Walsh, NaomiZhang, HanHyland, Paula LYang, QiMocci, EvelinaZhang, MingfengChilds, Erica JCollins, IreneWang, ZhaomingArslan, Alan ABeane-Freeman, LauraBracci, Paige MBrennan, PaulCanzian, FedericoDuell, Eric JGallinger, StevenGiles, Graham GGoggins, MichaelGoodman, Gary EGoodman, Phyllis JHung, Rayjean JKooperberg, CharlesKurtz, Robert CMalats, NúriaLeMarchand, LoicNeale, Rachel EOlson, Sara HScelo, GhislaineShu, Xiao OVan Den Eeden, Stephen KVisvanathan, KalaWhite, EmilyZheng, WeiAlbanes, DemetriusAndreotti, GabriellaBabic, AnaBamlet, William RBerndt, Sonja IBorgida, AyeletBoutron-Ruault, Marie-ChristineBrais, LaurenBrennan, PaulBueno-de-Mesquita, BasBuring, JulieChaffee, Kari GChanock, StephenCleary, SeanCotterchio, MichelleForetova, LenkaFuchs, CharlesM Gaziano, J MichaelGiovannucci, EdwardGoggins, MichaelHackert, ThiloHaiman, ChristopherHartge, PatriciaHasan, ManalHelzlsouer, Kathy JHerman, JosephHolcatova, IvanaHolly, Elizabeth AHoover, RobertHung, Rayjean JJanout, VladimirKlein, Eric AKurtz, Robert CLaheru, DanielLee, I-MinLu, LingengMalats, NúriaMannisto, SatuMilne, Roger LOberg, Ann LOrlow, IrenePatel, Alpa VPeters, UlrikePorta, MiquelReal, Francisco XRothman, NathanielSesso, Howard DSeveri, GianlucaSilverman, DebraStrobel, OliverSund, MalinThornquist, Mark DTobias, Geoffrey SWactawski-Wende, JeanWareham, NickWeiderpass, ElisabeteWentzensen, NicolasWheeler, WilliamYu, HerbertZeleniuch-Jacquotte, AnneKraft, PeterLi, DonghuiJacobs, Eric JPetersen, Gloria MWolpin, Brian MRisch, Harvey AAmundadottir, Laufey TYu, KaiKlein, Alison PStolzenberg-Solomon, Rachael Z2019-03-142019-03-142018-12-121460-21053054104210.1093/jnci/djy155http://hdl.handle.net/10029/622872Genome-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.enArticleJ Natl Cancer Inst 2019; 111(6):557-67