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Comparison of Markov Chain Monte Carlo Software for the Evolutionary Analysis of Y-Chromosomal Microsatellite Data.

Authors
  • Gundlach, Sven1
  • Junge, Olaf1
  • Wienbrandt, Lars2
  • Krawczak, Michael1
  • Caliebe, Amke1
  • 1 Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Brunswiker Strasse 10, 24105 Kiel, Germany. , (Germany)
  • 2 Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Rosalind-Franklin-Strasse 12, 24105 Kiel, Germany. , (Germany)
Type
Published Article
Journal
Computational and Structural Biotechnology Journal
Publisher
Elsevier
Publication Date
Jan 01, 2019
Volume
17
Pages
1082–1090
Identifiers
DOI: 10.1016/j.csbj.2019.07.014
PMID: 31452861
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The evolutionary analysis of genetic data is an important subject of modern bioscience, with practical applications in diverse fields. Parameters of interest in this context include effective population sizes, mutation rates, population growth rates and the times to most recent common ancestors. Studying Y-chromosomal microsatellite data, in particular, has proven useful to unravel the recent patrilineal history of Homo sapiens populations. We compared the individual analysis options and technical details of four software tools that are widely used for this purpose, namely BATWING, BEAST, IMa2 and LAMARC, all of which use Bayesian coalescent-based Markov chain Monte Carlo (MCMC) methods for parameter estimation. More specifically, we simulated datasets for either eight or 20 hypothetical Y-chromosomal microsatellites, assuming a mutation rate of 0.0030 per generation and a constant or exponentially increasing population size, and used these data to evaluate the parameter estimation capacity of each tool. The datasets comprised between 100 and 1000 samples. In addition to runtime, the practical utility of the tools of interest can also be expected to depend critically upon the convergence behavior of the actual MCMC implementation. In fact, we found that runtime increased, and convergence rate decreased, with increasing sample size as expected. BATWING performed best with respect to runtime and convergence behavior, but only supports simple evolutionary models. As regards the spectrum of evolutionary models covered, and also in terms of cross-platform usability, BEAST provided the greatest flexibility. Finally, IMa2 and LAMARC turned out best to incorporate elaborate migration models in the analysis process.

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