Non-Synonymous Single Nucleotide Polymorphism (nsSNPs) are the main cause of defects in Genotypes and are critical for the prediction of genetic basis of various diseases. The major issue in analysis of variation at genetic level is to differentiate between mutation that can influence gene function from those that are neutral. The present study employed multiple in silico tools to predict nsSNPs of GSDMA and GSDMD genes with functional implications, before proceeding to study them at population level. Seven different tools such as SIFT, PolyPhen2, PROVEAN, SNP & GO, MuPro, I-Mutant and MutPred were used for insilico analysis. 10 nsSNP (missense) of GSDMA and 11 nsSNPs of GSDMD were analyzed using above softwares. By combining results of different methods, 2 nsSNPs of GSDMA namely rs191833662 (T2I) and rs115509258 (G200D) and 1 ns SNP of GSDMD the rs62000416 (L186M) were predicted to be deleterious or disease related by all softwares. The study is the first approach for insilico analysis of polymorphism in GSDMA and GSDMD genes that will be useful for further population and functional analysis.
