Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication

DOI

10.1038/ng.2232

Type

Journal article

Journal

Nature genetics

Publication Date

25/03/2012

Volume

44

Pages

483 - 489

Addresses

Division of Rheumatology Immunology and Allergy, Brigham and Women's Hospital, Boston, Massachusetts, USA. estahl@rics.bwh.harvard.edu

Keywords

Diabetes Genetics Replication and Meta-analysis Consortium, Myocardial Infarction Genetics Consortium, Humans, Arthritis, Rheumatoid, Celiac Disease, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Bayes Theorem, Case-Control Studies, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Genetic Loci