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Abstract:
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Evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that model pleiotropy are often more powerful than univariate methods that model each phenotype separately. While many methods exist for testing pleiotropy for common variants, there is a lack of cross-phenotype tests for gene-based analysis of rare variants. Here, we introduce a new 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 can adjust for covariates. Our approach yields asymptotic p-values that permit 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.
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