title: Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits.
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.
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
authors.forename: Qianchuan
authors.initials: Q
authors.lastname: He
authors.affiliation: Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada T6G 2G1.
authors.forename: Linglong
authors.initials: L
authors.lastname: Kong
authors.affiliation: School of Mathematics, Beijing Institute of Technology, Beijing, 100081, China.
authors.forename: Yanhua
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Biostatistics & Medical Informatics and Department of Statistics, The University of Wisconsin-Madison, Madison, WI 53706, USA.
authors.forename: Sijian
authors.initials: S
authors.lastname: Wang
authors.affiliation: Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.
authors.forename: Timothy A
authors.initials: TA
authors.lastname: Chan
authors.affiliation: Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Holland
keywords: Genomic features; Heterogeneous sparsity; Quantile regression; Quantitative traits; Variable selection