Genome-wide gene⇓diabetes and gene⇓obesity interaction scan in 8,255 cases and 11,900 controls from panscan and PanC4 consortia

Hongwei Tang, Lai Jiang, Rachael Z. Stolzenberg-Solomon, Alan A. Arslan, Laura E. Beane Freeman, Paige M. Bracci, Paul Brennan, Federico Canzian, Mengmeng Du, Steven Gallinger, Graham G. Giles, Phyllis J. Goodman, Charles Kooperberg, Loc Le Marchand, Rachel E. Neale, Xiao Ou Shu, Kala Visvanathan, Emily White, Wei Zheng, Demetrius AlbanesGabriella Andreotti, Ana Babic, William R. Bamlet, Sonja I. Berndt, Amanda Blackford, Bas Bueno-De-Mesquita, Julie E. Buring, Daniele Campa, Stephen J. Chanock, Erica Childs, Eric J. Duell, Charles Fuchs, J. Michael Gaziano, Michael Goggins, Patricia Hartge, Manal H. Hassam, Elizabeth A. Holly, Robert N. Hoover, Rayjean J. Hung, Robert C. Kurtz, I. Min Lee, Nuria Malats, Roger L. Milne, Kimmie Ng, Ann L. Oberg, Irene Orlow, Ulrike Peters, Miquel Porta, Kari G. Rabe, Nathaniel Rothman, Ghislaine Scelo, Howard D. Sesso, Debra T. Silverman, Ian M. Thompson, Anne Tjønneland, Antonia Trichopoulou, Jean Wactawski-Wende, Nicolas Wentzensen, Lynne R. Wilkens, Herbert Yu, Anne Zeleniuch-Jacquotte, Laufey T. Amundadottir, Eric J. Jacobs, Gloria M. Petersen, Brian M. Wolpin, Harvey A. Risch, Nilanjan Chatterjee, Alison P. Klein, Donghui Li, Peter Kraft, Peng Wei

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: Obesity and diabetes are major modifiable risk factors for pancreatic cancer. Interactions between genetic variants and diabetes/obesity have not previously been comprehensively investigated in pancreatic cancer at the genome-wide level. Methods: We conducted a gene–environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I–III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case–control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics. Results: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 106) was observed in the meta-analysis (PGxE ¼ 1.2 106, PJoint ¼ 4.2 107). Conclusions: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans. Impact: This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.

Original languageEnglish (US)
Pages (from-to)1784-1791
Number of pages8
JournalCancer Epidemiology Biomarkers and Prevention
Volume29
Issue number9
DOIs
StatePublished - Sep 2020

ASJC Scopus subject areas

  • Epidemiology
  • Oncology

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