In Silico Analysis Identified Putative Pathogenic Missense Single Nucleotide Polymorphisms (SNPs) in the Human Gene.

Maturity-onset diabetes of the young (MODY) is a rare genetic condition that affects children, adolescents, and adults. Studies have shown that genetic changes in the gene are associated with MODY-3. However, most of the causative variants and the molecular mechanisms remain underexplored. This study aims to better understand MODY-3 by investigating HNF1A-missense variants with clinical uncertainty. Various bioinformatics tools were utilised to address the clinical uncertainty of missense variants in the gene that have not been linked with HNF1A-related conditions, sourced from the Genome Aggregation Database (GnomAD v4.1.0). Among the clinically uncertain 2444 variants, only 138 were classified as missense with clinically uncertain significance. Results show that four variants (Arg168Cys, Glu275Ala, Gly375Asp and Val411Phe) were consistently predicted as pathogenic by all tools. The allele frequency (AF) of the commonly predicted disease-causing variants was very low in the global population. The assessment of the secondary structure of filtered variants indicates that variants (Arg168Cys and Glu275Ala) are located in the helical region of the HNF1A protein. At the same time (Gly375Asp and Val411Phe) are found in the protein's coil, suggesting structural changes at the site of variations. The prediction of protein stability was conducted using I-Mutant and MuPro. Both tools collectively indicate decreased protein stability for the variants (Arg168Cys, Glu275Ala, Gly375Asp and Val411Phe). Predicting the protein's 3D structure for the HNF1A wild-type and mutants indicates potential structural damages in Arg168Cys and Gly375Asp. Additionally, results show that the amino acids at the variation sites of the variants (Arg168Cys, Glu275Ala, Gly375Asp and Val411Phe) were highly conserved. To conclude, 4 out of the 138 missense variants labelled as uncertain significance were found to be consistently pathogenic using in silico tools in this study. Our findings aim to support variant interpretation, understand the genotype-phenotype association of diabetes, and provide better healthcare services for patients with diabetes.