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 Type1 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 Type1 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.
Highlights
Genome-wide association studies (GWAS), which analyze a single trait each time, have identified thousands of genetic variants associated with an impressive number of complex traits and diseases [1]
We have introduced adaptive multi-trait association test (aMAT), a multi-trait association test, to conduct jointly analysis of any number of traits
Through simulations and real data analyses, we demonstrated that aMAT could yield well-controlled Type I error rates and achieve high statistical power across a wide range of scenarios
Summary
Genome-wide association studies (GWAS), which analyze a single trait each time, have identified thousands of genetic variants associated with an impressive number of complex traits and diseases [1]. AMAT yields well-controlled Type I error rates when analyzing any number (e.g., hundreds) of traits. This is achieved by taking the potential singularity of the trait correlation matrix into account. By analyzing several brain image-derived phenotypes GWAS summary results jointly, we demonstrate that our approach can reproducibly identify additional associated genetic variants that have been ignored by several existing methods. These newly identified genetic variants provide additional biological insights into brain structure
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