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Bayesian inference of natural selection from spatiotemporal phenotypic data

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  • David, Olivier
  • van Frank, Gaëlle
  • Goldringer, Isabelle
  • Rivière, Pierre
  • Turbet Delof, Michel

Abstract

Spatiotemporal variations of natural selection may influence the evolution of various features of organisms such as local adaptation or specialisation. This article develops a method for inferring how selection varies between locations and between generations from phenotypic data. It is assumed that generations are non-overlapping and that individuals reproduce by selfing or asexually. A quantitative genetics model taking account of the effects of stabilising natural selection, the environment and mutation on phenotypic means and variances is developed. Explicit results on the evolution of populations are derived and used to develop a Bayesian inference method. The latter is applied to simulated data and to data from a wheat participatory plant breeding programme. It has some ability to infer evolutionary parameters, but estimates may be sensitive to prior distributions, for example when phenotypic time series are short and when environmental effects are large. In such cases, sensitivity to prior distributions may be reported or more data may be collected.

Suggested Citation

  • David, Olivier & van Frank, Gaëlle & Goldringer, Isabelle & Rivière, Pierre & Turbet Delof, Michel, 2020. "Bayesian inference of natural selection from spatiotemporal phenotypic data," Theoretical Population Biology, Elsevier, vol. 131(C), pages 100-109.
    • Handle: RePEc:eee:thpobi:v:131:y:2020:i:c:p:100-109
    DOI: 10.1016/j.tpb.2019.11.007
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    References listed on IDEAS

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    1. Julie C. Dawson & Pierre Rivière & Jean-François Berthellot & Florent Mercier & Patrick de Kochko & Nathalie Galic & Sophie Pin & Estelle Serpolay & Mathieu Thomas & Simon Giuliano & Isabelle Goldring, 2011. "Collaborative Plant Breeding for Organic Agricultural Systems in Developed Countries," Sustainability, MDPI, vol. 3(8), pages 1-18, August.
    2. Baey, Charlotte & Cournède, Paul-Henry & Kuhn, Estelle, 2019. "Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 107-122.
    3. repec:dau:papers:123456789/1908 is not listed on IDEAS
    4. David, Olivier & Lannou, Christian & Monod, Hervé & Papaïx, Julien & Traore, Djidi, 2017. "Adaptive diversification in heterogeneous environments," Theoretical Population Biology, Elsevier, vol. 114(C), pages 1-9.
    5. Barton, N.H. & Etheridge, A.M. & Véber, A., 2017. "The infinitesimal model: Definition, derivation, and implications," Theoretical Population Biology, Elsevier, vol. 118(C), pages 50-73.
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    Cited by:

    1. Olivier David & Arnaud Le Rouzic & Christine Dillmann, 2022. "Optimization of sampling designs for pedigrees and association studies," Biometrics, The International Biometric Society, vol. 78(3), pages 1056-1066, September.

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