India accounts for over 17% of the global population and exhibits extensive genetic heterogeneity, including many clinically important rare variants distributed among numerous sub-groups. These variants remain underrepresented in widely used international reference datasets such as the 1000 Genomes Project (1KG), which contains comparatively few samples from Indian backgrounds. Such resources are crucial for drug discovery and pharmaceutical research, where population diversity is essential for identifying genetic factors that affect predisposition to adverse drug reactions.
In this work, we conducted a qualitative, comparative evaluation of sequence- and structure-level differences between kinase-gene variants documented in the newly released IndiGen database—the most comprehensive curated Indian genome dataset—and those cataloged in 1KG. Kinase genes represent one of the most frequently exploited drug-target classes. The sequence-based comparisons highlighted shared and divergent patterns among populations using nsSNVs and amino acid substitution trends, while structural analyses contrasted IndiGen variants with pathogenic alterations curated in the UniProtKB Humsavar dataset. We assessed how these substitutions influence protein features such as folding stability, hydrophobic interactions, solvent exposure, and hydrogen-bonding networks. Computational docking against known drugs targeting proteins harboring Indian-specific variants revealed several notable shifts in binding affinity attributable to these population-specific alterations. Overall, this study provides an in-depth overview of common variants in the world’s second-largest population and explores their implications for sequence properties, structural behavior, and pharmacogenomic relevance. These initial findings, which highlight Indian-specific ADR-linked variants, may support both pre-clinical and post-marketing surveillance workflows aimed at reducing adverse drug events in India.
