Agnostic pathway/gene set analysis of genome-wide association data identifies associations for pancreatic cancer

• Verfasst von: Walsh, Naomi [VerfasserIn]
• Verfasst von: Hackert, Thilo [VerfasserIn]
• Titel: Agnostic pathway/gene set analysis of genome-wide association data identifies associations for pancreatic cancer
• Verf.angabe: Naomi Walsh, Han Zhang, Paula L. Hyland, Qi Yang, Evelina Mocci, Mingfeng Zhang, Erica J. Childs, Irene Collins, Zhaoming Wang, Alan A. Arslan, Laura Beane-Freeman, Paige M. Bracci, Paul Brennan, Federico Canzian, Eric J. Duell, Steven Gallinger, Graham G. Giles, Michael Goggins, Gary E. Goodman, Phyllis J. Goodman, Rayjean J. Hung, Charles Kooperberg, Robert C. Kurtz, Núria Malats, Loic LeMarchand, Rachel E. Neale, Sara H. Olson, Ghislaine Scelo, Xiao O. Shu, Stephen K. Van Den Eeden, Kala Visvanathan, Emily White, Wei Zheng, PanScan and PanC4 consortia, Demetrius Albanes, Gabriella Andreotti, Ana Babic, William R. Bamlet, Sonja I. Berndt, Ayelet Borgida, Marie-Christine Boutron-Ruault, Lauren Brais, Paul Brennan, Bas Bueno-de-Mesquita, Julie Buring, Kari G. Chaffee, Stephen Chanock, Sean Cleary, Michelle Cotterchio, Lenka Foretova, Charles Fuchs, J. Michael M Gaziano, Edward Giovannucci, Michael Goggins, Thilo Hackert, Christopher Haiman, Patricia Hartge, Manal Hasan, Kathy J. Helzlsouer, Joseph Herman, Ivana Holcatova, Elizabeth A. Holly, Robert Hoover, Rayjean J. Hung, Vladimir Janout, Eric A. Klein, Robert C. Kurtz, Daniel Laheru, I.-Min Lee, Lingeng Lu, Núria Malats, Satu Mannisto, Roger L. Milne, Ann L. Oberg, Irene Orlow, Alpa V. Patel, Ulrike Peters, Miquel Porta, Francisco X. Real, Nathaniel Rothman, Howard D. Sesso, Gianluca Severi, Debra Silverman, Oliver Strobel, Malin Sund, Mark D. Thornquist, Geoffrey S. Tobias, Jean Wactawski-Wende, Nick Wareham, Elisabete Weiderpass, Nicolas Wentzensen, William Wheeler, Herbert Yu, Anne Zeleniuch-Jacquotte, Peter Kraft, Donghui Li, Eric J. Jacobs, Gloria M. Petersen, Brian M. Wolpin, Harvey A. Risch, Laufey T. Amundadottir, Kai Yu, Alison P. Klein, Rachael Z. Stolzenberg-Solomon
• E-Jahr: 2018
• Jahr: 12 December 2018
• Umfang: 11 S.
• Fussnoten: Epub ahead of print ; Gesehen am 26.04.2019
• Titel Quelle: Enthalten in: National Cancer InstituteJournal of the National Cancer Institute
• Ort Quelle: Oxford : Oxford Univ. Press, 1941
• Jahr Quelle: 2018
• Band/Heft Quelle: 111(2018), 6, Seite 1-11
• ISSN Quelle: 1460-2105
• DOI: doi:10.1093/jnci/djy155
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1663654328

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.