Data-adaptive multi-locus association testing in subjects with arbitrary genealogical relationships

Gong Gail, Wang Wei, Hsieh Chih-Lin, J. Van Den Berg David, Haiman Christopher, Oakley-Girvan Ingrid and Whittemore Alice S. ()
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Gong Gail: Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA
Wang Wei: Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA
Hsieh Chih-Lin: Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
J. Van Den Berg David: Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
Haiman Christopher: Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
Oakley-Girvan Ingrid: Public Health Institute, Oakland, CA, USA
Whittemore Alice S.: Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA

Statistical Applications in Genetics and Molecular Biology, 2019, vol. 18, issue 3, 15

Abstract: Genome-wide sequencing enables evaluation of associations between traits and combinations of variants in genes and pathways. But such evaluation requires multi-locus association tests with good power, regardless of the variant and trait characteristics. And since analyzing families may yield more power than analyzing unrelated individuals, we need multi-locus tests applicable to both related and unrelated individuals. Here we describe such tests, and we introduce SKAT-X, a new test statistic that uses genome-wide data obtained from related or unrelated subjects to optimize power for the specific data at hand. Simulations show that: a) SKAT-X performs well regardless of variant and trait characteristics; and b) for binary traits, analyzing affected relatives brings more power than analyzing unrelated individuals, consistent with previous findings for single-locus tests. We illustrate the methods by application to rare unclassified missense variants in the tumor suppressor gene BRCA2, as applied to combined data from prostate cancer families and unrelated prostate cancer cases and controls in the Multi-ethnic Cohort (MEC). The methods can be implemented using open-source code for public use as the R-package GATARS (Genetic Association Tests for Arbitrarily Related Subjects) .

Keywords: data-adaptive tests; multi-locus kernel tests; related subjects (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/sagmb-2018-0030

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