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Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer
N. Walsh, H. Zhang, PL. Hyland, Q. Yang, E. Mocci, M. Zhang, EJ. Childs, I. Collins, Z. Wang, AA. Arslan, L. Beane-Freeman, PM. Bracci, P. Brennan, F. Canzian, EJ. Duell, S. Gallinger, GG. Giles, M. Goggins, GE. Goodman, PJ. Goodman, RJ. Hung, C....
Language English Country United States
Document type Journal Article, Meta-Analysis
Grant support
U01 CA167462
NCI NIH HHS - United States
P50 CA062924
NCI NIH HHS - United States
UL1 TR001863
NCATS NIH HHS - United States
UG1 CA189974
NCI NIH HHS - United States
MC_UU_12015/1
Medical Research Council - United Kingdom
R01 CA154823
NCI NIH HHS - United States
U10 CA037429
NCI NIH HHS - United States
U01 CA063673
NCI NIH HHS - United States
UM1 CA182883
NCI NIH HHS - United States
UM1 CA167462
NCI NIH HHS - United States
U01 CA164973
NCI NIH HHS - United States
NLK
Free Medical Journals
from 1996 to 1 year ago
Open Access Digital Library
from 1996-01-01
PubMed
30541042
DOI
10.1093/jnci/djy155
Knihovny.cz E-resources
- MeSH
- Genome-Wide Association Study methods MeSH
- Carcinoma, Pancreatic Ductal genetics MeSH
- Genetic Predisposition to Disease MeSH
- Polymorphism, Single Nucleotide MeSH
- Humans MeSH
- Pancreatic Neoplasms genetics MeSH
- Models, Statistical MeSH
- Case-Control Studies MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Meta-Analysis MeSH
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.
Cancer Epidemiology Program University of Hawaii Cancer Center Honolulu HI
CIBER Epidemiología y Salud Pública Universitat Autònoma de Barcelona Barcelona Spain
Department of Cancer Epidemiology and Genetics Masaryk Memorial Cancer Institute Brno Czech Republic
Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT
Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY
Department of Epidemiology and Biostatistics University of California San Francisco CA
Department of Epidemiology and Environmental Health University at Buffalo Buffalo NY
Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD
Department of Epidemiology University of Texas MD Anderson Cancer Center Houston TX
Department of General Surgery University Hospital Heidelberg Heidelberg Germany
Department of Health Sciences Research Mayo Clinic College of Medicine Rochester MN
Department of Medical Oncology Dana Farber Cancer Institute Boston MA
Department of Medicine Memorial Sloan Kettering Cancer Center New York NY
Department of Public Health Solutions National Institute for Health and Welfare Helsinki Finland
Department of Surgical and Perioperative Sciences Umeå University Umeå Sweden
Division of Aging Brigham and Women's Hospital Boston MA Boston VA Healthcare System Boston MA
Division of Hepatobiliary and Pancreas Surgery Mayo Clinic Rochester MN
Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle WA
Division of Research Kaiser Permanente Northern California Oakland CA
Epidemiology Research Program American Cancer Society Atlanta GA
Genomic Epidemiology Group German Cancer Research Center Heidelberg Germany
Glickman Urological and Kidney Institute Cleveland Clinic Cleveland OH
Information Management Systems Silver Spring MD
International Agency for Research on Cancer Lyon France
MRC Epidemiology Unit University of Cambridge Cambridge UK
National Institute for Cellular Biotechnology Dublin City University Glasnevin Dublin Ireland
Perlmutter Cancer Center New York University School of Medicine New York NY
Population Health Department QIMR Berghofer Medical Research Institute Brisbane Queensland Australia
SWOG Statistical Center Fred Hutchinson Cancer Research Center Seattle WA
References provided by Crossref.org
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