germ cells and primitive streak cells. We further show that amniotic ectoderm-like cells function as a signalling centre to trigger the onset of gastrulation-like events in hPSCs. Given its controllability and scalability, the microfluidic model provides a powerful experimental system to advance knowledge of human embryology and reproduction. This model could assist in the rational design of differentiation protocols of hPSCs for disease modelling and cell therapy, and in high-throughput drug and toxicity screens to prevent pregnancy failure and birth defects.

  405. https://www.technologyreview.com/s/543541/how-to-really-engineer-a-human-baby/

  406. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655236/

  407. https://www.nytimes.com/2016/10/06/upshot/when-you-hear-the-margin-of-error-is-plus-or-minus-3-percent-think-7-instead.html

  408. http://www.afhalifax.ca/magazine/wp-content/sciences/Darwin/SelectionArtificielle/ArtificialSelection.pdf

  409. http://nitro.biosci.arizona.edu:80/zbook/NewVolume_2/pdf/Chapter26.pdf

  410. http://nitro.biosci.arizona.edu/workshops/Aarhus2006/pdfs/WalshLT.pdf

  411. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1200493/pdf/543.pdf

  412. 1911-surface.pdf: “The Result of Selecting Fluctuating Variations: Data from the Illinois Corn Breeding Experiments”⁠, Frank M. Surface

  413. http://www.genetics.org/content/genetics/144/1/205.full.pdf

  414. 2004-weber.pdf: “Population Size and Long-term Selection”⁠, Kenneth Weber

  415. 2018-walsh-c26-weberfruitflyselectivebreeding.png

  416. https://www.aipl.arsusda.gov/publish/other/2011/ColeVanRaden_JABGEV_41311.pdf

  417. ⁠, P. M. VanRaden (2004):

    Genetic selection has made dairy cows more profit-able producers of milk. Genetic evaluations began with 2 traits measured on a few cows but now include many traits measured on millions of cows. Selection indexes from USDA included yield traits beginning in 1971, productive life and somatic cell score beginning in 1994, conformation traits in 2000, and cow fertility and calving ease in 2003. This latest revision of net merit should result in 2% more progress, worth $7$52004 million/​​​​yr nationally, with improved cow health and fitness, but slightly less progress for yield. Fertility and longevity evaluations have similar reliability because cows can have several fertility records, each with lower heritability, compared with one longevity record with higher heritability. Lifetime profit can be estimated more accurately if less heritable traits are evaluated and included instead of ignored. Milk volume has a positive value for fluid use, but a negative value for cheese production. Thus, multiple selection indexes are needed for different markets and production systems. Breeding programs should estimate future rather than current costs and prices. Many other nations have derived selection indexes similar to US net merit.

  418. 2017-wiggans.pdf: ⁠, George R. Wiggans, John B. Cole, Suzanne M. Hubbard, Tad S. Sonstegard (2017; genetics  /​ ​​ ​selection):

    Genomic selection has revolutionized dairy cattle breeding. Since 2000, assays have been developed to genotype large numbers of single-nucleotide polymorphisms (SNPs) at relatively low cost. The first commercial SNP genotyping chip was released with a set of 54,001 SNPs in December 2007. Over 15,000 genotypes were used to determine which SNPs should be used in genomic evaluation of US dairy cattle. Official USDA genomic evaluations were first released in January 2009 for Holsteins and Jerseys, in August 2009 for Brown Swiss, in April 2013 for Ayrshires, and in April 2016 for Guernseys. Producers have accepted genomic evaluations as accurate indications of a bull’s eventual daughter-based evaluation. The integration of DNA marker technology and genomics into the traditional evaluation system has doubled the rate of genetic progress for traits of economic importance, decreased generation interval, increased selection accuracy, reduced previous costs of progeny testing, and allowed identification of recessive lethals.

    [Keywords: genetic evaluation, single-nucleotide polymorphism, SNP, reliability, imputation, haplotype, genotype]

  419. 2011-cole-selectionlimits-figure5-perchromosome.png

  420. ⁠, 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.

  421. https://www.nature.com/articles/d41586-018-00578-5

  422. ⁠, Naomi R. Wray, Cisca Wijmenga, Patrick F. Sullivan, Jian Yang, Peter M. Visscher (2018-06-14):

    The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up mechanisms is now overwhelming. In this context, we consider the recent “omnigenic” or “core genes” model. A key assumption of the model is that there is a relatively small number of core genes relevant to any disease. While intuitively appealing, this model may underestimate the biological complexity of common disease, and therefore, the goal to discover core genes should not guide experimental design. We consider other implications of polygenicity, concluding that a focus on patient stratification is needed to achieve the goals of precision medicine.

