Joint inference of adaptive and demographic history from temporal population genomic data
Disentangling the effects of selection and drift is a long-standing problem in population genetics. Simulations show that pervasive selection may bias the inference of demography. Ideally, models for the inference of demography and selection should account for the interaction between these two force...
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| Format: | Article |
| Language: | English |
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Peer Community In
2022-12-01
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| Series: | Peer Community Journal |
| Online Access: | https://peercommunityjournal.org/articles/10.24072/pcjournal.203/ |
| _version_ | 1797651174122323968 |
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| author | Pavinato, Vitor A. C. De Mita, Stéphane Marin, Jean-Michel de Navascués, Miguel |
| author_facet | Pavinato, Vitor A. C. De Mita, Stéphane Marin, Jean-Michel de Navascués, Miguel |
| author_sort | Pavinato, Vitor A. C. |
| collection | DOAJ |
| description | Disentangling the effects of selection and drift is a long-standing problem in population genetics. Simulations show that pervasive selection may bias the inference of demography. Ideally, models for the inference of demography and selection should account for the interaction between these two forces. With simulation-based likelihood-free methods such as Approximate Bayesian Computation (ABC), demography and selection parameters can be jointly estimated. We propose to use the ABC-Random Forests framework to jointly infer demographic and selection parameters from temporal population genomic data (e.g. experimental evolution, monitored populations, ancient DNA). Our framework allowed the separation of demography (census size, N) from the genetic drift (effective population size, Ne) and the estimation of genome-wide parameters of selection. Selection parameters informed us about the adaptive potential of a population (the scaled mutation rate of beneficial mutations, $\theta_{\mathrm{b}}$), the realized adaptation (the number of mutations under strong selection), and population fitness (genetic load). We applied this approach to a dataset of feral populations of honey bees (Apis mellifera) collected in California, and we estimated parameters consistent with the biology and the recent history of this species.
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| first_indexed | 2024-03-11T16:11:06Z |
| format | Article |
| id | doaj.art-566fdf39c00a4d23b6e83fc6e304c1cc |
| institution | Directory Open Access Journal |
| issn | 2804-3871 |
| language | English |
| last_indexed | 2024-03-11T16:11:06Z |
| publishDate | 2022-12-01 |
| publisher | Peer Community In |
| record_format | Article |
| series | Peer Community Journal |
| spelling | doaj.art-566fdf39c00a4d23b6e83fc6e304c1cc2023-10-24T14:38:35ZengPeer Community InPeer Community Journal2804-38712022-12-01210.24072/pcjournal.20310.24072/pcjournal.203Joint inference of adaptive and demographic history from temporal population genomic dataPavinato, Vitor A. C.0https://orcid.org/0000-0003-2483-1207De Mita, Stéphane1https://orcid.org/0000-0003-2752-865XMarin, Jean-Michel2https://orcid.org/0000-0001-7451-9719de Navascués, Miguel3https://orcid.org/0000-0001-8342-6047CBGP, INRAE, CIRAD, IRD, Institut Agro, Université de Montpellier, Montpellier, France; IMAG, Univ Montpellier, CNRS, UMR 5149, Montpellier, France; Entomology Dept., CFAES, The Ohio State University, Wooster, USAUMR Interactions Arbres-Microorganismes, INRAE, France; PHIM Plant Health Institute, Univ Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, FranceIMAG, Univ Montpellier, CNRS, UMR 5149, Montpellier, FranceCBGP, INRAE, CIRAD, IRD, Institut Agro, Université de Montpellier, Montpellier, France; Human Evolution, Department of Organismal Biology, Uppsala University, Uppsala, SwedenDisentangling the effects of selection and drift is a long-standing problem in population genetics. Simulations show that pervasive selection may bias the inference of demography. Ideally, models for the inference of demography and selection should account for the interaction between these two forces. With simulation-based likelihood-free methods such as Approximate Bayesian Computation (ABC), demography and selection parameters can be jointly estimated. We propose to use the ABC-Random Forests framework to jointly infer demographic and selection parameters from temporal population genomic data (e.g. experimental evolution, monitored populations, ancient DNA). Our framework allowed the separation of demography (census size, N) from the genetic drift (effective population size, Ne) and the estimation of genome-wide parameters of selection. Selection parameters informed us about the adaptive potential of a population (the scaled mutation rate of beneficial mutations, $\theta_{\mathrm{b}}$), the realized adaptation (the number of mutations under strong selection), and population fitness (genetic load). We applied this approach to a dataset of feral populations of honey bees (Apis mellifera) collected in California, and we estimated parameters consistent with the biology and the recent history of this species. https://peercommunityjournal.org/articles/10.24072/pcjournal.203/ |
| spellingShingle | Pavinato, Vitor A. C. De Mita, Stéphane Marin, Jean-Michel de Navascués, Miguel Joint inference of adaptive and demographic history from temporal population genomic data Peer Community Journal |
| title | Joint inference of adaptive and demographic history from temporal population genomic data |
| title_full | Joint inference of adaptive and demographic history from temporal population genomic data |
| title_fullStr | Joint inference of adaptive and demographic history from temporal population genomic data |
| title_full_unstemmed | Joint inference of adaptive and demographic history from temporal population genomic data |
| title_short | Joint inference of adaptive and demographic history from temporal population genomic data |
| title_sort | joint inference of adaptive and demographic history from temporal population genomic data |
| url | https://peercommunityjournal.org/articles/10.24072/pcjournal.203/ |
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