Insights into colon cancer etiology via a regularized approach to gene set analysis of GWAS data.

ABSTRACT: Genome-wide association studies (GWAS) have successfully identified susceptibility loci from marginal association analysis of SNPs. Valuable insight into genetic variation underlying complex diseases will likely be gained by considering functionally related sets of genes simultaneously. One approach is to further develop gene set enrichment analysis methods, which are initiated in gene expression studies, to account for the distinctive features of GWAS data. These features include the large number of SNPs per gene, the modest and sparse SNP associations, and the additional information provided by linkage disequilibrium (LD) patterns within genes. We propose a "gene set ridge regression in association studies (GRASS)" algorithm. GRASS summarizes the genetic structure for each gene as eigenS

SUBMITTER: Chen LS 

PROVIDER: S-EPMC3032068 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature