Abstract
Colorectal cancer (CRC) risk prediction models could be used to risk-stratify the population to provide individually tailored screening provision. Using participants from the UK Biobank prospective cohort study, we evaluated whether the addition of a genetic risk score (GRS) could improve the performance of two previously validated models. Inclusion of the GRS did not appreciably improve discrimination of either model, and led to substantial miscalibration. Following recalibration the discrimination did not change, but good calibration for models incorporating the GRS was recovered. Comparing predictions between models with and without the GRS, 5% of participants or fewer changed their absolute risk by ±0.3% or more in either model. In summary, addition of a GRS did not meaningfully improve the performance of validated CRC-risk prediction models. At present, provision of genetic information is not useful for risk stratification for CRC.
Highlights
Colorectal cancer (CRC) is a substantial global health burden[1] and there is strong evidence that screening can reduce CRC mortality.[2,3,4] The efficacy of screening programmes may be enhanced by targeting screening and screening intensity to those at greatest risk.[5]
It was weakly associated with selfreported family history of CRC, with a greater number of first degree relatives diagnosed with CRC associated with a higher genetic risk scores (GRS) (Supplementary Table 4)
In a large prospective cohort study, we showed that a GRS composed of 41 published, genome-wide significant single nucleotide polymorphisms (SNPs) for CRC, has poor discriminatory ability on its own and does not meaningfully improve model discrimination of established models, nor does it strongly influence the predicted probabilities for the vast majority of participants
Summary
Colorectal cancer (CRC) is a substantial global health burden[1] and there is strong evidence that screening can reduce CRC mortality.[2,3,4] The efficacy of screening programmes may be enhanced by targeting screening and screening intensity to those at greatest risk.[5] Genome-wide association studies (GWAS) have identified over 40 independent loci unequivocally associated with the risk of CRC,[6] and there is increasing interest in developing genetic risk scores (GRS) for a personalised risk assessment.[7] To justify their use in clinical or population health practice, GRS must provide additional information over and above previously validated risk models.[8,9] Here, using data from the UK Biobank, we examined the predictive value of a GRS for CRC either alone or in combination with validated CRC-risk models
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