Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis by 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 and 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 in Nature genetics (2012).

[PMID: 22446960] PubMed


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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