A Generalized Similarity U Test for Multivariate Analysis of Sequencing Data

TL;DR

This paper introduces GSU, a generalized similarity U test designed for multivariate analysis of high-dimensional sequencing data, addressing challenges of phenotype diversity and low-frequency variants in genetic association studies.

Contribution

The paper presents GSU, a novel similarity-based test that handles high-dimensional genotypes and phenotypes, with theoretical properties and practical advantages demonstrated through simulations and real data.

Findings

GSU outperforms existing methods in power and robustness

GSU effectively analyzes multiple phenotypes with different distributions

Identified joint gene associations in the Dallas Heart Study

Abstract

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.