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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.
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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

References

This article includes 61 references
  1. Ablondi M, Summer A, Vasini M, Simoni M, Sabbioni A. Genetic parameters estimation in an Italian horse native breed to support the conversion from agricultural uses to riding purposes. Journal of Animal Breeding and Genetics 137(2), 200-210.
    doi: 10.1111/jbg.12425google scholar: lookup
  2. Akaike H. Information theory and an extensión of the maximum likelihood principle. International Symposium on Information Theory 1973, 267-281.
    doi: 10.1007/978-1-4612-1694-0google scholar: lookup
  3. Bartlett M S. Tests of significance in factor analysis. British Journal of Statistical Psychology 3(2), 77-85.
    doi: 10.1111/j.2044-8317.1950.tb00285.xgoogle scholar: lookup
  4. Bignardi A B, El Faro L, Rosa G J M, Cardoso V L, Machado P F, Albuquerque L G. Short communication: Principal components and factor analytic models for test-day milk yield in Brazilian Holstein cattle. Journal of Dairy Science 95(4), 2157-2164.
    doi: 10.3168/jds.2011-4494google scholar: lookup
  5. Bignardi A B, Santana M L, Eler J P, Ferraz J B S. Models for genetic evaluation of growth of Brazilian Bonsmara cattle. Livestock Science 162, 50-58.
    doi: 10.1016/j.livsci.2014.01.031google scholar: lookup
  6. Boligon A A, Bignardi A B, Mercadante M E Z, Lôbo R B, Albuquerque L G. Principal components and factor analytic models for birth to mature weights in Nellore cattle. Livestock Science 152(2-3), 135-142.
    doi: 10.1016/j.livsci.2013.01.005google scholar: lookup
  7. Boligon A A, de Albuquerque L G, Mercadante M E Z, Lôbo R B. Herdabilidades e correlações entre pesos do nascimento à idade adulta em rebanhos da raça Nelore. Revista Brasileira de Zootecnia 38(12), 2320-2326.
    doi: 10.1590/s1516-35982009001200005google scholar: lookup
  8. Boligon A A, Silveira F A, Silveira D D, Dionello N J L, Santana M L, Bignardi A B, Souza F R P. Reduced-rank models of growth and reproductive traits in Nelore cattle. Theriogenology 83(8), 1338-1343.
    doi: 10.1016/j.theriogenology.2015.01.025google scholar: lookup
  9. Burnham K P, Anderson D R. Multimodel Inference: Understanding AIC and BIC in model selection. Sociological Methods & Research 33(2), 261-304.
    doi: 10.1177/0049124104268644google scholar: lookup
  10. Bussiman F D O, Perez B D C, Ventura R V, Silva F F E, Peixoto M G C D, Vizoná R G, Mattos E C, Ferraz J B S, Eler J P, Curi R A, Balieiro J C D C. Genetic analysis of morphological and functional traits in Campolina horses using Bayesian multi-trait model. Livestock Science 216, 119-129.
    doi: 10.1016/j.livsci.2018.08.002google scholar: lookup
  11. Buzanskas M E, Savegnago R P, Grossi D A, Venturini G C, Queiroz S A, Silva L O C, Júnior R A A T, Munari D P, Alencar M M. Genetic parameter estimates and principal component analysis of breeding values of reproduction and growth traits in female Canchim cattle. Reproduction, Fertility and Development 25(5), 775.
    doi: 10.1071/rd12132google scholar: lookup
  12. Cabral G C, de Almeida F Q, Quirino C R, de Azevedo P C N, Pinto L F B, Santos E M. Avaliação morfométrica de eqüinos da raça Mangalarga Marchador: índices de conformação e proporções corporais. Revista Brasileira de Zootecnia 33(6 Suppl. 1), 1798-1805.
    doi: 10.1590/s1516-35982004000700018google scholar: lookup
  13. Cattell R B. The Scree test for the number of factors. Multivariate Behavioral Research 1(2), 245-276.
    doi: 10.1207/s15327906mbr0102_10google scholar: lookup
  14. de Sousa A S, de Jesus I I C, de Araújo Oliveira C A, Costa R B, de Godoi F N. How is the morphometry of stallions and mares show-winning and nonwinning Campolina Brazilian breed with Batida and Picada Gaits?. Journal of Equine Veterinary Science 64, 34-40.
