Identification of Breast Cancer Associated Putative Functional Single Nucleotide Polymorphisms in the Iranian Population through In Silico Analyses - Vida Nadafi Sichani; Seyed Alireza Emami; Morteza Bitaraf Sani; Mohammadreza Nassiri; Vinod Gopalan (Journal of Cell and Molecular Research, 2020)
Previous studies have found several distinct alleles at both levels of transcriptional activity and protein-DNA binding manners in breast cancer patients vs. healthy individuals through multi-step experimental approaches. This study presents a computational-based model to investigate the regulatory potential and functional properties of disease-related non-coding single nucleotide polymorphisms (SNPs) variants through several online in silico tools in the Iranian population. The association between the risk of breast cancer and its putative single nucleotide polymorphisms in the Iranian population was investigated through SNPedia database and genome-wide association studies (GWAS). Furthermore, a meta-analysis was performed by Comprehensive Meta-Analysis (CMA) software. Functional analyses were carried out through LDlink, HaploReg, and RegulomeDB. The impact of each single nucleotide polymorphism on gene expression profiles and transcription factor binding sites were predicted by the RegulomeDB. "5", "6", and "1d" scores were assigned to rs3746444, rs1062577, and rs1049174 by this scoring system, respectively. RegulomeDB scores of rs3746444-MYH7B/MIR499A and rs1062577-ESR1 suggested that they are not putative functional single nucleotide polymorphisms; and may not associate with significant eQTL signals. The “1d” score for rs1049174-RP11-277P12.20 confirmed an association with the expression of the target gene. Proxy variants rs6088678 and rs2617160 have been identified using LDlink in non-coding segments. They were in strong linkage disequilibrium (LD) with single nucleotide polymorphisms rs3746444 and rs1049174, respectively. Also, non-coding variants rs6088678-TRPC4AP and rs2617160- RP11-277P12.20 with high-ranked scores showed the strongest related-expression. This work provides a rapid and direct in silico-based approach for the identification of functional genetic variants in the breast cancer. These analyses were conducted to evaluate the association of intended SNPs with the regulatory elements of histones, DNases, motif changes, and selected eQTL signals. It can be extended to some other complex single nucleotide polymorphism-related diseases.