Incorporating Alternative Polygenic Risk Scores into the BOADICEA Breast Cancer Risk Prediction Model

Nasim Mavaddat*, Lorenzo Ficorella, Tim Carver, Andrew Lee, Alex P. Cunningham, Michael Lush, Joe Dennis, Marc Tischkowitz, Kate Downes, Donglei Hu, Eric Hahnen, Rita K. Schmutzler, Tracy L. Stockley, Gregory S. Downs, Tong Zhang, Anna M. Chiarelli, Stig E. Bojesen, Cong Liu, Wendy K. Chung, Monica PardoLidia Feliubadalo, Judith Balmaña, Jacques Simard, Antonis C. Antoniou, Douglas F. Easton

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

17 Citationer (Scopus)
10 Downloads (Pure)

Abstract

Background: The multifactorial risk prediction model BOADICEA enables identification of women at higher or lower risk of developing breast cancer. BOADICEA models genetic susceptibility in terms of the effects of rare variants in breast cancer susceptibility genes and a polygenic component, decomposed into an unmeasured and a measured component - the polygenic risk score (PRS). The current version was developed using a 313 SNP PRS. Here, we evaluated approaches to incorporating this PRS and alternative PRS in BOADICEA. Methods: The mean, SD, and proportion of the overall polygenic component explained by the PRS (α2) need to be estimated. a was estimated using logistic regression, where the age-specific log-OR is constrained to be a function of the age-dependent polygenic relative risk in BOADICEA; and using a retrospective likelihood (RL) approach that models, in addition, the unmeasured polygenic component. Results: Parameters were computed for 11 PRS, including 6 variations of the 313 SNP PRS used in clinical trials and implementation studies. The logistic regression approach underestimates a, as compared with the RL estimates. The RL a estimates were very close to those obtained by assuming proportionality to the OR per 1 SD, with the constant of proportionality estimated using the 313 SNP PRS. Small variations in the SNPs included in the PRS can lead to large differences in the mean. Conclusions: BOADICEA can be readily adapted to different PRS in a manner that maintains consistency of the model. Impact : The methods described facilitate comprehensive breast cancer risk assessment.

OriginalsprogEngelsk
TidsskriftCancer Epidemiology Biomarkers and Prevention
Vol/bind32
Udgave nummer3
Sider (fra-til)422-427
Antal sider6
ISSN1055-9965
DOI
StatusUdgivet - 2023

Bibliografisk note

Publisher Copyright:
© 2023 The Authors.

Citationsformater