Multi-SKAT: General framework to test multiple phenotype associations of rare variants

Abstract

In genetic association analysis, a joint test of multiple distinct phenotypes can increase power to identify sets of trait-associated variants within genes or regions of interest. Existing multi-phenotype tests for rare variants make specific assumptions about the patterns of association with underlying causal variants and the violation of these assumptions can reduce power to detect association. Here we develop a general framework for testing pleiotropic effects of rare variants on multiple continuous phenotypes using multivariate kernel regression (Multi-SKAT). Multi-SKAT models effect sizes of variants on the phenotypes through a kernel matrix and performs a variance component test of association. We show that many existing tests are equivalent to specific choices of kernel matrices with the Multi-SKAT framework. To increase power of detecting association across tests with different kernel matrices, we developed a fast and accurate approximation of the significance of the minimum observed p-value across tests. To account for related individuals, our framework uses random effects for the kinship matrix. Using simulated data and amino acid and exome-array data from the METSIM study, we show that Multi-SKAT can improve power over single-phenotype SKAT-O test and existing multiple phenotype tests, while maintaining type I error rate.\n\nGrant NumbersThis research was conducted with support from the following grants from National Institute of Health: O_LIR01 HG008773 (D.D. and S.L.)\nC_LIO_LIR01 LM012535 (D.D. and S.L.)\nC_LIO_LIR01 HG000376 (L.S. and M.B.)\nC_LIO_LIU01 DK062370 (L.S. and M.B.)\nC_LI