StaRQR-K: False Discovery Rate Controlled Regional Quantile Regression

TL;DR

StaRQR-K is a novel statistical framework that identifies genomic regions associated with specific parts of an outcome distribution, controlling false discoveries in ultrahigh-dimensional settings, demonstrated through simulations and cancer data analysis.

Contribution

It introduces a stabilized regional quantile regression method with FDR control using model-X knockoffs, tailored for high-dimensional genomic data analysis.

Findings

Achieves valid FDR control in simulations

Detects region-specific effects in cancer data

Improves out-of-sample prediction accuracy

Abstract

Quantifying how genomic features influence different parts of an outcome distribution requires statistical tools that go beyond mean regression, especially in ultrahigh-dimensional settings. Motivated by the study of LINE-1 activity in cancer, we propose StaRQR-K, a stabilized regional quantile regression framework with model-X knockoffs for false discovery rate control. StaRQR-K identifies CpG sites whose methylation levels are associated with specific quantile regions of an outcome, allowing detection of heterogeneous and tail-sensitive effects. The method combines an efficient regional quantile sure independence screening procedure with a winsorizing-based model-X knockoff filter, providing false discovery rate (FDR) control for regional quantile regression. Simulation studies show that StaRQR-K achieves valid FDR control and substantially higher power than existing approaches. In an application to The Cancer Genome Atlas head and neck cancer cohort, StaRQR-K reveals quantile-region-specific associations between CpG methylation and LINE-1 activity that improve out-of-sample prediction and highlight genomic regions with known functional relevance.