  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.
