Robust Bayesian FDR Control using Bayes Factors, with Applications to Multi-tissue eQTL Discovery

TL;DR

This paper introduces a robust Bayesian method for controlling false discovery rate in multiple hypothesis testing, especially suited for large-scale genomic data like multi-tissue eQTL mapping, with strong theoretical and empirical support.

Contribution

The paper presents a new FDR control procedure based on Bayes factors that is robust to model misspecification and computationally efficient for large genomic datasets.

Findings

Effective FDR control under model misspecification

High computational efficiency for big data

Demonstrated power in multi-tissue eQTL mapping

Abstract

Motivated by the genomic application of expression quantitative trait loci (eQTL) mapping, we propose a new procedure to perform simultaneous testing of multiple hypotheses using Bayes factors as input test statistics. One of the most significant features of this method is its robustness in controlling the targeted false discovery rate (FDR) even under misspecifications of parametric alternative models. Moreover, the proposed procedure is highly computationally efficient, which is ideal for treating both complex system and big data in genomic applications. We discuss the theoretical properties of the new procedure and demonstrate its power and computational efficiency in applications of single-tissue and multi-tissue eQTL mapping.