B1862
Title: FDR control for high dimensional quantile regression
Authors: Zhigen Zhao - Temple University (United States)
Yan Yu - University of Cincinnati (United States)
Tianhai Zu - University of Texas at San Antonio (United States) [presenting]
Abstract: Multiple testing is a significant challenge in genetic research, particularly when investigating complex diseases. The quantile regression is increasingly critical for providing a more comprehensive view of heterogeneous relationships between genetic markers and complex conditions like diabetes. However, despite their sophistication, existing mechanisms for false discovery rate (FDR) control are not tailored to the framework of quantile regression. To tackle these challenges, a novel FDR control method is presented for linear quantile regression, utilizing data-splitting mirror statistics. The approach addresses the current limitations in existing FDR control methods for quantile regression and is especially advantageous in preserving high power. Theoretical justifications are provided, highlighting that this is the first attempt of its kind for controlling FDR for linear quantile regression. Extensive simulations confirm the efficacy of the approach. Furthermore, its use case is demonstrated through a case study on diabetes data, with particular emphasis on high-risk quantiles. The method effectively identifies genetic factors across various diabetes risk quantiles that may benefit improved diagnostics and treatments.
