SNP-based pathway enrichment analysis for genome-wide association studies

Background: Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most sig...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Lingjie Weng, Fabio Macciardi, Aravind Subramanian, Guia Guffanti, Steven G. Potkin, Zhaoxia Yu, Xiaohui Xie
• Keywords: Artificial Intelligence, BMCBI 2011, Genetic Variations, Multiple Snps, SNPs