Most DNA tests were built on reference databases dominated by people of European ancestry, which means the interpretations they produce of ancestry, disease risk, and trait predictions are less accurate when applied to Indian users. The gap is real, documented, and significant. The good news is that it is being actively closed, driven by growing Indian genomic data and a national effort to map India's genetic diversity. But it has not closed yet, and Indian users taking DNA tests today should understand what this means for reading their results.
This is not a criticism of the technology itself. The science of reading genetic data from a sample is accurate and well-established. The gap is in the interpretation layer, the step where raw genetic data is compared against population reference data to produce the ancestry percentages, disease-risk scores, and trait predictions in a report. That comparison is only as meaningful as the reference it is compared against, and for Indian users that reference has historically been a poor fit.
Why reference databases matter so much. Think of a DNA test as a translation exercise. The lab reads the letters of your genetic code accurately. Then the test interprets those letters by comparing your code against large databases of people with known origins and health outcomes. If those databases contain millions of European-ancestry profiles and relatively few Indian profiles, the interpretations produced for a European user rest on solid statistical ground. The same interpretations produced for an Indian user rest on much thinner ground, because the comparison is being made against a population that does not adequately represent them. This is not a hypothetical concern. It shows up in measurable ways across different types of genetic output.
Ancestry estimates: where the gap is most visible. For ancestry results, the reference population problem is most obvious. India is one of the most genetically diverse countries on Earth, shaped by a complex population history spanning thousands of years, with distinct genetic signatures across different states, communities, and historical groups. Capturing that diversity accurately requires reference data from the full range of Indian populations. Historically, most commercial ancestry databases had thin, broad-brush coverage of South Asian populations, which produced results that might say 'South Asian' where a richer database would say something far more specific. This is why Indian users have historically received less granular, less informative ancestry results than European users from the same tests. The data to do better simply was not there, and the test could not manufacture precision it did not have.
Health risk scores: where the stakes are higher. For health-related results, the gap matters more, because the consequences of a miscalibrated risk score are not just frustrating but potentially misleading. Many genetic risk assessments, including polygenic risk scores for conditions like type 2 diabetes, heart disease, and cancer, were developed and validated primarily on datasets of European ancestry. When these tools are applied to Indian users, their accuracy drops, because the genetic patterns the tool learned to recognise were patterns in a different population. A risk score calibrated on the wrong reference population is not a precise verdict. It is an estimate wearing the clothes of precision, and reading it as though it were a perfectly calibrated assessment of your individual risk can lead to either false reassurance or unnecessary alarm. This is especially consequential for South Asians given that conditions like type 2 diabetes and cardiovascular disease are known to arrive earlier and at lower body weights in South Asian populations than in the populations many of these tools were built on.
What the GenomeIndia project is doing about it. The most significant development in this area is the systematic effort to build Indian-specific genomic reference data. The GenomeIndia project, launched in 2020 by India's Department of Biotechnology, has sequenced whole genomes from thousands of individuals across India's diverse population groups. Published findings from this project have identified hundreds of millions of genetic variants, including many unique to India or to specific Indian communities, that were simply absent from international reference databases. As this data grows and feeds into the tools that interpret genetic tests, the accuracy of those tests for Indian users improves directly and measurably. This is genuinely significant progress. Better reference data means more accurate ancestry breakdowns, more precisely calibrated health risk scores, and the identification of genetic variants relevant to diseases that disproportionately affect Indian populations. The gap is not fixed, but it is being fixed, and the direction is unambiguously toward more accurate and more useful results for Indian users over time.
What Indian users should do right now. Three practical things, all actionable today. First, treat health-risk scores from consumer DNA tests as informative starting points rather than precise verdicts. A risk score is only as reliable as the population it was calibrated on, and for many tools applied to Indian users that calibration is still incomplete. Use results to start conversations with a doctor, not to make independent health decisions. Second, ask about reference populations when choosing a test. Providers who use or are building Indian-specific reference data are producing more meaningful results for Indian users than those relying entirely on globally pooled data dominated by European-ancestry samples. Third, pay attention to when results update. As reference databases improve, the estimates in your report can and will change to reflect better data, even though your DNA has not changed. This is a feature, not a bug.
The one rule that matters most. Never make significant health decisions, including starting, stopping, or changing medications, beginning major preventive treatments, or dismissing genuine symptoms, based solely on a consumer DNA test result. This is true for any user anywhere in the world. It is especially true for Indian users right now, where the interpretive accuracy gap means the margin for error in a result is wider than the confident-looking output typically suggests. The result is useful context. A doctor who can interpret it alongside your full clinical picture is what makes it actionable.