qpAdm ancestry analysis

Price

€49.90, one-time. No subscription.

Method

qpadm() from ADMIXTOOLS 2, run in R on our infrastructure

Reference panel

Allen Ancient DNA Resource (AADR) v66 — ~23,265 samples, ~6,015 population labels

Statistics reported

Model p-value; per-source weight, standard error and z-score; the right-population set used

Eras

Two — Hunter-Gatherer & Neolithic Farmer, and Classical Antiquity — across 34 curated populations

Accepted files

.txt, .csv or .zip raw-data exports up to 50 MB

Also included

Ancestry maps, server-rendered cinematic ancestry videos, and shareable image cards in three formats

Data location

European Union (Germany and Finland), under GDPR

Most consumer ancestry tools fit your sample to a set of reference populations and return whatever weights fit best. Those weights always exist, whether or not the model makes any sense — there is no output that says "this combination cannot have produced you".

qpAdm works differently. It operates on allele-frequency statistics against a set of outgroup populations, and it tests a model rather than merely fitting one. The p-value is the point: it is what allows a proposed ancestry model to be rejected. A standard error on each proportion tells you how tightly that number is pinned down, and a z-score tells you whether a source is contributing at all.

That is the entire reason this product costs more and takes longer than our Global25 analysis. You are paying for a model that could have failed and did not.

We built automated model rotation — the approach of screening many candidate source combinations and surfacing whichever reaches an acceptable p-value — and then removed it from the product. Measured against real targets it selected models that scored well and were wrong: with sources that carry the same ancestral stream, the search has enough freedom to shrink the residual while the weights drift into nonsense.

So a person does that step. Your model is selected, run and reviewed before it is published to your report. It is slower, and it is the reason the number you are given is worth reading.

• Is this real qpAdm, or an approximation?

  It is real qpAdm. We run the qpadm() function from ADMIXTOOLS 2 — the same package used in published ancient-DNA research — against your genotypes merged with the Allen Ancient DNA Resource. It is not a coordinate-fitting method dressed up in qpAdm language.

• How is qpAdm different from Global25 and nMonte percentages?

  They answer different questions. Global25 and nMonte fit your coordinate to a weighted combination of reference coordinates, and will always return some percentages. qpAdm works from allele-frequency statistics and outgroups, and can reject a model outright: it returns a p-value that tells you whether the proposed ancestry model is compatible with the data at all. Percentages from a coordinate fit are estimates; qpAdm output is a statistical test.

• Which statistics do you actually show me?

  For each era you get the model's p-value, and for each source population its weight, its standard error and its z-score, alongside the set of right (outgroup) populations the model was run against. Those are the numbers needed to judge a model rather than just read it.

• Which reference dataset do you use?

  The Allen Ancient DNA Resource (AADR) v66 — roughly 23,265 samples across roughly 6,015 distinct population labels. We operate the merge ourselves: your file is converted, filtered of indels and strand-ambiguous SNPs, and intersected against the panel with Poseidon's trident before any model is run.

• Which files can I upload?

  A raw-data export from a consumer testing company — 23andMe, AncestryDNA, MyHeritage, FamilyTreeDNA and similar — as a .txt, .csv or .zip file up to 50 MB. We validate the file by its actual content rather than by vendor, so exports that follow the usual microarray text layout are accepted even when the company is not named here.

• Do I get results instantly?

  No, and deliberately not. Merging your genotypes against the AADR panel is a heavy job that runs on our own infrastructure, and a scientist then builds and checks your model before it is published. A qpAdm report is reviewed work, not a page that renders the moment you pay.

• Why is a person involved at all?

  Because automated model search is the part of qpAdm that goes wrong. We built automated rotation, measured it, and removed it: rotating large numbers of candidate models has a high false-discovery rate, so the best-scoring model is frequently not the right one. A scientist selecting and checking the model is a deliberate design choice, not a missing feature.

• Where is my data stored?

  On EU infrastructure, in Germany and Finland, under GDPR. Your raw file is processed only for your own analysis, you can delete it at any time, and full account deletion and data export are self-service.