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Mantel test in population genetics.

José Alexandre F Diniz-Filho1, Thannya N Soares2, Jacqueline S Lima3

  • 1Departamento de Ecologia, Universidade Federal de Goiás, Goiânia, GO, Brazil .

Genetics and Molecular Biology
|January 4, 2014
PubMed
Summary

The Mantel test effectively analyzes spatial genetic divergence, despite recent criticisms. Careful application, especially with Mantel correlograms, remains a valuable tool for understanding population structure.

Keywords:
genetic distancesgeographical geneticspartial correlationpartial regression“Baru” tree

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Area of Science:

  • Population genetics
  • Spatial analysis
  • Evolutionary biology

Background:

  • The Mantel test is widely used to correlate genetic and geographic distances, assessing spatial processes in population structure.
  • Recent statistical performance concerns have prompted discussions and the development of alternative analytical frameworks.

Purpose of the Study:

  • To review the Mantel test and its variations for analyzing spatial genetic divergence.
  • To compare Mantel test performance with alternative methods using a case study of Dipteryx alata.

Main Methods:

  • Review of Mantel test, Mantel correlograms, partial correlations, and regressions.
  • Analysis of spatial genetic divergence in 25 Dipteryx alata populations using 8 microsatellite loci.
  • Application of multivariate Spatial Eigenfunction Analysis (SEA) based on redundancy analysis (RDA).

Main Results:

  • Different analytical approaches yielded similar estimates of spatial genetic structure magnitude.
  • Results aligned with existing knowledge of Dipteryx alata's ecological and evolutionary processes.
  • Careful application of Mantel tests, particularly correlograms, can mitigate statistical issues.

Conclusions:

  • Mantel tests, when applied judiciously, remain a useful and simple tool for multivariate analysis of spatial genetic patterns.
  • Alternative methods like SEA-RDA provide complementary insights into spatial genetic structure.
  • Understanding spatial processes requires robust statistical approaches in population genetics.