Multi-trait Genome-Wide Analyses of the Brain Imaging Phenotypes in UK Biobank.

ABSTRACT: Many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated, traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits, and may yield inflated Type 1 error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a gen

SUBMITTER: Wu C 

PROVIDER: S-EPMC7404235 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature