Pathway-based analysis using reduced gene subsets in genome-wide association studies

Background: Single Nucleotide Polymorphism (SNP) analysis only captures a small proportion of associated genetic variants in Genome-Wide Association Studies (GWAS) partly due to small marginal effects. Pathway level analysis incorporating prior biological information offers another way to analyze GWAS’s of complex diseases, and promises to reveal the mechanisms leading to complex diseases. Biologically defined pathways are typically comprised of numerous genes. If only a subset of genes in the pathways is associated with disease then a joint analysis including all individual genes would result in a loss of power. To address this issue, we propose a pathway-based method that allows us to test for joint effects by using a pre-selected gene subset. In the proposed approach, each gene is considered as the basic unit, which reduces the number of genetic variants considered and hence reduces the degrees of freedom in the joint analysis. The proposed approach also can be used to investigat...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Jingyuan Zhao, Simone Gupta, Mark Seielstad, Jianjun Liu, Anbupalam Thalamuthu
• Keywords: Analysis, Artificial Intelligence, BMCBI 2011, Disease, Genetic Variants