Using genome-scale bioinformatics platforms to investigate the role of single nucleotide polymorphisms in the BRCA1 gene in key molecular pathways of disease

Single nucleotide polymorphisms (SNPs) are often associated with conferring risk for disease, and are associated with many complex diseases such as breast and ovarian cancer. The BRCA1 gene is known to carry mutations that can predispose an individual to such diseases. Currently, the clinical significance of most SNPs remains unknown due to the lack of successful and reliable classification tools, leading to the possibility that many pathogenic SNPs are not considered during genetic screening. In order to investigate the role of SNPs within crucial pathways and the structural effects of SNPs, a database and data collection pipeline was constructed that sourced information from Reactome, ClinVar, and UniProt. A second pipeline was created that allowed for the modelling of variant proteins. Through querying the database, direct pathway associations with BRCA1 were identified. Protein variant modelling revealed a novel approach to structural analysis of SNPs, allowing for differences in heuristic structural functions to be measured between pathogenic and benign variants. Of particular interest, the heuristic functions that showed the most significant differences were the van der Waals contacts and strict hydrogen bonds. Identification of SNPs within genes linked to complex diseases, such as BRCA1, can inform better targets of genetic screening and potentially provide new drug targets