GWAS summary-based pathway analysis correcting for the genetic confounding impact of environmental exposures

• Published in: Briefings in bioinformatics Vol. 19; no. 5; pp. 725 - 730
• Main Authors: Fan, Qianrui, Zhang, Feng, Wang, Wenyu, Xu, Jiawen, Hao, Jingcan, He, Awen, Wen, Yan, Li, Ping, Liang, Xiao, Du, Yanan, Liu, Li, Wu, Cuiyan, Wang, Sen, Wang, Xi, Ning, Yujie, Guo, Xiong
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 28.09.2018; Oxford Publishing Limited (England)
• Subjects: Association analysis; Cardiovascular diseases; Cardiovascular Diseases - etiology; Cardiovascular Diseases - genetics; Cigarette smoking; Computational Biology - methods; Computer Simulation; Environmental effects; Environmental Exposure - adverse effects; Environmental Exposure - statistics & numerical data; Environmental impact; Exposure; Genetic analysis; Genetic Variation; Genome-wide association studies; Genome-Wide Association Study - statistics & numerical data; Genomes; Humans; Models, Genetic; Multifactorial Inheritance; Polymorphism, Single Nucleotide; Risk Factors; Smoking; Smoking - adverse effects; Smoking - genetics; pathway analysis; complex diseases; genome-wide association studies; environmental exposures
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbx025

Abstract Genome-wide association study (GWAS)-based pathway association analysis is a powerful approach for the genetic studies of human complex diseases. However, the genetic confounding effects of environment exposure-related genes can decrease the accuracy of GWAS-based pathway association analysis of target diseases. In this study, we developed a pathway association analysis approach, named Mendelian randomization-based pathway enrichment analysis (MRPEA), which was capable of correcting the genetic confounding effects of environmental exposures, using the GWAS summary data of environmental exposures. After analyzing the real GWAS summary data of cardiovascular disease and cigarette smoking, we observed significantly improved performance of MRPEA compared with traditional pathway association analysis (TPAA) without adjusting for environmental exposures. Further, simulation studies found that MRPEA generally outperformed TPAA under various scenarios. We hope that MRPEA could help to fill the gap of TPAA and identify novel causal pathways for complex diseases.