Incorporating multiple sets of eQTL weights into gene‐by‐environment interaction analysis identifies novel susceptibility loci for pancreatic cancer

• Published in: Genetic epidemiology 2020-11, Vol.44 (8), p.880-892
• Main Authors: Yang, Tianzhong, Tang, Hongwei, Risch, Harvey A., Olson, Sarah H., Peterson, Gloria, Bracci, Paige M., Gallinger, Steven, Hung, Rayjean J., Neale, Rachel E., Scelo, Ghislaine, Duell, Eric J., Kurtz, Robert C., Khaw, Kay‐Tee, Severi, Gianluca, Sund, Malin, Wareham, Nick, Amos, Christopher I., Li, Donghui, Wei, Peng
• Format: Article
• Language: English
• Subjects: Case-Control Studies; Cohort Studies; Computer Simulation; Data Interpretation, Statistical; Data-adaptive association testing; eQTL; Gene Expression Regulation; gene-by-environment interaction; Gene-Environment Interaction; Genetic diversity; Genetic Predisposition to Disease; Genome-wide association studies; Genome-Wide Association Study; Humans; Models, Genetic; Molecular modelling; multiple functional weights; Pancreatic cancer; Pancreatic Neoplasms - genetics; Polymorphism, Single Nucleotide - genetics; PrediXCan; Quantitative trait loci; Quantitative Trait Loci - genetics; Smoking; Smoking - genetics
• ISSN: 0741-0395; 1098-2272; 1098-2272
• DOI: 10.1002/gepi.22348

It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene‐by‐environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data‐adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case–Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome‐wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking‐related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE.