Statistical Resolution and Clinical Utility in Population Genomics: A Critical Audit of the Genome India Project (GIP) Sampling Architecture

The Genome India Project (GIP) represents a landmark achievement in South Asian population genomics, sequencing 9,772 individuals across 83 endogamous subpopulations. However, our audit identifies a critical architectural Resolution Gap resulting from uniform distribution of samples across these subpopulations. Using Monte Carlo simulation (10⁶ iterations) we demonstrate a 22.17% non-detection probability for rare alleles (1% MAF) in tribal cohorts at the current median sample size of n=75. We propose the Two-Stage Adaptive Sentinel Model (TASM), modelled after India's NACO-IBBS sentinel surveillance framework, which uses a Composite Trigger (FST > 0.02 and private allele frequency ≥2.5%) to adaptively scale sampling to n=300 in high-drift isolates. This reduces non-detection probability to 0.24%, achieving 99.76% detection power using only 8,660 total genomes — fewer than the GIP's current 10,074. TASM is directly transferable to Gulf genome programmes including the Saudi Human Genome Programme, Qatar Genome Programme, and UAE Genome Programme (Wareed).