Demographic history and genetic variation of the Armenian population
Bibliographic record
Abstract
We introduce a sizable (n = 34) whole-genome dataset on Armenians, a population inhabiting the region in West Asia known as the Armenian highlands. Equipped with this genetic data, we conducted a whole-genome study of Armenians and deciphered their fine-scale population structure and complex demographic history. We demonstrated that the Armenian populations from western, central, and eastern parts of the highlands are relatively homogeneous. The Sasun, a population in the south that had been argued to have received a major genetic contribution from Assyrians, was instead shown to have derived its slightly divergent genetic profile from a bottleneck that occurred in the recent past. We also investigated the debated question on the genetic origin of Armenians and failed to find any significant support for historical suggestions by Herodotus of their Balkan-related ancestry. We checked the degree of continuity of modern Armenians with ancient inhabitants of the eastern Armenian highlands and detected a genetic input into the region from a source linked to Neolithic Levantine Farmers at some point after the Early Bronze Age. Additionally, we cataloged an abundance of new mutations unique to the population, including a missense mutation predicted to cause familial Mediterranean fever, an autoinflammatory disorder highly prevalent in Armenians. Thus, we highlight the importance of further genetic and medical studies of this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".