Identifying disease related sub-pathways for analysis of genome-wide association studies

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• 作者：Chunquan Li¹ ; Junwei Han¹ ; Desi Shang¹ ; Jing Li ; Yan Wang ; YingYing Wang ; Yunpeng Zhang ; Qianlan Yao ; Chunlong Zhang ; Kongning Li ; ^{kongningli@hotmail.com} ; Xia Li ; ^{lixia@hrbmu.edu.cn}
• 关键词：GWAS ; genome-wide association studies ; SNPs ; single-nucleotide polymorphisms ; RA ; rheumatoid arthritis ; CD ; Crohn's disease ; T1D ; type 1 diabetes ; BD ; bipolar disorder ; HT ; hypertension ; CAD ; coronary artery disease ; T2D ; type 2 diabetes ; KEGG ; Kyoto Ency
• 刊名：Gene
• 出版年：2012
• 期刊代码：73_03781119
• 类别：bio
• 出版时间：15 July, 2012
• 卷：503
• 期：1
• 页码：101-109
• 文件大小：1597 K

摘要

Most methods for genome-wide association studies (GWAS) focus on discovering a single genetic variant, but the pathogenesis of complex diseases is thought to arise from the joint effect of multiple genetic variants. Information about pathway structure, such as the interactions and distances between gene products within pathways, can help us learn more about the functions and joint effect of genes associated with disease risk. We developed a novel sub-pathway based approach to study the joint effect of multiple genetic variants that are modestly associated with disease. The approach prioritized sub-pathways based on the significance values of single nucleotide polymorphisms (SNPs) and the interactions and distances between gene products within pathways. We applied the method to seven complex diseases. The result showed that our method can efficiently identify statistically significant sub-pathways associated with the pathogenesis of complex diseases. The approach identified sub-pathways that may inform the interpretation of GWAS data.