Covariate Adaptive Family-wise Error Rate Control for Genome-Wide Association Studies

TL;DR

This paper introduces a novel covariate-adaptive method for controlling the family-wise error rate in genome-wide association studies, leveraging functional annotations to improve detection power while maintaining robustness.

Contribution

It develops a new covariate-adaptive FWER control procedure that incorporates external genomic annotations, with proven asymptotic validity and improved power over existing methods.

Findings

More powerful than competing methods in simulations

Detects more significant loci in UK Biobank data

Maintains robustness across different settings

Abstract

The family-wise error rate (FWER) has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase the detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focus on the false discovery rate control while covariate-adaptive FWER-controlling procedures remain under-developed. Here we propose a novel covariate-adaptive FWER-controlling procedure that incorporates external covariates which are potentially informative of either the statistical power or the prior null probability. An efficient algorithm is developed to implement the proposed method. We prove its asymptotic validity and obtain the rate of convergence through a perturbation-type argument. Our numerical studies show that the new procedure is more powerful than competing methods and maintains robustness across different settings. We apply the proposed approach to the UK Biobank data and analyze 27 traits with 9 million single-nucleotide polymorphisms tested for associations. Seventy-five genomic annotations are used as covariates. Our approach detects more genome-wide significant loci than other methods in 21 out of the 27 traits.