A Generalized Similarity U Test for Multivariate Analysis of Sequencing Data
This addresses the problem of genetic association studies for complex diseases in biology and epidemiology, offering a method to handle multivariate data with non-standard distributions, though it appears incremental as it builds on similarity-based tests.
The authors tackled the challenge of analyzing high-dimensional sequencing data with multiple phenotypes that may follow different distributions, by proposing a generalized similarity U test (GSU) that showed advantages in power and robustness over existing methods in simulations and identified a joint association of 4 genes with 5 phenotypes in a real study.
Sequencing-based studies are emerging as a major tool for genetic association studies of complex diseases. These studies pose great challenges to the traditional statistical methods (e.g., single-locus analyses based on regression methods) because of the high-dimensionality of data and the low frequency of genetic variants. In addition, there is a great interest in biology and epidemiology to identify genetic risk factors contributed to multiple disease phenotypes. The multiple phenotypes can often follow different distributions, which violates the assumptions of most current methods. In this paper, we propose a generalized similarity U test, referred to as GSU. GSU is a similarity-based test and can handle high-dimensional genotypes and phenotypes. We studied the theoretical properties of GSU, and provided the efficient p-value calculation for association test as well as the sample size and power calculation for the study design. Through simulation, we found that GSU had advantages over existing methods in terms of power and robustness to phenotype distributions. Finally, we used GSU to perform a multivariate analysis of sequencing data in the Dallas Heart Study and identified a joint association of 4 genes with 5 metabolic related phenotypes.