Bayesian Variable Selection with Genome-wide Association Studies

Abstract

Genome Wide Association Studies (GWAS) are a type of experiment that aim to detect genetic variation that may be linked to a type of disease. GWAS typically contain many thousands of covariates, which makes variable selection an exceptionally computationally intensive process. In variable selection, one of the biggest challenges is the extremely large potential set of variants but a limited sample size. Hence, there are two problems: huge computational time burdens for analysing each dataset and another is the sparsity in the number of covariates associated to the response. In this study, we use variable selection via using Bayesian variable selection and LASSO method in logistic regression model. Moreover, the results are expanded for application in real dataset about cardiovascular disease.