Pathway-based analysis for genome-wide association studies using supervised principal components.

ABSTRACT: Many complex diseases are influenced by genetic variations in multiple genes, each with only a small marginal effect on disease susceptibility. Pathway analysis, which identifies biological pathways associated with disease outcome, has become increasingly popular for genome-wide association studies (GWAS). In addition to combining weak signals from a number of SNPs in the same pathway, results from pathway analysis also shed light on the biological processes underlying disease. We propose a new pathway-based analysis method for GWAS, the supervised principal component analysis (SPCA) model. In the proposed SPCA model, a selected subset of SNPs most associated with disease outcome is used to estimate the latent variable for a pathway. The estimated latent variable for each pathway is an opt

SUBMITTER: Chen X 

PROVIDER: S-EPMC3480088 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature