medRxivGenetic & Genomic MedicinePreprint
Genetic assessments of breast cancer risk that do not account for polygenic background are incomplete and lead to incorrect preventative strategies
George BusbyiD, Paul Craig, Nesrine Yousfi, Saurabh Hebbalkar, Paolo Di Domenico, Giordano BottàiD
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.
- organism: human
- assay: whole-exome sequencing
- assay: whole-genome sequencing
- research focus: variant effect prediction
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 4
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