Comparative GWAS using global and Indian Reference Panels reveals non-coding drivers of COVID-19 severity and mortality

India remains underrepresented in global genomic studies. We hypothesized that population-specific genetic variants contribute to COVID-19 severity and outcomes, and that the choice of reference panel during imputation impacts Genome-Wide Association Studies (GWAS) resolution. Integrating both global and indigenous reference panels may unravel unique and shared genetic associations that are otherwise missed during standard analyses. In this study, we aimed to perform a comparative GWAS using Indian population-specific (IndiGen) and global (1000 Genomes Project/1KGenomes) reference panels to identify potential genetic loci associated with the COVID-19 differential severity and mortality among the Indian patients. Genomic DNA was extracted and genotyped from the patients who were stratified based on the clinical data capturing COVID-19 symptoms and clinical outcomes. Quality control, liftover, phasing and imputation were performed on the genotypic data. GWAS was performed separately for the severity and mortality phenotypes. Significant loci were functionally annotated using Linkage Disequilibrium (LD) analysis, eQTL mapping, and gene annotation tools. Comparative GWAS with 1KGenomes and IndiGen panels revealed both shared and unique loci. 1KGenomes identified protective variants near MIR4432HG involved in endothelial stability, while IndiGen uncovered risk variants with rs10096505 (SFTPC/BMP1) linked to alveolar collapse and fibrotic remodelling. rs9547631 was common to both panels for mortality, whereas IndiGen-specific risk variants (rs78554880, rs112982286, rs111390553, and rs79900659) were associated with immune dysregulation. Functional annotation of these loci pointed to key biologically plausible links to COVID-19 severity and fatal outcomes. Briefly, the use of an indigenous reference panel improved variant discovery and LD resolution, highlighting that population-specific signals are missed by the generic global datasets. Our findings underscore the importance of inclusive genomic resources for accurate association mapping in the underrepresented populations.Author summary: Most genetic studies have focused on people of European descent, leaving South Asian populations, especially those from India, largely underrepresented. To help fill this gap, we studied the genetic makeup of Indian individuals with different COVID-19 severity levels and outcomes, ranging from recovery to death. We wanted to understand why some people become severely ill and while others recover, and whether genetic differences might help explain this. To study this, we analysed each person’s DNA and used two different datasets to fill in missing genetic information. First, represents global populations (1KGenomes), and secondly, IndiGen, which is specific for the Indian population. The Indian specific dataset helped us discover more genetic differences, including some that were missed by the global reference. These differences were linked to important biological processes such as lung function and immune response. For instance, we identified a variant located near the SFTPC and BMP1 genes, which are associated with impaired surfactant production and lung fibrosis. In patients who did not survive, we saw strong genetic signals associated with immune system regulation. In a nutshell, our study captures novel genetic signals with potential links to COVID-19 pathophysiology and highlights the importance of using tailored genomic resources to improve the accuracy of association findings.