The main objective of work presented in this thesis was to explore the clinical utility of the Polygenic Risk Score (PRS) based on breast cancer associated common low risk variants, which explain ... Show moreThe main objective of work presented in this thesis was to explore the clinical utility of the Polygenic Risk Score (PRS) based on breast cancer associated common low risk variants, which explain ~18% of the familial relative risk, for individual breast cancer risk prediction. It did so by generating knowledge about the PRS in the Dutch general population and in clinic-based breast cancer families, as well as in a large international population of BRCA1/2 pathogenic variant carriers. We have validated the association of the PRS with breast cancer for women in both the Dutch population and breast cancer families and showed a better risk-discrimination by adding the PRS to family-based risk prediction. Secondly, we have shown that addition of the PRS to family-based risk prediction has an impact on screening recommendations for many non-carriers and carriers of a pathogenic variant in a moderate breast cancer gene. The results will support implementation of comprehensive risk prediction in the clinic, and may help women to make more informed choices about their optimal clinical management. Show less
Background: Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are... Show moreBackground: Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are uncertain. Methods: We analyzed data on 59,639 breast cancer cases and 53,165 controls from studies participating in the Breast Cancer Association Consortium BRIDGES project. We sampled training (80%) and validation (20%) sets to analyze rare missense variants in ATM (1146 training variants), BRCA1 (644), BRCA2 (1425), CHEK2 (325), and PALB2 (472). We evaluated breast cancer risks according to five in silico prediction-of-deleteriousness algorithms, functional protein domain, and frequency, using logistic regression models and also mixture models in which a subset of variants was assumed to be risk-associated. Results: The most predictive in silico algorithms were Helix (BRCA1, BRCA2 and CHEK2) and CADD (ATM). Increased risks appeared restricted to functional protein domains for ATM (FAT and PIK domains) and BRCA1 (RING and BRCT domains). For ATM, BRCA1, and BRCA2, data were compatible with small subsets (approximately 7%, 2%, and 0.6%, respectively) of rare missense variants giving similar risk to those of protein truncating variants in the same gene. For CHEK2, data were more consistent with a large fraction (approximately 60%) of rare missense variants giving a lower risk (OR 1.75, 95% CI (1.47-2.08)) than CHEK2 protein truncating variants. There was little evidence for an association with risk for missense variants in PALB2. The best fitting models were well calibrated in the validation set. Conclusions: These results will inform risk prediction models and the selection of candidate variants for functional assays and could contribute to the clinical reporting of gene panel testing for breast cancer susceptibility. Show less
