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Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank

View ORCID ProfileChong Wu
doi: https://doi.org/10.1101/758326
Chong Wu
Department of Statistics, Florida State University, Tallahassee, FL, USA
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  • For correspondence: cwu3@fsu.edu
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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 I 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 I error rates were 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 02, 2020.
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Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank
Chong Wu
bioRxiv 758326; doi: https://doi.org/10.1101/758326
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Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank
Chong Wu
bioRxiv 758326; doi: https://doi.org/10.1101/758326

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