In-Silico Approach for Finding Consistent Deleterious Non-Synonymous SNPs in Diabetes Mellitus

Diabetes mellitus is a metabolic disorder in human beings. Genetic mutation affects the body to resist the production of insulin inadequate amount, abnormally glucose level becomes very high than normal. As a result, body organs and tissues can be damaged, which has a severe effect on human health if it is not treated properly. Genetic mutations are present in DNA among the genes and Single Nucleotide Polymorphism is one of the forms of mutation. The occurrence of SNPs within a gene may lead to a direct role in disease development and altering gene functionality, allowing scientist to easily identify the genes associated with specific diseases. In this study, the most consistent deleterious nsSNP’s associated with 'Diabetes Mellitus' are identified using an in-silico approach. A variety of computational analysis tools are combined to provide a more comprehensive and reliable analysis by integrating each method's strengths and cross-validating across platforms. The results indicates that the list of 44 genes with their selected number of nsSNP’s have a strong association with Diabetes Mellitus risk as it remains common in all analysis tools used in the identification of deleterious non-synonymous single nucleotide polymorphism.