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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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Consequences of the Last Glacial Period on the Genetic Diversity of Southeast Asians

Authors: Branco, Catarina; Kanellou, Marina; González-Martín, Antonio; Arenas, Miguel;

Consequences of the Last Glacial Period on the Genetic Diversity of Southeast Asians

Abstract

********* Observed data ********* The file ObsData.arp contains the sequences of the mtDNA hypervariable I region from 720 individuals belonging to 25 Southeast Asian populations used as input file to compute the summary statistics with Arlequin. For further details on the format and available Summary statistics see the manual of Arlequin. ********* Input files for simulations ********* For each evolutionary scenario (NONE, LGP, LDD and LGP&LDD) find a folder (named after the scenario) containing the input files to perform 100 simulations. To run the simulations one should access the command line and execute: ./ABCsampler abc_sensitivity.input Input files for SPLATCHE3, Arlequin and ABCtoolbox are included (for further details on them see the manual of these software). ********* Selection of the best-fitting evolutionary scenario ********* The R script (ModelSelection.R) can be used to select the evolutionary scenario that better fits the observed data, using the multinomial logistic regression method and the neural networks based method. Firstly, one will need the summary statistics obtained from observed data (the file entitled ObsSS.txt). Then, one will need the files containing the output files of the simulations under each scenario, i.e., the genetic parameters used under each simulation and the computed summary statistics. Please, note that the output of the ABCtoolbox is a single file containing all this information, but we prefer to use a file with the summary statistics and another with the parameters. Here, we provide example files obtained from 100 simulations of each scenario: - ssNONE.txt, the summary statistics computed from 100 simulations under the scenario NONE - parNONE.txt, the genetic and demographic parameters per simulation under the scenario NONE - ssLGP.txt, the summary statistics computed from 100 simulations under the scenario LGP - parLGP.txt, the genetic and demographic parameters per simulation under the scenario LGP - ssLDD.txt, the summary statistics computed from 100 simulations under the scenario LDD - parLDD.txt, the genetic and demographic parameters per simulation under the scenario LDD - ssLGP_LDD.txt, the summary statistics computed from 100 simulations under the scenario LGP&LDD - parLGP_LDD.txt, the genetic and demographic parameters per simulation under the scenario LGP&LDD To run the script the directory containing these files has to be specified in the script. For details see Csilléry, et al. (2012): "Approximate Bayesian computation (ABC) in R: a Vignette." ********* Parameters estimation ********* The folder named ParametersEstimation contains all the input files to estimate the genetic and demographic parameters under the selected evolutionary scenario (LGP&LDD). Within the folder, one will find the summary statistics obtained under the selected scenario and the corresponding parameters (completeEstimator_LGP-LDD.txt), the summary statists from observed data (obs11SS.txt) and all the remaining input files to run ABCestimator (for further detail on these files see the manual of ABCtoolbox).

Keywords

Long-distance dispersal, Population genetics, Southeast Asians, Modern humans evolution, Last glacial period

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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