In silico analysis of functional non-synonymous and intronic variants found in a polycystic ovarian syndrome (PCOS) candidate gene: DENND1A

Abstract

The major thrust of our study confers to the identification of non-synonymous and intronic variants of the DENND1A gene via in silico methods and determination of its effect on the structural integrity of the protein. The outcome identifies potential disease -causing SNPs. The pathogenic variants of DENND1A were deduced via in silico analysis using various tools that include SIFT, PolyPhen-2, PROVEAN, SNP & GO, and PANTHER. The intronic variants were analysed using RegulomeDB. The 3D protein structure was obtained using the SWISS PDB modeler and validated by Ramachandran plots and QMEAN server. The effect on the stability of the protein structure caused by the SNPs was evaluated on the PYMOL and SWISS model platform. The functional changes caused by the SNPs were analysed in silico with I Mutant and Mutation Taster. The post-translational modifications were also predicted. STRING database was used for screening the protein interaction network. The SNPs rs2479106 and rs10986105 on the splice sites were found to be pathogenic for PCOS. The amino acid changes V179G and P331L were found to be disease-causing but the disease association with PCOS is yet to be validated.