|Publication Type||Journal Article|
|Year of Publication||2012|
|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, C, Myocardial Infarction Genetics, C, Kathiresan, S, Wijmenga, C, Gregersen, PK, Alfredsson, L, Siminovitch, KA, Worthington, J, de Bakker, PI, Raychaudhuri, S, Plenge, RM|
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.