Multiple phenotype association tests using summary statistics in genome-wide association studies.

ABSTRACT: We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models

SUBMITTER: Liu Z 

PROVIDER: S-EPMC5743780 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature