A General Framework for Powerful Confounder Adjustment in Omics Association Studies

TL;DR

This paper introduces 2dFDR+, a novel framework for confounder adjustment in omics studies that improves statistical power by leveraging auxiliary information and provides valid inference under complex, unknown distributions.

Contribution

The study proposes a new two-dimensional FDR control method that outperforms traditional approaches in confounder adjustment for genomic data analysis.

Findings

2dFDR+ significantly increases detection power in simulations.

The method maintains asymptotic FDR control.

Real data applications demonstrate practical effectiveness.

Abstract

Genomic data are subject to various sources of confounding, such as demographic variables, biological heterogeneity, and batch effects. To identify genomic features associated with a variable of interest in the presence of confounders, the traditional approach involves fitting a confounder-adjusted regression model to each genomic feature, followed by multiplicity correction. This study shows that the traditional approach was sub-optimal and proposes a new two-dimensional false discovery rate control framework (2dFDR+) that provides significant power improvement over the conventional method and applies to a wide range of settings. 2dFDR+ uses marginal independence test statistics as auxiliary information to filter out less promising features, and FDR control is performed based on conditional independence test statistics in the remaining features. 2dFDR+ provides (asymptotically) valid inference from samples in settings where the conditional distribution of the genomic variables given the covariate of interest and the confounders is arbitrary and completely unknown. To achieve this goal, our method requires the conditional distribution of the covariate given the confounders to be known or can be estimated from the data. We develop a new procedure to simultaneously select the two cutoff values for the marginal and conditional independence test statistics. 2dFDR+ is proved to offer asymptotic FDR control and dominate the power of the traditional procedure. Promising finite sample performance is demonstrated via extensive simulations and real data applications.