    doi: 10.1016/j.jevs.2018.02.012google scholar: lookup
  15. Giontella A, Sarti F M, Biggio G P, Giovannini S, Cherchi R, Pieramati C, Silvestrelli M. Genetic parameters and inbreeding effect of morphological traits in Sardinian Anglo Arab Horse. Animals 10(5), 791.
    doi: 10.3390/ani10050791google scholar: lookup
  16. Henderson C R. Best linear unbiased estimation and prediction under a selection model. Biometrics 31(2), 423-447.
    doi: 10.2307/2529430google scholar: lookup
  17. Henderson C R. Rapid method for computing the inverse of a relationship matrix. Journal of Dairy Science 58(11), 1727-1730.
    doi: 10.3168/jds.s0022-0302(75)84776-xgoogle scholar: lookup
  18. Hill W G. Genetic correlation. .
  19. Im S, Fernando R, Gianola D. Likelihood inferences in animal breeding under selection: A missing-data theory view point. Genetics Selection Evolution 21(4), 399.
    doi: 10.1186/1297-9686-21-4-399google scholar: lookup
  20. Johnson D L, Thompson R. Restricted Maximum Likelihood Estimation of variance components for univariate animal models using sparse matrix techniques and average information. Journal of Dairy Science 78(2), 449-456.
    doi: 10.3168/jds.s0022-0302(95)76654-1google scholar: lookup
  21. Kaiser H F. The application of electronic computers to factor analysis. Educational and Psychological Measurement 20(1), 141-151.
    doi: 10.1177/001316446002000116google scholar: lookup
  22. Kirkpatrick M, Meyer K. Direct estimation of genetic principal components: Simplified analysis of complex phenotypes. Genetics 168(4), 2295-2306.
    doi: 10.1534/genetics.104.029181google scholar: lookup
  23. Lattin J M, Carroll J D, Green P E. Analyzing multivariate data (1re éd.). .
  24. Manly B J F, Navarro Alberto J A. Multivariate statistical methods: A primer (4e éd.). .
  25. Meyer K. Estimates of genetic parameters for mature weight of Australian beef cows and its relationship to early growth and skeletal measures. Livestock Production Science 44(2), 125-137.
    doi: 10.1016/0301-6226(95)00067-4google scholar: lookup
  26. Meyer K. An « average information » restricted maximum likelihood algorithm for estimating reduced rank genetic covariance matrices or covariance functions for animal models with equal design matrices. Genetics Selection Evolution 29(2), 97.
    doi: 10.1186/1297-9686-29-2-97google scholar: lookup
  27. Meyer K. Genetic principal components for live ultrasound scan traits of Angus cattle. Animal Science 81(3), 337-345.
    doi: 10.1079/asc50850337google scholar: lookup
  28. Meyer K. Multivariate analyses of carcass traits for Angus cattle fitting reduced rank and factor analytic models. Journal of Animal Breeding and Genetics 124(2), 50-64.
    doi: 10.1111/j.1439-0388.2007.00637.xgoogle scholar: lookup
  29. Meyer K. WOMBAT-A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University Science B 8(11), 815-821.
    doi: 10.1631/jzus.2007.b0815google scholar: lookup
  30. Meyer K. Factor-analytic models for genotype × environment type problems and structured covariance matrices. Genetics Selection Evolution 41(1), 21.
    doi: 10.1186/1297-9686-41-21google scholar: lookup
  31. Meyer K, Kirkpatrick M. Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices. Genetics Selection Evolution 37(1), 1-30.
    doi: 10.1051/gse:2004034google scholar: lookup
  32. Meyer K, Kirkpatrick M. Up hill, down dale: Quantitative genetics of curvaceous traits. Philosophical Transactions of the Royal Society B: Biological Sciences 360(1459), 1443-1455.
