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Demographic inference for spatially heterogeneous populations using long shared haplotypes.

Raphaël Forien1, Harald Ringbauer2, Graham Coop3

  • 1INRAE - BioSP, Centre INRAE PACA, 228 route de l'aérodrome, Domaine St-Paul - Site Agroparc, 84914, Avignon Cedex 9, France.

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PubMed
Summary

We developed a new spatial model to track population ancestry across habitats with differing dispersal rates. This method accurately infers population density and dispersal parameters using genetic data.

Keywords:
isolation by distancepopulation geneticssegments of shared haplotypesskew Brownian motionspatial coalescentspatial Λ-Fleming-Viot process

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Area of Science:

  • Population genetics
  • Spatial modeling
  • Mathematical biology

Background:

  • Understanding how population structure and dispersal influence genetic diversity is crucial in evolutionary biology.
  • Previous models often simplified spatial habitats, limiting their applicability to complex environments.

Approach:

  • Introduced a modified spatial Λ-Fleming-Viot process to model population ancestry in a two-region habitat with a discontinuity in dispersal rate and population density.
  • Derived an analytical formula for the expected number of shared haplotype segments between individuals based on their locations.
  • Utilized the transition density of a skew diffusion, identified as a scaling limit of ancestral lineages, within the derived formula.

Key Points:

  • The model accounts for sharp changes in dispersal rate and population density between habitat regions.
  • An analytical formula was derived for expected shared haplotype segments, linking genetic data to spatial parameters.
  • Skew diffusion's transition density is central to understanding ancestral lineage scaling limits.

Conclusions:

  • The derived formula enables inference of dispersal parameters and effective population density in distinct regions.
  • A composite likelihood approach demonstrated the method's efficiency on simulated datasets.
  • This work provides a robust framework for analyzing population structure and history in spatially heterogeneous environments.