GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation.

ABSTRACT: Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects. Identifications of these risk variants remain a very challenging problem. There is a need to develop more powerful statistical methods to leverage available information to improve upon traditional approaches that focus on a single GWAS dataset without incorporating additional data. In this paper, we propose a novel statistical approach, GPA (Genetic analysis incorporating Pleiotropy and Annotation), to increase statistical power to identify risk variants through joint analysis of multiple GWAS data sets and annotation information because: (1) accumulating evidence suggests that different complex diseases share common risk bases, i.

SUBMITTER: Chung D 

PROVIDER: S-EPMC4230845 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature