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Genotyping and Quantification of In Situ Hybridization Staining in Zebrafish
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HZAR: hybrid zone analysis using an R software package.

Elizabeth P Derryberry1, Graham E Derryberry, James M Maley

  • 1Department of Ecology and Evolutionary Biology, Tulane University, New Orleans, LA, 70118, USA; Museum of Natural Science and Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA.

Molecular Ecology Resources
|December 31, 2013
PubMed
Summary
This summary is machine-generated.

We introduce HZAR, a new R software package for analyzing hybrid zones. HZAR fits genetic and morphological data to cline models using Markov chain Monte Carlo methods, aiding evolutionary studies.

Keywords:
ManacusR packageclineshybrid zonesmanakin

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Area of Science:

  • Evolutionary Biology
  • Population Genetics
  • Bioinformatics

Background:

  • Hybrid zones are crucial for understanding evolutionary processes.
  • Analyzing genetic and morphological data in hybrid zones requires specialized tools.
  • Classic equilibrium cline models provide a framework for studying admixture.

Purpose of the Study:

  • To present HZAR, a novel software package for analyzing hybrid zones.
  • To enable fitting of molecular genetic and morphological data to equilibrium cline models.
  • To facilitate automated model selection and statistical comparison of cline parameters.

Main Methods:

  • Utilizes the Metropolis-Hastings Markov chain Monte Carlo (MCMC) algorithm.
  • Applies likelihood functions for diploid/haploid genetic markers and quantitative traits.
  • Features a modular design for flexible cline model fitting and an autofit function for model selection.

Main Results:

  • HZAR successfully fits hybrid zone data to various cline models.
  • Parameter estimation (cline centre, width) allows statistical comparisons.
  • Validation with a Panamanian manakin hybrid zone confirms software accuracy.

Conclusions:

  • HZAR provides a flexible and powerful tool for analyzing hybrid zones.
  • The software aids in hypothesis testing and understanding evolutionary dynamics in contact zones.
  • Automated model selection and statistical comparisons enhance hybrid zone research.