Recent genomic studies indicate that heritability of complex human traits and diseases originate from the effects of hundreds to thousands of genetic variants, with each variant explaining only a slight fraction of the total heritability of the trait. However, variation within the human genome is finite. Therefore, population genetics theory predicts that pleiotropic effects—that is, genetic variants within a single gene or locus affecting multiple phenotypes—must be ubiquitous within humans. While several statistical approaches exist for testing pleiotropy in genome-wide association studies, there are no existing pleiotropic tests that perform the standard rare-variant analytic strategy of aggregating information on rare variants across a gene of interest.

In order to fill this important gap, we introduce a new statistical method for pleiotropic analysis of rare variants using a nonparametric distance-covariance approach. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Like the popular SKAT framework for univariate rare variant analysis, our approach allows for inclusion of prior information, such as biological plausibility of the variants under study, and further remains powerful when a gene harbors a mixture of rare causal variants that act in different directions on phenotype. Our approach derives analytic P-values based on a gamma approximation of the test statistic, 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 Gamma Approximation for Multiple Traits (GAMuT) test, provides increased power over univariate SKAT testing of individual traits when pleiotropy exists. We also illustrate our approach using resequencing data from the Grady Trauma Project.