The Bayesian lasso for genome-wide association studies.

ABSTRACT:

Motivation

Despite their success in identifying genes that affect complex disease or traits, current genome-wide association studies (GWASs) based on a single SNP analysis are too simple to elucidate a comprehensive picture of the genetic architecture of phenotypes. A simultaneous analysis of a large number of SNPs, although statistically challenging, especially with a small number of samples, is crucial for genetic modeling.

Method

We propose a two-stage procedure for multi-SNP modeling and analysis in GWASs, by first producing a 'preconditioned' response variable using a supervised principle component analysis and then formulating Bayesian lasso to select a subset of significant SNPs. The Bayesian lasso is implemented with a hierarchical model, in which scale mixtures of n

SUBMITTER: Li J 

PROVIDER: S-EPMC3105480 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature