RT Journal Article SR Electronic T1 Cumulative impact of common genetic variants and other risk factors on colorectal cancer risk in 42 103 individuals JF Gut JO Gut FD BMJ Publishing Group Ltd and British Society of Gastroenterology SP 871 OP 881 DO 10.1136/gutjnl-2011-300537 VO 62 IS 6 A1 Malcolm G Dunlop A1 Albert Tenesa A1 Susan M Farrington A1 Stephane Ballereau A1 David H Brewster A1 Thibaud Koessler A1 Paul Pharoah A1 Clemens Schafmayer A1 Jochen Hampe A1 Henry Völzke A1 Jenny Chang-Claude A1 Michael Hoffmeister A1 Hermann Brenner A1 Susanna von Holst A1 Simone Picelli A1 Annika Lindblom A1 Mark A Jenkins A1 John L Hopper A1 Graham Casey A1 David Duggan A1 Polly A Newcomb A1 Anna Abulí A1 Xavier Bessa A1 Clara Ruiz-Ponte A1 Sergi Castellví-Bel A1 Iina Niittymäki A1 Sari Tuupanen A1 Auli Karhu A1 Lauri Aaltonen A1 Brent Zanke A1 Tom Hudson A1 Steven Gallinger A1 Ella Barclay A1 Lynn Martin A1 Maggie Gorman A1 Luis Carvajal-Carmona A1 Axel Walther A1 David Kerr A1 Steven Lubbe A1 Peter Broderick A1 Ian Chandler A1 Alan Pittman A1 Steven Penegar A1 Harry Campbell A1 Ian Tomlinson A1 Richard S Houlston YR 2013 UL http://gut.bmj.com/content/62/6/871.abstract AB 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.