Temporal population structure, a genetic dating method for ancient Eurasian genomes from the past 10,000 years
(2022) In Cell reports methods 2(8).- Abstract
Radiocarbon dating is the gold standard in archeology to estimate the age of skeletons, a key to studying their origins. Many published ancient genomes lack reliable and direct dates, which results in obscure and contradictory reports. We developed the temporal population structure (TPS), a DNA-based dating method for genomes ranging from the Late Mesolithic to today, and applied it to 3,591 ancient and 1,307 modern Eurasians. TPS predictions aligned with the known dates and correctly accounted for kin relationships. TPS dating of poorly dated Eurasian samples resolved conflicting reports in the literature, as illustrated by one test case. We also demonstrated how TPS improved the ability to study phenotypic traits over time. TPS can be... (More)
Radiocarbon dating is the gold standard in archeology to estimate the age of skeletons, a key to studying their origins. Many published ancient genomes lack reliable and direct dates, which results in obscure and contradictory reports. We developed the temporal population structure (TPS), a DNA-based dating method for genomes ranging from the Late Mesolithic to today, and applied it to 3,591 ancient and 1,307 modern Eurasians. TPS predictions aligned with the known dates and correctly accounted for kin relationships. TPS dating of poorly dated Eurasian samples resolved conflicting reports in the literature, as illustrated by one test case. We also demonstrated how TPS improved the ability to study phenotypic traits over time. TPS can be used when radiocarbon dating is unfeasible or uncertain or to develop alternative hypotheses for samples younger than 10,000 years ago, a limitation that may be resolved over time as ancient data accumulate.
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- author
- Behnamian, Sara LU ; Esposito, Umberto ; Holland, Grace ; Alshehab, Ghadeer ; Dobre, Ann M ; Pirooznia, Mehdi ; Brimacombe, Conrad S and Elhaik, Eran LU
- organization
- publishing date
- 2022-08-22
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Cell reports methods
- volume
- 2
- issue
- 8
- article number
- 100270
- publisher
- Cell Press
- external identifiers
-
- scopus:85136051702
- pmid:36046618
- ISSN
- 2667-2375
- DOI
- 10.1016/j.crmeth.2022.100270
- language
- English
- LU publication?
- yes
- additional info
- © 2022 The Authors.
- id
- 0fc4a967-9d33-4a40-b32d-35ee6bbf81ae
- date added to LUP
- 2022-09-11 01:50:47
- date last changed
- 2024-09-19 23:12:16
@article{0fc4a967-9d33-4a40-b32d-35ee6bbf81ae, abstract = {{<p>Radiocarbon dating is the gold standard in archeology to estimate the age of skeletons, a key to studying their origins. Many published ancient genomes lack reliable and direct dates, which results in obscure and contradictory reports. We developed the temporal population structure (TPS), a DNA-based dating method for genomes ranging from the Late Mesolithic to today, and applied it to 3,591 ancient and 1,307 modern Eurasians. TPS predictions aligned with the known dates and correctly accounted for kin relationships. TPS dating of poorly dated Eurasian samples resolved conflicting reports in the literature, as illustrated by one test case. We also demonstrated how TPS improved the ability to study phenotypic traits over time. TPS can be used when radiocarbon dating is unfeasible or uncertain or to develop alternative hypotheses for samples younger than 10,000 years ago, a limitation that may be resolved over time as ancient data accumulate.</p>}}, author = {{Behnamian, Sara and Esposito, Umberto and Holland, Grace and Alshehab, Ghadeer and Dobre, Ann M and Pirooznia, Mehdi and Brimacombe, Conrad S and Elhaik, Eran}}, issn = {{2667-2375}}, language = {{eng}}, month = {{08}}, number = {{8}}, publisher = {{Cell Press}}, series = {{Cell reports methods}}, title = {{Temporal population structure, a genetic dating method for ancient Eurasian genomes from the past 10,000 years}}, url = {{http://dx.doi.org/10.1016/j.crmeth.2022.100270}}, doi = {{10.1016/j.crmeth.2022.100270}}, volume = {{2}}, year = {{2022}}, }