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A Spatial Statistical Model for Landscape Genetics

Gilles Guillot, Arnaud Estoup, Frédéric Mortier and Jean François Cosson
Genetics July 1, 2005 vol. 170 no. 3 1261-1280; https://doi.org/10.1534/genetics.104.033803
Gilles Guillot
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Arnaud Estoup
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Frédéric Mortier
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Jean François Cosson
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Abstract

Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., FST < 0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites.

Footnotes

  • Communicating editor: L. Excoffier

  • Received July 21, 2004.
  • Accepted October 31, 2004.
  • Genetics Society of America
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PUBLICATION INFORMATION

Volume 170 Issue 3, July 2005

Genetics: 170 (3)

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A Spatial Statistical Model for Landscape Genetics

Gilles Guillot, Arnaud Estoup, Frédéric Mortier and Jean François Cosson
Genetics July 1, 2005 vol. 170 no. 3 1261-1280; https://doi.org/10.1534/genetics.104.033803
Gilles Guillot
  • Find this author on Google Scholar
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Arnaud Estoup
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Frédéric Mortier
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Jean François Cosson
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Citation

A Spatial Statistical Model for Landscape Genetics

Gilles Guillot, Arnaud Estoup, Frédéric Mortier and Jean François Cosson
Genetics July 1, 2005 vol. 170 no. 3 1261-1280; https://doi.org/10.1534/genetics.104.033803
Gilles Guillot
  • Find this author on Google Scholar
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  • Search for this author on this site
Arnaud Estoup
  • Find this author on Google Scholar
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Frédéric Mortier
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Jean François Cosson
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  • Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use
Show more Investigations
  • Top
  • Article
    • Abstract
    • HIERARCHICAL SPATIAL MODEL
    • FULL BAYESIAN SPECIFICATION
    • MARKOV CHAIN MONTE CARLO INFERENCE
    • RESULTS FROM SIMULATED DATA SETS
    • APPLICATION TO MONTANA WOLVERINES (GULO GULO)
    • DISCUSSION
    • APPENDIX
    • Acknowledgments
    • Footnotes
    • References
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The Genetics Society of America (GSA), founded in 1931, is the professional membership organization for scientific researchers and educators in the field of genetics. Our members work to advance knowledge in the basic mechanisms of inheritance, from the molecular to the population level.

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