    …In conclusion, are congratulated for their synthesis of current data and for articulation of a biological framework that has prompted extensive constructive discussion. We agree that understanding the cell-specific role of disease-associated variants is a crucial step for advancing knowledge of common disease. However, whereas those authors extrapolate results of analyses of GWAS summary statistics to make fundamental assumptions that rare variants of large effect in a small number of genes play the most critical roles in clinical conditions that attract a common disease diagnosis, we believe it would be a major disservice to the field to allow these assumptions to guide the next steps of research. To assume that a limited number of core genes are key to our understanding of common disease may underestimate the true biological complexity, which is better represented by systems genetics and network approaches (Baliga et al 2017, Parikshak et al 2015). While Boyle et al. advocate for WES studies, they did not discuss the sample sizes needed for such discovery. We believe that in the short term, large samples recorded for key measures of phenotypic heterogeneity and genome-wide SNP data are the best next steps for research using human DNA samples in moving forward our understanding of complex genetic diseases. Large numbers of samples, biobanked for cellular reprogramming, will position us well for the next generation of sequencing and other new technologies. High-throughput phenotyping to characterize cellular properties associated with disease-associated genomes may be the key to penetrate the polygenic complexity of common disease and provide the data needed for patient stratification, as well as to progress toward the goal of new drug treatments. These are research paths that need to advance in parallel to advance the promise of precision medicine.

  423. ⁠, Soke Yuen Yong, Timothy G. Raben, Louis Lello, Stephen D. H. Hsu (2020-02-13):

    Genomic prediction of complex human traits (e.g., height, cognitive ability, bone density) and disease risks (e.g., breast cancer, diabetes, heart disease, atrial fibrillation) has advanced considerably in recent years. Predictors have been constructed using penalized algorithms that favor sparsity: i.e., which use as few genetic variants as possible. We analyze the specific genetic variants (SNPs) utilized in these predictors, which can vary from dozens to as many as thirty thousand. We find that the fraction of SNPs in or near genic regions varies widely by phenotype. For the majority of disease conditions studied, a large amount of the variance is accounted for by SNPs outside of coding regions. The state of these SNPs cannot be determined from exome-sequencing data. This suggests that data alone will miss much of the heritability for these traits—i.e., existing PRS cannot be computed from exome data alone. We also study the fraction of SNPs and of variance that is in common between pairs of predictors. The DNA regions used in disease risk predictors so far constructed seem to be largely disjoint (with a few interesting exceptions), suggesting that individual genetic disease risks are largely uncorrelated. It seems possible in theory for an individual to be a low-risk outlier in all conditions simultaneously.

  424. https://www.hindawi.com/journals/ijg/2018/5121540/

  425. 1933-student.pdf: “Evolution By Selection: The Implications of Winter's Selection Experiment”⁠, Student (William Sealy Gosset)

  426. 1929-winter.pdf: “The mean and variability as affected by continuous selection for composition in corn”⁠, F. L. Winter

  427. 1974-dudley-seventygenerationsofselectionforoilandproteininmaize.pdf: “Seventy Generations of Selection for Oil and Protein in Maize”⁠, J. W. Dudley

  428. 1933-fisher.pdf: “Number of Mendelian Factors in Quantitative Inheritance”⁠, R. A. Fisher

  429. https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1469-1809.1939.tb02192.x#page=10

  430. 1930-fisher-thegeneticaltheoryofnaturalselection.pdf#page=119

  431. http://ssgac.org/documents/CHIC_Summary_Benyamin2014.txt.gz

  432. http://ssgac.org/documents/CHIC_Benyamin2014_Summary_Notes.pdf

  433. https://www.broadinstitute.org/snap/snap

  434. #glue-robbers-sequencing-nobelists-using-collectible-letters

  435. https://www.cbsnews.com/news/the-clones-of-polo/

  436. Clone

  437. Regression

  438. 1960-robertson.pdf: ⁠, A. Robertson (1960-11-29; genetics  /​ ​​ ​selection):

