Many Phenotypes without Many False Discoveries: Error Controlling Strategies for Multi-Traits Association Studies

TL;DR

This paper introduces a hierarchical testing method for multi-traits genetic association studies that effectively controls false discoveries at both variant and phenotype levels, improving reliability over traditional FDR approaches.

Contribution

The authors propose a simple hierarchical testing procedure that better controls false discovery rates for variants and phenotypes in multi-trait studies, enhancing interpretability and reliability.

Findings

Hierarchical testing controls false discoveries more effectively.

Simulation studies show improved error rate control and power.

Application to Arabidopsis identifies new genetic variants affecting flowering.

Abstract

The genetic basis of multiple phenotypes such as gene expression, metabolite levels, or imaging features is often investigated by testing a large collection of hypotheses, probing the existence of association between each of the traits and hundreds of thousands of genotyped variants. Appropriate multiplicity adjustment is crucial to guarantee replicability of findings, and False Discovery Rate (FDR) is frequently adopted as a measure of global error. In the interest of interpretability, results are often summarized so that reporting focuses on variants discovered to be associated to some phenotypes. We show that applying FDR-controlling procedures on the entire collection of hypotheses fails to control the rate of false discovery of associated variants as well as the average rate of false discovery of phenotypes influenced by such variants. We propose a simple hierarchical testing procedure which allows control of both these error rates and provides a more reliable basis for the identification of variants with functional effects. We demonstrate the utility of this approach through simulation studies comparing various error rates and measures of power for genetic association studies of multiple traits. Finally, we apply the proposed method to identify genetic variants which impact flowering phenotypes in Arabdopsis thaliana, expanding the set of discoveries.