Identification and Characterization of Rare Genetic Variants Associated with pathogenicity Breast Cancer Susceptibility Across Thousand Genome and GnomAD population
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Background
Inherited variants in cancer susceptibility genes play a critical role in breast cancer risk, yet comprehensive comparative analyses across clinically relevant genes remain limited. While BRCA1 and BRCA2 are well-established high-risk contributors, the broader landscape of germline pathogenicity across breast cancer associated genes warrants systematic evaluation.
Methods
We curated a panel of 15 breast cancer associated genes ( BRCA1, BRCA2, TP53, PTEN, CHEK2, ATM, CDH1, PALB2, LSP1, MAP3K1, NF1, RECQL, TOX3, FANCD2, RAD51C ) based on clinical and epidemiological relevance. Publicly available germline variant data from gnomAD v4.1 and the 1000 Genomes Project were integrated with ClinVar annotations. Variants were classified by clinical significance and filtered using allele frequency and phenotype annotation to stratify into high-risk and non-high-risk groups. We compared the distribution of variants and assessed penetrance patterns, and we evaluated the discriminatory power of computational pathogenicity predictors including CADD, REVEL, SIFT, and PolyPhen-2.
Results
BRCA1 and BRCA2 harbored the highest burden of pathogenic variants, consistent with their high penetrance status. Moderate-risk genes ( PALB2, RAD51C, CHEK2 ) showed intermediate levels of pathogenicity, while low-risk genes such as RECQL, TOX3 , and FANCD2 were predominantly enriched for variants of uncertain significance. High-risk variants exhibited significantly elevated CADD (>30), REVEL (>0.75), and SIFT (<0.05) scores compared to non-high-risk variants (p < 1×10− 20 ), whereas PolyPhen-2 showed limited discriminatory power. Odds ratio analysis further supported CADD, REVEL, and ClinVar pathogenicity labels as strong discriminators between variant categories.
Conclusions
Our analysis reaffirms the pathogenic burden concentrated in high penetrance genes and highlights the utility of combined in silico prediction tools and population frequency filters in variant prioritization. These findings support refined variant curation pipelines for hereditary breast cancer and provide a framework for interpreting gene-level pathogenicity across diverse susceptibility loci.
