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Investigating population continuity with ancient DNA under a spatially explicit simulation framework
BACKGROUND: Recent advances in sequencing technologies have allowed for the retrieval of ancient DNA data (aDNA) from skeletal remains, providing direct genetic snapshots from diverse periods of human prehistory. Comparing samples taken in the same region but at different times, hereafter called “se...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5731203/ https://www.ncbi.nlm.nih.gov/pubmed/29246100 http://dx.doi.org/10.1186/s12863-017-0575-6 |
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author | Silva, Nuno Miguel Rio, Jeremy Currat, Mathias |
author_facet | Silva, Nuno Miguel Rio, Jeremy Currat, Mathias |
author_sort | Silva, Nuno Miguel |
collection | PubMed |
description | BACKGROUND: Recent advances in sequencing technologies have allowed for the retrieval of ancient DNA data (aDNA) from skeletal remains, providing direct genetic snapshots from diverse periods of human prehistory. Comparing samples taken in the same region but at different times, hereafter called “serial samples”, may indicate whether there is continuity in the peopling history of that area or whether an immigration of a genetically different population has occurred between the two sampling times. However, the exploration of genetic relationships between serial samples generally ignores their geographical locations and the spatiotemporal dynamics of populations. Here, we present a new coalescent-based, spatially explicit modelling approach to investigate population continuity using aDNA, which includes two fundamental elements neglected in previous methods: population structure and migration. The approach also considers the extensive temporal and geographical variance that is commonly found in aDNA population samples. RESULTS: We first showed that our spatially explicit approach is more conservative than the previous (panmictic) approach and should be preferred to test for population continuity, especially when small and isolated populations are considered. We then applied our method to two mitochondrial datasets from Germany and France, both including modern and ancient lineages dating from the early Neolithic. The results clearly reject population continuity for the maternal line over the last 7500 years for the German dataset but not for the French dataset, suggesting regional heterogeneity in post-Neolithic migratory processes. CONCLUSIONS: Here, we demonstrate the benefits of using a spatially explicit method when investigating population continuity with aDNA. It constitutes an improvement over panmictic methods by considering the spatiotemporal dynamics of genetic lineages and the precise location of ancient samples. The method can be used to investigate population continuity between any pair of serial samples (ancient-ancient or ancient-modern) and to investigate more complex evolutionary scenarios. Although we based our study on mitochondrial DNA sequences, diploid molecular markers of different types (DNA, SNP, STR) can also be simulated with our approach. It thus constitutes a promising tool for the analysis of the numerous aDNA datasets being produced, including genome wide data, in humans but also in many other species. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12863-017-0575-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5731203 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57312032017-12-19 Investigating population continuity with ancient DNA under a spatially explicit simulation framework Silva, Nuno Miguel Rio, Jeremy Currat, Mathias BMC Genet Methodology Article BACKGROUND: Recent advances in sequencing technologies have allowed for the retrieval of ancient DNA data (aDNA) from skeletal remains, providing direct genetic snapshots from diverse periods of human prehistory. Comparing samples taken in the same region but at different times, hereafter called “serial samples”, may indicate whether there is continuity in the peopling history of that area or whether an immigration of a genetically different population has occurred between the two sampling times. However, the exploration of genetic relationships between serial samples generally ignores their geographical locations and the spatiotemporal dynamics of populations. Here, we present a new coalescent-based, spatially explicit modelling approach to investigate population continuity using aDNA, which includes two fundamental elements neglected in previous methods: population structure and migration. The approach also considers the extensive temporal and geographical variance that is commonly found in aDNA population samples. RESULTS: We first showed that our spatially explicit approach is more conservative than the previous (panmictic) approach and should be preferred to test for population continuity, especially when small and isolated populations are considered. We then applied our method to two mitochondrial datasets from Germany and France, both including modern and ancient lineages dating from the early Neolithic. The results clearly reject population continuity for the maternal line over the last 7500 years for the German dataset but not for the French dataset, suggesting regional heterogeneity in post-Neolithic migratory processes. CONCLUSIONS: Here, we demonstrate the benefits of using a spatially explicit method when investigating population continuity with aDNA. It constitutes an improvement over panmictic methods by considering the spatiotemporal dynamics of genetic lineages and the precise location of ancient samples. The method can be used to investigate population continuity between any pair of serial samples (ancient-ancient or ancient-modern) and to investigate more complex evolutionary scenarios. Although we based our study on mitochondrial DNA sequences, diploid molecular markers of different types (DNA, SNP, STR) can also be simulated with our approach. It thus constitutes a promising tool for the analysis of the numerous aDNA datasets being produced, including genome wide data, in humans but also in many other species. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12863-017-0575-6) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-15 /pmc/articles/PMC5731203/ /pubmed/29246100 http://dx.doi.org/10.1186/s12863-017-0575-6 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Silva, Nuno Miguel Rio, Jeremy Currat, Mathias Investigating population continuity with ancient DNA under a spatially explicit simulation framework |
title | Investigating population continuity with ancient DNA under a spatially explicit simulation framework |
title_full | Investigating population continuity with ancient DNA under a spatially explicit simulation framework |
title_fullStr | Investigating population continuity with ancient DNA under a spatially explicit simulation framework |
title_full_unstemmed | Investigating population continuity with ancient DNA under a spatially explicit simulation framework |
title_short | Investigating population continuity with ancient DNA under a spatially explicit simulation framework |
title_sort | investigating population continuity with ancient dna under a spatially explicit simulation framework |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5731203/ https://www.ncbi.nlm.nih.gov/pubmed/29246100 http://dx.doi.org/10.1186/s12863-017-0575-6 |
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