    1. The paper presents a theory of selection limits in ⁠. It is, however, developed primarily in terms of single genes.
    2. For a single gene with selective advantage s, the chance of (the expected gene frequency at the limit) is a function only of Ns, where N is the ⁠. In artificial selection based on individual measurements, where the selection differential is ī standard deviations, the expected Limit of individual selection in any population is a function only of .
    3. For low values of , the total advance by selection is, for additive genes, 2N× the gain in the first generation but may be much greater than this for recessives, particularly if their initial frequency is low.
    4. The half-life of any selection process will, for additive genes, not be greater than 1.4N generations but may for rare recessives equal 2N.
    5. The effect of an initial period of selection or inbreeding or of both together on the limits in further selection is discussed. It appears that the effects of restrictions in population size on the selection limit may be a useful diagnostic tool in the laboratory.
    6. The treatment can be extended to deal with the limits of further selection after the crossing of replicate lines from the same population when the initial response has ceased.
    7. In a selection programme of individual selection of equal intensity in both sexes, the furthest limit should be attained when half the population is selected from each generation.
    8. The treatment can also be extended to include selection based on progeny or family records.

    Consideration of the optimum structure, as far as the limit is concerned, shows that the use of the information on relatives is always a sacrifice on the eventual limit for the sake of immediate gain in the early generations. The loss may, however, be small in large populations.

  439. https://www.pnas.org/content/79/1/142.full.pdf

  440. http://www.genetics.org/content/genetics/125/3/585.full.pdf

  441. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1210208/pdf/407.pdf

  442. 1966-roberts.pdf: “The limits to artificial selection for body weight in the mouse II. The Genetic Nature of the Limits”⁠, R. C. Roberts

  443. 1980-yoo-3.pdf: ⁠, B. H. Yoo (1980; genetics  /​ ​​ ​selection):

    Six replicate lines of Drosophila melanogaster, which had been selected for increased abdominal bristle number for more than 85 generations, were assayed by hierarchical analysis of variance and offspring on parent regression immediately after selection ceased, and by single-generation realised heritability after more than 25 generations of subsequent relaxed selection.

    Half-sib estimates of heritability in 5 lines were as high as in the base population and much higher than observed genetic gains would suggest, excluding lack of sufficient additive genetic variance as a cause of ineffective selection in these lines. Also, there was considerable diversity among the six lines in composition of phenotypic variability: in addition to differences in the additive genetic component, one or more of the components due to dominance, epistasis, sex-linkage or genotype-environment interaction appeared to be important in different lines.

    Even after relaxed selection, single-generation realised heritabilities in four lines were as high as in the base population. As a large proportion of total genetic gain must have been made by fixation of favourable alleles, the compensatory increase of genetic variability has been sought in a genetic model involving genes at low initial frequencies, enhancement of gene effects during selection and/​​​​or new mutations.

    [See also: {#1980-yoo-1-responsetoselection-2}, {#1980-yoo-2-responsetoselection-largeeffects}, {#1980-yoo-3-responsetoselection-relaxedreversed}, {#1980-yoo-5-responsetoselection-inbreeding}.]

  444. https://www.nature.com/articles/hdy19514.pdf

  445. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1210626/pdf/935.pdf

  446. 1971-wilson.pdf

  447. 1963-sheldon.pdf

  448. 1957-clayton.pdf

  449. ⁠, Jeffrey L. Neyhart, Tyler Tiede, Aaron J. Lorenz, Kevin P. Smith (2016-11-10):

    Genomewide selection is hailed for its ability to facilitate greater genetic gains per unit time. Over breeding cycles, the requisite linkage disequilibrium (LD) between quantitative trait loci (QTL) and markers is expected to change as a result of recombination, selection, and drift, leading to a decay in prediction accuracy. Previous research has identified the need to update the training population using data that may capture new LD generated over breeding cycles, however optimal methods of updating have not been explored. In a barley (Hordeum vulgare L.) breeding simulation experiment, we examined prediction accuracy and response to selection when updating the training population each cycle with the best predicted lines, the worst predicted lines, random lines, criterion-selected lines, or no lines. In the short-term, we found that updating with the best predicted lines resulted in greater prediction accuracy and genetic gain, but in the long-term, all methods (besides not updating) performed similarly. We also examined the impact of including all data in the training population or only the most recent data. Though patterns among update methods were similar, using a smaller, but more recent training population provided a slight advantage in prediction accuracy and genetic gain. In an actual breeding program, a breeder might desire to gather phenotypic data on lines predicted to be the best, perhaps to evaluate possible cultivars. Therefore, our results suggest that the most optimal method of updating the training population is also the most practical.

  450. 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]

  451. 1972-smith-onevolution.pdf#page=82: “On Evolution”⁠, John Maynard Smith

  452. Iodine

  453. Hunter

  454. https://slatestarcodex.com/2014/09/10/society-is-fixed-biology-is-mutable/

  455. http://www.rfreitas.com/Astro/Xenopsychology.htm

  456. http://dominiccummings.wordpress.com/2014/10/30/the-hollow-men-ii-some-reflections-on-westminster-and-whitehall-dysfunction/

  457. 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.

  458. https://www.amazon.com/Genetics-Human-Populations-L-Cavalli-Sforza/dp/0486406938

  459. 1994-charlesworth-evolutioninagestructuredpopulations.pdf

  460. 1938-cattell.pdf

  461. http://www.danielascur.com/wp-content/uploads/2019/01/the-ties-that-bind-lemos-and-scur.pdf

  462. https://www.mdpi.com/2079-3200/6/2/18/htm

  463. https://palladiummag.com/2019/05/09/what-botswana-can-teach-us-about-political-stability/

  464. http://galton.org/books/hereditary-genius/text/pdf/galton-1869-genius-v3.pdf#page=162

  465. https://jeremyturcotte.wordpress.com/2013/09/21/a-listing-of-extinct-british-peerages/

  466. https://archive.org/details/mindandsocietyvo029378mbp/page/n559

  467. ⁠, Tanner Greer (2014-03-08):

    …The Manchus, before the founding of the Qing, also rarely encountered smallpox, but they knew of its danger. Mongols and Manchus who had not been exposed to the disease were exempted from coming to Beijing to receive titles of succession. The main response of the Mongols and Manchus to those who did fall ill was quarantine. Li Xinheng commented that if anyone in a tribe caught smallpox, his relatives abandoned him in a cave or distant grassland. 70 to 80% of those infected died. The German traveler Peter Simon Pallas, who visited the Mongols three times front 1768 to I771, commented that smallpox was the only disease they greatly feared. It occurred very seldom, but spread rapidly when it struck: “If someone catches it, they abandon him in his tent; they only approach front the windward side to provide food. Children who catch it are sold to the Russians very cheaply.” The Mongols whom Pallas visited lived far from the Chinese border, but they knew well that smallpox was highly contagious and nearly fatal.

    The Chinese discovery of variolation—a method of inoculation—was of great aid in reducing the severity of attacks. The Kangxi emperor himself was selected as heir in part because he had survived the disease in childhood; his father had died of it. In 1687 he inaugurated regular inoculation of the royal family, and his successor extended mandatory inoculation to all Manchu children. The Manchus adopted this Chinese medical practice in order to protect themselves against the virulent strains that were absent from the steppe. Only Manchus who had survived the disease were allowed to be sent to the Mongolian steppe. Mongols close to the Manchu and Chinese border gradually grew immune, but those farther away suffered great losses in the nineteenth century when Chinese penetration increased. [1]

    …For several millennia historians have tried to explain the generally superior strength and endurance of steppe warriors, often focusing on the demands of life in the saddle or the nomads’ protein-rich diets as the explanation for their vitality. A more powerful explanation may be the absence of the debilitating and deadly diseases of settled life among the peoples of the steppe.

  468. https://longreads.com/2018/09/27/queens-of-infamy-the-early-trials-of-catherine-de-medici/

  469. https://www.imdb.com/title/tt0551569/characters/nm2205890

  470. 2011-arbesman.pdf: ⁠, Samuel Arbesman (2011-07-28; history):

    Figure 1: Lifetime distribution of empires. The best-fit line for the exponential distribution is overlaid on the lifetime distribution of 41 empires. The bin height is the frequency of empires in each bin, divided by the bin width, to arrive at a probability density.

    The collapse of empires is exceedingly difficult to understand.

    The author examined the distribution of imperial lifetimes using a data set that spans more than 3 millennia and found that it conforms to a memoryless in which the rate of collapse of an empire is independent of its age.

    Comparing this distribution to similar of other complex systems—specifically, biological species and corporate firms—the author explores the reasons behind their lifetime distributions and how this approach can yield insights into empires.

    [Keywords: empires, exponential lifetime, longevity, species]

  471. https://www.historytoday.com/gemma-masson/ottoman-empire-succession-deposition-and-fratricide

  472. https://dash.harvard.edu/bitstream/handle/1/23490131/16-044.pdf?sequence=1

  473. 2015-mi.pdf: “Selectiongain: an R package for optimizing multi-stage selection”⁠, Xuefei Mi, H. Friedrich Utz, Albrecht E. Melchinger

  474. #georges-massey-1991

  475. #shulman-2009

  476. #sparrow-2013

  477. #bogliotti-et-al-2018

  478. #goszczynski-et-al-2018

  479. #hou-et-al-2018

  480. 1991-georges.pdf: ⁠, M. Georges, J. M. Massey (1991; genetics  /​ ​​ ​selection):

    Until recently, artificial selection has relied on the biometrical evaluation of individual breeding values from an animal’s own performance and from performance of its relatives. This biometrical strategy is based on relatively simple genetic premises, operating within a “black box”. Briefly, the majority of economically important traits are so-called complex or quantitative traits, meaning that the phenotype of an animal is determined by both environment and a large number of genes with individually small, additive effects. The proportion of the phenotypic variation observed in a given population that is genetic in nature is the heritability of the trait. Substantial genetic progress has been obtained using this approach. One of the powers of the biometrical approach is that it obviates the need for any detailed molecular knowledge of the underlying genes or Economic Trait Loci (ETL).

    However, it is believed that the molecular identification of these BTLs should allow for an increased genetic response by affecting both time and accuracy of selection, through a procedure called Marker Assisted Selection (MAS) (1,2). Moreover, we propose to use a scheme that we call “velogenetics”, or the combined use of Marker Assisted Selection and germ-line manipulations aimed at shortening the generation interval of domestic species (especially cattle), which would allow the efficient introgression of mapped Economic Trait Loci between genetic backgrounds.

  481. 1998-haley.pdf: ⁠, C. S. Haley, P. M. Visscher (1998; genetics  /​ ​​ ​selection):

    Marker-assisted selection holds promise because genetic markers provide completely heritable traits that can be measured at any age in either sex and that are potentially correlated with traits of economic value. Theoretical and simulation studies show that the advantage of using marker-assisted selection can be substantial, particularly when marker information is used, because normal selection is less effective, for example, for sex-limited or carcass traits. Assessment of the available information and its most effective use is difficult, but approaches such as crossvalidation may help in this respect. Marker systems are now becoming available that allow the high density of markers required for close associations between marker loci and trait loci. Emerging technologies could allow large numbers of polymorphic sites to be identified, practically guaranteeing that markers will be available that are in complete association with any trait locus. Identifying which polymorphism out of many that is associated with any trait will remain problematic, but multiple-locus disequilibrium measures may allow performance to be associated with unique marker haplotypes. This type of approach, combined with cheap and high density markers, could allow a move from selection based on a combination of “infinitesimal” effects plus individual loci to effective total genomic selection. In such a unified model, each region of the genome would be given its appropriate weight in a breeding program. However, the collection of good quality trait information will remain central to the use of these technologies for the foreseeable future.

    [Keywords: markers, breeding, quantitative trait loci, selection]

  482. http://theuncertainfuture.com/

  483. https://reflectivedisequilibrium.blogspot.com/

  484. https://www.amazon.com/Singularity-Rising-Surviving-Thriving-Dangerous/dp/1936661659

  485. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.434.5233&rep=rep1&type=pdf

  486. https://www.amazon.com/End-Science-Religion-about-Apocalypse-ebook/dp/B019BNG5BO/

  487. https://www.duo.uio.no/bitstream/handle/10852/52700/1/Masteroppgave_Filosofi_V-r2016_4427.pdf

  488. ⁠, Mathews, Debra J. H Donovan, Peter J. Harris, John Lovell-Badge, Robin Savulescu, Julian Faden, Ruth (2009):

    An emerging body of data suggests that pluripotent stem cells may be able to differentiate to form eggs and sperm. We discuss the state of the science and the potential social implications and offer recommendations for addressing some of the ethical and policy issues that would be raised by the availability of stem cell-derived gametes.

  489. 2014-sparrow.pdf: “Reproductive technologies, risk, enhancement and the value of genetic relatedness”⁠, Robert Sparrow

  490. 2013-dafonseca.pdf: “Human in vitro eugenics: close, yet far away”⁠, Flávio Guimarães da Fonseca, Daniel Mendes Ribeiro, Nara Pereira Carvalho, Brunello Stancioli

  491. https://www.pnas.org/content/pnas/early/2018/02/08/1716161115.full.pdf

  492. https://caes.ucdavis.edu/news/articles/2018/february/isolating-embryonic-stem-cells-in-cows-just-got-easier

  493. https://nitter.hu/BioBeef/status/979482261430009856

  494. https://pdfs.semanticscholar.org/6df9/96a72b537f97121cbaaadcf49fc95be3a5b5.pdf

  495. 2015-zulkarnain.pdf: “Chapter 10: Applications of In Vitro Techniques in Plant Breeding”⁠, Zul Zulkarnain, Tanya Tapingkae, Acram Taji

  496. https://www.findagrave.com/memorial/7333144/john-von_neumann

  497. https://www.wired.com/story/a-new-method-of-dna-testing-could-solve-more-shootings/

  498. https://www.themarshallproject.org/2018/04/19/framed-for-murder-by-his-own-dna

  499. https://www.theatlantic.com/magazine/archive/2015/10/a-death-at-torrey-pines/403186/

  500. https://search.wikileaks.org/plusd/cables/08STATE30340_a.html

  501. https://search.wikileaks.org/plusd/cables/09STATE37561_a.html

  502. https://www.theatlantic.com/magazine/archive/2012/11/hacking-the-presidents-dna/309147/

  503. https://www.theatlantic.com/science/archive/2016/02/the-unexplored-marvels-locked-away-in-our-natural-history-museums/459306/

  504. ⁠, Qian Cong, Jinhui Shen, Jing Zhang, Wenlin Li, Lisa N. Kinch, John V. Calhoun, Andrew D. Warren, Nick V. Grishin (2019-09-04):

    Centuries of zoological studies amassed billions of specimens in collections worldwide. Genomics of these specimens promises to rejuvenate biodiversity research. The obstacles stem from DNA degradation with specimen age. Overcoming this challenge, we set out to resolve a series of long-standing controversies involving a group of butterflies. We deduced geographical origins of several ancient specimens of uncertain provenance that are at the heart of these debates. Here, genomics tackles one of the greatest problems in zoology: countless old, poorly documented specimens that serve as irreplaceable embodiments of species concepts. The ability to figure out where they were collected will resolve many on-going disputes. More broadly, we show the utility of genomics applied to ancient museum specimens to delineate the boundaries of species and populations, and to hypothesize about genotypic determinants of phenotypic traits.

  505. ⁠, Katarina C. Stuart, William B. Sherwin, Jeremy J. Austin, Melissa Bateson, Marcel Eens, Matthew C. Brandley, Lee A. Rollins (2021-08-23):

    During the Anthropocene, Earth has experienced unprecedented habitat loss, native species decline, and global climate change. Concurrently, greater globalisation is facilitating species movement, increasing the likelihood of alien species establishment and propagation. There is a great need to understand what influences a species9 ability to persist or perish within a new or changing environment. Examining genes that may be associated with a species9 invasion success or persistence informs invasive species management, assists with native species preservation, and sheds light on important evolutionary mechanisms that occur in novel environments. This approach can be aided by coupling spatial and temporal investigations of evolutionary processes. Here we use the common starling, Sturnus vulgaris, to identify parallel and divergent evolutionary change between contemporary native and invasive range samples and their common ancestral population. To do this, we use reduced-representation sequencing of native samples collected recently in north-western Europe and invasive samples from Australia, together with museum specimens sampled in the UK during the mid-19th Century. We found evidence of parallel selection on both continents, possibly resulting from common global selective forces such as exposure to pollutants (e.g. TCDD) and food carbohydrate content. We also identified divergent selection in these populations, which might be related to adaptive changes in response to the novel environment encountered in the introduced Australian range. Interestingly, signatures of selection are equally as common within both invasive and native range contemporary samples. Our results demonstrate the value of including historical samples in genetic studies of invasion and highlight the ongoing and occasionally parallel role of adaptation in both native and invasive ranges.

