Ontology highlight
ABSTRACT:
SUBMITTER: Li D
PROVIDER: S-EPMC3405651 | biostudies-literature | 2012 Jul
REPOSITORIES: biostudies-literature
Li Donghui D Duell Eric J EJ Yu Kai K Risch Harvey A HA Olson Sara H SH Kooperberg Charles C Wolpin Brian M BM Jiao Li L Dong Xiaoqun X Wheeler Bill B Arslan Alan A AA Bueno-de-Mesquita H Bas HB Fuchs Charles S CS Gallinger Steven S Gross Myron M Hartge Patricia P Hoover Robert N RN Holly Elizabeth A EA Jacobs Eric J EJ Klein Alison P AP LaCroix Andrea A Mandelson Margaret T MT Petersen Gloria G Zheng Wei W Agalliu Ilir I Albanes Demetrius D Boutron-Ruault Marie-Christine MC Bracci Paige M PM Buring Julie E JE Canzian Federico F Chang Kenneth K Chanock Stephen J SJ Cotterchio Michelle M Gaziano J Michael JM Giovannucci Edward L EL Goggins Michael M Hallmans Göran G Hankinson Susan E SE Hoffman Bolton Judith A JA Hunter David J DJ Hutchinson Amy A Jacobs Kevin B KB Jenab Mazda M Khaw Kay-Tee KT Kraft Peter P Krogh Vittorio V Kurtz Robert C RC McWilliams Robert R RR Mendelsohn Julie B JB Patel Alpa V AV Rabe Kari G KG Riboli Elio E Shu Xiao-Ou XO Tjønneland Anne A Tobias Geoffrey S GS Trichopoulos Dimitrios D Virtamo Jarmo J Visvanathan Kala K Watters Joanne J Yu Herbert H Zeleniuch-Jacquotte Anne A Amundadottir Laufey L Stolzenberg-Solomon Rachael Z RZ
Carcinogenesis 20120420 7
Four loci have been associated with pancreatic cancer through genome-wide association studies (GWAS). Pathway-based analysis of GWAS data is a complementary approach to identify groups of genes or biological pathways enriched with disease-associated single-nucleotide polymorphisms (SNPs) whose individual effect sizes may be too small to be detected by standard single-locus methods. We used the adaptive rank truncated product method in a pathway-based analysis of GWAS data from 3851 pancreatic ca ...[more]