Conceptualization of functional single nucleotide polymorphisms of polycystic ovarian syndrome genes: an in silico approach.

ABSTRACT

PURPOSE: Polycystic ovarian syndrome (PCOS) is a multi-faceted endocrinopathy frequently observed in reproductive-aged females, causing infertility. Cumulative evidence revealed that genetic and epigenetic variations, along with environmental factors, were linked with PCOS. Deciphering the molecular pathways of PCOS is quite complicated due to the availability of limited molecular information. Hence, to explore the influence of genetic variations in PCOS, we mapped the GWAS genes and performed a computational analysis to identify the SNPs and their impact on the coding and non-coding sequences. METHODS: The causative genes of PCOS were searched using the GWAS catalog, and pathway analysis was performed using ClueGO. SNPs were extracted using an Ensembl genome browser, and missense variants were shortlisted. Further, the native and mutant forms of the deleterious SNPs were modeled using I-TASSER, Swiss-PdbViewer, and PyMOL. MirSNP, PolymiRTS, miRNASNP3, and SNP2TFBS, SNPInspector databases were used to find SNPs in the miRNA binding site and transcription factor binding site (TFBS), respectively. EnhancerDB and HaploReg were used to characterize enhancer SNPs. Linkage Disequilibrium (LD) analysis was performed using LDlink. RESULTS: 25 PCOS genes showed interaction with 18 pathways. 7 SNPs were predicted to be deleterious using different pathogenicity predictions. 4 SNPs were found in the miRNA target site, TFBS, and enhancer sites and were in LD with reported PCOS GWAS SNPs. CONCLUSION: Computational analysis of SNPs residing in PCOS genes may provide insight into complex molecular interactions among genes involved in PCOS pathophysiology. It may also aid in determining the causal variants and consequently contributing to predicting disease strategies.