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

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, standard deviation (SD), and proportion of the overall polygenic component explained by the PRS (α2) need to be estimated. α was estimated using logistic regression, where the age-specific log-odds ratio 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 α, as compared with the RL estimates. The RL α estimates were very close to those obtained by assuming proportionality to the odds ratio 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.

Validation of an Abridged Breast Cancer Risk Prediction Model for the General Population

“…All SNPs were available in the UK Biobank for the 77-SNP PRS (8) while 8 (3%) SNPs for the 313-SNP PRS (7) were not available in the UK Biobank. For the 77-SNP PRS, 55% of women had all SNP genotypes and 34% were missing only one SNP, while for the 313-SNP PRS, 61% had at least 98% of the 305 available SNP genotypes.…”

“…The estimates for body mass index for pre-and post-menopausal women were taken from Hopper et al (52). The 77-SNP PRS was from Mavaddat et al (8) and the 313-SNP PRS was from Mavaddat et al (7).…”

“…These include high penetrance hereditary risk, family history, breast architecture (which includes everything from mammographic density to biopsy status), and polygenic risk. Polygenic risk is a promising epidemiological risk factor (7,8) with a robust amount of evidence derived from multiple large international datasets but its value is greater when integrated with other risk factors (9,10).…”

“…Herein, we compare the performance of two versions of our clinical model against the performance of the commonly used BCRAT in the UK Biobank. For clarity, the single variable that differs between the BRISK versions is the integrated PRS SNP panel and throughout the manuscript we use the following names to distinguish the two BRISK models: 77-SNP BRISK and 313-SNP BRISK (7,8). A general population cohort such as the UK Biobank is of interest because it represents the large number of women who are typically considered as being at average risk of developing breast cancer.…”

CanRisk-Prostate: A Comprehensive, Externally Validated Risk Model for the Prediction of Future Prostate Cancer

“…45,[70][71][72][73][74][75] Similarly, although the model incorporates the latest 268-SNP PGS, 18 the model is flexible and can incorporate alternative PGSs, provided that an estimate of the proportion of the PGC that is explained by the PGS is available. 76 As further risk variants are identified, the model discrimination is expected to improve.…”