An Adaptive and Robust Method for Multi-trait Analysis of Genome-wide Association Studies Using Summary Statistics

TL;DR

This paper introduces MTAFS, an efficient and robust multi-trait analysis method for GWAS summary statistics, improving power and computational performance over existing methods, demonstrated on UK Biobank brain phenotypes.

Contribution

The paper presents MTAFS, a novel adaptive Fisher method that enhances multi-trait GWAS analysis using summary statistics, addressing performance and efficiency issues of prior methods.

Findings

MTAFS outperforms existing methods in simulations.

It effectively controls type 1 error.

Demonstrated on UK Biobank data with large trait sets.

Abstract

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. In contrast to individual-level data, GWAS summary statistics are usually publicly available, and thus methods using only summary statistics have greater usage. Although many methods have been developed for joint analysis of multiple traits using summary statistics, there are many issues, including inconsistent performance, computational inefficiency, and numerical problems when considering lots of traits. To address these challenges, we propose a multi-trait adaptive Fisher method for summary statistics (MTAFS), a computationally efficient method with robust power performance. We applied MTAFS to two sets of brain image-derived phenotypes (IDPs) from the UK Biobank, including a set of 58 Volumetric IDPs and a set of 212 Area IDPs. Together with results from a simulation study, MTAFS shows its advantage over existing multi-trait methods, with robust performance across a range of underlying settings. It controls type 1 error well, and can efficiently handle a large number of traits.