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A method for identifying local adaptation in structured populations.

Isabela do O1, Oscar E Gaggiotti2, Pierre de Villemereuil3,4

  • 1Department of Ecology and Evolution, University of Lausanne, Lausanne, Vaud, Switzerland.

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A new method improves the detection of local adaptation by accounting for complex population structures. This approach uses relatedness matrices to compare ancestral genetic variances, offering a more accurate neutral baseline for population differentiation.

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Area of Science:

  • Evolutionary biology
  • Population genetics

Background:

  • Species inhabit diverse environments, leading to spatially varied selective pressures.
  • Population divergence can occur due to local adaptation or neutral evolution.
  • Accurate detection of local adaptation requires a reliable neutral baseline for population differentiation.

Purpose of the Study:

  • To address limitations of the classical QST-FST comparison in complex population structures.
  • To develop a novel statistical method for detecting local adaptation.
  • To provide a more robust test for adaptive divergence in metapopulations.

Main Methods:

  • Utilized estimates of between- and within-population relatedness to model population structure.
  • Inferred between- and within-population ancestral additive genetic variances using a mixed-effects model.
  • Proposed a new test comparing these inferred variances under a neutrality hypothesis.

Main Results:

  • The proposed method is well-calibrated across various population structures.
  • The new test demonstrates high power for detecting adaptive divergence.
  • The method effectively accounts for complexities in population structure that affect neutral divergence.

Conclusions:

  • The developed method offers a significant improvement over classical approaches for detecting local adaptation.
  • Accurate modeling of population structure is crucial for distinguishing adaptive from neutral divergence.
  • This approach enhances our ability to study evolutionary processes in natural populations.