newsletter/2017/08 (Link Bibliography)

“newsletter/​2017/​08” links:


  2. 07

  3. newsletter

  4. Changelog


  6. ⁠, Hong Ma, Nuria Marti-Gutierrez, Sang-Wook Park, Jun Wu, Yeonmi Lee, Keiichiro Suzuki, Amy Koski, Dongmei Ji, Tomonari Hayama, Riffat Ahmed, Hayley Darby, Crystal Van Dyken, Ying Li, Eunju Kang, A.-Reum Park, Daesik Kim, Sang-Tae Kim, Jianhui Gong, Ying Gu, Xun Xu, David Battaglia, Sacha A. Krieg, David M. Lee, Diana H. Wu, Don P. Wolf, Stephen B. Heitner, Juan Carlos Izpisua Belmonte, Paula Amato, Jin-Soo Kim, Sanjiv Kaul, Shoukhrat Mitalipov (2017-08-02):

    Genome editing has potential for the targeted correction of germline mutations. Here we describe the correction of the heterozygous MYBPC3 mutation in human preimplantation embryos with precise -Cas9-based targeting accuracy and high homology-directed repair efficiency by activating an endogenous, germline-specific DNA repair response. Induced double-strand breaks (DSBs) at the mutant paternal allele were predominantly repaired using the homologous wild-type maternal gene instead of a synthetic DNA template. By modulating the cell cycle stage at which the DSB was induced, we were able to avoid mosaicism in cleaving embryos and achieve a high yield of embryos carrying the wild-type MYBPC3 gene without evidence of off-target mutations. The efficiency, accuracy and safety of the approach presented suggest that it has potential to be used for the correction of heritable mutations in human embryos by complementing preimplantation genetic diagnosis. However, much remains to be considered before clinical applications, including the reproducibility of the technique with other heterozygous mutations.

  7. 2017-niu.pdf: ⁠, Dong Niu, HongJiang Wei, Lin Lin, Haydy George, Tao Wang, IHsiu Lee, HongYe Zhao, Yong Wang, Yinan Kan, Ellen Shrock, Emal Lesha, Gang Wang, Yonglun Luo, Yubo Qing, Deling Jiao, Heng Zhao, Xiaoyang Zhou, Shouqi Wang, Hong Wei, Marc Gell, George M. Church, Luhan Yang (2017-08-10; genetics  /​ ​​ ​editing):

    Xenotransplantation is a promising strategy to alleviate the shortage of organs for human transplantation. In addition to the concern on pig-to-human immunological compatibility, the risk of cross-species transmission of porcine endogenous retroviruses (PERVs) has impeded the clinical application of this approach. Earlier, we demonstrated the feasibility of inactivating PERV activity in an immortalized pig cell line. Here, we confirmed that PERVs infect human cells, and observed the horizontal transfer of PERVs among human cells. Using CRISPR-Cas9, we inactivated all the PERVs in a porcine primary cell line and generated PERV-inactivated pigs via somatic cell nuclear transfer. Our study highlighted the value of PERV inactivation to prevent cross-species viral transmission and demonstrated the successful production of PERV-inactivated animals to address the safety concern in clinical xenotransplantation.



  10. 2017-scheufele.pdf: “Science Magazine”



  13. 2017-hickey.pdf

  14. 2017-latham.pdf: “Mothers want extraversion over conscientiousness or intelligence for their children”⁠, Rachel M. Latham, Sophie von Stumm


  16. ⁠, Lawrence H. Uricchio, Hugo C. Kitano, Alexander Gusev, Noah A. Zaitlen (2017-08-08):

    Selection alters human genetic variation, but the evolutionary mechanisms shaping complex traits and the extent of selection’s impact on polygenic trait evolution remain largely unknown. Here, we develop a novel polygenic selection inference method (Polygenic Ancestral Selection Test Encompassing Linkage, or PASTEL) relying on summary data from a single population. We use model-based simulations of complex traits that incorporate human demography, stabilizing selection, and polygenic adaptation to show how shifts in the fitness landscape generate distinct signals in GWAS summary data. Our test retains power for relatively ancient selection events and controls for potential from linkage disequilibrium. We apply PASTEL to nine complex traits, and find evidence for selection acting on five of them (height, ⁠, schizophrenia, Crohn’s disease, and educational attainment). This study provides evidence that selection modulates the relationship between frequency and of trait-altering alleles for a wide range of traits, and provides a flexible framework for future investigations of selection on complex traits using GWAS data.


  18. ⁠, 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 ⁠, is complicated by incidental structure present when collecting such large cohorts. For instance, UK Biobank comprises 107,162 third degree or closer related participants. Traditionally, GWAS have removed related individuals because they comprised an insignificant proportion of the overall sample size, however, removing related individuals in UK would entail a substantial loss of power. Furthermore, modelling such structure using is computationally expensive, which requires a computational infrastructure that may not be accessible to all researchers. Here we present an atlas of genetic associations for 118 non-binary and 599 binary traits of 408,455 related and unrelated UK Biobank participants of White-British descend. Results are compiled in a publicly accessible database that allows querying genome-wide association summary results for 623,944 genotyped and HapMap2 imputed SNPs, as well downloading whole GWAS summary statistics for over 30 million imputed SNPs from the Haplotype Reference Consortium panel. Our atlas of associations (GeneAtlas, http:/​​​​/​​​​ will help researchers to query UK Biobank results in an easy way without the need to incur in high computational costs.


  20. ⁠, 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.

  21. 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.

  22. ⁠, 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 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.

  23. 2018-zhang.pdf: “Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits”⁠, Yan Zhang, Guanghao Qi, Ju-Hyun Park, Nilanjan Chatterjee

  24. ⁠, 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 ⁠, 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 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% 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.

  25. ⁠, 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 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 -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.

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

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

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

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

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

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

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

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

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


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

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


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

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

  34. 2017-silventoinen.pdf

  35. ⁠, Moira Verbelen, Michael E. Weale, Cathryn M. Lewis (2016-07-23):

    Pharmacogenetics (PGx) has the potential to personalize pharmaceutical treatments. Many relevant gene-drug associations have been discovered, but PGx guided treatment needs to be cost-effective as well as clinically beneficial to be incorporated into standard healthcare. Progress in this area can be assessed by reviewing economic evaluations to determine the cost-effectiveness of PGx testing versus standard treatment. We performed a review of economic evaluations for PGx associations listed in the US Food and Drug Administration (FDA) Table of Pharmacogenomic Biomarkers in Drug Labeling (http:/​​​​/​​​​​​​​Drugs/​​​​ScienceResearch/​​​​ResearchAreas/​​​​Pharmacogenetics/​​​​ucm083378.htm). We determined the proportion of evaluations that found PGx guided treatment to be cost-effective or dominant over the alternative strategies, and we estimated the impact on this proportion of removing the cost of genetic testing. Of the 130 PGx associations in the FDA table, 44 economic evaluations, relating to 10 drugs, were identified. Of these evaluations, 57% drew conclusions in favour of PGx testing, of which 30% were cost-effective and 27% were dominant (cost-saving). If genetic information was freely available, 75% of economic evaluations would support PGx guided treatment, of which 25% would be cost-effective and 50% would be dominant. Thus, PGx guided treatment can be a cost-effective and even cost-saving strategy. Having genetic information readily available in the clinical health record is a realistic future prospect, and would make more genetic tests economically worthwhile. However, few drugs with PGx associations have been studied and more economic evaluations are needed to underpin the uptake of genetic testing in clinical practice.


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

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

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

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

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

  38. 2011-villani.pdf: ⁠, Natalie Adele Villani (2011; cat  /​ ​​ ​catnip):

    The response to is unique in nature as it represents a repeatable, recognizable behavioral response to an olfactory stimulus that appears to have little evolutionary importance. There is clear variation in response between cats and this has been attributed to genetic factors in the past. These factors are explored in this study using behavioral observation after presenting of catnip to cats in two different research colonies with different environmental and genetic backgrounds. The response trait is defined and methods are used to explore a mixed model for the trait to determine genetic effects. Heritabilities obtained in the two colonies for the most important response behaviors, the head over roll and cheek rub, were 0.511 and 0.794 using catnip spray and dried catnip respectively. No clear Mendelian mode of inheritance was ascertained in either colony. The variation in response behaviors and intensity seen in the two colonies reflects the complex nature of expression of the catnip response, but there is a clear genetic influence on the feline predisposition to responding.

  39. 1962-todd.pdf: ⁠, Neil B. Todd (1962; cat  /​ ​​ ​catnip):

    Four behavioral components of the catnip response are described briefly. The analysis of a indicates that responding is inherited as an autosomal dominant. Other aspects of inheritance of the catnip response are discussed.

    An essential oil, was isolated from the catnip plant (Nepeta cataria) by McElvain et al. 2, 3, 4 and Meinwald 5. McElvain2 demonstrated with lions that the oil is the substance which is responsible for the attraction of cats to the plant and the only constituent capable of inducing a response. This familiar response has been broken down into four components, viz, 1. sniffing, 2. licking and chewing with head shaking, 3. chin and cheek rubbing and 4. head-over roll and body rubbing. None of these automatisms is unique to catnip, each of them apparently belonging normally to sexual or ingestive behavior1. These components almost invariably appear in the above sequence. In fact, among 58 responding cats, all tested with dried leaves, only 3 individuals deviated from this sequence and omitted the licking and chewing with head shaking. These animals went immediately into the rolling phase, which seemed to be exceptionally violent. Component four may last from three to six minutes before all response is extinguished. Additional behavior patterns noted occasionally are claw sharpening and washing, both of which occur as displacement activities in the ethological sense in sexual behavior1.

    Among responding animals the response may occasionally be inhibited for obscure reasons, necessitating repeated testing of non-responders before drawing conclusions. Also, the response is not manifested in kittens under 6 to 8 weeks of age and may not develop fully until three months of age. In fact, catnip often produces a distinct avoidance response in young kittens which is gradually replaced by indifference in non-responders and by heightened curiosity in responders. Whether nursing is in any way connected with inhibiting the response has not yet been determined. In one case a 6- to 7-week-old nursing kitten gave a total response, but this seems exceptional. A distressed or enraged animal may still respond, and neutering appears to have no effect on behavior towards catnip.

  40. ⁠, Andrew Brock, Theodore Lim, J. M. Ritchie, Nick Weston (2017-08-17):

    Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model’s architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ⁠, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10⁠, ModelNet10, and Imagenet32×32, achieving competitive performance with similarly-sized hand-designed networks. Our code is available at https:/​​​​/​​​​​​​​ajbrock/​​​​SMASH




  44. ⁠, Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, Marian Mazzone (2017-06-21):

    We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (), which have shown the ability to learn to generate novel images simulating a given distribution. We argue that such networks are limited in their ability to generate creative products in their original design. We propose modifications to its objective to make it capable of generating creative art by maximizing deviation from established styles and minimizing deviation from art distribution. We conducted experiments to compare the response of human subjects to the generated art with their response to art created by artists. The results show that human subjects could not distinguish art generated by the proposed system from art generated by contemporary artists and shown in top art fairs. Human subjects even rated the generated images higher on various scales.

  45. ⁠, Wei Ren Tan, Chee Seng Chan, Hernan Aguirre, Kiyoshi Tanaka (2017-02-11):

    This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ARTGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics. This is in contrast to most of the current solutions that focused on generating natural images such as room interiors, birds, flowers and faces. The key innovation of our work is to allow back-propagation of the w.r.t. the labels (randomly assigned to each generated images) to the generator from the discriminator. With the feedback from the label information, the generator is able to learn faster and achieve better generated image quality. Empirically, we show that the proposed ARTGAN is capable to create realistic artwork, as well as generate compelling real world images that globally look natural with clear shape on CIFAR-10.


  47. ⁠, Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen (2017-07-07):

    Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance. The algorithm addresses a broad range of problems in a computationally efficient manner and is therefore enjoying wide use. This tutorial covers the algorithm and its application, illustrating concepts through a range of examples, including Bernoulli bandit problems, shortest path problems, product recommendation, assortment, active learning with neural networks, and in Markov decision processes. Most of these problems involve complex information structures, where information revealed by taking an action informs beliefs about other actions. We will also discuss when and why Thompson sampling is or is not effective and relations to alternative algorithms.


  49. ⁠, Yu Wang, Jiayi Liu, Yuxiang Liu, Jun Hao, Yang He, Jinghe Hu, Weipeng P. Yan, Mantian Li (2017-08-18):

    We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of high-level semantic information. The agent is based on an asynchronous stochastic variant of (Deep Q Network) named DASQN. The inputs of the agent are plain-text descriptions of states of a game of incomplete information, i.e. real-time large scale online auctions, and the rewards are auction profits of very large scale. We apply the agent to an essential portion of JD’s online RTB (real-time bidding) advertising business and find that it easily beats the former state-of-the-art bidding policy that had been carefully engineered and calibrated by human experts: during’s June 18th anniversary sale, the agent increased the company’s ads revenue from the portion by more than 50%, while the advertisers’ ROI (return on investment) also improved significantly.



  52. ⁠, Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Joshua B. Tenenbaum (2017-07-30):

    We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of ⁠. The model combines techniques from deep learning and program synthesis. We learn a that proposes plausible drawing primitives that explain an image. These drawing primitives are like a trace of the set of primitive commands issued by a graphics program. We learn a model that uses program synthesis techniques to recover a graphics program from that trace. These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals. With a graphics program in hand, we can correct errors made by the deep network, measure similarity between drawings by use of similar high-level geometric structures, and extrapolate drawings. Taken together these results are a step towards agents that induce useful, human-readable programs from perceptual input.


