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Spatial analysis method (sam): a software tool combining molecular and environmental data to identify candidate loci

Stéphane Joost1, Michael Kalbermatten, Aurélie Bonin

  • 1Istituto di Zootecnica, Università Cattolica del S.Cuore, via E. Parmense 84, 29100 Piacenza, Italy, MicroGIS Foundation for Spatial Analysis (MFSA), La Frasse, 1618 Châtel-Saint-Denis, Switzerland, Laboratoire de Systèmes d'Information Géographique, Ecole Polytechnique Fédérale de Lausanne (EPFL), Bâtiment GC, Station 18, 1015 Lausanne, Switzerland, Laboratoire d'Ecologie Alpine, CNRS-UMR 5553, Université Joseph Fourier, BP 53, 38041 Grenoble cedex 09, France.

Molecular Ecology Resources
|May 19, 2011
PubMed
Summary

SAM software identifies genetic loci under selection and links them to ecological factors using logistic regression. This tool aids in understanding evolutionary adaptation by analyzing marker and environmental data.

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Area of Science:

  • Population Genetics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Identifying genetic loci under selection is crucial for understanding adaptation.
  • Environmental variables are key drivers of evolutionary processes.
  • Computational tools are needed to analyze complex genomic and environmental datasets.

Purpose of the Study:

  • To introduce SAM, a novel software for detecting candidate loci under selection.
  • To enable the investigation of ecological factors associated with selection.
  • To provide a user-friendly tool for whole-genome scan analysis.

Main Methods:

  • Utilizes multiple univariate logistic regression models.
  • Tests for associations between marker loci allelic frequencies and environmental variables.
  • Processes presence/absence matrices of molecular markers and environmental parameters.

Main Results:

  • SAM successfully detects candidate loci for selection.
  • The software provides insights into the ecological factors driving selection.
  • Dynamic analysis tables facilitate result interpretation.

Conclusions:

  • SAM is a valuable tool for population geneticists and evolutionary biologists.
  • It facilitates the integration of genomic and environmental data for selection studies.
  • The software is freely available, promoting wider research accessibility.