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 genomic inflation factor of 1.04, indicating the Type 1 error rate was well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.

journal_name

Genetics

journal_title

Genetics

authors

Wu C

doi

10.1534/genetics.120.303242

subject

Has Abstract

pub_date

2020-08-01 00:00:00

pages

947-958

issue

4

eissn

0016-6731

issn

1943-2631

pii

genetics.120.303242

journal_volume

215

pub_type

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