  54. ⁠, Yanghua Jin, Jiakai Zhang, Minjun Li, Yingtao Tian, Huachun Zhu, Zhihao Fang (2017-08-18):

    Automatic generation of facial images has been well studied after the Generative Adversarial Network (GAN) came out. There exists some attempts applying the GAN model to the problem of generating facial images of anime characters, but none of the existing work gives a promising result. In this work, we explore the training of GAN models specialized on an anime facial image dataset. We address the issue from both the data and the model aspect, by collecting a more clean, well-suited dataset and leverage proper, empirical application of DRAGAN. With quantitative analysis and case studies we demonstrate that our efforts lead to a stable and high-quality model. Moreover, to assist people with anime character design, we build a website (http:/​​​​/​​​​ with our pre-trained model available online, which makes the model easily accessible to general public.


  56. 2016-pica.pdf: “PEDS_20160223.indd

  57. ⁠, Gordon J. Lithgow, Monica Driscoll, Patrick Phillips (2017-08-22):

    About 15 years ago, one of us (G.J.L.) got an uncomfortable phone call from a colleague and collaborator. After nearly a year of frustrating experiments, this colleague was about to publish a paper1 chronicling his team’s inability to reproduce the results of our high-profile paper2 in a mainstream journal. Our study was the first to show clearly that a drug-like molecule could extend an animal’s lifespan. We had found over and over again that the treatment lengthened the life of a roundworm by as much as 67%. Numerous phone calls and e-mails failed to identify why this apparently simple experiment produced different results between the labs. Then another lab failed to replicate our study. Despite more experiments and additional publications, we couldn’t work out why the labs were getting different lifespan results. To this day, we still don’t know. A few years later, the same scenario played out with different compounds in other labs…In another, now-famous example, two cancer labs spent more than a year trying to understand inconsistencies6. It took scientists working side by side on the same tumour biopsy to reveal that small differences in how they isolated cells—vigorous stirring versus prolonged gentle rocking—produced different results. Subtle tinkering has long been important in getting biology experiments to work. Before researchers purchased kits of reagents for common experiments, it wasn’t unheard of for a team to cart distilled water from one institution when it moved to another. Lab members would spend months tweaking conditions until experiments with the new institution’s water worked as well as before. Sources of variation include the quality and purity of reagents, daily fluctuations in microenvironment and the idiosyncratic techniques of investigators7. With so many ways of getting it wrong, perhaps we should be surprised at how often experimental findings are reproducible.

    …Nonetheless, scores of publications continued to appear with claims about compounds that slow ageing. There was little effort at replication. In 2013, the three of us were charged with that unglamorous task…Our first task, to develop a protocol, seemed straightforward.

    But subtle disparities were endless. In one particularly painful teleconference, we spent an hour debating the proper procedure for picking up worms and placing them on new agar plates. Some batches of worms lived a full day longer with gentler technicians. Because a worm’s lifespan is only about 20 days, this is a big deal. Hundreds of e-mails and many teleconferences later, we converged on a technique but still had a stupendous three-day difference in lifespan between labs. The problem, it turned out, was notation—one lab determined age on the basis of when an egg hatched, others on when it was laid. We decided to buy shared batches of reagents from the start. Coordination was a nightmare; we arranged with suppliers to give us the same lot numbers and elected to change lots at the same time. We grew worms and their food from a common stock and had strict rules for handling. We established protocols that included precise positions of flasks in autoclave runs. We purchased worm incubators at the same time, from the same vendor. We also needed to cope with a large amount of data going from each lab to a single database. We wrote an iPad app so that measurements were entered directly into the system and not jotted on paper to be entered later. The app prompted us to include full descriptors for each plate of worms, and ensured that data and metadata for each experiment were proofread (the strain names MY16 and my16 are not the same). This simple technology removed small recording errors that could disproportionately affect statistical analyses.

    Once this system was in place, variability between labs decreased. After more than a year of pilot experiments and discussion of methods in excruciating detail, we almost completely eliminated systematic differences in worm survival across our labs9 (see ‘Worm wonders’)…Even in a single lab performing apparently identical experiments, we could not eliminate run-to-run differences.

    …We have found one compound that lengthens lifespan across all strains and species. Most do so in only two or three strains, and often show detrimental effects in others.