    doi: 10.1098/rstb.2005.1681google scholar: lookup
  33. Meyer K, Kirkpatrick M. Perils of parsimony: Properties of reduced-rank estimates of genetic covariance matrices. Genetics 180(2), 1153-1166.
    doi: 10.1534/genetics.108.090159google scholar: lookup
  34. Molina A, Valera M, Dos Santos R, Rodero A. Genetic parameters of morphofunctional traits in Andalusian horse. Livestock Production Science 60(2-3), 295-303.
    doi: 10.1016/s0301-6226(99)00101-3google scholar: lookup
  35. Nascimento G B, Savegnago R P, Chud T C S, Ledur M C, Figueiredo E A P, Munari D P. Genetic parameter estimates and principal component analysis on performance and carcass traits of a terminal pig sire line. Acta Agriculturae Scandinavica, Section A - Animal Science 64(3), 137-144.
    doi: 10.1080/09064702.2014.950322google scholar: lookup
  36. Pollak E J, van der Werf J, Quaas R L. Selection bias and multiple trait evaluation. Journal of Dairy Science 67(7), 1590-1595.
    doi: 10.3168/jds.s0022-0302(84)81481-2google scholar: lookup
  37. Procópio A M M, Bergmann J A G A G, Costa M D D. Formação e demografia da raça Campolina. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 55(3), 361-365.
    doi: 10.1590/s0102-09352003000300018google scholar: lookup
  38. R Core Team. R: A language and environment for statistical computing. .
  39. Rezende A S C, Moura R S. Raças de equídeos no Brasil. .
  40. Rubin D B. Inference and missing data. Biometrika 63(3), 581-592.
    doi: 10.1093/biomet/63.3.581google scholar: lookup
  41. Rustin M, Janssens S, Buys N, Gengler N. Multi-trait animal model estimation of genetic parameters for linear type and gait traits in the Belgian warmblood horse. Journal of Animal Breeding and Genetics 126(5), 378-386.
    doi: 10.1111/j.1439-0388.2008.00798.xgoogle scholar: lookup
  42. Sánchez-Guerrero M J, Cervantes I, Molina A, Gutiérrez J P, Valera M. Designing an early selection morphological linear traits index for dressage in the Pura Raza Español horse. Animal 11(6), 948-957.
    doi: 10.1017/s1751731116002214google scholar: lookup
  43. Schaeffer L R. Sire and cow evaluation under multiple trait models. Journal of Dairy Science 67(7), 1567-1580.
    doi: 10.3168/jds.s0022-0302(84)81479-4google scholar: lookup
  44. Schaeffer L R. Estimation of variances and covariances within the allowable parameter space. Journal of Dairy Science 69(1), 187-194.
    doi: 10.3168/jds.s0022-0302(86)80385-xgoogle scholar: lookup
  45. Schwarz G. Estimating the dimension of a model. The Annals of Statistics 6(2), 461-464.
    doi: 10.1214/aos/1176344136google scholar: lookup
  46. Smith A, Cullis B, Thompson R. Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57(4), 1138-1147.
    doi: 10.1111/j.0006-341x.2001.01138.xgoogle scholar: lookup
  47. Solar Diaz I D P, Strauss Borges Junqueira G, Aparecida Rocha Cruz V, Albano Araújo de Oliveira C, Nunes de Oliveira H, Miguel Ferreira de Camargo G, Bermal Costa R. Principal components for morphometric traits in Campolina horses. Journal of Animal Breeding and Genetics 138(2), 179-187.
    doi: 10.1111/jbg.12521google scholar: lookup
  48. Thompson B. Exploratory and confirmatory factor analysis: Understanding concepts and applications (1re éd.). .
  49. Thompson R, Cullis B, Smith A, Gilmour A. A sparse implementation of the average information algorithm for factor analytic and reduced rank variance models. Australian New Zealand Journal of Statistics 45(4), 445-459.
    doi: 10.1111/1467-842x.00297google scholar: lookup
  50. Thompson R, Meyer K. A review of theoretical aspects in the estimation of breeding values for multi-trait selection. Livestock Production Science 15(4), 299-313.
