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arXiv · 2511.21562

StaRQR-K: False Discovery Rate Controlled Regional Quantile Regression

Sang Kyu Lee·Tongwu Zhang·Hyokyoung G. Hong·Haolei Weng
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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.

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Sang Kyu Lee, Tongwu Zhang, Hyokyoung G. Hong, Haolei Weng. 2025-11-26. StaRQR-K: False Discovery Rate Controlled Regional Quantile Regression. https://arxiv.org/abs/2511.21562

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