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Polygenic model for predicting breast cancer risk via genome-wide polymorphisms
Stephen R Piccolo1, Nicola Camp, Lewis J Frey
1University of Utah, Salt Lake City, UT, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|November 13, 2008
Summary
A new polygenic model predicts breast cancer risk using single nucleotide polymorphism (SNP) profiles. This genetic risk prediction tool shows promising accuracy in preliminary validation studies.
Area of Science:
- Genetics
- Oncology
- Bioinformatics
Background:
- Sporadic, late-onset breast cancer lacks accurate predictive models.
- Genetic factors significantly influence breast cancer susceptibility.
Purpose of the Study:
- To develop and validate a polygenic model for predicting breast cancer risk.
- To assess the model's performance using genome-wide single nucleotide polymorphism (SNP) data.
Main Methods:
- Development of a polygenic risk score model.
- Validation using a publicly available dataset of cases and controls.
- Analysis of genome-wide SNP markers.
Main Results:
- A polygenic model for predicting susceptibility to late-onset, sporadic breast cancer was successfully developed.
- The model demonstrated predictive performance exceeding chance levels upon validation.
- The model utilizes an individual's single nucleotide polymorphism (SNP) profile.
Conclusions:
- Polygenic models based on SNP profiles offer a promising approach for breast cancer risk prediction.
- This validated model can potentially aid in identifying individuals at higher risk for sporadic, late-onset breast cancer.
- Further research and validation are warranted to refine the model for clinical application.
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