A variable selection method for genome-wide association studies

A variable selection method for genome-wide association studies

MOTIVATION Genome-wide association studies (GWAS) involving half a million or more single nucleotide polymorphisms (SNPs) allow genetic dissection of complex diseases in a holistic manner. The common practice of analyzing one SNP at a time does not fully realize the potential of GWAS to identify multiple causal variants and to predict risk of disease. Existing methods for joint analysis of GWAS...

A Parallel Forward Selection Wrapper for Genome Wide Association Studies

A pathway analysis method for genome-wide association studies.

For genome-wide association studies, we propose a new method for identifying significant biological pathways. In this approach, we aggregate data across single-nucleotide polymorphisms to obtain summary measures at the gene level. We then use a hierarchical Bayesian model, which takes the gene-level summary measures as data, in order to evaluate the relevance of each pathway to an outcome of in...

Bayesian Variable Selection Regression for Genome - Wide Association Studies , and Other Large - Scale Problems

We consider applying Bayesian Variable Selection Regression, or BVSR, to genome-wide association studies, and similar large-scale regression problems. Currently, typical genome-wide association studies measure hundreds of thousands, or millions, of genetic variants (SNPs), in thousands or tens of thousands of individuals, and attempt to identify regions harboring SNPs that affect some phenotype...

Model Selection Methods for Genome Wide Association Studies∗

Due to the multiple loci control nature of complex phenotypes, there is great interest to test markers simultaneously instead of one by one. In this paper, we compare three model selection methods for genome wide association studies using simulations: the Stochastic Search Variable Selection (SSVS), the Least Absolute Shrinkage and Selection Operator (LASSO) and the Elastic Net. We also apply t...