Genome-wide association studies of cardiovascular risk factors: design, conduct and interpretation

Abstract

Relying on known biology, candidate-gene studies have been only modestly successful in identifying genetic variants associated with cardiovascular risk factors. Genome-wide association (GWA) studies, in contrast, allow broad scans across millions of loci in search of unsuspected genetic associations with phenotypes. The large numbers of statistical tests in GWA studies and the large sample sizes required to detect modest-sized associations have served as a powerful incentive for the development of large collaborative efforts such as the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. This article uses published data on three phenotypes, fibrinogen, uric acid, and electrocardiographic QT interval duration, from the CHARGE Consortium to describe several methodologic issues in the design, conduct, and interpretation of GWA studies, including the use of imputation and the need for additional genotyping. Even with large studies, novel genetic loci explain only a small proportion of the variance of cardiovascular phenotypes.