Genetic assessments of breast cancer risk that do not account for polygenic background are incomplete and lead to incorrect preventative strategies
Busby, G. B.; Craig, P.; Yousfi, N.; Hebbalker, S.; Di Domenico, P.; Botta, G.
Show abstract
Breast cancer is the most common cancer among women and is a leading cause of cancer mortality worldwide. There is a significant genetic component to breast cancer risk which is the result of both rare pathogenic mutations and common genome-wide variation. However, the penetrance of pathogenic mutations varies widely and their frequency is low, both at a population level and amongst breast cancer cases. Polygenic risk scores, which aggregate the effect of hundreds to millions of common genome-wide variants offer a way to further understand the contribution of genetics to disease risk. Here we analyse genome-wide data from 221,479 women and 90,307 high coverage exomes to understand how rare and common variation affect lifetime breast cancer risk. We show that PRS strongly modulates the penetrance of mutations in 8 breast cancer susceptibility genes. For example, lifetime risk in BRCA1 carriers with low polygenic risk is almost one third that of carriers with high PRS (26% v 69% in the bottom and top PRS deciles, respectively). Adding family history of breast cancer provides additional stratification on the potential outcome of disease in carriers of rare mutations. PRS also identifies a significant fraction of the population at equivalent risk to carriers of moderate impact pathogenic variants and who are an order of magnitude more common at a population level. These results have important implications for breast cancer risk mitigation strategies, indicating that the genetic risk of breast cancer is determined by both monogenic mutation and polygenic background, and that assessments of genetic risk for breast cancer risk that do not consider the polygenic background are imprecise and unreliable.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Risk factors for eight common cancers revealed from a phenome-wide Mendelian randomisation analysis of 378,142 cases and 485,715 controls 95%
- Whole-genome analysis of Nigerian patients with breast cancer reveals ethnic-driven somatic evolution and distinct genomic subtypes 94%
- Identifying therapeutic targets for cancer: 2,094 circulating proteins and risk of nine cancers 94%
Similar papers in this journal
- Performance of polygenic risk scores for cancer prediction in a racially diverse academic biobank 95%
- Classification of Variants of Reduced Penetrance in High Penetrance Cancer Susceptibility Genes: Framework for Genetics Clinicians and Clinical Scientists by CanVIG-UK (Cancer Variant Interpretation Group-UK) 92%
- Impact of genetic counselling strategy on diagnostic yield and workload for whole genome sequencing-based tumour diagnostics 91%
Similar papers in this journal
- Segregation analysis of 17,425 population-based breast cancer families: evidence for genetic susceptibility and risk prediction 96%
- A joint transcriptome-wide association study across multiple tissues identifies new candidate susceptibility genes for breast cancer 96%
- A polygenic score-based approach to identify gene-drug interactions stratifying breast cancer risk 96%
Similar papers in this journal
- Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses 96%
- Genomic profiling defines variable clonal relatedness between invasive breast cancer and primary ductal carcinoma in situ 94%
- Fine-mapping of 150 breast cancer risk regions identifies 178 high confidence target genes 92%
Similar papers in this journal
- A Comprehensive Epithelial Tubo-Ovarian Cancer Risk Prediction Model Incorporating Genetic and Epidemiological Risk Factors 95%
- Heritable genetic variants in key cancer genes link cancer risk with anthropometric traits 94%
- Estimating cancer risk in carriers of Lynch syndrome variants in UK Biobank 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.