Finding associated variants in genome-wide association studies on multiple traits.

ABSTRACT:

Motivation

Many variants identified by genome-wide association studies (GWAS) have been found to affect multiple traits, either directly or through shared pathways. There is currently a wealth of GWAS data collected in numerous phenotypes, and analyzing multiple traits at once can increase power to detect shared variant effects. However, traditional meta-analysis methods are not suitable for combining studies on different traits. When applied to dissimilar studies, these meta-analysis methods can be underpowered compared to univariate analysis. The degree to which traits share variant effects is often not known, and the vast majority of GWAS meta-analysis only consider one trait at a time.

Results

Here, we present a flexible method for finding associated variants from GWAS s

SUBMITTER: Gai L 

PROVIDER: S-EPMC6022769 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature