FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie2023; 140(4); 366-375; doi: 10.1111/jbg.12764

Factor analysis of evaluated and linearly scored traits in Swedish Warmblood horses.

Abstract: Assessment protocols to describe the various aspects of conformation, gait and jumping traits on a linear scale were introduced at young horse tests for Swedish Warmblood horses in 2013. The traits scored on a linear scale are assumed to be less subjective and more easily compared across populations than the traditional evaluated traits that are scored relative to the breeding goal. However, the resulting number of traits is considerable, and several of the traits are correlated. The aim of this study was to investigate the interrelationship between the different evaluated and linearly scored traits in Swedish Warmbloods using factor analysis. In total, 20,935 horses born 1996-2017 had information on evaluated traits, and 5450 of these also had linearly scored trait records assessed since 2014 when the protocol was updated. A factor analysis with varimax rotation was performed separately for evaluated and linearly scored traits using the Psych package in R. Height at withers was included in both analyses. A total of four factors for evaluated traits and 14 factors for linearly scored traits were kept for further analysis. Missing values for individual traits in horses with linearly scored trait records were imputed based on correlated traits before factor scores were calculated using factor loadings. Genetic parameters for, and correlations between, the resulting underlying factors were estimated using multiple-trait animal models in the BLUPF90 package. Heritability estimates were on a similar level as for the traits currently used in the genetic evaluation, ranging from 0.05 for the factor for linearly scored traits named L.behaviour (dominated by traits related to behaviour) to 0.59 for the factor for evaluated traits named E.size (dominated by height at withers and conformation). For both types of traits, separate factors were formed for jumping and gait traits, as well as for body size. High genetic correlations were estimated between such corresponding factors for evaluated traits and factors for linearly scored traits. In conclusion, factor analysis could be used to reduce the number of traits to be included in multiple-trait genetic evaluation or in genomic analysis for warmblood horses. It can also contribute to a better understanding of the interrelationships among the assessed traits and be useful to decide on subgroups of traits to be used in several multiple-trait evaluations on groups of original traits.
© 2023 The Authors. Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.
Get Full Text
Publication Date: 2023-02-27 PubMed ID: 36852464DOI: 10.1111/jbg.12764Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

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 researchers conducted a study to understand the relationship between different traditionally evaluated and linearly scored traits in Swedish Warmblood horses. The results suggested that factor analysis could simplify the traits for detailed genetic evaluation or genomic analysis of horses.

Overview of the Study

  • The researchers used factor analysis to study the correlation between different traits in Swedish Warmblood horses. The traits were traditionally scored qualitatively and on a linear scale, which would be less subjective and more comparative across different populations.
  • The study was conducted on about 20,935 horses born between 1996 to 2017, out of which 5450 horses also had linearly scored traits since a new protocol was introduced in 2014.
  • The factor analysis was done separately for evaluated and linearly scored traits using a statistical computing tool called the Psych package in R-language. All the evaluated and linearly scored traits were considered, including the height of the horse.

Results of the Study

  • The study named four factors for evaluated traits and 14 factors for linearly scored traits for further analysis.
  • For missing values, the researchers used data from correlated traits to fill the gaps before calculating factor scores.
  • They estimated the genetic parameters and correlations using the calculated factors through multiple-trait animal models.
  • The estimated heritability ranged from 0.05 for behaviors relating to linearly scored traits to 0.59 for anatomical aspects related to traditionally evaluated traits.
  • The separate factors were formed for jumping and gait traits, and body size.
  • They discovered high genetic correlations between evaluated traits and linearly scored traits for similar aspects.

Applications of the Research Findings

  • The use of factor analysis could reduce the number of traits to be included multiple-trait genetic evaluations or genomic analysis in horses thereby making the analyses simpler and more efficient.
  • In addition, the factor analysis results could provide better insight into the relationships among the assessed traits and help in deciding subgroups of traits to be used in multiple-trait evaluations of different groups of original traits.

Cite This Article

APA
Nazari-Ghadikolaei A, Fikse F, Gelinder Viklund Å, Eriksson S. (2023). Factor analysis of evaluated and linearly scored traits in Swedish Warmblood horses. J Anim Breed Genet, 140(4), 366-375. https://doi.org/10.1111/jbg.12764

Publication

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
ISSN: 1439-0388
NlmUniqueID: 100955807
Country: Germany
Language: English
Volume: 140
Issue: 4
Pages: 366-375

Researcher Affiliations

Nazari-Ghadikolaei, Anahit
  • Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Fikse, Freddy
  • Växa, Uppsala, Sweden.
Gelinder Viklund, Åsa
  • Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Eriksson, Susanne
  • Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden.

MeSH Terms

  • Horses / genetics
  • Animals
  • Sweden
  • Gait / genetics
  • Phenotype
  • Body Size
  • Factor Analysis, Statistical

References

This article includes 40 references
  1. Abdi H. Factor rotations in factor analyses. In Encyclopedia for research methods for the social sciences (pp. 792-795).
  2. Aguilar I, Tsuruta S, Masuda Y, Lourenco D, Legarra A, Misztal I. BLUPF90 suite of programs for animal breeding with focus on genomics. The 11th world congress on genetics applied to livestock production 11-16 February.
  3. Arvelius P, Asp H E, Fikse W, Strandberg E, Nilsson K. Genetic analysis of a temperament test as a tool to select against everyday life fearfulness in rough collie. Journal of Animal Science 92(11), 4843-4855.
    doi: 10.2527/jas.2014-8169google scholar: lookup
  4. Bonow S, Eriksson S, Thorén Hellsten E, Gelinder Viklund Å. Consequences of specialized breeding in the Swedish warmblood horse population. Journal of Animal Breeding and Genetics 00, 1-13.
    doi: 10.1111/jbg.12731google scholar: lookup
  5. Clayton H M, Hobbs S J. A review of biomechanical gait classification with reference to collected trot, passage and piaffe in dressage horses. Animals 9(10), 763.
    doi: 10.3390/ani9100763google scholar: lookup
  6. de Oliveira Bussiman F, Carvalho R S B, Silva F F E, Ventura R V, Ferraz J B S, Mattos E C, Eler J P, Balieiro J C D C. Reduced rank analysis of morphometric and functional traits in Campolina horses. Journal of Animal Breeding and Genetics 139(2), 231-246.
    doi: 10.1111/jbg.12658google scholar: lookup
  7. Ducro B J, Koenen E P C, Van Tartwijk J M F M, Bovenhuis H. Genetic relations of movement and free-jumping traits with dressage and show-jumping performance in competition of Dutch warmblood horses. Livestock Science 107(2-3), 227-234.
    doi: 10.1016/j.livsci.2006.09.018google scholar: lookup
  8. Duensing J, Stock K F, Krieter J. Implementation and prospects of linear profiling in the warmblood horse. Journal of Equine Veterinary Science 34(3), 360-368.
    doi: 10.1016/j.jevs.2013.09.002google scholar: lookup
  9. Guo G, Zhao F, Wang Y, Zhang Y, Du L, Su G. Comparison of single-trait and multiple-trait genomic prediction models. BMC Genetics 15, 30.
    doi: 10.1186/1471-2156-15-30google scholar: lookup
  10. Jönsson L, Näsholm A, Roepstorff L, Egenvall A, Dalin G, Philipsson J. Conformation traits and their genetic and phenotypic associations with health status in young Swedish warmblood riding horses. Livestock Science 163, 12-25.
    doi: 10.1016/j.livsci.2014.02.010google scholar: lookup
  11. Josse J, Husson F. missMDA: A package for handling missing values in multivariate data analysis. Journal of Statistical Software 70(1), 1-31.
    doi: 10.18637/jss.v070.i01google scholar: lookup
  12. Kern E L, Cobuci J A, Costa C N, Pimentel C M M. Factor analysis of linear type traits and their relation with longevity in Brazilian Holstein cattle. Asian-Australasian Journal of Animal Sciences 27(6), 784-790.
    doi: 10.5713/ajas.2013.13817google scholar: lookup
  13. Koenen E P C, Van Veldhuizen A E, Brascamp E W. Genetic parameters of linear scored conformation traits and their relation to dressage and show-jumping performance in the Dutch Warmblood riding horse population. Livestock Production Science 43(1), 85-94.
    doi: 10.1016/0301-6226(95)00010-igoogle scholar: lookup
  14. Kristjansson T, Bjornsdottir S, Albertsdóttir E, Sigurdsson A, Pourcelot P, Crevier-Denoix N, Arnason T. Association of conformation and riding ability in Icelandic horses. Livestock Science 189, 91-101.
    doi: 10.1016/j.livsci.2016.05.010google scholar: lookup
  15. Miles A M, Posbergh C J, Huson H J. Direct phenotyping and principal component analysis of type traits implicate novel QTL in bovine mastitis through genome-wide association. Animals 11(4), 1147.
    doi: 10.3390/ani11041147google scholar: lookup
  16. Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee D H. BLUPF90 and related programs (BGF90). The 7th world congress on genetics applied to livestock production 19-23 august.
  17. Mrode R A. Linear models for the prediction of animal breeding values (3rd ed.). CAB International.
  18. Nazari-Ghadikolaei A, Mehrabani-Yeganeh H, Miarei-Aashtiani S R, Staiger E A, Rashidi A, Huson H J. Genome-wide association studies identify candidate genes for coat color and mohair traits in the Iranian Markhoz goat. Frontiers in Genetics 9, 105.
    doi: 10.3389/fgene.2018.00105google scholar: lookup
  19. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing .
  20. Revelle W. How To: Use the psych package for Factor Analysis and data reduction. Northwestern University .
  21. Ricard A, Dumont Saint Priest B, Chassier M, Sabbagh M, Danvy S. Genetic consistency between gait analysis by accelerometry and evaluation scores at breeding shows for the selection of jumping competition horses. PLoS One 15(12), e0244064.
    doi: 10.1371/journal.pone.0244064google scholar: lookup
  22. 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
  23. Samoré A B, Pagnacco G, Miglior F. Genetic parameters and breeding values for linear type traits in the Haflinger horse. Livestock Production Science 52(2), 105-111.
    doi: 10.1016/s0301-6226(97)00143-7google scholar: lookup
  24. Sánchez M J, Gomez M D, Peña F, Monterde J G, Morales J L, Molina A. Relationship between conformation traits and gait characteristics in Pura Raza Espanol horses. Archives Animal Breeding 56(1), 137-148.
    doi: 10.7482/0003-9438-56-013google scholar: lookup
  25. Sigurðardóttir H, Albertsdóttir E, Eriksson S. Analysis of new temperament traits to better understand the trait spirit assessed in breeding field tests for Icelandic horses. Acta Agriculturae Scandinavica, Section A-Animal Science 67(1-2), 46-57.
    doi: 10.1080/09064702.2017.1383507google scholar: lookup
  26. Sperrle F, Viklund Å, Thorén-Hellsten E, Schulze-Schleppinghoff W, Stock K F. Implications of across-studbook genetic correlations between linear traits for sport horse breeding. Paper presented at the Proc. 67th annual meeting of the EAAP.
  27. Staiger E A, Al Abri M A, Pflug K M, Kalla S E, Ainsworth D M, Miller D, Raudsepp T, Sutter N B, Brooks S A. Skeletal variation in Tennessee walking horses maps to the LCORL/NCAPG gene region. Physiological Genomics 48(5), 325-335.
    doi: 10.1152/physiolgenomics.00100.2015google scholar: lookup
  28. Staiger E A, Albright J D, Brooks S A. Genome-wide association mapping of heritable temperament variation in the Tennessee walking horse. Genes, Brain and Behavior 15(5), 514-526.
    doi: 10.1111/gbb.12290google scholar: lookup
  29. Steiger J H. Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research 25(2), 173-180.
    doi: 10.1207/s15327906mbr2502_4google scholar: lookup
  30. Swedish Warmblood Association. (2022). Retrieved from https://www.blup.se/en-US/page/short_intro
  31. Thurstone L L. The vectors of mind: Multiple-factor analysis for the isolation of primary traits. University of Chicago Press.
  32. Tucker L R, Lewis C. A reliability coefficient for maximum likelihood factor analysis. Psychometrika 38(1), 1-10.
    doi: 10.1007/bf02291170google scholar: lookup
  33. Van Bergen H M J M, Van Arendonk J A M. Genetic parameters for linear type traits in Shetland ponies. Livestock Production Science 36(3), 273-284.
    doi: 10.1016/0301-6226(93)90058-pgoogle scholar: lookup
  34. Van Buuren S, Groothuis-Oudshoorn K. Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software 45, 1-67.
    doi: 10.18637/jss.v045.i03google scholar: lookup
  35. Verkade M E, Back W, Birch H L. Equine digital tendons show breed-specific differences in their mechanical properties that may relate to athletic ability and predisposition to injury. Equine Veterinary Journal 52(2), 320-325.
    doi: 10.1111/evj.13169google scholar: lookup
  36. Viklund Å, Eriksson S. Genetic analyses of linear profiling data on 3-year-old Swedish Warmblood horses. Journal of Animal Breeding and Genetics 135(1), 62-72.
    doi: 10.1111/jbg.12311google scholar: lookup
  37. Viklund Å, Hellsten E T, Näsholm A, Strandberg E, Philipsson J. Genetic parameters for traits evaluated at field tests of 3-and 4-year-old Swedish Warmblood horses. Animal 2(12), 1832-1841.
    doi: 10.1017/s1751731108003030google scholar: lookup
  38. Viklund Å, Näsholm A, Strandberg E, Philipsson J. Genetic trends for performance of Swedish Warmblood horses. Livestock Science 141(2-3), 113-122.
    doi: 10.1016/j.livsci.2011.05.006google scholar: lookup
  39. Wobbe M, Alkhoder H, Stock K F, Liu Z, Vosgerau S, Krattenmacher N, von Depka-Prondzinski M, Kalm E, Reents R, Nolte W, Kühn C, Tetens J, Thaller G. Single-step genomic evaluation in German riding horses. 72th Annual Meeting of the European Federation of Animal Science (EAAP) 30 August-3 September.
  40. Zhang W, Gao X, Shi X, Zhu B, Wang Z, Gao H, Xu L, Zhang L, Li J, Chen Y. PCA-based multiple-trait GWAS analysis: A powerful model for exploring pleiotropy. Animals 8(12), 239.
    doi: 10.3390/ani8120239google scholar: lookup

Citations

This article has been cited 8 times.
  1. Li A, Zhao K, Wang T, Shi G. Transcriptome and single-cell RNA sequencing analysis with 101 machine learning combinations and experimental verification reveals the mechanism of action of mannose metabolism in bladder cancer. Front Immunol 2026;17:1710823.
    doi: 10.3389/fimmu.2026.1710823pubmed: 41685329google scholar: lookup
  2. Li H, Ding R, Zheng Y, Wang D, Deng T, Wu Z, Li L, Ma X, Cheng C, Zhang Y, Luo Y, Zhang H, Chen Y, Yu Y, Hou W, Ding T, Xie M, Bai X, Yang H, Wang Y, Zheng M. Identification and experimental validation of biomarkers associated with paraptosis in meningioma. Medicine (Baltimore) 2025 Dec 26;104(52):e47116.
    doi: 10.1097/MD.0000000000047116pubmed: 41465897google scholar: lookup
  3. Zhang Y, Meng X, Li M. Association of chaperone-mediated autophagy with the mechanisms of vascular calcification in diabetic nephropathy. Ren Fail 2025 Dec;47(1):2542530.
    doi: 10.1080/0886022X.2025.2542530pubmed: 40764924google scholar: lookup
  4. Yong C, Yu Y, Wei Y, Huang G, Shu L, Gao K, Zhou E. To reveal biomarkers related to macrophage and lactic acid metabolism in renal fibrosis and explore their mechanisms. Front Immunol 2025;16:1609903.
    doi: 10.3389/fimmu.2025.1609903pubmed: 40755781google scholar: lookup
  5. Walz KR, McCormick ME, Fedorka CE. The Thoroughbred Theory: Influence of Breed on Performance at the CCI5*-L Level of Eventing. Animals (Basel) 2025 Jun 18;15(12).
    doi: 10.3390/ani15121796pubmed: 40564347google scholar: lookup
  6. Nazari-Ghadikolaei A, Fikse WF, Viklund ÅG, Mikko S, Eriksson S. Single-Step Genome-Wide Association Study of Factors for Evaluated and Linearly Scored Traits in Swedish Warmblood Horses. J Anim Breed Genet 2025 Sep;142(5):499-512.
    doi: 10.1111/jbg.12923pubmed: 39754479google scholar: lookup
  7. Borowska A, Lewczuk D. Comparison of Conformation and Movement Characteristics in Dressage and Jumping Sport Warmblood Mares Based on Point Evaluation and Linear Scoring System. Animals (Basel) 2023 Oct 4;13(19).
    doi: 10.3390/ani13193101pubmed: 37835707google scholar: lookup
  8. 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.
    doi: 10.1186/s12711-023-00837-8pubmed: 37723416google scholar: lookup
Subscribe to our newsletter
Join 99,658 horse owners like you who receive our email updates on horse health and nutrition.