  506. https://www.theatlantic.com/science/archive/2019/03/dna-tests-for-envelopes-have-a-price/583636/

  507. https://www.nature.com/articles/s42003-019-0399-1

  508. ⁠, Theis Z. T. Jensen, Jonas Niemann, Katrine Højholt Iversen, Anna K. Fotakis, Shyam Gopalakrishnan, Åshild J. Vågene, Mikkel Winther Pedersen, Mikkel-Holger S. Sinding, Martin R. Ellegaard, Morten E. Allentoft, Liam T. Lanigan, Alberto J. Taurozzi, Sofie Holtsmark Nielsen, Michael W. Dee, Martin N. Mortensen, Mads C. Christensen, Søren A. Sørensen, Matthew J. Collins, M. Thomas P. Gilbert, Martin Sikora, Simon Rasmussen, Hannes Schroeder (2019-12-17):

    The rise of ancient genomics has revolutionised our understanding of human prehistory but this work depends on the availability of suitable samples. Here we present a complete ancient human genome and oral sequenced from a 5700 year-old piece of chewed birch pitch from Denmark. We sequence the human genome to an average depth of 2.3× and find that the individual who chewed the pitch was female and that she was genetically more closely related to western hunter-gatherers from mainland Europe than hunter-gatherers from central Scandinavia. We also find that she likely had dark skin, dark brown hair and blue eyes. In addition, we identify DNA fragments from several bacterial and viral taxa, including Epstein-Barr virus, as well as animal and plant DNA, which may have derived from a recent meal. The results highlight the potential of chewed birch pitch as a source of ancient DNA.

  509. ⁠, Andrew T. Ozga, Timothy H. Webster, Ian C. Gilby, Melissa A. Wilson, Rebecca S. Nockerts, Michael L. Wilson, Anne E. Pusey, Yingying Li, Beatrice H. Hahn, Anne C. Stone (2020-02-20):

    The ability to generate genomic data from wild animal populations has the potential to give unprecedented insight into the population history and dynamics of species in their natural habitats. However, in the case of many species, it is impossible legally, ethically, or logistically to obtain tissues samples of high-quality necessary for genomic analyses. In this study we evaluate the success of multiple sources of genetic material (feces, urine, dentin, and dental calculus) and several capture methods (shotgun, whole-genome, exome) in generating genome-scale data in wild eastern chimpanzees (Pan troglodytes schweinfurthii) from Gombe National Park, Tanzania. We found that urine harbors statistically-significantly more host DNA than other sources, leading to broader and deeper coverage across the genome. Urine also exhibited a lower rate of allelic dropout. We found exome sequencing to be far more successful than both shotgun sequencing and whole-genome capture at generating usable data from low-quality samples such as feces and dental calculus. These results highlight urine as a promising and untapped source of DNA that can be noninvasively collected from wild populations of many species.

  510. https://royalsocietypublishing.org/doi/10.1098/rsos.170988

  511. ⁠, Toni de-Dios, Lucy van Dorp, Philippe Charlier, Sofia Morfopoulou, Esther Lizano, Celine Bon, Corinne Le Bitouzé, Marina Álvarez-Estapé, Tomas Marquès-Bonet, François Balloux, Carles Lalueza-Fox (2019-10-31):

    The French revolutionary Jean-Paul Marat was assassinated in 1793 in his bathtub, where he was trying to find relief from the debilitating skin disease he was suffering from. At the time of his death, Marat was annotating newspapers, which got stained with his blood and were subsequently preserved by his sister. We extracted and sequenced DNA from the blood stain and also from another section of the newspaper, which we used for comparison. Analysis of human DNA sequences supported the heterogeneous ancestry of Marat, with his mother being of French origin and his father born in Sardinia, although bearing more affinities to mainland Italy or Spain. Metagenomic analyses of the non-human reads uncovered the presence of fungal, bacterial and low levels of viral DNA. Relying on the presence/​​​​absence of microbial species in the samples, we could confidently rule out several putative infectious agents that had been previously hypothesised as the cause of his condition. Conversely, some of the detected species are uncommon as environmental contaminants and may represent plausible infective agents. Based on all the available evidence, we hypothesize that Marat may have suffered from a primary fungal infection (seborrheic dermatitis), superinfected with bacterial opportunistic pathogens.

    Significance: The advent of second-generation sequencing technologies allows for the retrieval of ancient genomes from long-dead people and, using non-human sequencing reads, of the pathogens that infected them. In this work we combined both approaches to gain insights into the ancestry and health of the controversial French revolutionary leader and physicist Jean-Paul Marat (1743-1793). Specifically, we investigate the pathogens, which may have been the cause of the debilitating skin condition that was affecting him, by analysing DNA obtained from a paper stained with his blood at the time of his death. This allowed us to confidently rule out several conditions that have been put forward. To our knowledge, this represents the oldest successful retrieval of genetic material from cellulose paper.

  512. ⁠, Pere Gelabert, Susanna Sawyer, Anders Bergström, Thomas C. Collin, Tengiz Meshveliani, Anna Belfer-Cohen, David Lordkipanidze, Nino Jakeli, Zinovi Matskevich, Guy Bar-Oz, Daniel M. Fernandes, Olivia Cheronet, Kadir T. Özdoğan, Victoria Oberreiter, Robin N. M. Feeney, Mareike C. Stahlschmidt, Pontus Skoglund, Ron Pinhasi (2021-01-08):

    Archaeological sediments have been shown to preserve ancient DNA, but so far have not yielded genome-scale information of the magnitude of skeletal remains. We retrieved and analysed human and mammalian low-coverage nuclear and high-coverage mitochondrial genomes from Upper Palaeolithic sediments from Satsurblia cave, western Georgia, dated to 25,000 years ago. First, a human female genome with substantial basal Eurasian ancestry, which was an ancestry component of the majority of post-Ice Age people in the Near East, North Africa, and parts of Europe. Second, a wolf genome that is basal to extant Eurasian wolves and dogs and represents a previously unknown, likely extinct, Caucasian lineage that diverged from the ancestors of modern wolves and dogs before these diversified. Third, a bison genome that is basal to present-day populations, suggesting that population structure has been substantially reshaped since the Last Glacial Maximum. Our results provide new insights into the late Pleistocene genetic histories of these three species, and demonstrate that sediment DNA can be used not only for species identification, but also be a source of genome-wide ancestry information and genetic history.

    Highlights: We demonstrate for the first time that genome sequencing from sediments is comparable to that of skeletal remains

    A single Pleistocene sediment sample from the Caucasus yielded three low-coverage mammalian ancient genomes

    We show that sediment ancient DNA can reveal important aspects of the human and faunal past

    Evidence of an uncharacterized human lineage from the Caucasus before the Last Glacial Maximum

    ~0.01× coverage wolf and bison genomes are both basal to present-day diversity, suggesting reshaping of population structure in both species

  513. 2021-vernot.pdf: ⁠, Benjamin Vernot, Elena I. Zavala, Asier Gómez-Olivencia, Zenobia Jacobs, Viviane Slon, Fabrizio Mafessoni, Frédéric Romagné, Alice Pearson, Martin Petr, Nohemi Sala, Adrián Pablos, Arantza Aranburu, José María Bermúdez de Castro, Eudald Carbonell, Bo Li, Maciej T. Krajcarz, Andrey I. Krivoshapkin, Kseniya A. Kolobova, Maxim B. Kozlikin, Michael V. Shunkov, Anatoly P. Derevianko, Bence Viola, Steffi Grote, Elena Essel, David López Herráez, Sarah Nagel, Birgit Nickel, Julia Richter, Anna Schmidt, Benjamin Peter, Janet Kelso, Richard G. Roberts, Juan-Luis Arsuaga, Matthias Meyer (2021-04-15; genetics  /​ ​​ ​selection):

    Bones and teeth are important sources of Pleistocene hominin DNA, but are rarely recovered at archaeological sites. Mitochondrial DNA has been retrieved from cave sediments, but provides limited value for studying population relationships.

    We therefore developed methods for the enrichment and analysis of nuclear DNA from sediments, and applied them to cave deposits in western Europe and southern Siberia dated to between approximately 200,000 and 50,000 years ago.

    We detect a population replacement in northern Spain approximately 100,000 years ago, accompanied by a turnover of mitochondrial DNA. We also identify 2 radiation events in Neanderthal history during the early part of the Late Pleistocene.

    Our work lays the ground for studying the population history of ancient hominins from trace amounts of nuclear DNA in sediments.

  514. https://www.theguardian.com/science/2015/mar/12/italian-scientists-dna-fascist-warrior-poet-semen-gabriele-dannunzio

  515. https://familytreewebinars.com/uploads/video_vtt/MHLive-GiladJaphet1.vtt

  516. https://vimeo.com/299232829/89a3ff9ae4

  517. Emergenesis

  518. https://www.rrauction.com/past_auction_results.cfm?SearchCrit=albert%20einstein&order=LP&oper=exact&scope=titleonly

  519. https://www.rrauction.com/PastAuctionItem/3190905

  520. https://www.rrauction.com/browse_gallery.cfm?Category=319&SortOrder=LP&SearchCrit=%20%20&ByItem=

  521. https://www.rrauction.com/browse_gallery.cfm?Category=318&SortOrder=LP&SearchCrit=%20%20&ByItem=

  522. https://www.rrauction.com/PastAuctionItem/3393305