  58. ⁠, Mark Lucanic, W. Todd Plummer, Esteban Chen, Jailynn Harke, Anna C. Foulger, Brian Onken, Anna L. Coleman-Hulbert, Kathleen J. Dumas, Suzhen Guo, Erik Johnson, Dipa Bhaumik, Jian Xue, Anna B. Crist, Michael P. Presley, Girish Harinath, Christine A. Sedore, Manish Chamoli, Shaunak Kamat, Michelle K. Chen, Suzanne Angeli, Christina Chang, John H. Willis, Daniel Edgar, Mary Anne Royal, Elizabeth A. Chao, Shobhna Patel, Theo Garrett, Carolina Ibanez-Ventoso, June Hope, Jason L. Kish, Max Guo, Gordon J. Lithgow, Monica Driscoll, Patrick C. Phillips (2017-02-21):

    Limiting the debilitating consequences of ageing is a major medical challenge of our time. Robust pharmacological interventions that promote healthy ageing across diverse genetic backgrounds may engage conserved longevity pathways. Here we report results from the Caenorhabditis Intervention Testing Program in assessing longevity variation across 22 Caenorhabditis strains spanning 3 species, using multiple replicates collected across three independent laboratories. Reproducibility between test sites is high, whereas individual trial reproducibility is relatively low. Of ten pro-longevity chemicals tested, six statistically-significantly extend lifespan in at least one strain. Three reported dietary restriction mimetics are mainly effective across C. elegans strains, indicating species and strain-specific responses. In contrast, the amyloid dye ThioflavinT is both potent and robust across the strains. Our results highlight promising pharmacological leads and demonstrate the importance of assessing lifespans of discrete cohorts across repeat studies to capture biological variation in the search for reproducible ageing interventions.

  59. ⁠, Andrew Gelman, Daniel Simpson, Michael Betancourt (2017-08-24):

    A key sticking point of is the choice of prior distribution, and there is a vast literature on potential defaults including uniform priors, Jeffreys’ priors, reference priors, maximum priors, and weakly informative priors. These methods, however, often manifest a key conceptual tension in prior modeling: a model encoding true information should be chosen without reference to the model of the measurement process, but almost all common prior modeling techniques are implicitly motivated by a reference likelihood. In this paper we resolve this apparent paradox by placing the choice of prior into the context of the entire Bayesian analysis, from inference to prediction to model evaluation.






  65. 2017-kowarsky.pdf





  70. ⁠, Sebastiaan Bol, Jana Caspers, Lauren Buckingham, Gail Denise Anderson-Shelton, Carrie Ridgway, C. A. Tony Buffington, Stefan Schulz, Evelien M. Bunnik (2017-03-16; cat  /​ ​​ ​catnip⁠, cat  /​ ​​ ​silvervine⁠, cat  /​ ​​ ​tartarian-honeysuckle⁠, cat  /​ ​​ ​valerian):

    Background: Olfactory stimulation is an often overlooked method of environmental enrichment for cats in captivity. The best known example of olfactory enrichment is the use of catnip, a plant that can cause an apparently euphoric reaction in domestic cats and most of the Pantherinae. It has long been known that some domestic cats and most tigers do not respond to catnip. Although many anecdotes exist of other plants with similar effects, data are lacking about the number of cats that respond to these plants, and if cats that do not respond to catnip respond to any of them. Furthermore, much is still unknown about which chemicals in these plants cause this response.

    Methods: We tested catnip, ⁠, Tatarian honeysuckle and root on 100 domestic cats and observed their response. Each cat was offered all four plant materials and a control, multiple times. Catnip and silver vine also were offered to nine tigers. The plant materials were analyzed by gas chromatography coupled with mass spectrometry to quantify concentrations of compounds believed to exert stimulating effects on cats.

    Results: Nearly all domestic cats responded positively to olfactory enrichment. In agreement with previous studies, one out of every three cats did not respond to catnip. Almost 80% of the domestic cats responded to silver vine and about 50% to Tatarian honeysuckle and valerian root. Although cats predominantly responded to fruit galls of the silver vine plant, some also responded positively to its wood. Of the cats that did not respond to catnip, almost 75% did respond to silver vine and about one out of three to ⁠. Unlike domestic cats, tigers were either not interested in silver vine or responded disapprovingly. The amount of nepetalactone was highest in catnip and only present at marginal levels in the other plants. Silver vine contained the highest concentrations of all other compounds tested.

    Conclusions: Olfactory enrichment for cats may have great potential. Silver vine powder from dried fruit galls and catnip were most popular among domestic cats. Silver vine and Tatarian honeysuckle appear to be good alternatives to catnip for domestic cats that do not respond to catnip.

  71. Catnip#optimal-catnip-alternative-selection-solving-the-mdp

  72. 2017-samson.pdf







  79. 2017-chalfin.pdf









  88. Bakker

  89. Anime#youjo-senki

  90. Anime#school-live