Subtype-specific prediction using the lasso analysis was optimized using case-only lasso analysis. The OR per 1 SD in the validation set was 1.81 (95%CI: 1.73–1.89) for ER-positive and 1.48 (95%CI: 1.37–1.59) for ER-negative disease (Tables 2 and S8).
Table 2 Association between PRS and Breast Cancer Risk in the Validation Set and Prospective Test Datasets
Validation Set Prospective Test Set
ORa 95% CI AUC ORa 95% CI AUC
77 SNP PRS (PRS77)
Overall BC 1.49 1.44–1.56 0.612 1.46 1.42–1.49 0.603
ER-positive 1.56 1.49–1.63 0.623 1.52 1.48–1.56 0.615
ER-negative 1.40 1.30–1.50 0.596 1.35 1.27–1.43 0.584
313 SNP PRS (PRS313)
Overall BC 1.65 1.59–1.72 0.639 1.61 1.57–1.65 0.630
ER-positive 1.74 1.66–1.82 0.651 1.68 1.63–1.73 0.641
ER-negative 1.47 1.37–1.58 0.611 1.45 1.37–1.53 0.601
3,820 SNP PRS (PRS3820)
Overall BC 1.71 1.64–1.79 0.646 1.66 1.61–1.70 0.636
ER-positive 1.81 1.73–1.89 0.659 1.73 1.68–1.78 0.647
ER-negative 1.48 1.37–1.59 0.611 1.44 1.36–1.53 0.600
Parameter selection and effect size estimation for derivation of the PRS was carried out in the training set as described in the Material and Methods. The optimal subtype-specific PRS was obtained by carrying out case-only logistic regression and estimating effect sizes in the relevant subtype for SNPs passing a p value of 0.025 in case-only ordinary logistic regression (ER-positive versus ER-negative disease). OR for association with breast cancer in the validation set derived using logistic regression adjusting for country and ten PCs. AUCs were adjusted for by country. In the prospective test set, logistic regression models were adjusted for study and 15 PCs. AUCs were adjusted for by study.
a OR per 1 SD for the PRS.