Graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture.

ABSTRACT: Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. However, identification of risk variants associated with complex diseases remains challenging as they are often affected by many genetic variants with small or moderate effects. There has been accumulating evidence suggesting that different complex traits share common risk basis, namely pleiotropy. Recently, several statistical methods have been developed to improve statistical power to identify risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging pleiotropy. While these methods were shown to im

SUBMITTER: Chung D 

PROVIDER: S-EPMC5347371 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature