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Ecology and evolution2020; 10(10); 4261-4279; doi: 10.1002/ece3.6195

Major inconsistencies of inferred population genetic structure estimated in a large set of domestic horse breeds using microsatellites.

Abstract: STRUCTURE remains the most applied software aimed at recovering the true, but unknown, population structure from microsatellite or other genetic markers. About 30% of structure-based studies could not be reproduced (, 21, 2012, 4925). Here we use a large set of data from 2,323 horses from 93 domestic breeds plus the Przewalski horse, typed at 15 microsatellites, to evaluate how program settings impact the estimation of the optimal number of population clusters that best describe the observed data. Domestic horses are suited as a test case as there is extensive background knowledge on the history of many breeds and extensive phylogenetic analyses. Different methods based on different genetic assumptions and statistical procedures (dapc, flock, PCoA, and structure with different run scenarios) all revealed general, broad-scale breed relationships that largely reflect known breed histories but diverged how they characterized small-scale patterns. structure failed to consistently identify using the most widespread approach, the Δ method, despite very large numbers of MCMC iterations (3,000,000) and replicates (100). The interpretation of breed structure over increasing numbers of , without assuming a , was consistent with known breed histories. The over-reliance on should be replaced by a qualitative description of clustering over increasing , which is scientifically more honest and has the advantage of being much faster and less computer intensive as lower numbers of MCMC iterations and repetitions suffice for stable results. Very large data sets are highly challenging for cluster analyses, especially when populations with complex genetic histories are investigated.
© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
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Publication Date: 2020-04-12 PubMed ID: 32489595PubMed Central: PMC7246218DOI: 10.1002/ece3.6195Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The study presents an evaluation of the STRUCTURE software, commonly used in genetic research to infer population structure through genetic markers, specifically microsatellites. The researchers chose domestic horse breeds as their subjects, examining data from 2323 horses across 93 breeds, including the Przewalski horse. Despite using a range of methods and numerous iterations and replicates, the study suggests that STRUCTURE often fails to provide consistent results, impacting understanding of population genetic structures.

Research Objectives and Methodology

  • The primary goal of this research was to assess the performance of STRUCTURE, a widely-used genetic analysis software, in accurately establishing the population structure of domestic horses using microsatellite markers. The evaluation focused on the influence of program settings on estimating optimal population clusters.
  • The researchers examined a large data set drawn from 2323 horses across 93 different domestic breeds, in addition to the Przewalski horse. The horses were assessed on the basis of 15 microsatellites.
  • The research methodology employed both genetic assumptions and statistical procedures, including Differential Analysis of Principal Components (DAPC), Flock, Principal Coordinate Analysis (PCoA), and other STRUCTURE run scenarios.

Findings

  • The study found that the different methods revealed general, broad-scale breed relationships that largely matched known breed histories. However, there was divergence in the characterisation of small-scale patterns.
  • Despite the use of a large number of Markov Chain Monte Carlo (MCMC) iterations (3,000,000) and replicates (100), the Δ method used by STRUCTURE often failed to consistently identify clusters.
  • The inference of breed structure across an increasing number of clusters, without assuming a specific number, was more in line with known breed histories.

Implications

  • The researchers argue that the over-reliance on a specific number of clusters should be replaced with a qualitative description of clustering over increasing clusters. This approach is considered more scientifically honest, faster, and less resource-intense, requiring fewer MCMC iterations and repetitions to yield stable results.
  • The study suggests that the challenges posed by large data sets, especially when studying populations with complex genetic histories, require an update to current methodologies.

Cite This Article

APA
Funk SM, Guedaoura S, Juras R, Raziq A, Landolsi F, Luís C, Martínez AM, Musa Mayaki A, Mujica F, Oom MDM, Ouragh L, Stranger YM, Vega-Pla JL, Cothran EG. (2020). Major inconsistencies of inferred population genetic structure estimated in a large set of domestic horse breeds using microsatellites. Ecol Evol, 10(10), 4261-4279. https://doi.org/10.1002/ece3.6195

Publication

Ecology and evolution
ISSN: 2045-7758
NlmUniqueID: 101566408
Country: England
Language: English
Volume: 10
Issue: 10
Pages: 4261-4279

Researcher Affiliations

Funk, Stephan Michael
  • Centro de Excelencia de Modelación y Computación Científica Universidad de La Frontera Temuco Chile.
  • Nature Heritage St. Lawrence UK.
Guedaoura, Sonya
  • Faculté des Sciences de la Nature et de la Vie Université d'El-Tarf El-Tarf Algeria.
  • Faculté de Pharmacie Université Laval Québec City QC Canada.
Juras, Rytis
  • College of Veterinary Medicine and Biomedical Science Texas A&M University College Station TX USA.
Raziq, Absul
  • Society of Veterinary, Environment and Agriculture Scientists (SAVES) Quetta Pakistan.
Landolsi, Faouzi
  • Ecole Nationale de Médecine Vétérinaire Sidi Thabet Tunisie.
Luís, Cristina
  • Centro Interuniversitário de História das Ciências e da Tecnologia (CIUHCT) Faculdade de Ciências Universidade de Lisboa Lisboa Portugal.
Martínez, Amparo Martínez
  • Departamento de Genética Universidad de Córdoba Córdoba Spain.
Musa Mayaki, Abubakar
  • Department of Veterinary Medicine Usmanu Danfodiyo University Sokoto Nigeria.
Mujica, Fernando
  • Instituto de Producción Animal Universidad Austral de Chile Valdivia Chile.
Oom, Maria do Mar
  • CE3C - Centre for Ecology, Evolution and Environmental Changes Faculdade de Ciências Universidade de Lisboa Lisboa Portugal.
Ouragh, Lahoussine
  • Institut Agronomique et Vétérinaire Hassan II Rabat Morocco.
Stranger, Yves-Marie
  • Marmande France.
Vega-Pla, Jose Luis
  • Laboratorio de Investigación Aplicada Crıa Caballar de las Fuerzas Armadas Cordoba Spain.
Cothran, Ernest Gus
  • College of Veterinary Medicine and Biomedical Science Texas A&M University College Station TX USA.

Conflict of Interest Statement

None declared.

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