Breast cancer risk prediction using a polygenic risk score in the familial setting: a prospective study from the Breast Cancer Family Registry and kConFab
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Author
Li, Hongyan
Feng, Bingjian
Miron, Alexander
Chen, Xiaoqing
Beesley, Jonathan
Bimeh, Emmanuella
Barrowdale, Daniel
John, Esther M.
Daly, Mary B.
Andrulis, Irene L.
Buys, Saundra S.
Thorne, Heather
Chenevix-Trench, Georgia
Southey, Melissa
Antoniou, Antonis C.
James, Paul A.
Terry, Mary Beth
Phillips, Kelly-Anne
Hopper, John L.
Mitchell, Gillian
Goldgar, David E.
Note: Order does not necessarily reflect citation order of authors.
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https://doi.org/10.1038/gim.2016.43Metadata
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Li, H., B. Feng, A. Miron, X. Chen, J. Beesley, E. Bimeh, D. Barrowdale, et al. 2016. “Breast cancer risk prediction using a polygenic risk score in the familial setting: a prospective study from the Breast Cancer Family Registry and kConFab.” Genetics in medicine : official journal of the American College of Medical Genetics :10.1038/gim.2016.43. doi:10.1038/gim.2016.43. http://dx.doi.org/10.1038/gim.2016.43.Abstract
Purpose This study examined the utility of sets of Single Nucleotide Polymorphisms (SNPs) in familial but non-BRCA-associated breast cancer (BC). Methods: We derived a polygenic risk score (PRS) based on 24 known BC risk SNPs for 4,365 women from the BCFR and kConFab familial BC cohorts. We compared scores in women based on cancer status at baseline. 2,599 women unaffected at enrollment were followed for an average of 7.4 years. Cox proportional hazards regression was used to analyze the association of PRS with BC risk. The BOADICEA risk prediction algorithm was used to measure risk based on family history alone. Results: The mean (SD) PRS baseline was 2.25 (0.35) for the affected and 2.17 (0.35) for unaffected women from combined cohorts (p<10−6). During follow-up, 205 BCs occurred. The hazard ratios for continuous PRS (per SD), and upper vs. lower quintiles were 1.38 (95% CI: 1.22–1.56) and 3.18 (95% CI: 1.84–5.23) respectively. Based on their PRS-based predicted risk, management for up to 23% of women could be altered. Conclusion: Including BC-associated SNPs in risk assessment can provide more accurate risk prediction than family history alone and can influence recommendations for cancer screening and prevention modalities for high-risk women.Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5107177/pdf/Terms of Use
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http://nrs.harvard.edu/urn-3:HUL.InstRepos:29626175
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