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dc.contributor.authorStahl EA
dc.contributor.authorWegmann D
dc.contributor.authorTrynka G
dc.contributor.authorGutierrez-Achury J
dc.contributor.authorDo R
dc.contributor.authorVoight BF
dc.contributor.authorKraft P
dc.contributor.authorChen R
dc.contributor.authorKallberg HJ
dc.contributor.authorKurreeman FA
dc.contributor.authorDIAGRAM Consortium
dc.contributor.authorMyocardial Infarction Genetics Consortium
dc.contributor.authorKathiresan S
dc.contributor.authorWijmenga C
dc.contributor.authorGregersen PK
dc.contributor.authorAlfredsson L
dc.contributor.authorSiminovitch KA
dc.contributor.authorWorthington J
dc.contributor.authorde Bakker PI
dc.contributor.authorRaychaudhuri S
dc.contributor.authorPlenge RM
dc.description.abstractThe genetic architectures of common, complex diseases are largely uncharacterized. We modeled the genetic architecture underlying genome-wide association study (GWAS) data for rheumatoid arthritis and developed a new method using polygenic risk-score analyses to infer the total liability-scale variance explained by associated GWAS SNPs. Using this method, we estimated that, together, thousands of SNPs from rheumatoid arthritis GWAS explain an additional 20% of disease risk (excluding known associated loci). We further tested this method on datasets for three additional diseases and obtained comparable estimates for celiac disease (43% excluding the major histocompatibility complex), myocardial infarction and coronary artery disease (48%) and type 2 diabetes (49%). Our results are consistent with simulated genetic models in which hundreds of associated loci harbor common causal variants and a smaller number of loci harbor multiple rare causal variants. These analyses suggest that GWAS will continue to be highly productive for the discovery of additional susceptibility loci for common diseases.en_US
dc.titleBayesian inference analyses of the polygenic architecture of rheumatoid arthritisen_US
dc.journal.titleNature genetics

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