Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits

TL;DR

This paper introduces a regularized quantile regression method tailored for high-dimensional genetic data with heterogeneous sparsity, enabling more accurate identification of genetic factors influencing quantitative traits.

Contribution

It develops a novel regularized quantile regression approach that accounts for heterogeneity and sparsity in genetic data, with theoretical analysis and practical validation.

Findings

Method effectively identifies genetic features influencing traits.

Theoretical properties are rigorously established.

Simulation and real data demonstrate superior performance.

Abstract

Genetic studies often involve quantitative traits. Identifying genetic features that influence quantitative traits can help to uncover the etiology of diseases. Quantile regression method considers the conditional quantiles of the response variable, and is able to characterize the underlying regression structure in a more comprehensive manner. On the other hand, genetic studies often involve high dimensional genomic features, and the underlying regression structure may be heterogeneous in terms of both effect sizes and sparsity. To account for the potential genetic heterogeneity, including the heterogeneous sparsity, a regularized quantile regression method is introduced. The theoretical property of the proposed method is investigated, and its performance is examined through a series of simulation studies. A real dataset is analyzed to demonstrate the application of the proposed method.