Common SNPs explain a large proportion of the heritability for human height
Jian Yang,Beben Benyamin,Brian P. McEvoy,Scott D. Gordon,Anjali K. Henders,Dale R. Nyholt,Pamela A. F. Madden,Andrew C. Heath,Nicholas G. Martin,Grant W. Montgomery,Michael E. Goddard,Peter M. Visscher +11 more
TLDR
Evidence is provided that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.Abstract:
SNPs discovered by genome-wide association studies (GWASs) account for only a small fraction of the genetic variation of complex traits in human populations. Where is the remaining heritability? We estimated the proportion of variance for human height explained by 294,831 SNPs genotyped on 3,925 unrelated individuals using a linear model analysis, and validated the estimation method with simulations based on the observed genotype data. We show that 45% of variance can be explained by considering all SNPs simultaneously. Thus, most of the heritability is not missing but has not previously been detected because the individual effects are too small to pass stringent significance tests. We provide evidence that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.read more
Citations
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GCTA: a tool for genome-wide complex trait analysis.
TL;DR: The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets and focuses on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation.
Journal ArticleDOI
An atlas of genetic correlations across human diseases and traits.
Brendan Bulik-Sullivan,Brendan Bulik-Sullivan,Hilary K. Finucane,Verneri Anttila,Verneri Anttila,Alexander Gusev,Felix R. Day,Po-Ru Loh,Po-Ru Loh,Laramie E. Duncan,Laramie E. Duncan,John R. B. Perry,Nick Patterson,Elise B. Robinson,Elise B. Robinson,Mark J. Daly,Mark J. Daly,Alkes L. Price,Alkes L. Price,Benjamin M. Neale,Benjamin M. Neale +20 more
TL;DR: This work introduces a technique—cross-trait LD Score regression—for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap, and uses this method to estimate 276 genetic correlations among 24 traits.
Journal ArticleDOI
10 Years of GWAS Discovery: Biology, Function, and Translation
Peter M. Visscher,Naomi R. Wray,Qian Zhang,Pamela Sklar,Mark I. McCarthy,Matthew A. Brown,Jian Yang +6 more
TL;DR: The remarkable range of discoveriesGWASs has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics are reviewed.
Journal ArticleDOI
Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci
Andre Franke,Dermot P.B. McGovern,Jeffrey C. Barrett,Kai Wang,Graham L. Radford-Smith,Tariq Ahmad,Charlie W. Lees,Tobias Balschun,James Lee,Rebecca L. Roberts,Carl A. Anderson,Joshua C. Bis,Suzanne Bumpstead,David Ellinghaus,Eleonora M. Festen,Michel Georges,Todd Green,Talin Haritunians,Luke Jostins,Anna Latiano,Christopher G. Mathew,Grant W. Montgomery,Natalie J. Prescott,Soumya Raychaudhuri,Jerome I. Rotter,Philip Schumm,Yashoda Sharma,Lisa A. Simms,Kent D. Taylor,David C. Whiteman,Cisca Wijmenga,Robert N. Baldassano,Murray L. Barclay,Theodore M. Bayless,Stephan Brand,Carsten Büning,Albert Cohen,Jean Frederick Colombel,Mario Cottone,Laura Stronati,Ted Denson,Martine De Vos,Renata D'Incà,Marla Dubinsky,Cathryn Edwards,Timothy H. Florin,Denis Franchimont,Richard B. Gearry,Jürgen Glas,Jürgen Glas,Jürgen Glas,André Van Gossum,Stephen L. Guthery,Jonas Halfvarson,Hein W. Verspaget,Jean-Pierre Hugot,Amir Karban,Debby Laukens,Ian C. Lawrance,Marc Lémann,Arie Levine,Cécile Libioulle,Edouard Louis,Craig Mowat,William G. Newman,Julián Panés,Anne M. Phillips,Deborah D. Proctor,Miguel Regueiro,Richard K Russell,Paul Rutgeerts,Jeremy D. Sanderson,Miquel Sans,Frank Seibold,A. Hillary Steinhart,Pieter C. F. Stokkers,Leif Törkvist,Gerd A. Kullak-Ublick,David C. Wilson,Thomas D. Walters,Stephan R. Targan,Steven R. Brant,John D. Rioux,Mauro D'Amato,Rinse K. Weersma,Subra Kugathasan,Anne M. Griffiths,John C. Mansfield,Severine Vermeire,Richard H. Duerr,Mark S. Silverberg,Jack Satsangi,Stefan Schreiber,Judy H. Cho,Vito Annese,Hakon Hakonarson,Mark J. Daly,Miles Parkes +97 more
TL;DR: A meta-analysis of six Crohn's disease genome-wide association studies and a series of in silico analyses highlighted particular genes within these loci implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP.
Journal ArticleDOI
Five years of GWAS discovery
Peter M. Visscher,Peter M. Visscher,Matthew A. Brown,Mark I. McCarthy,Mark I. McCarthy,Jian Yang +5 more
TL;DR: The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs), which 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.
References
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Common polygenic variation contributes to risk of schizophrenia and bipolar disorder
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TL;DR: The extent to which common genetic variation underlies the risk of schizophrenia is shown, using two analytic approaches, and the major histocompatibility complex is implicate, which is shown to involve thousands of common alleles of very small effect.
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Population structure and eigenanalysis
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