Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer.

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 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

J Natl Cancer Inst 2018 Dec 12. Epub 2018 Dec 12.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD.

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.

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December 2018
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