Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer.
Journal of the National Cancer Institute 2018 ; 111: 557-567.
Walsh N, Zhang H, Hyland PL, Yang Q, Mocci E, Zhang M, Childs EJ, Collins I, Wang Z, Arslan AA, Beane-Freeman L, Bracci PM, Brennan P, Canzian F, Duell EJ, Gallinger S, Giles GG, Goggins M, Goodman GE, Goodman PJ, Hung RJ, Kooperberg C, Kurtz RC, Malats N, Lemarchand L, Neale RE, Olson SH, Scelo G, Shu XO, Van Den Eeden SK, Visvanathan K, White E, Zheng W, PanScan and PanC4 consortia PanScan and PanC4 consortia, Albanes D, Andreotti G, Babic A, Bamlet WR, Berndt SI, Borgida A, Boutron-Ruault MC, Brais L, Bueno-de-Mesquita B, Buring J, Chaffee KG, Chanock S, Cleary S, Cotterchio M, Foretova L, Fuchs C, M Gaziano JM, Giovannucci E, Hackert T, Haiman C, Hartge P, Hasan M, Helzlsouer KJ, Herman J, Holcátová I, Holly EA, Hoover R, Janout V, Klein EA, Laheru D, Lee IM, Lu L, Männistö S, Milne RL, Oberg AL, Orlow I, Patel AV, Peters U, Porta M, Real FX, Rothman N, Sesso HD, Severi G, Silverman D, Strobel O, Sund M, Thornquist MD, Tobias GS, Wactawski-Wende J, Wareham N, Weiderpass E, Wentzensen N, Wheeler W, Yu H, Zeleniuch-Jacquotte A, Kraft P, Li D, Jacobs EJ, Petersen GM, Wolpin BM, Risch HA, Amundadottir LT, Yu K, Klein AP, and Stolzenberg-Solomon RZ
DOI : 10.1093/jnci/djy155
PubMed ID : 30541042
PMCID : PMC6579744
URL : https://academic.oup.com/jnci/article/111/6/557/5240954
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.