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Related Experiment Video

Updated: Jul 12, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

A step-by-step tutorial to use HierFstat to analyse populations hierarchically structured at multiple levels.

Thierry de Meeûs¹, Jérôme Goudet

¹Génétique et Evolution des Maladies Infectieuses, Unité Mixte de Recherche 2724, Institute de Recherche pour le Développement, Centre National de la Recherche Scientifique, Centre IRD, BP 64501, 34394 Montpellier Cedex 5, France. demeeus@mpl.ird.fr

Infection, Genetics and Evolution : Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases

|September 4, 2007

Summary

This study introduces HierFstat software for analyzing complex population structures beyond traditional F-statistics. It provides tools for calculating and testing fixation indices in hierarchical populations.

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Last Updated: Jul 12, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Published on: December 7, 2021

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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Published on: December 12, 2019

Area of Science:

Population Genetics
Evolutionary Biology
Parasitology

Background:

Classical population structure analysis often uses Wright's F-statistics (F(IS), F(ST), F(IT)) for nested designs.
Parasite and infectious agent populations frequently exhibit more complex hierarchical structures.
Existing methods may not adequately capture these intricate population hierarchies.

Purpose of the Study:

To present a user-friendly guide for the HierFstat software.
To enable computation and testing of fixation indices for any hierarchical population structure.
To offer practical guidance for specialized data types and analytical procedures.

Main Methods:

Utilizing the HierFstat software for hierarchical analysis.
Step-by-step instructions for software implementation.
Incorporating procedures for haploid data, single loci, bootstrap analysis over loci, and crossed factors.

Main Results:

Demonstration of HierFstat's capability to handle complex population hierarchies.
Successful computation and testing of fixation indices in non-nested structures.
Practical solutions for analyzing diverse and complex population genetic data.

Conclusions:

HierFstat provides a robust and accessible tool for analyzing complex population structures.
The software and accompanying guidance facilitate advanced population genetic analyses.
Researchers can now more effectively study the population genetics of parasites and infectious agents.