A Weighted U Statistic for Genetic Association Analyses of Sequencing Data

TL;DR

This paper introduces WU-seq, a non-parametric weighted U statistic method for genetic association analysis of high-dimensional sequencing data, demonstrating improved robustness and comparable performance to existing methods.

Contribution

The paper presents WU-seq, a novel non-parametric method that enhances association analysis of sequencing data without assuming specific phenotype distributions.

Findings

WU-seq outperforms SKAT when assumptions are violated

WU-seq performs comparably to SKAT under correct assumptions

Identified association between ANGPTL4 and VLDL cholesterol in DHS data

Abstract

With advancements in next generation sequencing technology, a massive amount of sequencing data are generated, offering a great opportunity to comprehensively investigate the role of rare variants in the genetic etiology of complex diseases. Nevertheless, this poses a great challenge for the statistical analysis of high-dimensional sequencing data. The association analyses based on traditional statistical methods suffer substantial power loss because of the low frequency of genetic variants and the extremely high dimensionality of the data. We developed a weighted U statistic, referred to as WU-seq, for the high-dimensional association analysis of sequencing data. Based on a non-parametric U statistic, WU-SEQ makes no assumption of the underlying disease model and phenotype distribution, and can be applied to a variety of phenotypes. Through simulation studies and an empirical study, we showed that WU-SEQ outperformed a commonly used SKAT method when the underlying assumptions were violated (e.g., the phenotype followed a heavy-tailed distribution). Even when the assumptions were satisfied, WU-SEQ still attained comparable performance to SKAT. Finally, we applied WU-seq to sequencing data from the Dallas Heart Study (DHS), and detected an association between ANGPTL 4 and very low density lipoprotein cholesterol.