    doi: 10.1016/0301-6226(86)90071-0google scholar: lookup
  51. Tyrisevä A-M, Meyer K, Fikse W F, Ducrocq V, Jakobsen J, Lidauer M H, Mäntysaari E A. Principal component approach in variance component estimation for international sire evaluation. Genetics Selection Evolution 43(1), 21.
    doi: 10.1186/1297-9686-43-21google scholar: lookup
  52. van der Werf J H J, van Arendonk J A M, De Vries A G. Improving selection of pigs using correlated characters. Dans Book of abstracts of European Federation of Animal Science Madrid: Proceedings of the 43th EAAP Annual Meeting.
  53. Vargas G, Schenkel F S, Brito L F, Neves H H D R, Munari D P, Boligon A A, Carvalheiro R. Unravelling biological biotypes for growth, visual score and reproductive traits in Nellore cattle via principal component analysis. Livestock Science 217, 37-43.
    doi: 10.1016/j.livsci.2018.09.010google scholar: lookup
  54. Velie B D, Hamilton N A, Wade C M. Heritability of racing performance in the Australian Thoroughbred racing population. Animal Genetics 46(1), 23-29.
    doi: 10.1111/age.12234google scholar: lookup
  55. Vicente A A, Carolino N, Ralão-Duarte J, Gama L T. Selection for morphology, gaits and functional traits in Lusitano horses: I. Genetic parameter estimates. Livestock Science 164(1), 1-12.
    doi: 10.1016/j.livsci.2014.01.020google scholar: lookup
  56. Vicente A A, Carolino N, Ralão-Duarte J, Gama L T. Selection for morphology, gaits and functional traits in Lusitano horses: II. Fixed effects, genetic trends and selection in retrospect. Livestock Science 164(1), 13-25.
    doi: 10.1016/j.livsci.2014.03.017google scholar: lookup
  57. Vieira E R, Rezende A S C D, Lana A, Barcelos K, Santiago J M, Lage J, Fonseca M G, Bergmann J. Caracterização da equideocultura no estado de Minas Gerais. Arquivo Brasileiro de Medicina Veterinaria e Zootecnia 67(1), 319-323.
    doi: 10.1590/1678-7460google scholar: lookup
  58. Vostry L, Vostrà-vydrovà H, Hofmanovà B, Veselà Z, Schmidovà J, Majzlik I. Genetic parameters for linear type traits in three Czech draught horse breeds. Agriculturae Conspectus Scientificus 82(2), 111-115.
  59. Wickham H. ggplot2: Elegant graphics for data analysis (2e éd.). .
    doi: 10.1007/978-3-319-24277-4google scholar: lookup
  60. Wolfinger R. Covariance structure selection in general mixed models. Communications in Statistics - Simulation and Computation 22(4), 1079-1106.
    doi: 10.1080/03610919308813143google scholar: lookup
  61. Wright S. Coefficients of inbreeding and relationship. The American Naturalist 56(645), 330-338.
    doi: 10.1086/279872google scholar: lookup

Citations

This article has been cited 3 times.
  1. Bussiman F, Richter J, Hidalgo J, Silva FFE, Ventura RV, Carvalho RSB, Mattos EC, Ferraz JBS, Eler JP, de Carvalho Balieiro JC. Bayesian Recursive and Structural Equation Models to Infer Causal Links Among Gait Visual Scores on Campolina Horses. J Anim Breed Genet 2025 Sep;142(5):463-477.
    doi: 10.1111/jbg.12919pubmed: 39698947google scholar: lookup
  2. Bussiman F, Alves AAC, Richter J, Hidalgo J, Veroneze R, Oliveira T. Supervised Machine Learning Techniques for Breeding Value Prediction in Horses: An Example Using Gait Visual Scores. Animals (Basel) 2024 Sep 20;14(18).
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  3. Ricard A, Crevier-Denoix N, Pourcelot P, Crichan H, Sabbagh M, Dumont-Saint-Priest B, Danvy S. Genetic analysis of geometric morphometric 3D visuals of French jumping horses. Genet Sel Evol 2023 Sep 18;55(1):63.
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