Common genetic variation has been shown to contribute to Colorectal cancer risk.
Dr Malcolm Dunlop and colleagues from the United Kingdom conducted a large multi-population study to assess the feasibility of colorectal cancer 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.
The doctors evaluated 9 populations of European descent to develop and validate colorectal cancer risk prediction models.
Binary logistic regression was used to assess the combined effect of age, gender, family history and genotypes at 10 susceptibility loci that individually only modestly influence colorectal cancer risk.
The research team assessed that risk models were generated from case-control data incorporating genotypes alone and in combination with gender, age and family history.
Model discriminatory performance was assessed using 10-fold internal cross-validation, and externally using 4187 independent samples.
The doctors examined that the 10-year absolute risk was estimated by modelling genotype and family history with age- and gender-specific population risks.
The median number of risk alleles was greater in cases than controls, confirmed in external validation sets.
The research team noted that the mean per-allele increase in risk was 9% .
|The mean per-allele increase in risk was 9%|
The research team reported that discriminative performance was poor across the risk spectrum.
However, modelling genotype data,family history, age and gender with Scottish population data shows the practicalities of identifying a subgroup with 5% predicted 10-year absolute risk.
Dr Dunlop's team concluded, "Genotype data provide additional information that complements age, gender and family history as risk factors, but individualized genetic risk prediction is not currently feasible."
"Nonetheless, the modelling exercise suggests public health potential since it is possible to stratify the population into colorectal cancer risk categories, thereby informing targeted prevention and surveillance."