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

2018; Oxford University Press; Volume: 111; Issue: 6 Linguagem: Inglês

10.1093/jnci/djy155

1460-2105

Naomi Walsh, Han Zhang, Paula L. Hyland, Qi Yang, Evelina Mocci, Mingfeng Zhang, Erica J. Childs, Irene Collins, Zhaoming Wang, Alan A. Arslan, Laura E. 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, Loı̈c Le Marchand, Rachel Ε. Neale, Sara H. Olson, Ghislaine Scélo, Xiao Ou Shu, Stephen K. Van Den Eeden, Kala Visvanathan, Emily White, Wei Zheng, Demetrius Albanes, Gabriella Andreotti, Ana Babić, William R. Bamlet, Sonja I. Berndt, Ayelet Borgida, Marie‐Christine Boutron‐Ruault, Lauren K. Brais, Paul Brennan, Bas Bueno‐de‐Mesquita, Julie E. Buring, Kari G. Chaffee, Stephen J. Chanock, Sean P. Cleary, Michelle Cotterchio, Lenka Foretová, Charles S. Fuchs, J. Michael Gaziano, Edward Giovannucci, Michael Goggins, Thilo Hackert, Christopher A. Haiman, Patricia Hartge, Manal Hasan, Kathy J. Helzlsouer, Joseph M. Herman, Ivana Holcátová, Elizabeth A. Holly, Robert N. Hoover, Rayjean J. Hung, Vladimí­r Janout, Eric A. Klein, Robert C. Kurtz, Daniel A. Laheru, I‐Min Lee, Lingeng Lu, Núria Malats, Satu Männistö, 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 T. Silverman, Oliver Strobel, Malin Sund, Mark Thornquist, Geoffrey S. Tobias, Jean Wactawski‐Wende, Nicholas J. 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. Ámundadóttir, Kai Yu, Alison P. Klein, Rachael Z. Stolzenberg‐Solomon,

Pancreatic and Hepatic Oncology Research

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. 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. 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. 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.