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Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis.
|Publication Type||Journal Article|
|Authors||Stahl, EA, Wegmann D., Trynka G., Gutierrez-Achury J., Do R., Voight BF, Kraft P., Chen R., Kallberg HJ, Kurreeman FA, Diabetes Genetics Replication and Meta-analysis Consortium, Myocardial Infarction Genetics Consortium, Kathiresan S., Wijmenga C., Gregersen PK, Alfredsson L., Siminovitch KA, Worthington J., de Bakker PI, Raychaudhuri S., and Plenge RM|
|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.|
|Year of Publication||2012|
|Date Published (YYYY/MM/DD)||2012/03/25|