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Genome-Wide Gene-Diabetes and Gene-Obesity Interaction Scan in 8,255 Cases and 11,900 Controls from PanScan and PanC4 Consortia.

About this Publication
Title
Genome-Wide Gene-Diabetes and Gene-Obesity Interaction Scan in 8,255 Cases and 11,900 Controls from PanScan and PanC4 Consortia.
Pubmed ID
32546605 (View this publication on the PubMed website)
Digital Object Identifier
Publication
Cancer Epidemiol. Biomarkers Prev. 2020 Jun 16
Authors
Tang H, Jiang L, Stolzenberg-Solomon RZ, Arslan AA, Beane Freeman LE, Bracci PM, Brennan P, Canzian F, Du M, Gallinger S, Giles GG, Goodman PJ, Kooperberg C, Le Marchand L, Neale RE, Shu XO, Visvanathan K, White E, Zheng W, Albanes D, ...show more Andreotti G, Babic A, Bamlet WR, Berndt SI, Blackford A, Bueno-de-Mesquita B, Buring JE, Campa D, Chanock SJ, Childs E, Duell EJ, Fuchs C, Gaziano JM, Goggins M, Hartge P, Hassam MH, Holly EA, Hoover RN, Hung RJ, Kurtz RC, Lee IM, Malats N, Milne RL, Ng K, Oberg AL, Orlow I, Peters U, Porta M, Rabe KG, Rothman N, Scelo G, Sesso HD, Silverman DT, Thompson IM, Tjønneland A, Trichopoulou A, Wactawski-Wende J, Wentzensen N, Wilkens LR, Yu H, Zeleniuch-Jacquotte A, Amundadottir LT, Jacobs EJ, Petersen GM, Wolpin BM, Risch HA, Chatterjee N, Klein AP, Li D, Kraft P, Wei P
Affiliations
  • Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
  • Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland.
  • Department of Obstetrics and Gynecology, New York University School of Medicine, New York, New York.
  • Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California.
  • International Agency for Research on Cancer, Lyon, France.
  • Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Lunenfeld-Tanenbaum Research Institute, Sinai Health System and University of Toronto, Toronto, Ontario, Canada.
  • Division of Cancer Epidemiology, Cancer Council Victoria, Melbourne, Victoria, Australia.
...show more
  • SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington.
  • Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.
  • Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii.
  • Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
  • Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee.
  • Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
  • Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, Washington.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
  • Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota.
  • Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland.
  • Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
  • Department of Biology, University of Pisa, Pisa, Italy.
  • Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain.
  • Yale Cancer Center, New Haven, Connecticut.
  • Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
  • Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, Maryland.
  • Gastroenterology, Hepatology, and Nutrition Service, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre, Madrid, Spain.
  • CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.
  • CHRISTUS Santa Rosa Hospital - Medical Center, San Antonio, Texas.
  • Department of Public Health, University of Copenhagen and Danish Cancer Society Research Center Diet, Genes and Environment, Copenhagen, Denmark.
  • Hellenic Health Foundation, World Health Organization Collaborating Center of Nutrition, Medical School, University of Athens, Athens, Greece.
  • Department of Epidemiology and Environmental Health, University of Buffalo, Buffalo, New York.
  • Department of Population Health, New York University School of Medicine, New York, New York.
  • Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut.
  • Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
  • Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas. pwei2@mdanderson.org pkraft@hsph.harvard.edu dli@mdanderson.org.
  • Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. pwei2@mdanderson.org pkraft@hsph.harvard.edu dli@mdanderson.org.
  • Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas. pwei2@mdanderson.org pkraft@hsph.harvard.edu dli@mdanderson.org.
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 GWAS datasets (PanScan I-III and PanC4). Obesity (BMI=30 kg/m2) and diabetes (duration = 3 years) were the environmental variables of interest. Approximately 870,000 SNPs 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 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<1.25E-6) was observed in the meta-analysis (PGxE= 1.2E-6, PJoint= 4.2E-7).

CONCLUSIONS: Our current analyses 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.

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