| Title: | Clinically relevant combined effect of polygenic background, rare pathogenic germline variants, and family history on colorectal cancer incidence |
| Journal: | BMC Medical Genomics |
| Published: | 5 Mar 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36872334/ |
| DOI: | https://doi.org/10.1186/s12920-023-01469-z |
| URL: | https://bmcmedgenomics.biomedcentral.com/counter/pdf/10.1186/s12920-023-01469-z |
| Citations: | 13 (13 in last 2 years) as of 8 Aug 2024 |
Publication 11173
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Abstract
Background and aimsSummarised in polygenic risk scores (PRS), the effect of common, low penetrant genetic variants associated with colorectal cancer (CRC), can be used for risk stratification.MethodsTo assess the combined impact of the PRS and other main factors on CRC risk, 163,516 individuals from the UK Biobank were stratified as follows: 1. carriers status for germline pathogenic variants (PV) in CRC susceptibility genes (APC, MLH1, MSH2, MSH6, PMS2), 2. low (< 20%), intermediate (20-80%), or high PRS (> 80%), and 3. family history (FH) of CRC. Multivariable logistic regression and Cox proportional hazards models were applied to compare odds ratios and to compute the lifetime incidence, respectively.</p>
ResultsDepending on the PRS, the CRC lifetime incidence for non-carriers ranges between 6 and 22%, compared to 40% and 74% for carriers. A suspicious FH is associated with a further increase of the cumulative incidence reaching 26% for non-carriers and 98% for carriers. In non-carriers without FH, but high PRS, the CRC risk is doubled, whereas a low PRS even in the context of a FH results in a decreased risk. The full model including PRS, carrier status, and FH improved the area under the curve in risk prediction (0.704).ConclusionThe findings demonstrate that CRC risks are strongly influenced by the PRS for both a sporadic and monogenic background. FH, PV, and common variants complementary contribute to CRC risk. The implementation of PRS in routine care will likely improve personalized risk stratification, which will in turn guide tailored preventive surveillance strategies in high, intermediate, and low risk groups.</p>
18 Authors
- Emadeldin Hassanin
- Isabel Spier
- Dheeraj R. Bobbili
- Rana Aldisi
- Hannah Klinkhammer
- Friederike David
- Nuria Dueñas
- Robert Hüneburg
- Claudia Perne
- Joan Brunet
- Gabriel Capella
- Markus M. Nöthen
- Andreas J. Forstner
- Andreas Mayr
- Peter Krawitz
- Patrick May
- Stefan Aretz
- Carlo Maj
1 Application
| Application ID | Title |
|---|---|
| 52446 | Predictive and integrative models for disease prevention using the UK Biobank Data |
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