Genome-Wide Gene–Diabetes and Gene–Obesity Interaction Scan in 8,255 Cases and 11,900 Controls from PanScan and PanC4 Consortia
Authors: Tang Hongwei , Jiang Lai , Stolzenberg-Solomon Rachael Z. , Arslan Alan A. , Beane Freeman Laura E. , Bracci Paige M. , Brennan Paul , Canzian Federico , Du Mengmeng , Gallinger Steven , Giles Graham G. , Goodman Phyllis J. , Kooperberg Charles , Le Marchand Loïc , Neale Rachel E. , Shu Xiao-Ou , Visvanathan Kala , White Emily , Zheng Wei , Albanes Demetrius , Andreotti Gabriella , Babic Ana , Bamlet William R. , Berndt Sonja I. , Blackford Amanda , Bueno-de-Mesquita Bas , Buring Julie E. , Campa Daniele , Chanock Stephen J. , Childs Erica , Duell Eric J. , Fuchs Charles , Gaziano J. Michael , Goggins Michael , Hartge Patricia , Hassam Manal H. , Holly Elizabeth A. , Hoover Robert N. , Hung Rayjean J. , Kurtz Robert C. , Lee I.-Min , Malats Núria , Milne Roger L. , Ng Kimmie , Oberg Ann L. , Orlow Irene , Peters Ulrike , Porta Miquel , Rabe Kari G. , Rothman Nathaniel , Scelo Ghislaine , Sesso Howard D. , Silverman Debra T. , Thompson Ian M. , Tjønneland Anne , Trichopoulou Antonia , Wactawski-Wende Jean , Wentzensen Nicolas , Wilkens Lynne R. , Yu Herbert , Zeleniuch-Jacquotte Anne , Amundadottir Laufey T. , Jacobs Eric J. , Petersen Gloria M. , Wolpin Brian M. , Risch Harvey A. , Chatterjee Nilanjan , Klein Alison P. , Li Donghui , Kraft Peter , Wei Peng 
Published: June 2020

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 &lt; 1.25 × 10−6) was observed in the meta-analysis (PGxE = 1.2 ×10−6, PJoint = 4.2 ×10−7).

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.