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Home / Equine Research Bank / J Anim Breed Genet / Reduced rank analysis of morphometric and functional traits ...
Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie2021; 139(2); 231-246; doi: 10.1111/jbg.12658

Reduced rank analysis of morphometric and functional traits in Campolina horses.

Abstract: Multitrait models can increase the accuracy of breeding value prediction and reduce bias due to selection by using traits measured before and after it has occurred. However, as the number of traits grows, a similar trend is expected for the number of parameters to be estimated, which directly affects the computing power and the amount of data required. The aim of the present study was to apply reduced rank (principal components model-PCM) and factor analytical models (FAM), to estimate (co)variance components for nineteen traits, jointly evaluated in a single analysis in Campolina horses. A total of 18 morphometric traits (MT) and one gait visual score (GtS), along with genealogical records of 48,806 horses, were analysed under a restricted maximum likelihood framework. Nine PCM, nine FAM and one standard multitrait model (MTM) were fitted to the data and compared to find the best suitable model. Based on Bayesian information criterion, the best model was the FAM option, considering five common factors (FAM5). After performing an intraclass analysis, none of MT were genetically negatively correlated, whereas GtS was negatively related to all MT, except for the genetic correlations among GtS and BLL, and between GtS and BLLBL (0.01 and 0.10 respectively). From all MT, two traits were derived computing ratios involving other traits, those had negative correlations with others MT, but all favourable for selection. Similar patterns were observed between the genetic parameters obtained from MTM and FAM5 respectively. The heritability estimates ranged from 0.09 (head width) to 0.47 (height at withers). Our results indicated that FAM was efficient to reduce the multitrait analysis dimensionality, and therefore, traits can be combined based on the first three eigenvectors from the additive genetic (co)variance matrix. In addition, there was sufficient genetic variation for selection, benefiting its potential implementation in a breeding program.
© 2021 Wiley-VCH GmbH.
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Publication Date: 2021-11-28 PubMed ID: 34841593DOI: 10.1111/jbg.12658Google 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.

This research aims to optimize the efficiency of breeding value prediction in campolina horses by using a reduced rank model. Focusing on 19 traits, this study applies the principal components model (PCM) and factor analytical models (FAM), concluding that the FAM offers the most effective way of reducing the dimensionality of multitrait analysis, paving the way for more successful breeding program planning.

Introduction

  • The objective of this research was to evaluate PCM and FAM models as alternatives to predict breeding values accurately in Campolina horses.
  • The authors aimed to minimize computing power and data requirements by reducing the number of traits being evaluated and by maximizing the accuracy of breeding value predictions.

Methodology

  • The study incorporated genealogical and phenotypic data for 19 traits from a total of 48,806 horses.
  • The traits encompassed 18 morphometric traits (MT) and one gait visual score (GtS).
  • The restricted maximum likelihood framework was used to analyze the data.
  • Nine PCM, nine FAM, and one standard multitrait model (MTM) were each applied to the dataset and compared to identify the most suitable

Results

  • The FAM model using five common factors (FAM5) was found to be the best fit for the dataset based on the Bayesian information criterion, evidencing superior efficiency in reducing the multitrait analysis dimensionality.
  • The research found that none of the MT were genetically negatively correlated, while GtS was negatively related to all MT except those associated with BLL and BLLBL (0.01 and 0.10 respectively).
  • Two traits were derived from ratios involving other traits, with these displaying negative correlations with other MT. Despite this, the correlations were all favorable for selection.
  • The genetic parameters obtained from both MTM and FAM5 exhibited similar patterns.
  • The heritability estimates ranged from 0.09 (for head width) and 0.47 (height at withers), indicating significant genetic variation and implying ample potential for selection in breeding programs.

Conclusion

  • The results suggest that principal component and factor analysis models have the potential to improve breeding value predictions in campolina horses by reducing the number of traits required for analysis.
  • The FAM5 model, in particular, stands out as an efficient way of reducing multitrait analysis dimensionality while maintaining satisfactory levels of accuracy in breeding value predictions.
  • This study provides a backbone for future research in the area and contributes to breeding program planning designed to maximize genetic variation and, therefore, selection potential.

Cite This Article

APA
de Oliveira Bussiman F, Carvalho RSB, E Silva FF, Ventura RV, Ferraz JBS, Mattos EC, Eler JP, Balieiro JCC. (2021). Reduced rank analysis of morphometric and functional traits in Campolina horses. J Anim Breed Genet, 139(2), 231-246. https://doi.org/10.1111/jbg.12658

Publication

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
ISSN: 1439-0388
NlmUniqueID: 100955807
Country: Germany
Language: English
Volume: 139
Issue: 2
Pages: 231-246

Researcher Affiliations

de Oliveira Bussiman, Fernando
  • Bioinformatic and Animal Breeding Lab., Department of Animal Nutrition and Production, College of Veterinary Medicine and Animal Science, University of São Paulo (BIOMA-VNP/FMVZ-USP), Pirassununga, Brazil.
Carvalho, Rachel Santos Bueno
  • Department of Basic Sciences, College of Animal Science and Food Engineering, University of São Paulo (ZAB/FZEA-USP), Pirassununga, Brazil.
E Silva, Fabyano Fonseca
  • Department of Animal Science, Federal University of Viçosa (DZO/UFV), Viçosa, Brazil.
Ventura, Ricardo Vieira
  • Bioinformatic and Animal Breeding Lab., Department of Animal Nutrition and Production, College of Veterinary Medicine and Animal Science, University of São Paulo (BIOMA-VNP/FMVZ-USP), Pirassununga, Brazil.
Ferraz, José Bento Sterman
  • Group of Animal Breeding and Biotechnology, Department of Veterinary Medicine, College of Animal Science and Food Engineering, University of São Paulo (GMAB-ZMV/FZEA-USP), Pirassununga, Brazil.
Mattos, Elisângela Chicaroni
  • Group of Animal Breeding and Biotechnology, Department of Veterinary Medicine, College of Animal Science and Food Engineering, University of São Paulo (GMAB-ZMV/FZEA-USP), Pirassununga, Brazil.
Eler, Joanir Pereira
  • Group of Animal Breeding and Biotechnology, Department of Veterinary Medicine, College of Animal Science and Food Engineering, University of São Paulo (GMAB-ZMV/FZEA-USP), Pirassununga, Brazil.
Balieiro, Júlio Cesar de Carvalho
  • Bioinformatic and Animal Breeding Lab., Department of Animal Nutrition and Production, College of Veterinary Medicine and Animal Science, University of São Paulo (BIOMA-VNP/FMVZ-USP), Pirassununga, Brazil.

MeSH Terms

  • Animals
  • Bayes Theorem
  • Gait
  • Horses / genetics
  • Phenotype

Grant Funding

  • 2018/26465-3 / Fundau00e7u00e3o de Amparo u00e0 Pesquisa do Estado de Su00e3o Paulo
  • 001 / Coordenau00e7u00e3o de Aperfeiu00e7oamento de Pessoal de Nu00edvel Superior

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