Edited by Liisa Loog, University of Oxford, Cambridge, UK; received November 1, 2022; accepted December 5, 2022, by Editorial Board Member Richard G. Klein
February 23, 2023
120 (9) e2218375120
Significance
Ancient human DNA (aDNA) extracted from archaeological contexts allows reconstructing past population movements. Previous methods work by calculating proportions of shared ancestry among individuals or groups in order to answer specific, regional research questions. Here, we propose a large-scale algorithm to quantify human mobility through time and space using bulk aDNA data. The algorithm has two core components: i) interpolation of the spatiotemporal distribution of genetic ancestry to obtain a continuous ancestry information field and ii) probabilistic estimation of a spatial genetic similarity surface for each input sample by projecting its ancestry profile into this field. We apply this to thousands of published genomic samples in the last 10,000 y to trace diachronic mobility patterns in Western Eurasia.
Abstract
The recent increase in openly available ancient human DNA samples allows for large-scale meta-analysis applications. Trans-generational past human mobility is one of the key aspects that ancient genomics can contribute to since changes in genetic ancestry—unlike cultural changes seen in the archaeological record—necessarily reflect movements of people. Here, we present an algorithm for spatiotemporal mapping of genetic profiles, which allow for direct estimates of past human mobility from large ancient genomic datasets. The key idea of the method is to derive a spatial probability surface of genetic similarity for each individual in its respective past. This is achieved by first creating an interpolated ancestry field through space and time based on multivariate statistics and Gaussian process regression and then using this field to map the ancient individuals into space according to their genetic profile. We apply this algorithm to a dataset of 3138 aDNA samples with genome-wide data from Western Eurasia in the last 10,000 y. Finally, we condense this sample-wise record with a simple summary statistic into a diachronic measure of mobility for subregions in Western, Central, and Southern Europe. For regions and periods with sufficient data coverage, our similarity surfaces and mobility estimates show general concordance with previous results and provide a meta-perspective of genetic changes and human mobility.
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Data, Materials, and Software Availability
The code for this paper is available in a repository here: https://doi.org/10.17605/OSF.IO/6UWM5. From that, we outsourced the main similarity search and mobility estimation workflow into an R package available here: https://github.com/nevrome/mobest. All data analysis and plotting was done in R (87) with the following packages: checkmate (88), cowplot (89), fractional (90), future (91), ggh4x (92), ggnewscale (93), ggpubr (94), ggrepel (95), ggridges (96), igraph (97), khroma (98), latex2exp (99), lemon (100), progress (101), rnaturalearth (102), sf (103), smartsnp (104), viridis (105), and, finally, the tidyverse and the many packages within it ref. 106. Previously published data were used for this work (Allen Ancient DNA Resource https://reich.hms.harvard.edu/allen-ancient-dna-resource-aadr-downloadable-genotypes-present-day-and-ancient-dna-dataversion 50.0).
Acknowledgments
This research was financed by the International Max Planck Research School for the Science of Human History (IMPRS-SHH) and carried out on computational facilities of the Max Planck Institutes for Geoanthropology (formerly for the Science of Human History) and for Evolutionary Anthropology. Data collection was significantly simplified thanks to the Allen Ancient DNA Resource and the Poseidon genotype data initiative. We gratefully acknowledge insightful discussions with Joscha Gretzinger and helpful advice from Thiseas C. Lamnidis, James A. Fellows Yates, He Yu, Ayshin Ghalichi, Ke Wang (all currently or formerly affiliated with MPI-EVA), Martin Hinz (University Bern), Martin J. Kümmel (University Jena), Oliver Nakoinz (University Kiel), and all members of the Population genetics working group at the MPI-EVA. This project has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement number 851511).
Author contributions
C.S. and S.S. designed research; C.S. and S.S. performed research; C.S. analyzed data; and C.S. and S.S. wrote the paper.
Competing interests
The authors declare no competing interest.
Supporting Information
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Information & Authors
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Published in
Proceedings of the National Academy of Sciences
Vol. 120 | No. 9
February 28, 2023
Classifications
Copyright
Data, Materials, and Software Availability
The code for this paper is available in a repository here: https://doi.org/10.17605/OSF.IO/6UWM5. From that, we outsourced the main similarity search and mobility estimation workflow into an R package available here: https://github.com/nevrome/mobest. All data analysis and plotting was done in R (87) with the following packages: checkmate (88), cowplot (89), fractional (90), future (91), ggh4x (92), ggnewscale (93), ggpubr (94), ggrepel (95), ggridges (96), igraph (97), khroma (98), latex2exp (99), lemon (100), progress (101), rnaturalearth (102), sf (103), smartsnp (104), viridis (105), and, finally, the tidyverse and the many packages within it ref. 106. Previously published data were used for this work (Allen Ancient DNA Resource https://reich.hms.harvard.edu/allen-ancient-dna-resource-aadr-downloadable-genotypes-present-day-and-ancient-dna-dataversion 50.0).
Submission history
Received: November 1, 2022
Accepted: December 5, 2022
Published online: February 23, 2023
Published in issue: February 28, 2023
Change history
March 2, 2023: The supporting datasets have been updated.
Keywords
- aDNA
- prehistory
- mobility estimation
- Gaussian process regression
Acknowledgments
This research was financed by the International Max Planck Research School for the Science of Human History (IMPRS-SHH) and carried out on computational facilities of the Max Planck Institutes for Geoanthropology (formerly for the Science of Human History) and for Evolutionary Anthropology. Data collection was significantly simplified thanks to the Allen Ancient DNA Resource and the Poseidon genotype data initiative. We gratefully acknowledge insightful discussions with Joscha Gretzinger and helpful advice from Thiseas C. Lamnidis, James A. Fellows Yates, He Yu, Ayshin Ghalichi, Ke Wang (all currently or formerly affiliated with MPI-EVA), Martin Hinz (University Bern), Martin J. Kümmel (University Jena), Oliver Nakoinz (University Kiel), and all members of the Population genetics working group at the MPI-EVA. This project has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement number 851511).
Author Contributions
C.S. and S.S. designed research; C.S. and S.S. performed research; C.S. analyzed data; and C.S. and S.S. wrote the paper.
Competing Interests
The authors declare no competing interest.
Notes
This article is a PNAS Direct Submission. L.L. is a guest editor invited by the Editorial Board.
Authors
Affiliations
Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany
International Max Planck Research School for the Science of Human History, Max Planck Institute for Geoanthropology (formerly known as Max Planck Institute for the Science of Human History), Jena 07745, Germany
Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany
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