Gene–environment interactions have important implications for elucidating the genetic basis
of complex diseases beyond the joint function of multiple genetic factors and their …

Beyond the main genetic and environmental effects, gene–environment (G–E) interactions
have been demonstrated to significantly contribute to the development and progression of …

In this article, our goal is to develop a method for Bayesian model averaging in linear
regression models to accommodate heavier tailed error distributions than the normal …

Rapid advancements in high-throughput biological techniques have facilitated the
generation of high-dimensional omics datasets, which have provided a solid foundation for …

Variable selection methods have been extensively developed for and applied to cancer
genomics data to identify important omics features associated with complex disease traits …

In addition to considering the main effects, understanding gene‐environment (G× E)
interactions is imperative for determining the etiology of diseases and the factors that affect …

The quantile varying coefficient (VC) model can flexibly capture dynamical patterns of
regression coefficients. In addition, due to the quantile check loss function, it is robust …

Background In the context of biomedical and epidemiological research, gene-environment
(GE) interaction is of great significance to the etiology and progression of many complex …

With the development of data collection techniques, analysis with a survival response and
high-dimensional covariates has become routine. Here we consider an interaction model …

Data irregularity in cancer genomics studies has been widely observed in the form of outliers
and heavy-tailed distributions in the complex traits. In the past decade, robust variable …