Section: Evolutionary Biology
Topic: Evolution, Genetics/Genomics, Population biology

Joint inference of adaptive and demographic history from temporal population genomic data

Pavinato, Vitor A. C.1, 2, 3 ; De Mita, Stéphane4, 5 ; Marin, Jean-Michel2 ; de Navascués, Miguel1, 6  
1 CBGP, INRAE, CIRAD, IRD, Institut Agro, Université de Montpellier, Montpellier, France
2 IMAG, Univ Montpellier, CNRS, UMR 5149, Montpellier, France
3 Entomology Dept., CFAES, The Ohio State University, Wooster, USA
4 UMR Interactions Arbres-Microorganismes, INRAE, France
5 PHIM Plant Health Institute, Univ Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France
6 Human Evolution, Department of Organismal Biology, Uppsala University, Uppsala, Sweden

Corresponding author(s): de Navascués, Miguel (miguel.navascues@inrae.fr)

10.24072/pcjournal.203 - Peer Community Journal, Volume 2 (2022), article no. e78.

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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, θb), 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.

Published online:
DOI: 10.24072/pcjournal.203
Type: Research article

Pavinato, Vitor A. C. 1, 2, 3; De Mita, Stéphane 4, 5; Marin, Jean-Michel 2; de Navascués, Miguel 1, 6

1 CBGP, INRAE, CIRAD, IRD, Institut Agro, Université de Montpellier, Montpellier, France
2 IMAG, Univ Montpellier, CNRS, UMR 5149, Montpellier, France
3 Entomology Dept., CFAES, The Ohio State University, Wooster, USA
4 UMR Interactions Arbres-Microorganismes, INRAE, France
5 PHIM Plant Health Institute, Univ Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France
6 Human Evolution, Department of Organismal Biology, Uppsala University, Uppsala, Sweden
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
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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, Volume 2 (2022), article  no. e78. doi : 10.24072/pcjournal.203. https://peercommunityjournal.org/articles/10.24072/pcjournal.203/

PCI peer reviews and recommendation, and links to data, scripts, code and supplementary information: 10.24072/pci.evolbiol.100158

Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.

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