Cumulative impact of common genetic variants and other risk factors on colorectal cancer risk in 42 103 individuals

Dunlop MG, Tenesa A, Farrington SM, Ballereau S, Brewster DH, Koessler T, Pharoah P, Schafmayer C, Hampe J, Volzke H, Chang-Claude J, Hoffmeister M, Brenner H, von Holst S, Picelli S, Lindblom A, Jenkins MA, Hopper JL, Casey G, Duggan D, Newcomb PA, Abuli A, Bessa X, Ruiz-Ponte C, Castellvi-Bel S, Niittymaki I, Tuupanen S, Karhu A, Aaltonen L, Zanke B, Hudson T, Gallinger S, Barclay E, Martin L, Gorman M, Carvajal-Carmona L, Walther A, Kerr D, Lubbe S, Broderick P, Chandler I, Pittman A, Penegar S, Campbell H, Tomlinson I, Houlston RS (2012) Cumulative impact of common genetic variants and other risk factors on colorectal cancer risk in 42 103 individuals. Gut

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<2.2x10(-16)), confirmed="" in="" external="" validation="" sets="" (sweden="" p="1.2x10(-6)," finland="" p="2x10(-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.