| Title: | Prediction of colorectal cancer risk based on profiling with common genetic variants |
| Journal: | International Journal of Cancer |
| Published: | 20 Jul 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/32638365/ |
| DOI: | https://doi.org/10.1002/ijc.33191 |
| URL: | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ijc.33191 |
| Citations: | 21 (10 in last 2 years) as of 8 Aug 2024 |
Publication 5593
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Abstract
Increasing numbers of common genetic variants associated with colorectal cancer (CRC) have been identified. Our study aimed to determine whether risk prediction based on common genetic variants might enable stratification for CRC risk. Meta-analysis of 11 genome-wide association studies comprising 16 871 cases and 26 328 controls was performed to capture CRC susceptibility variants. Genetic prediction models with several candidate polygenic risk scores (PRSs) were generated from Scottish CRC case-control studies (6478 cases and 11 043 controls) and the score with the best performance was then tested in UK Biobank (UKBB) (4800 cases and 20 287 controls). A weighted PRS of 116 CRC single nucleotide polymorphisms (wPRS116 ) was found with the best predictive performance, reporting a c-statistics of 0.60 and an odds ratio (OR) of 1.46 (95% confidence interval [CI] = 1.41-1.50, per SD increase) in Scottish data set. The predictive performance of this wPRS116 was consistently validated in UKBB data set with c-statistics of 0.61 and an OR of 1.49 (95% CI = 1.44-1.54, per SD increase). Modeling the levels of PRS with age and sex in the general UK population shows that employing genetic risk profiling can achieve a moderate degree of risk discrimination that could be helpful to identify a subpopulation with higher CRC risk due to genetic susceptibility.</p>
10 Keywords
- Case-Control Studies
- Colorectal Neoplasms
- Female
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Male
- Models, Genetic
- Multifactorial Inheritance
- Polymorphism, Single Nucleotide
13 Authors
- Xue Li
- Maria Timofeeva
- Athina Spiliopoulou
- Paul McKeigue
- Yazhou He
- Xiaomeng Zhang
- Victoria Svinti
- Harry Campbell
- Richard S. Houlston
- Ian P. M. Tomlinson
- Susan M. Farrington
- Malcolm G. Dunlop
- Evropi Theodoratou
Enabling scientific discoveries that improve human health