Domain-specific Analysis of BRCA1 Variants Revealed Enrichment of G → T and C → A Transversions

The increasing availability of large-scale variants datasets have enabled systematic in silico analyses of mutation patterns in different disease-associated genes. In this study, we have performed a comprehensive computational analysis of BRCA1 variants spanning intronic, coding, and non-coding regions using publicly available repositories. The BRCA1 gene, which is essential for tumor suppression function becomes a major driver of hereditary breast and ovarian cancer (HBOC). A domain-specific nucleotide-level mutational trend analyses will provide a useful framework for translational research. Our study revealed a predominance of GàT and CàA transversions across BRCA1 variants, with notable enrichment for specific predicted impact categories. Our domain-specific analyses revealed that the RING domain (exons 2-7) and BRCT domain (exons 17-22), known mutation hotspots, frequently exhibited GàT and GàC transversions. The PolyPhen-based classification indicated that a substantial proportion of variants fall into higher predicted impact groups, which exhibited distinct substitution biases compared to benign categories. AI-assisted computational analyses using SpliceAI and RegulomeDB indicated predominantly moderate to benign splicing effects among intronic variants, while a subset showed potential regulatory impact that requires validation in large-scale studies. To our observation, BRCA1 possessed a balanced codon usage with high Nc and Nc' values. Furthermore, we highlight BRCA1 evolutionary and mutational patterns, particularly substitution trends, at nucleotide level. Therefore, the study offers a computational framework for prioritizing variants through AI-enabled tools and substitution patterns for experimental validation and functional characterization.