False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks

TL;DR

This paper introduces the Screen-T-Rex selector, a fast and scalable method for controlling false discovery rate in large-scale genomics biobanks, improving reproducibility and reducing computation time significantly.

Contribution

The paper presents a novel FDR-controlling method tailored for large-scale biobank screening that requires no additional parameter tuning and outperforms existing benchmark methods.

Findings

Superior performance in simulations and real-world HIV-1 drug resistance data

Significantly lower computation time than benchmark knockoff methods

Effective in high-dimensional, multivariate genomic screening

Abstract

Genomics biobanks are information treasure troves with thousands of phenotypes (e.g., diseases, traits) and millions of single nucleotide polymorphisms (SNPs). The development of methodologies that provide reproducible discoveries is essential for the understanding of complex diseases and precision drug development. Without statistical reproducibility guarantees, valuable efforts are spent on researching false positives. Therefore, scalable multivariate and high-dimensional false discovery rate (FDR)-controlling variable selection methods are urgently needed, especially, for complex polygenic diseases and traits. In this work, we propose the Screen-T-Rex selector, a fast FDR-controlling method based on the recently developed T-Rex selector. The method is tailored to screening large-scale biobanks and it does not require choosing additional parameters (sparsity parameter, target FDR level, etc). Numerical simulations and a real-world HIV-1 drug resistance example demonstrate that the performance of the Screen-T-Rex selector is superior, and its computation time is multiple orders of magnitude lower compared to current benchmark knockoff methods.