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Sequencing-based studies are emerging as a major tool for genetic associationstudies of complex diseases. These studies pose great challenges to thetraditional statistical methods (e.g., single-locus analyses based onregression methods) because of the high-dimensionality of data and the lowfrequency of genetic variants. In addition, there is a great interest inbiology and epidemiology to identify genetic risk factors contributed tomultiple disease phenotypes. The multiple phenotypes can often follow differentdistributions, which violates the assumptions of most current methods. In thispaper, we propose a generalized similarity U test, referred to as GSU. GSU is asimilarity-based test and can handle high-dimensional genotypes and phenotypes.We studied the theoretical properties of GSU, and provided the efficientp-value calculation for association test as well as the sample size and powercalculation for the study design. Through simulation, we found that GSU hadadvantages over existing methods in terms of power and robustness to phenotypedistributions. Finally, we used GSU to perform a multivariate analysis ofsequencing data in the Dallas Heart Study and identified a joint association of4 genes with 5 metabolic related phenotypes.

A Generalized Similarity U Test for Multivariate Analysis of Sequencing Data