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., Völzke 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., Abulí A., Bessa X., Ruiz-Ponte C., Castellví-Bel S., Niittymäki 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.
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.