Dataset Information

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

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.

SUBMITTER: Walsh N

PROVIDER: S-EPMC6579744 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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

Walsh Naomi N Zhang Han H Hyland Paula L PL Yang Qi Q Mocci Evelina E Zhang Mingfeng M Childs Erica J EJ Collins Irene I Wang Zhaoming Z Arslan Alan A AA Beane-Freeman Laura L Bracci Paige M PM Brennan Paul P Canzian Federico F Duell Eric J EJ Gallinger Steven S Giles Graham G GG Goggins Michael M Goodman Gary E GE Goodman Phyllis J PJ Hung Rayjean J RJ Kooperberg Charles C Kurtz Robert C RC Malats Núria N LeMarchand Loic L Neale Rachel E RE Olson Sara H SH Scelo Ghislaine G Shu Xiao O XO Van Den Eeden Stephen K SK Visvanathan Kala K White Emily E Zheng Wei W Albanes Demetrius D Andreotti Gabriella G Babic Ana A Bamlet William R WR Berndt Sonja I SI Borgida Ayelet A Boutron-Ruault Marie-Christine MC Brais Lauren L Brennan Paul P Bueno-de-Mesquita Bas B Buring Julie J Chaffee Kari G KG Chanock Stephen S Cleary Sean S Cotterchio Michelle M Foretova Lenka L Fuchs Charles C M Gaziano J Michael JM Giovannucci Edward E Goggins Michael M Hackert Thilo T Haiman Christopher C Hartge Patricia P Hasan Manal M Helzlsouer Kathy J KJ Herman Joseph J Holcatova Ivana I Holly Elizabeth A EA Hoover Robert R Hung Rayjean J RJ Janout Vladimir V Klein Eric A EA Kurtz Robert C RC Laheru Daniel D Lee I-Min IM Lu Lingeng L Malats Núria N Mannisto Satu S Milne Roger L RL Oberg Ann L AL Orlow Irene I Patel Alpa V AV Peters Ulrike U Porta Miquel M Real Francisco X FX Rothman Nathaniel N Sesso Howard D HD Severi Gianluca G Silverman Debra D Strobel Oliver O Sund Malin M Thornquist Mark D MD Tobias Geoffrey S GS Wactawski-Wende Jean J Wareham Nick N Weiderpass Elisabete E Wentzensen Nicolas N Wheeler William W Yu Herbert H Zeleniuch-Jacquotte Anne A Kraft Peter P Li Donghui D Jacobs Eric J EJ Petersen Gloria M GM Wolpin Brian M BM Risch Harvey A HA Amundadottir Laufey T LT Yu Kai K Klein Alison P AP Stolzenberg-Solomon Rachael Z RZ

Journal of the National Cancer Institute 20190601 6

<h4>Background</h4>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.<h4>Methods</h4>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 pathway ...[more]

PMID: 30541042

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Dataset Information

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

Publications

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

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