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Am J Hum Genet. 2016 Mar 3;98(3):525-540. doi: 10.1016/j.ajhg.2016.01.017.

A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants.

Author information

1
Department of Human Genetics, Emory University, Atlanta, GA 30322, USA.
2
Department of Evolution and Ecology, University of California, Davis, Davis, CA 95616, USA.
3
Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, USA; Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA.
4
Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109, USA.
5
Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA.
6
Department of Human Genetics, Emory University, Atlanta, GA 30322, USA. Electronic address: mpepste@emory.edu.

Abstract

Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy.

KEYWORDS:

complex human traits; gene mapping; pleiotropy; rare variant

PMID:
26942286
PMCID:
PMC4800053
DOI:
10.1016/j.ajhg.2016.01.017
[Indexed for MEDLINE]
Free PMC Article

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