Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis |
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Authors: | Stahl Eli A,Wegmann Daniel,Trynka Gosia,Gutierrez-Achury Javier,Do Ron,Voight Benjamin F,Kraft Peter,Chen Robert,Kallberg Henrik J,Kurreeman Fina A S Diabetes Genetics Replication Meta-analysis Consortium Myocardial Infarction Genetics Consortium,Kathiresan Sekar,Wijmenga Cisca,Gregersen Peter K,Alfredsson Lars,Siminovitch Katherine A,Worthington Jane,de Bakker Paul I W,Raychaudhuri Soumya,Plenge Robert M |
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Affiliation: | Division of Rheumatology Immunology and Allergy, Brigham and Women's Hospital, Boston, Massachusetts, USA. estahl@rics.bwh.harvard.edu |
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Abstract: | The 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. |
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