A W-test collapsing method for rare variant testing with applications to exome sequencing data of hypertensive disorder

TL;DR

This paper introduces a W-test collapsing method for rare variant association testing that improves power and speed, demonstrated on hypertensive disorder exome data and identifying biologically relevant genes.

Contribution

The paper presents a novel W-test collapsing method that outperforms existing tests in power and computational efficiency for rare variant analysis.

Findings

Better performance than Weighted-Sum Statistic and SKAT in simulations

Identified genes related to metabolism and inflammation in hypertensive disorder data

Faster computation speed for rare variant association testing

Abstract

Advancement in sequencing technology enables the study of association between complex disorders and rare variants with low minor allele frequencies. One of the major challenges in rare variant testing is lack of statistical power of traditional testing methods due to extremely low variances of single nucleotide polymorphisms. In this paper, we introduce a W-test collapsing method that evaluates the distributional differences in cases and controls using a combined log of odds ratio. The proposed method is compared with the Weighted-Sum Statistic and Sequence Kernel Association Test using simulation data sets and showed better performances and faster computing speed. In the study of real next generation sequencing data set of hypertensive disorder, we identified genes of interesting biological functions that are associated to metabolism disorder and inflammation, which include the MACROD1, NLRP7, AGK, PAK6 and APBB1. The W-test collapsing method offers a fast, effective and alternative way for rare variants association analysis.