Exploiting population samples to enhance genome-wide association studies of disease.

ABSTRACT: It is widely acknowledged that genome-wide association studies (GWAS) of complex human disease fail to explain a large portion of heritability, primarily due to lack of statistical power-a problem that is exacerbated when seeking detection of interactions of multiple genomic loci. An untapped source of information that is already widely available, and that is expected to grow in coming years, is population samples. Such samples contain genetic marker data for additional individuals, but not their relevant phenotypes. In this article we develop a highly efficient testing framework based on a constrained maximum-likelihood estimate in a case-control-population setting. We leverage the available population data and optional modeling assumptions, such as Hardy-Weinberg equilibrium (HWE) in the

SUBMITTER: Kaufman S 

PROVIDER: S-EPMC4012491 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature