Tiered genetic testing reveals CACNA1A allelic series and RNA modeling elucidates repeat expansion mechanism in an Indian spinocerebellar ataxias cohort

Objectives: Spinocerebellar ataxias (SCAs) represent a heterogeneous group of neurodegenerative disorders with significant diagnostic challenges, particularly in genetically underrepresented populations. While tiered genetic testing approaches have been previously applied in Indian cohorts, diagnostic gaps persist with approximately 40-75% of cases remaining undiagnosed. This study integrates established genetic diagnostics with in silico structural modeling to improve diagnostic yield and provide mechanistic insights in 137 clinically suspected Indian SCA cases. Materials and Methods: We employed standard polymerase chain reaction (PCR) and triple repeat-primed PCR (TP-PCR) for repeat expansions in SCA1-3,6,7,8,10,12, and 36, followed by whole-exome sequencing (WES) in repeat-negative cases. In silico RNA and protein modeling studies, along with protein-protein interaction analysis in the aggregates, they explored the disease mechanisms. Results: This tiered approach achieved a combined diagnostic yield of 48.9%, with PCR/TP-PCR identifying pathogenic repeat expansions in 45.98% cases, while WES performed on 17/137 (12.4%) repeat-negative cases identified pathogenic variants in 4/17 (23.53% of those sequenced, 2.92% of the total cohort). Notably, CACNA1A emerged as the predominant gene harboring allelic variants in three unrelated families presenting with progressive cerebellar ataxia. These included a missense variant (c.1745G>A), a novel duplication (c.3058_3075dup), and a heterozygous CAG repeat expansion (expCAG:13/23). Our RNA modeling predicted that exp CAG generates an unstable hairpin loop in the case of exp CAG in SCA2, 3, and 7 and a stable hairpin loop structure in the case of SCA1 and SCA6, with increased propensity for RNA foci via RNA-RNA and RNA-binding protein aggregation. Conclusion: Our study demonstrates that integrating tiered genetic testing with in silico structural modeling enhances diagnostic yield and functional understanding of SCA pathogenesis. By combining molecular genomic screening with RNA and protein modeling, this study helps to close the diagnostic gap in SCAs and guide therapeutic strategy development.