Abstract
We propose a novel aggregating U-test for gene-based association analysis. The method considers both rare and common variants. It adaptively searches for potential disease-susceptibility rare variants and collapses them into a single “supervariant.” A forward U-test is then used to assess the joint association of the supervariant and other common variants with quantitative traits. Using 200 simulated replicates from the Genetic Analysis Workshop 17 mini-exome data, we compare the performance of the proposed method with that of a commonly used approach, QuTie. We find that our method has an equivalent or greater power than QuTie to detect nine genes that influence the quantitative trait Q1. This new approach provides a powerful tool for detecting both common and rare variants associated with quantitative traits.
Highlights
In the past decade, extensive genome-wide association studies (GWAS) have been conducted to understand the genetic etiology of complex diseases
We propose an aggregating U-test to examine the association between the quantitative traits and multiple genetic variants, including both rare and common variants
The threshold for rare variants was chosen as a minor allele frequency (MAF) less than 0.01
Summary
Extensive genome-wide association studies (GWAS) have been conducted to understand the genetic etiology of complex diseases. The common variants identified so far explain only a small fraction of the variations of complex diseases [1]. It seems clear that the genetic etiology of complex diseases is highly heterogeneous. Individually rare, may impose a high risk for the development of diseases [2]. These rare variants have been the recipients of growing attention by investigators. Statistical methods are greatly needed to detect the association between these genetic variants and common complex diseases
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