Naomi Walsh1, Han Zhang2, Paula L Hyland2,3, Qi Yang2, Evelina Mocci4, Mingfeng Zhang5,6, Erica J Childs4, Irene Collins5, Zhaoming Wang5,7, Alan A Arslan8,9,10, Laura Beane-Freeman2, Paige M Bracci11, Paul Brennan12, Federico Canzian13, Eric J Duell14, Steven Gallinger15, Graham G Giles16,17,18, Michael Goggins19, Gary E Goodman20, Phyllis J Goodman21, Rayjean J Hung15, Charles Kooperberg20, Robert C Kurtz22, Núria Malats23,24, Loic LeMarchand25, Rachel E Neale26, Sara H Olson27, Ghislaine Scelo12, Xiao O Shu28, Stephen K Van Den Eeden29, Kala Visvanathan30, Emily White20,31, Wei Zheng28, Demetrius Albanes2, Gabriella Andreotti2, Ana Babic32, William R Bamlet33, Sonja I Berndt2, Ayelet Borgida15, Marie-Christine Boutron-Ruault34, Lauren Brais32, Paul Brennan12, Bas Bueno-de-Mesquita35,36,37, Julie Buring38,39, Kari G Chaffee33, Stephen Chanock2, Sean Cleary40, Michelle Cotterchio41,42, Lenka Foretova43, Charles Fuchs44, J Michael M Gaziano45,46, Edward Giovannucci32, Michael Goggins19, Thilo Hackert47, Christopher Haiman48, Patricia Hartge2, Manal Hasan49, Kathy J Helzlsouer50, Joseph Herman51, Ivana Holcatova52, Elizabeth A Holly11, Robert Hoover2, Rayjean J Hung15, Vladimir Janout53,54, Eric A Klein55, Robert C Kurtz22, Daniel Laheru4, I-Min Lee38,39, Lingeng Lu56, Núria Malats23,24, Satu Mannisto57, Roger L Milne16,17, Ann L Oberg33, Irene Orlow27, Alpa V Patel58, Ulrike Peters20, Miquel Porta59,60, Francisco X Real24,61,62, Nathaniel Rothman2, Howard D Sesso38,39, Gianluca Severi16,17,34, Debra Silverman2, Oliver Strobel47, Malin Sund63, Mark D Thornquist20, Geoffrey S Tobias2, Jean Wactawski-Wende64, Nick Wareham65, Elisabete Weiderpass66,67,68,69, Nicolas Wentzensen2, William Wheeler70, Herbert Yu71, Anne Zeleniuch-Jacquotte10,72, Peter Kraft39,73, Donghui Li73, Eric J Jacobs58, Gloria M Petersen33, Brian M Wolpin32, Harvey A Risch56, Laufey T Amundadottir5, Kai Yu2, Alison P Klein4,19, Rachael Z Stolzenberg-Solomon1. 1. National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin, Ireland. 2. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD. 3. Division of Applied Regulatory Science, Office of Translational Science, Center for Drug Evaluation & Research, U.S. Food and Drug Administration, Silver Spring, MD. 4. Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD. 5. Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD. 6. Division of Epidemiology II, Office of Surveillance and Epidemiology, Center for Drug Evaluation & Research, U.S. Food and Drug Administration, Silver Spring, MD. 7. Department of Computational Biology, St Jude Children's Research Hospital, Memphis, Tennessee. 8. Department of Obstetrics and Gynecology, New York University School of Medicine, New York, NY. 9. Department of Environmental Medicine, New York University School of Medicine, New York, NY. 10. Department of Population Health, New York University School of Medicine, New York, NY. 11. Department of Epidemiology and Biostatistics, University of California, San Francisco, CA. 12. International Agency for Research on Cancer (IARC), Lyon, France. 13. Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany. 14. Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Bellvitge Biomedical Research Institute (IDIBELL), Catalan Institute of Oncology (ICO), Barcelona, Spain. 15. Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada. 16. Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia. 17. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia. 18. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia. 19. Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD. 20. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA. 21. SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA. 22. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY. 23. Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Center (CNIO), Madrid, Spain. 24. CIBERONC, Madrid, Spain. 25. Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI. 26. Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. 27. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY. 28. Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN. 29. Division of Research, Kaiser Permanente Northern California, Oakland, CA. 30. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. 31. Department of Epidemiology, University of Washington, Seattle, WA. 32. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA. 33. Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN. 34. Centre de Recherche en Épidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Medicine, Université Paris-Saclay, UPS, UVSQ, Gustave Roussy, Villejuif, France. 35. Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands. 36. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. 37. Department of Social & Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia. 38. Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA. 39. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA. 40. Division of Hepatobiliary and Pancreas Surgery, Mayo Clinic, Rochester, MN. 41. Cancer Care Ontario, University of Toronto, Toronto, ON, Canada. 42. Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada. 43. Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic. 44. Yale Cancer Center, New Haven, CT. 45. Division of Aging, Brigham and Women's Hospital, Boston, MA. 46. Boston VA Healthcare System, Boston, MA. 47. Department of General Surgery, University Hospital Heidelberg, Heidelberg, Germany. 48. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA. 49. Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX. 50. Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD. 51. Department of Radiation Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD. 52. Institute of Public Health and Preventive Medicine, Charles University, 2nd Faculty of Medicine, Prague, Czech Republic. 53. Department of Epidemiology and Public Health, Faculty of Medicine, University of Ostrava, Czech Republic. 54. Faculty of Medicine, University of Olomouc, Olomouc, Czech Republic. 55. Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH. 56. Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT. 57. Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland. 58. Epidemiology Research Program, American Cancer Society, Atlanta, GA. 59. CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain. 60. Hospital del Mar Institute of Medical Research (IMIM), Universitat Autònoma de Barcelona, Barcelona, Spain. 61. Epithelial Carcinogenesis Group, Spanish National Cancer Research Centre-CNIO, Madrid, Spain. 62. Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain. 63. Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden. 64. Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY. 65. MRC Epidemiology Unit, University of Cambridge, Cambridge, UK. 66. Department of Research, Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway. 67. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 68. Genetic Epidemiology Group, Folkhälsan Research Center and Faculty of Medicine, University of Helsinki, Helsinki, Finland. 69. Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, The Arctic University of Norway, Tromsø, Norway. 70. Information Management Systems, Silver Spring, MD. 71. Perlmutter Cancer Center, New York University School of Medicine, New York, NY. 72. Department of Biostatistics, Harvard School of Public Health, Boston, MA. 73. Department of Gastrointestinal Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX.
Abstract
BACKGROUND: Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes. METHODS: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided. RESULTS: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets. CONCLUSION: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.
BACKGROUND: Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes. METHODS: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided. RESULTS: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets. CONCLUSION: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.
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