Multiple-trait Adaptive Fisher's Method for Genome-wide Association Studies

TL;DR

This paper introduces a novel adaptive Fisher's method for multi-trait GWAS that improves power and error control by aggregating evidence across traits, accommodating both continuous and binary data.

Contribution

The paper proposes the multiple-trait adaptive Fisher's (MTAF) method, a new approach for multi-trait GWAS that enhances detection power and flexibility over existing methods.

Findings

MTAF controls type I error effectively.

MTAF demonstrates higher power in simulations.

Identified genes associated with substance dependence.

Abstract

In genome-wide association studies (GWASs), there is an increasing need for detecting the associations between a genetic variant and multiple traits. In studies of complex diseases, it is common to measure several potentially correlated traits in a single GWAS. Despite the multivariate nature of the studies, single-trait-based methods remain the most widely-adopted analysis procedure, owing to their simplicity for studies with multiple traits as their outcome. However, the association between a genetic variant and a single trait sometimes can be weak, and ignoring the actual correlation among traits may lose power. On the contrary, multiple-trait analysis, a method analyzes a group of traits simultaneously, has been proven to be more powerful by incorporating information from the correlated traits. Although existing methods have been developed for multiple traits, several drawbacks limit their wide application in GWASs. In this paper, we propose a multiple-trait adaptive Fisher's (MTAF) method to test associations between a genetic variant and multiple traits at once, by adaptively aggregating evidence from each trait. The proposed method can accommodate both continuous and binary traits and it has reliable performance under various scenarios. Using a simulation study, we compared our proposed method with several existing methods and demonstrated its competitiveness in terms of type I error control and statistical power. By applying the method to the Study of Addiction: Genetics and Environment (SAGE) dataset, we successfully identified several genes associated with substance dependence.