Cumulative impact of 10 common genetic variants on colorectal cancer risk in 42,333 individuals from eight populations
Author: Dunlop, Malcolm G; Tenesa, Albert; Farrington, Susan M; Ballereau, Stephane; Brewster, David H; Koessler, Thibaud; Pharoah, Paul; Schafmayer, Clemens; Hampe, Jochen; Voelzke, Henry; Chang-Claude, Jenny; Hoffmeister, Michael; Brenner, Hermann; von Holst, Susanna; Picelli, Simone; Lindblom, Annika; Jenkins, Mark A; Hopper, John L; Casey, Graham; Duggan, David J; Newcomb, Polly A; Abuli, Anna; Bessa, Xavier; Ruiz-Ponte, Clara; Castellvi-Bel, Sergi; Niittymaeki, Iina; Tuupanen, Sari; Karhu, Auli; Aaltonen, Lauri A; Zanke, Brent; Hudson, Tom; Gallinger, Steven; Barclay, Ella; Martin, Lynn; Gorman, Maggie; Carvajal-Carmona, Luis G; Walther, Axel; Kerr, David J; Lubbe, Steven; Broderick, Peter; Chandler, Ian; Pittman, Alan; Penegar, Steven; Campbell, Harry; Tomlinson, Ian; Houlston, Richard S
Department: Inst för molekylär medicin och kirurgi / Dept of Molecular Medicine and Surgery
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Abstract
OBJECTIVE: Colorectal cancer (CRC) has a substantial heritable component. Common genetic variation has been shown to contribute to CRC risk. A study was conducted in a large multi-population study to assess the feasibility of CRC risk prediction using common genetic variant data combined with other risk factors. A risk prediction model was built and applied to the Scottish population using available data.
DESIGN: Nine populations of European descent were studied to develop and validate CRC risk prediction models. Binary logistic regression was used to assess the combined effect of age, gender, family history (FH) and genotypes at 10 susceptibility loci that individually only modestly influence CRC risk. Risk models were generated from case-control data incorporating genotypes alone (n=39 266) and in combination with gender, age and FH (n=11 324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4187 independent samples. The 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks.
RESULTS: The median number of risk alleles was greater in cases than controls (10 vs 9, p<2.2×10(-16)), confirmed in external validation sets (Sweden p=1.2×10(-6), Finland p=2×10(-5)). The mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05 to 1.13). Discriminative performance was poor across the risk spectrum (area under curve for genotypes alone 0.57; area under curve for genotype/age/gender/FH 0.59). However, modelling genotype data, FH, age and gender with Scottish population data shows the practicalities of identifying a subgroup with >5% predicted 10-year absolute risk.
CONCLUSION: Genotype data provide additional information that complements age, gender and FH as risk factors, but individualised genetic risk prediction is not currently feasible. Nonetheless, the modelling exercise suggests public health potential since it is possible to stratify the population into CRC risk categories, thereby informing targeted prevention and surveillance.
DESIGN: Nine populations of European descent were studied to develop and validate CRC risk prediction models. Binary logistic regression was used to assess the combined effect of age, gender, family history (FH) and genotypes at 10 susceptibility loci that individually only modestly influence CRC risk. Risk models were generated from case-control data incorporating genotypes alone (n=39 266) and in combination with gender, age and FH (n=11 324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4187 independent samples. The 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks.
RESULTS: The median number of risk alleles was greater in cases than controls (10 vs 9, p<2.2×10(-16)), confirmed in external validation sets (Sweden p=1.2×10(-6), Finland p=2×10(-5)). The mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05 to 1.13). Discriminative performance was poor across the risk spectrum (area under curve for genotypes alone 0.57; area under curve for genotype/age/gender/FH 0.59). However, modelling genotype data, FH, age and gender with Scottish population data shows the practicalities of identifying a subgroup with >5% predicted 10-year absolute risk.
CONCLUSION: Genotype data provide additional information that complements age, gender and FH as risk factors, but individualised genetic risk prediction is not currently feasible. Nonetheless, the modelling exercise suggests public health potential since it is possible to stratify the population into CRC risk categories, thereby informing targeted prevention and surveillance.
Institution:
- Colon Cancer Genetics Group, MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Roslin Institute, University of Edinburgh, Roslin, UK
- Scottish Cancer Registry, Information Services Division, NHS National Services Scotland, Edinburgh, UK
- Department of Oncology, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
- POPGEN Biobank, University Hospital Schleswig-Holstein, Kiel, Germany
- Department of General Internal Medicine, University Hospital, Schleswig-Holstein, Kiel, Germany
- Institute for Community Medicine, University Hospital Greifswald, Greifswald, Germany
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Division of Clinical Epidemiology and Ageing Research, German Cancer Research Center, Heidelberg, Germany
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, Parkville, Victoria, Australia
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Translational Genomics Research Institute, Phoenix, Arizona, USA
- Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Gastroenterology, Hospital del Mar, Institut Municipal d’Investigació Mèdica, Pompeu Fabra University, Barcelona, Catalonia, Spain
- Fundación Pública Galega de Medicina Xenómica, Genomic Medicine Group, University of Santiago de Compostela, Galicia, Spain
- Department of Gastroenterology, Hospital Clínic, CIBERehd, IDIBAPS, University of Barcelona, Catalonia, Spain
- Department of Medical Genetics, University of Helsinki, Helsinki, Finland
- Cancer Care Ontario, Toronto, Ontario, Canada Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Samuel Lunenfeld Research Institute, Mount Sinai Hospital and University of Toronto, Toronto, Ontario, Canada
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Department of Clinical Pharmacology, University of Oxford, Oxford, UK
- Section of Cancer Genetics, Institute of Cancer Research, Sutton, UK
- Public Health Sciences, University of Edinburgh, Edinburgh, UK
Citation: Gut. 2013 Jun;62(6):871-81.
Citation DOI: 10.1136/gutjnl-2011-300537
Citation PMID: 22490517
Citation ISI: 000318230300011
Publishing journal: Gut
Eprint status: Peer Reviewed
Version: Accepted
Issue date: 2013-02-07
Sponsorship:
- Cancer Research UK
- Swedish Cancer Society
- Stockholm Cancer Foundation
- NIH National Cancer Institute
- Asociacion Espanola contra el Cancer
- Finnish Cancer Society
- Ontario Institute for Cancer Research
- National Cancer Institute of Canada
- European Union
- CORE Charity
- Swedish Research Council
- Medical Research Council UK
- German Research Council
- Academy of Finland
- Scottish Government Chief Scientist Office
- Fondo de Investigacion Sanitaria/FEDER
- Fundacion de Investigacion Medica Mutua Madrilena
- Ontario Ministry of Research and Innovation
- Foundation Dr Henri Dubois-Ferriere Dinu Lipatti
- Bobby Moore Fund
- German National Genome Research Network
- German Federal Ministry for Education and Research
- Stockholm County Council (ALF)
- Fundacion Olga Torres
- Instituto de Salud Carlos III
- Sigrid Juselius Foundation
- Genome Canada
- Genome Quebec
- Ministere du Developement Economique et Regional et de la Recherche du Quebec
Rights:
CC BY-NC 4.0
Publication year: 2012
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