You can run an admixture calculator on your raw DNA file for free, in a browser, in under a minute, and it will give you a tidy list of percentages. A qpAdm analysis costs 29.99 EUR one-time and takes a person's working hours to produce. The obvious question is what the money buys, and the honest answer is: a different kind of statement, not a better version of the same one. This post is written by someone who sells the paid version, so it tries to be exact about where the free tool is the right choice, because it often is.
What a percentage calculator actually optimises#
There are two common families of consumer calculator (the full anatomy of them is here), and they optimise different things, but both share one property that matters for this comparison.
Cluster-based (ADMIXTURE-style, K components). The tool holds a set of K allele-frequency profiles, learned by clustering a reference set. For your genotypes it finds the mixing proportions of those K profiles that maximise the likelihood of your data. The output is a vector of K numbers that sums to 100%.
Coordinate-based (Global25 nMonte and similar). Your genome is first reduced to a coordinate row in a principal-component space, and the tool then finds the non-negative combination of reference rows that sits closest to yours, by Euclidean distance. The output is a set of percentages and a fit distance.
In both cases the optimiser is answering: given these references, what proportions come closest? Note what is not being asked. It is not being asked whether the references are the right ones, or whether "closest" is close enough to mean anything. An ADMIXTURE-style solver with K = 6 will distribute every genome on Earth across those six profiles, because that is the only thing it can do. An nMonte fit will always return a best combination, and a distance of 0.02 versus 0.04 is a measure of how well the geometry closed, not a test of whether the story is true.
This is not a defect. It is what an optimiser is. But it means a calculator cannot say no. Give it a Yoruba genome and only European references and it will return a confident European breakdown. Give it a Sardinian genome and references that lack Neolithic Anatolians and it will spread the farmer ancestry over whatever is nearest. The absence of a failure mode is the thing to keep in mind whenever a calculator output looks precise.
What qpAdm adds#
qpAdm is a different kind of object. It is a test, not an optimiser, and the difference is visible in what it returns.
- A p-value for the whole model. qpAdm proposes that the target is a mixture of the chosen sources and tests that proposal against a set of distant right populations (outgroups) using f4-statistics. If the pattern of shared drift in your genome is incompatible with the mixture, the p-value falls below 0.05 and the model is rejected. The weights are then not worth reading.
- A standard error on every weight, from a block jackknife across the genome, so a weight is a range rather than a point.
- A Z-score on every weight, so a source that the data cannot distinguish from zero is visible as such, whatever its headline percentage.
- A stated right set. The outgroups are part of the model and are published with it, because changing them changes the answer. A weight without its right set cannot be evaluated by anyone.
The method is set out in Understanding qpAdm and the three numbers are read in How to read qpAdm results. The short version is that qpAdm can fail, and a method that can fail is one whose passing means something.
When a calculator is the right tool#
Most of the time, honestly. A calculator is the right instrument when:
- You are exploring. You want to see which references your genome leans toward, try five different reference sets, and get a feel for the landscape. Speed matters more than rigour.
- The question is about resemblance, not descent. "Which modern populations am I closest to" is a distance question, and a coordinate tool answers it directly and well.
- It is free and instant. Ancestrify's own lab tools and the in-browser Global25 calculators cost nothing and need no account for the browser ones. Nobody should pay for a formal model before they have played with the free version.
- It is fun. There is nothing wrong with that. A breakdown that says 12% Western Hunter-Gatherer is a pleasant thing to look at, provided nobody mistakes it for a finding.
When qpAdm is the right tool#
qpAdm earns its cost when you want to defend a claim. Some examples of claims:
- "My genome cannot be modelled without a steppe-related source." A calculator will happily give you 0% steppe if the references make that closest; qpAdm will tell you whether the two-source model without steppe is rejected, and by how much.
- "The Iranian-related component in my breakdown is real, not an artefact of the reference set." A Z-score of 1.1 on that source says it is not distinguishable from nothing; a Z-score of 6 says it is.
- "These percentages are compatible with the data at a stated level of confidence." Only a method with a p-value can say that.
If you plan to post a result on a forum, compare it to a published paper, or argue about it with somebody who knows the method, you need the numbers that make the result checkable. That is what a qpAdm analysis provides: the p-value, per-source weight, SE, Z-score and 95% confidence interval, the full right set with sample counts, the nested-model table and the rank test, published on screen and as a plain-text record at every tier. The full record is described in The model record explained.
The cost comparison, plainly#
| Admixture calculator | qpAdm analysis | |
|---|---|---|
| Price | Free | 29.99 EUR one-time (deeper searches 39.99, 49.99, 59.99 EUR) |
| Returns | Percentages, sometimes a fit distance | Weights, SE, Z, 95% CI, p-value, right set, nested models |
| Can reject a model | No | Yes |
| Who builds it | Nobody; it runs | A person composes, runs and checks every model |
| Reference | Whatever the tool ships | AADR v66, merged with your genotypes |
| Effort | Seconds | Hours to days of analyst time |
| Publish bar | None | p above 0.05, every source with Z above 3 in absolute value and SE below 0.10 |
The four Ancestrify tiers do not buy a different standard. Every published model has to clear the same bar. What a deeper tier buys is a longer search past the first model that clears it, so that more alternatives have been tried and rejected before one is published. The buyer's guide goes into what the tiers change and what they do not, and how much a qpAdm analysis costs breaks down the add-ons.
An honest "not worth it if" list#
This is the section a seller usually leaves out. qpAdm is not worth paying for if:
- Your file has very low coverage. Standard error is set by how many markers survive the merge with the reference panel. A sparse file fixes SE above the bar before any analyst touches it, and no amount of search will shrink an error the file has already set. Run the free Raw DNA File Check first; if it says the file cannot support qpAdm, believe it and keep your money.
- Your question is recent genealogy. qpAdm models ancestry in terms of populations thousands of years old. It cannot tell you whether a great-grandparent was Irish or Italian, and it cannot find relatives. That is a matching question, not an admixture one.
- You expect a single-number answer. A qpAdm result is a range with a test attached. If what you want is "I am 34% steppe", a calculator will give you that number with fewer caveats, and it will be exactly as meaningful as any other single number.
- You want percentages at a fine geographic grain. No population-genetic method places ancestry inside a modern country. A source labelled Anatolia_N is a reference group from a set of Neolithic sites, not a place anyone in your family lived.
- You have Global25 coordinates and want the coordinate view. That is a different product answering a different question; qpAdm vs Global25 sets out which one fits which question.
The same person through both lenses#
An illustrative example, with numbers invented for the purpose, showing what the two tools return for one genome. It is not a customer's result.
Calculator output (K = 6, cluster-based):
Anatolian farmer 48.2%
Steppe pastoralist 31.5%
Western hunter-gatherer 12.1%
Iranian / Caucasus 5.9%
North African 1.4%
East Asian 0.9%
Six components, a clean sum, and no way to know whether the 5.9% Iranian-related figure is a signal or a rounding of noise onto the nearest available profile. The 0.9% East Asian is almost certainly the latter, but the tool cannot say so.
qpAdm output for the same genome (illustrative):
Sources: Anatolia_N + Yamnaya_Samara + WHG
p-value: 0.184 chi-square: 11.06 dof: 8 f4 rank: 2
Anatolia_N 0.512 SE 0.031 Z 16.5 95% CI 0.451 to 0.573
Yamnaya_Samara 0.339 SE 0.034 Z 9.97 95% CI 0.272 to 0.406
WHG 0.149 SE 0.026 Z 5.73 95% CI 0.098 to 0.200
Right set (11): Mbuti.DG, Ust_Ishim.DG, Kostenki14, MA1, Han.DG,
Papuan.DG, Onge.DG, Karitiana.DG, Iran_N, Levant_N, EHG
Three sources, not six. The model was also run with a fourth source, CHG, standing in for the calculator's Iranian-related component: its weight came back at 0.038 with SE 0.037, Z about 1.0, and the three-source nested model passed at p = 0.184 on its own. So the fourth source was not earning its place and was dropped. The North African and East Asian slivers never appeared, because nothing in the f4 pattern required them.
Both outputs describe the same genome. The calculator's numbers are a best fit among six profiles; the qpAdm numbers are a tested claim with stated uncertainty, computed against eleven named outgroups, that a reader can reproduce. Neither is "the truth". One of them can be argued with.
So, is it worth it?#
If you want a quick, free, enjoyable picture of what your genome resembles, use a calculator and keep your money. If you want a claim you can defend, with the numbers that let someone else check it, a formal model is the only tool that produces one. Try the free AdmixTools 2 Lab to see how a rejection feels, look at the demo to see the shape of a full report, and if that is the kind of answer you want, the qpAdm analysis starts at 29.99 EUR.
References#
- Alexander, D. H., Novembre, J. & Lange, K. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome Research, 19(9), 1655 to 1664.
- Haak, W. et al. (2015). Massive migration from the steppe was a source for Indo-European languages in Europe. Nature, 522, 207 to 211.
- Harney, É., Patterson, N., Reich, D. & Wakeley, J. (2021). Assessing the performance of qpAdm: a statistical tool for studying population admixture. Genetics, 217(4), iyaa045.
- Lawson, D. J., van Dorp, L. & Falush, D. (2018). A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots. Nature Communications, 9, 3258.
