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

  • Yang, Tianzhong1, 2
  • Tang, Hongwei3
  • Risch, Harvey A4
  • Olson, Sarah H5
  • Peterson, Gloria6
  • Bracci, Paige M7
  • Gallinger, Steven8
  • Hung, Rayjean J8
  • Neale, Rachel E9
  • Scelo, Ghislaine10
  • Duell, Eric J11
  • Kurtz, Robert C12
  • Khaw, Kay-Tee13
  • Severi, Gianluca14, 15
  • Sund, Malin16
  • Wareham, Nick17
  • Amos, Christopher I18
  • Li, Donghui3
  • Wei, Peng1
  • 1 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • 2 Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota.
  • 3 Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • 4 Department of Epidemiology, Yale University School of Public Health, New Haven, Connecticut.
  • 5 Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York.
  • 6 Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota.
  • 7 Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.
  • 8 Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System and University of Toronto, Toronto, Canada. , (Canada)
  • 9 Cancer Aetiology and Prevention Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. , (Australia)
  • 10 International Agency for Research on Cancer, Lyon, France. , (France)
  • 11 Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (ICO-IDIBELL), Barcelona, Spain. , (Spain)
  • 12 Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
  • 13 Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  • 14 Gustave Roussy, Villejuif, France. , (France)
  • 15 CESP, Faculty of Medicine, INSERM, University of Paris-Saclay, Villejuif, France. , (France)
  • 16 Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden. , (Sweden)
  • 17 MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK.
  • 18 Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas.
Published Article
Genetic Epidemiology
Wiley (John Wiley & Sons)
Publication Date
Nov 01, 2020
DOI: 10.1002/gepi.22348
PMID: 32779232


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. © 2020 Wiley Periodicals LLC.

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