Demographic inferences using short-read genomic data in an approximate Bayesian computation framework: in silico evaluation of power, biases and proof of concept in Atlantic walrus.
Approximate Bayesian computation (ABC) is a powerful tool for model-based inference of demographic histories from large genetic data sets. For most organisms, its implementation has been hampered by the lack of sufficient genetic data. Genotyping-by-sequencing (GBS) provides cheap genome-scale data...
Published in: | Molecular Ecology |
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Online Access: | https://doi.org/10.1111/mec.13034 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fmec.13034 http://onlinelibrary.wiley.com/wol1/doi/10.1111/mec.13034/fullpdf https://onlinelibrary.wiley.com/doi/full/10.1111/mec.13034 https://www.ncbi.nlm.nih.gov/pubmed/25482153 http://www.diva-portal.org/smash/record.jsf?pid=diva2:794032 http://onlinelibrary.wiley.com/doi/10.1111/mec.13034/full http://swepub.kb.se/bib/swepub:oai:DiVA.org:uu-245521 https://academic.microsoft.com/#/detail/2167551683 |
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fttriple:oai:gotriple.eu:50|dedup_wf_001::79ca95a78c32faa6ef9bc439a67931a3 2023-05-15T15:13:17+02:00 Demographic inferences using short-read genomic data in an approximate Bayesian computation framework: in silico evaluation of power, biases and proof of concept in Atlantic walrus. Lucie M. Gattepaille Jochen B. W. Wolf Robert Ea Stewart Aaron B. A. Shafer 2014-12-09 https://doi.org/10.1111/mec.13034 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fmec.13034 http://onlinelibrary.wiley.com/wol1/doi/10.1111/mec.13034/fullpdf https://onlinelibrary.wiley.com/doi/full/10.1111/mec.13034 https://www.ncbi.nlm.nih.gov/pubmed/25482153 http://www.diva-portal.org/smash/record.jsf?pid=diva2:794032 http://onlinelibrary.wiley.com/doi/10.1111/mec.13034/full http://swepub.kb.se/bib/swepub:oai:DiVA.org:uu-245521 https://academic.microsoft.com/#/detail/2167551683 undefined unknown https://dx.doi.org/10.1111/mec.13034 http://dx.doi.org/10.1111/mec.13034 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fmec.13034 http://onlinelibrary.wiley.com/wol1/doi/10.1111/mec.13034/fullpdf https://onlinelibrary.wiley.com/doi/full/10.1111/mec.13034 https://www.ncbi.nlm.nih.gov/pubmed/25482153 http://www.diva-portal.org/smash/record.jsf?pid=diva2:794032 http://onlinelibrary.wiley.com/doi/10.1111/mec.13034/full http://swepub.kb.se/bib/swepub:oai:DiVA.org:uu-245521 https://academic.microsoft.com/#/detail/2167551683 undefined 25482153 10.1111/mec.13034 2167551683 10|opendoar____::8b6dd7db9af49e67306feb59a8bdc52c 10|openaire____::55045bd2a65019fd8e6741a755395c8c 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 10|issn___print::2392968e93a62f95e3cd5ee67f4c9d5c 10|openaire____::5f532a3fc4f1ea403f37070f59a7a53a 10|openaire____::806360c771262b4d6770e7cdf04b5c5a Genetics Ecology Evolution Behavior and Systematics stat psy Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2014 fttriple https://doi.org/10.1111/mec.13034 2023-01-22T17:17:22Z Approximate Bayesian computation (ABC) is a powerful tool for model-based inference of demographic histories from large genetic data sets. For most organisms, its implementation has been hampered by the lack of sufficient genetic data. Genotyping-by-sequencing (GBS) provides cheap genome-scale data to fill this gap, but its potential has not fully been exploited. Here, we explored power, precision and biases of a coalescent-based ABC approach where GBS data were modelled with either a population mutation parameter () or a fixed site (FS) approach, allowing single or several segregating sites per locus. With simulated data ranging from 500 to 50000 loci, a variety of demographic models could be reliably inferred across a range of timescales and migration scenarios. Posterior estimates were informative with 1000 loci for migration and split time in simple population divergence models. In more complex models, posterior distributions were wide and almost reverted to the uninformative prior even with 50000 loci. ABC parameter estimates, however, were generally more accurate than an alternative composite-likelihood method. Bottleneck scenarios proved particularly difficult, and only recent bottlenecks without recovery could be reliably detected and dated. Notably, minor-allele-frequency filters - usual practice for GBS data - negatively affected nearly all estimates. With this in mind, we used a combination of FS and approaches on empirical GBS data generated from the Atlantic walrus (Odobenus rosmarus rosmarus), collectively providing support for a population split before the last glacial maximum followed by asymmetrical migration and a high Arctic bottleneck. Overall, this study evaluates the potential and limitations of GBS data in an ABC-coalescence framework and proposes a best-practice approach. Article in Journal/Newspaper Arctic Odobenus rosmarus walrus* Unknown Arctic Molecular Ecology 24 2 328 345 |
institution |
Open Polar |
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Unknown |
op_collection_id |
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language |
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topic |
Genetics Ecology Evolution Behavior and Systematics stat psy |
spellingShingle |
Genetics Ecology Evolution Behavior and Systematics stat psy Lucie M. Gattepaille Jochen B. W. Wolf Robert Ea Stewart Aaron B. A. Shafer Demographic inferences using short-read genomic data in an approximate Bayesian computation framework: in silico evaluation of power, biases and proof of concept in Atlantic walrus. |
topic_facet |
Genetics Ecology Evolution Behavior and Systematics stat psy |
description |
Approximate Bayesian computation (ABC) is a powerful tool for model-based inference of demographic histories from large genetic data sets. For most organisms, its implementation has been hampered by the lack of sufficient genetic data. Genotyping-by-sequencing (GBS) provides cheap genome-scale data to fill this gap, but its potential has not fully been exploited. Here, we explored power, precision and biases of a coalescent-based ABC approach where GBS data were modelled with either a population mutation parameter () or a fixed site (FS) approach, allowing single or several segregating sites per locus. With simulated data ranging from 500 to 50000 loci, a variety of demographic models could be reliably inferred across a range of timescales and migration scenarios. Posterior estimates were informative with 1000 loci for migration and split time in simple population divergence models. In more complex models, posterior distributions were wide and almost reverted to the uninformative prior even with 50000 loci. ABC parameter estimates, however, were generally more accurate than an alternative composite-likelihood method. Bottleneck scenarios proved particularly difficult, and only recent bottlenecks without recovery could be reliably detected and dated. Notably, minor-allele-frequency filters - usual practice for GBS data - negatively affected nearly all estimates. With this in mind, we used a combination of FS and approaches on empirical GBS data generated from the Atlantic walrus (Odobenus rosmarus rosmarus), collectively providing support for a population split before the last glacial maximum followed by asymmetrical migration and a high Arctic bottleneck. Overall, this study evaluates the potential and limitations of GBS data in an ABC-coalescence framework and proposes a best-practice approach. |
format |
Article in Journal/Newspaper |
author |
Lucie M. Gattepaille Jochen B. W. Wolf Robert Ea Stewart Aaron B. A. Shafer |
author_facet |
Lucie M. Gattepaille Jochen B. W. Wolf Robert Ea Stewart Aaron B. A. Shafer |
author_sort |
Lucie M. Gattepaille |
title |
Demographic inferences using short-read genomic data in an approximate Bayesian computation framework: in silico evaluation of power, biases and proof of concept in Atlantic walrus. |
title_short |
Demographic inferences using short-read genomic data in an approximate Bayesian computation framework: in silico evaluation of power, biases and proof of concept in Atlantic walrus. |
title_full |
Demographic inferences using short-read genomic data in an approximate Bayesian computation framework: in silico evaluation of power, biases and proof of concept in Atlantic walrus. |
title_fullStr |
Demographic inferences using short-read genomic data in an approximate Bayesian computation framework: in silico evaluation of power, biases and proof of concept in Atlantic walrus. |
title_full_unstemmed |
Demographic inferences using short-read genomic data in an approximate Bayesian computation framework: in silico evaluation of power, biases and proof of concept in Atlantic walrus. |
title_sort |
demographic inferences using short-read genomic data in an approximate bayesian computation framework: in silico evaluation of power, biases and proof of concept in atlantic walrus. |
publishDate |
2014 |
url |
https://doi.org/10.1111/mec.13034 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fmec.13034 http://onlinelibrary.wiley.com/wol1/doi/10.1111/mec.13034/fullpdf https://onlinelibrary.wiley.com/doi/full/10.1111/mec.13034 https://www.ncbi.nlm.nih.gov/pubmed/25482153 http://www.diva-portal.org/smash/record.jsf?pid=diva2:794032 http://onlinelibrary.wiley.com/doi/10.1111/mec.13034/full http://swepub.kb.se/bib/swepub:oai:DiVA.org:uu-245521 https://academic.microsoft.com/#/detail/2167551683 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Odobenus rosmarus walrus* |
genre_facet |
Arctic Odobenus rosmarus walrus* |
op_source |
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op_relation |
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Molecular Ecology |
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24 |
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328 |
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