Why Two DNA Services Give You Different Ancestry Results

Same DNA, different percentages. The reference panels, algorithms, and region definitions behind conflicting ancestry estimates - and how to read them sensibly.

Upload the same raw DNA file to two services and you can get noticeably different ancestry breakdowns. One says 40 percent one region, the other says 25. It is a common surprise, and it does not mean either is broken. Ancestry estimates are informed guesses, and different tools guess differently.

An estimate, not a measurement

Your DNA does not carry a label reading “30 percent from here.” Ancestry is inferred by comparing your variants against groups of people whose backgrounds are known, then estimating which mixtures best explain your pattern. Change the inputs to that calculation and the output changes too - even though your DNA is identical.

Where the differences come from

A handful of design choices drive most of the disagreement:

  • Reference panels. Each service compares you against its own collection of reference populations. If one has deep sampling for a region and another has little, their estimates for that region will differ.
  • Region definitions. Companies draw the map differently. One might report a single broad region where another splits it into three, so the percentages simply do not line up.
  • Algorithms. The statistical models that assign ancestry are not identical, and each makes different trade-offs between precision and confidence.
  • Marker sets. Different chips read different positions, so each service is working from a slightly different view of your genome.
  • Updates over time. As reference data grows, services revise their models. Your result can shift months later without you sending a new sample.

The parts that tend to agree

Broad, continent-scale signals are usually stable across services. The disagreements cluster in fine detail - neighboring regions with shared history, or small percentages near the edge of what the data can resolve. That pattern is a useful tell: the coarse story is trustworthy, the precise slivers less so.

How to read your results

  • Focus on the broad strokes rather than exact percentages
  • Treat small single-digit regions as low confidence
  • Expect estimates to change as models improve
  • Remember that recent, documented family history often beats a fine-grained algorithmic guess

Keeping control of the file

Every extra upload is another copy of your genetic data on someone else’s servers. If you want to explore ancestry signals without adding to that trail, our origins analysis runs in your browser and weighs many positions across your file locally. It also pairs well with understanding haplogroups, which trace single lines rather than your whole mixture, and knowing the real risks of uploading DNA online.

You can start privately with on device DNA analysis - no uploads required.

This article is educational and is not medical advice.

Further reading