Logistic Bayesian LASSO for genetic association analysis of data from complex sampling designs.

ABSTRACT: Detecting gene-environment interactions with rare variants is critical in dissecting the etiology of common diseases. Interactions with rare haplotype variants (rHTVs) are of particular interest. At the same time, complex sampling designs, such as stratified random sampling, are becoming increasingly popular for designing case-control studies, especially for recruiting controls. The US Kidney Cancer Study (KCS) is an example, wherein all available cases were included while the controls at each site were randomly selected from the population by frequency matching with cases based on age, sex and race. There is currently no rHTV association method that can account for such a complex sampling design. To fill this gap, we consider logistic Bayesian LASSO (LBL), an existing rHTV approach for ca

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC5572548 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature