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Genes2025; 16(2); 131; doi: 10.3390/genes16020131

Comparative Analysis of Genomic and Pedigree-Based Approaches for Genetic Evaluation of Morphological Traits in Pura Raza Española Horses.

Abstract: The single-step best linear unbiased predictor (ssGBLUP) has emerged as a reference method for genomic selection in recent years due to its advantages over traditional approaches. Although its application in horses remains limited, ssGBLUP has demonstrated the potential to improve the reliability of estimated breeding values in livestock species. This study aimed to assess the impact of incorporating genomic data using single-step restricted maximum likelihood (ssGREML) on reliability (R) in the Pura Raza Española (PRE) horse breed, compared to traditional pedigree-based REML. Methods: The analysis involved 14 morphological traits from 7152 animals, including 2916 genotyped individuals. Genetic parameters were estimated using a multivariate model. Results: Results showed that heritability estimates were similar between the two approaches, ranging from 0.08 to 0.76. However, a significant increase in reliability (R) was observed for ssGREML compared to REML across all morphological traits, with overall gains ranging from 1.56% to 13.30% depending on the trait evaluated. R ranged from 6.93% to 22.70% in genotyped animals, significantly lower in non-genotyped animals (0.82% to 12.37%). Interestingly, individuals with low R values in REML demonstrated the largest R gains in ssGREML. Additionally, this improvement was much greater (5.96% to 19.25%) when only considering stallions with less than 40 controlled foals. Conclusions: Hereby, we demonstrated that the application of genomic selection can contribute to improving the reliability of mating decisions in a large horse breeding program such as the PRE breed.
Publication Date: 2025-01-23 PubMed ID: 40004460PubMed Central: PMC11855142DOI: 10.3390/genes16020131Google Scholar: Lookup
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  • Journal Article
  • Comparative Study

Summary

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The article discusses a study aimed to compare the efficiency of the single-step best linear unbiased predictor (ssGBLUP), a genomic approach, and traditional pedigree-based restricted maximum likelihood (REML) in genetic evaluation of morphological traits in Pura Raza Española (PRE) horse breed. The study found that the ssGBLUP showed a significant increase in reliability for all morphological traits compared to REML.

Methodology

  • The study centered around the assessment of 14 morphological traits in 7152 Pura Raza Española (PRE) horses. It involved 2916 genotyped horses (animals whose genetic material has been analyzed).
  • Genetic parameters of the traits were estimated through a multivariate model, a mathematical model consisting of multiple variables.

Results

  • The study found little variation in heritability estimates between the two methods, with estimates ranging from 0.08 to 0.76.
  • The single-step genomic best linear unbiased predictor (ssGBLUP) method showed a significant increase in reliability over the traditional pedigree-based restricted maximum likelihood (REML) method. The overall gains ranged from 1.56% to 13.30% depending on the specific trait being evaluated.
  • For the genotyped animals, the reliability ranged between 6.93% and 22.70%. This was substantially higher than the reliability range for non-genotyped animals, which stood at 0.82% to 12.37%.
  • The study observed the largest reliability gains in individuals with low reliability values under the REML method when evaluated using the ssGBLUP. Additionally, this improvement was considerably greater (5.96% to 19.25%) when the focus lay on stallions with less than 40 controlled foals.

Conclusion

  • The research concluded that the use of genomic selection, particularly the ssGBLUP method, can significantly improve the reliability of mating decisions in large horse breeding programs, such as the PRE breed, thus optimizing the genetic evaluation process.

Cite This Article

APA
Ziadi C, Demyda-Peyrás S, Valera M, Perdomo-González D, Laseca N, Rodríguez-Sainz de Los Terreros A, Encina A, Azor P, Molina A. (2025). Comparative Analysis of Genomic and Pedigree-Based Approaches for Genetic Evaluation of Morphological Traits in Pura Raza Española Horses. Genes (Basel), 16(2), 131. https://doi.org/10.3390/genes16020131

Publication

ISSN: 2073-4425
NlmUniqueID: 101551097
Country: Switzerland
Language: English
Volume: 16
Issue: 2
PII: 131

Researcher Affiliations

Ziadi, Chiraz
  • Departamento de Genética, Universidad de Córdoba, 14014 Córdoba, Spain.
Demyda-Peyrás, Sebastián
  • Departamento de Genética, Universidad de Córdoba, 14014 Córdoba, Spain.
Valera, Mercedes
  • Departamento de Agronomía, ETSIA, Universidad de Sevilla, 41013 Sevilla, Spain.
Perdomo-González, Davinia
  • Departamento de Producción Animal, Universidad Complutense de Madrid, 28040 Madrid, Spain.
Laseca, Nora
  • Departamento de Agronomía, ETSIA, Universidad de Sevilla, 41013 Sevilla, Spain.
  • Real Asociación Nacional de Criadores de Caballos de Pura Raza Española (ANCCE), 41014 Sevilla, Spain.
Rodríguez-Sainz de Los Terreros, Arancha
  • Real Asociación Nacional de Criadores de Caballos de Pura Raza Española (ANCCE), 41014 Sevilla, Spain.
Encina, Ana
  • Real Asociación Nacional de Criadores de Caballos de Pura Raza Española (ANCCE), 41014 Sevilla, Spain.
Azor, Pedro
  • Real Asociación Nacional de Criadores de Caballos de Pura Raza Española (ANCCE), 41014 Sevilla, Spain.
Molina, Antonio
  • Departamento de Genética, Universidad de Córdoba, 14014 Córdoba, Spain.

MeSH Terms

  • Animals
  • Horses / genetics
  • Pedigree
  • Breeding
  • Genomics / methods
  • Phenotype
  • Genotype
  • Male
  • Quantitative Trait, Heritable
  • Female

Grant Funding

  • Equigenom GO / Ministry of Agriculture, Fisheries and Food, through the Spanish Agrarian Guarantee Fund, FEGA
  • RyC2022 Fellow / MINECO, Spain

Conflict of Interest Statement

The authors declare no conflicts of interest.

References

This article includes 50 references
  1. Ministerio de Agricultura, P.y.A.M Datos Censales (PURA RAZA ESPAÑOLA) 2023. [(accessed on 31 December 2024)]. Available online: https://servicio.mapa.gob.es/arca/flujos.html?_flowId=datosCensalesRaza-flow&tipoOperacion=CONSULTA&formatoPagina=0&id=50157.
  2. ANCCE Purebred Spanish Horse Breeding Program. [(accessed on 31 December 2024)]. Available online: https://www.lgancce.com/Documentacion/Normativa/Nacional/programa_cria_en.pdf.
  3. Sánchez-Guerrero M.J., Molina A., Gómez M.D., Peña F., Valera M.. Relationship between morphology and performance: Signature of mass-selection in Pura Raza Español horse. Livest. Sci. 2016;185:148–155.
  4. ANCCE New Assessment Record Sheet for Basic Approval as a Breeding Stock and Genetic Assessment for Morphology. [(accessed on 31 December 2024)]. Available online: https://www.lgancce.com/web/news/new-assessment-record-sheet-basic-approval-breeding-stock-and-genetic-assessment-morphology?lang=en.
  5. Blasco A.. Animal breeding methods and sustainability. Animal Breeding and Genetics Springer; New York, NY, USA: 2022; pp. 5–24.
  6. Meuwissen T.H., Hayes B.J., Goddard M.E.. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001;157:1819–1829.
    doi: 10.1093/genetics/157.4.1819pmc: PMC1461589pubmed: 11290733google scholar: lookup
  7. Aguilar I., Misztal I., Johnson D.L., Legarra A., Tsuruta S., Lawlor T.J.. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J. Dairy Sci. 2010;93:743–752.
    doi: 10.3168/jds.2009-2730pubmed: 20105546google scholar: lookup
  8. Christensen O.F., Lund M.S.. Genomic prediction when some animals are not genotyped. Genet. Sel. Evol. 2010;42:2.
    doi: 10.1186/1297-9686-42-2pmc: PMC2834608pubmed: 20105297google scholar: lookup
  9. Legarra A., Christensen O.F., Aguilar I., Misztal I.. Single Step, a general approach for genomic selection. Livest. Sci. 2014;166:54–65.
  10. Lourenco D., Legarra A., Tsuruta S., Masuda Y., Aguilar I., Misztal I.. Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90. Genes 2020;11:790.
    doi: 10.3390/genes11070790pmc: PMC7397237pubmed: 32674271google scholar: lookup
  11. Koivula M., Strandén I., Aamand G.P., Mäntysaari E.A.. Effect of cow reference group on validation reliability of genomic evaluation. Animal 2016;10:1061–1066.
    doi: 10.1017/S1751731115002864pubmed: 27075712google scholar: lookup
  12. Koivula M., Stranden I., Su G., Mantysaari E.A.. Different methods to calculate genomic predictions--comparisons of BLUP at the single nucleotide polymorphism level (SNP-BLUP), BLUP at the individual level (G-BLUP), and the one-step approach (H-BLUP). J. Dairy Sci. 2012;95:4065–4073.
    doi: 10.3168/jds.2011-4874pubmed: 22720963google scholar: lookup
  13. Mantysaari E.A., Koivula M., Stranden I.. Symposium review: Single-step genomic evaluations in dairy cattle. J. Dairy Sci. 2020;103:5314–5326.
    doi: 10.3168/jds.2019-17754pubmed: 32331883google scholar: lookup
  14. Oliveira H.R., Lourenco D.A.L., Masuda Y., Misztal I., Tsuruta S., Jamrozik J., Brito L.F., Silva F.F., Schenkel F.S.. Application of single-step genomic evaluation using multiple-trait random regression test-day models in dairy cattle. J. Dairy Sci. 2019;102:2365–2377.
    doi: 10.3168/jds.2018-15466pubmed: 30638992google scholar: lookup
  15. Massender E., Brito L.F., Maignel L., Oliveira H.R., Jafarikia M., Baes C.F., Sullivan B., Schenkel F.S.. Single-and multiple-breed genomic evaluations for conformation traits in Canadian Alpine and Saanen dairy goats. J. Dairy Sci. 2022;105:5985–6000.
    doi: 10.3168/jds.2021-21713pubmed: 35534269google scholar: lookup
  16. Mucha S., Mrode R., MacLaren-Lee I., Coffey M., Conington J.. Estimation of genomic breeding values for milk yield in UK dairy goats. J. Dairy Sci. 2015;98:8201–8208.
    doi: 10.3168/jds.2015-9682pubmed: 26342984google scholar: lookup
  17. Teissier M., Larroque H., Robert-Granie C.. Accuracy of genomic evaluation with weighted single-step genomic best linear unbiased prediction for milk production traits, udder type traits, and somatic cell scores in French dairy goats. J. Dairy Sci. 2019;102:3142–3154.
    doi: 10.3168/jds.2018-15650pubmed: 30712939google scholar: lookup
  18. Baloche G., Legarra A., Salle G., Larroque H., Astruc J.M., Robert-Granie C., Barillet F.. Assessment of accuracy of genomic prediction for French Lacaune dairy sheep. J. Dairy Sci. 2014;97:1107–1116.
    doi: 10.3168/jds.2013-7135pubmed: 24315320google scholar: lookup
  19. Li L., Gurman P.M., Swan A.A., Brown D.J.. Single-step genomic evaluation of lambing ease in Australian terminal sire breed sheep. Anim. Prod. Sci. 2021;61:1990–1999.
    doi: 10.1071/AN21257google scholar: lookup
  20. Wei C., Luo H., Zhao B., Tian K., Huang X., Wang Y., Fu X., Tian Y., Di J., Xu X.. The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep. Animals 2020;10:569.
    doi: 10.3390/ani10040569pmc: PMC7222387pubmed: 32231053google scholar: lookup
  21. Fu C., Ostersen T., Christensen O.F., Xiang T.. Single-step genomic evaluation with metafounders for feed conversion ratio and average daily gain in Danish Landrace and Yorkshire pigs. Genet. Sel. Evol. 2021;53:79.
    doi: 10.1186/s12711-021-00670-xpmc: PMC8499570pubmed: 34620083google scholar: lookup
  22. Song H., Zhang J., Zhang Q., Ding X.. Using Different Single-Step Strategies to Improve the Efficiency of Genomic Prediction on Body Measurement Traits in Pig. Front. Genet. 2018;9:730.
    doi: 10.3389/fgene.2018.00730pmc: PMC6340005pubmed: 30693018google scholar: lookup
  23. Xiang T., Nielsen B., Su G., Legarra A., Christensen O.F.. Application of single-step genomic evaluation for crossbred performance in pig. J. Anim. Sci. 2016;94:936–948.
    doi: 10.2527/jas.2015-9930pubmed: 27065256google scholar: lookup
  24. Haberland A.M., Konig von Borstel U., Simianer H., Konig S.. Integration of genomic information into sport horse breeding programs for optimization of accuracy of selection. Animal 2012;6:1369–1376.
    doi: 10.1017/S1751731112000626pubmed: 23031511google scholar: lookup
  25. Mark T., Jönsson L., Holm M., Christiansen K.. Towards genomic selection in Danish Warmblood horses: Expected impacts and selective genotyping strategy. Proceedings of the 10th World Congress on Genetics Applied to Livestock Production; Vancouver, Canada. 17–22 August 2014; pp. 17–22.
  26. Vosgerau S., Krattenmacher N., Falker-Gieske C., Seidel A., Tetens J., Stock K.F., Nolte W., Wobbe M., Blaj I., Reents R.. Genetic and genomic characterization followed by single-step genomic evaluation of withers height in German Warmblood horses. J. Appl. Genet. 2022;63:369–378.
    doi: 10.1007/s13353-021-00681-wpmc: PMC8979901pubmed: 35028913google scholar: lookup
  27. 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;55:63.
    doi: 10.1186/s12711-023-00837-8pmc: PMC10506242pubmed: 37723416google scholar: lookup
  28. Eggen A.. The development and application of genomic selection as a new breeding paradigm. Anim. Front. 2012;2:10–15.
    doi: 10.2527/af.2011-0027google scholar: lookup
  29. Stock K.F., Jönsson L., Ricard A., Mark T.. Genomic applications in horse breeding. Anim. Front. 2016;6:45–52.
    doi: 10.2527/af.2016-0007google scholar: lookup
  30. Sánchez M.J., Gómez M.D., Molina A., Valera M.. Genetic analyses for linear conformation traits in Pura Raza Español horses. Livest. Sci. 2013;157:57–64.
  31. Carter R.A., Geor R.J., Burton Staniar W., Cubitt T.A., Harris P.A.. Apparent adiposity assessed by standardised scoring systems and morphometric measurements in horses and ponies. Vet. J. 2009;179:204–210.
    doi: 10.1016/j.tvjl.2008.02.029pubmed: 18440844google scholar: lookup
  32. R-Core-Team. R: A Language and Environment for Statistical Computing V4.4.2 “Pile of Leaves”. 2024.
  33. Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M.A., Bender D., Maller J., Sklar P., de Bakker P.I., Daly M.J.. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007;81:559–575.
    doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
  34. Marschner I.. GLM2: Fitting generalized linear models with convergence problems. R. J. 2011;3:12–15.
    doi: 10.32614/RJ-2011-012google scholar: lookup
  35. VanRaden P.M.. Efficient methods to compute genomic predictions. J. Dairy Sci. 2008;91:4414–4423.
    doi: 10.3168/jds.2007-0980pubmed: 18946147google scholar: lookup
  36. Yin L., Zhang H., Tang Z., Yin D., Fu Y., Yuan X., Li X., Liu X., Zhao S.. HIBLUP: An integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data. Nucleic Acids Res. 2023;51:3501–3512.
    doi: 10.1093/nar/gkad074pmc: PMC10164590pubmed: 36809800google scholar: lookup
  37. Poyato-Bonilla J., Sanchez-Guerrero M.J., Cervantes I., Gutierrez J.P., Valera M.. Genetic parameters for canalization analysis of morphological traits in the Pura Raza Espanol horse. J. Anim. Breed. Genet. 2021;138:482–490.
    doi: 10.1111/jbg.12537pubmed: 33527529google scholar: lookup
  38. Sánchez M.J., Azor P.J., Molina A., Parkin T., Rivero J.L., Valera M.. Prevalence, risk factors and genetic parameters of cresty neck in Pura Raza Español horses. Equine Vet. J. 2017;49:196–200.
    doi: 10.1111/evj.12569pubmed: 26877245google scholar: lookup
  39. Poyato-Bonilla J., Perdomo-Gonzalez D.I., Sanchez-Guerrero M.J., Varona L., Molina A., Casellas J., Valera M.. Genetic inbreeding depression load for morphological traits and defects in the Pura Raza Espanola horse. Genet. Sel. Evol. 2020;52:62.
    doi: 10.1186/s12711-020-00582-2pmc: PMC7576714pubmed: 33081691google scholar: lookup
  40. Clement V., Bibe B., Verrier E., Elsen J.M., Manfredi E., Bouix J., Hanocq E.. Simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects. Genet. Sel. Evol. 2001;33:369–395.
    doi: 10.1186/1297-9686-33-4-369pmc: PMC2705412pubmed: 11563370google scholar: lookup
  41. Christensen O.F., Madsen P., Nielsen B., Ostersen T., Su G.. Single-step methods for genomic evaluation in pigs. Animal 2012;6:1565–1571.
    doi: 10.1017/S1751731112000742pubmed: 22717310google scholar: lookup
  42. Ziadi C., Perdomo-González D., Valera M., Laseca N., Encina A., Azor P., Rodríguez A., Demyda-Peyrás S., Molina A.. Genomics improves the reliability of Breeding Value Prediction of morphological and reproductive traits in the Pura Raza Español Horse: Preliminary results. Proceedings of the 46th ICAR Annual Conference; Toledo, Spain. 21–26 May 2023; pp. 311–316.
  43. VanRaden P.M., Tooker M.E., Wright J.R., Sun C., Hutchison J.L.. Comparison of single-trait to multi-trait national evaluations for yield, health, and fertility. J. Dairy Sci. 2014;97:7952–7962.
    doi: 10.3168/jds.2014-8489pubmed: 25282421google scholar: lookup
  44. Misztal I., Lourenco D., Legarra A.. Current status of genomic evaluation. J. Anim. Sci. 2020;98:skaa101.
    doi: 10.1093/jas/skaa101pmc: PMC7183352pubmed: 32267923google scholar: lookup
  45. Guarini A.R., Lourenco D.A.L., Brito L.F., Sargolzaei M., Baes C.F., Miglior F., Misztal I., Schenkel F.S.. Comparison of genomic predictions for lowly heritable traits using multi-step and single-step genomic best linear unbiased predictor in Holstein cattle. J. Dairy Sci. 2018;101:8076–8086.
    doi: 10.3168/jds.2017-14193pubmed: 29935829google scholar: lookup
  46. Gao H., Christensen O.F., Madsen P., Nielsen U.S., Zhang Y., Lund M.S., Su G.. Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population. Genet. Sel. Evol. 2012;44:8.
    doi: 10.1186/1297-9686-44-8pmc: PMC3400441pubmed: 22455934google scholar: lookup
  47. Valera M., Molina A., Gutiérrez J.P., Gómez J., Goyache F.. Pedigree analysis in the Andalusian horse: Population structure, genetic variability and influence of the Carthusian strain. Livest. Prod. Sci. 2005;95:57–66.
  48. Goddard M.. Genomic selection: Prediction of accuracy and maximisation of long term response. Genetica 2009;136:245–257.
    doi: 10.1007/s10709-008-9308-0pubmed: 18704696google scholar: lookup
  49. van den Berg I., Meuwissen T.H.E., MacLeod I.M., Goddard M.E.. Predicting the effect of reference population on the accuracy of within, across, and multibreed genomic prediction. J. Dairy Sci. 2019;102:3155–3174.
    doi: 10.3168/jds.2018-15231pubmed: 30738664google scholar: lookup
  50. Lozada-Soto E.A., Tiezzi F., Jiang J., Cole J.B., VanRaden P.M., Maltecca C.. Genomic characterization of autozygosity and recent inbreeding trends in all major breeds of US dairy cattle. J. Dairy Sci. 2022;105:8956–8971.
    doi: 10.3168/jds.2022-22116pubmed: 36153159google scholar: lookup

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