Decoding Disease in India — an interactive, plain-language atlas of what can affect each part of the body: what each disease is, what causes it, and where it hits India hardest. Click any organ to explore. Underneath sits a genomics layer for where Indian DNA differs from the populations medicine was built on.
Fewer than 2%of the people in the world’s genome-wide studies are South Asian — for nearly a quarter of humanity. When a risk model is trained on one ancestry and used on another, its numbers quietly drift. Not because the biology is different — because the calibrationis. Here’s what that drift does, measured on real South Asian genomes.
Diversity figures: Martin et al., Nature Genetics 2019 · Sirugo et al., Cell 2019.
A European-trained polygenic risk score for type 2 diabetes flags 30.6% of Telugu (India) people as high-risk - vs the 10% it was designed for. That's a 3.1x mis-stratification: the score's 'average' is set to European genetics, so it systematically mis-reads South Asians (a +0.80 SD mean shift).
The risk score that cries wolf for Indians — over the largest diabetes population on Earth
A European-trained polygenic risk score (PGS000033) flags 30.6% of Telugu Indians as high-risk, against the 10% it was calibrated to — a 3.1x mis-stratification (our analysis on 1000 Genomes South Asian samples). The score's baseline is European; applied to Indian DNA it reads risk off a mis-set ruler.
India has over 100 million adults with diabetes (ICMR-INDIAB national study, 2023), and South Asians develop it younger, at lower BMI, with more central fat — the 'thin-fat' phenotype. A tool that systematically mis-ranks them isn't academic: it mis-triages the largest diabetes population on Earth.
The allele-frequency mean-shift that drives the mis-calibration is real and measurable. What's missing is a South-Asian-calibrated score validated against Indian outcomes — the training cohorts barely include Indians. Effect sizes and linkage patterns may differ too, which frequency math alone can't capture.
Recalibrate PGS000033 on an Indian genotyped + phenotyped cohort (even a few thousand people), set the high-risk threshold on South-Asian rather than European risk, and measure how many people get correctly reclassified. A clean, publishable validation once a sample is in hand.
A European-trained polygenic risk score for coronary artery disease places only 0.7% of Sri Lankan Tamil people above its high-risk cut-off - far BELOW the 10% it was calibrated to. Yet South Asians carry a well-documented EXCESS of real-world heart disease. The score is blind to South-Asian coronary artery disease genetics: it under-warns exactly the group at higher true risk. This under-flagging is more dangerous than over-flagging.
The score that under-warns the very people most likely to have an early heart attack
For coronary disease, the European polygenic score places only 0.7% of Sri Lankan Tamils above its high-risk line — far BELOW the 10% design target (our analysis). It under-flags. Yet South Asians have among the highest real-world coronary rates in the world, often a decade earlier than Europeans.
The 'South Asian paradox' — high coronary disease at relatively low cholesterol and BMI — is long documented (INTERHEART and others). A genetic score that quietly reassures exactly this group is the most dangerous failure mode: false comfort for those at highest true risk.
The downward shift is measurable, and part of the biology is understood (lipoprotein(a), central adiposity, insulin resistance). What's missing is a South-Asian coronary score that recovers the high-risk fraction the European one drops.
Build or recalibrate a South-Asian coronary polygenic score against Indian cardiac cohorts and show it recovers the missing high-risk group — turning a falsely-reassuring number into an actionable one.
Ranked by India burden × how mis-calibrated or unstudied South Asians are × whether a genomic handle even exists. These are the tractable ones — where you could actually design the study.
No verified India incidence figure. No genomic handle yet. This is the quieter gap — the diseases of poverty and nutrition where the data simply hasn’t been gathered. You can’t close a gap you haven’t measured.
D2I2 is a decade-long project to map, and then close, this blind spot for India — one disease at a time.