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Home / Equine Research Bank / Genes (Basel) / Enhanced Reliability of the Evaluation of Fertility Traits i...
Genes2025; 16(5); 562; doi: 10.3390/genes16050562

Enhanced Reliability of the Evaluation of Fertility Traits in Pura Raza Española Horses Using Single-Step Genomic Best Linear Unbiased Prediction.

Abstract: By simultaneously integrating both genotyped and non-genotyped animals into genetic evaluation, the single-step genomic BLUP method enhanced the accuracy of genetic assessments. This study aimed to compare the increase in prediction reliability (R) between restricted maximum likelihood (REML) and single-step genomic REML (ssGREML) in the Pura Raza Española (PRE) horse breed. The dataset comprised reproductive records for seven fertility traits from 47,502 females, with a total of 57,316 animals represented in the pedigree. A total of 4009 animals were genotyped using the EQUIGENE 90K SNP array, and 71,322 SNPs were retained for analysis after quality control. Genetic parameters were estimated using a multivariate model with the BLUPF90+ v2.60 software. Heritability estimates were similar between REML and ssGREML, ranging from 0.07 for IF12 to 0.349 for ALF. An increase in R was observed with ssGREML compared to REML across all traits, with overall gains ranging from 2.20% to 3.71%. Among genotyped animals, R values ranged from 17.81% to 24.04%, while significantly lower values (0.80% to 2.34%) were observed in non-genotyped animals. Notably, individuals with low initial R values under the REML approach exhibited the most significant gains using ssGREML. This improvement was particularly pronounced among stallions with fewer than 40 controlled foals. Our results demonstrated that incorporating genomic data improves the reliability of genetic evaluations for mare fertility in PRE horses.
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Publication Date: 2025-05-09 PubMed ID: 40428384PubMed Central: PMC12111279DOI: 10.3390/genes16050562Google 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 research paper examines whether utilizing a single-step genomic BLUP (Best Linear Unbiased Prediction) method can enhance the precision of genetic evaluations in Pura Raza Española (PRE) horse breed. Results indicated that integrating genomic data notably improved the reliability of fertility trait evaluations, especially among horses with initially low reliability values.

Methodology and Study Population

  • The study analyzed fertility data from a group of 47,502 females from the PRE horse breed. In total, the data represented the pedigree of 57,316 animals.
  • To carry out the genomic aspect of the study, 4009 of the animals were genotyped using the EQUIGENE 90K SNP array, an array used for genotyping horses and determining genetic variations. After quality assessments, 71,322 SNPs (single nucleotide polymorphisms) were retained for the analysis.

Analysis Process

  • The researchers estimated genetic parameters using a multivariate model with the BLUPF90+ v2.60 software.
  • They compared the prediction reliability between two methods: restricted maximum likelihood (REML) and single-step genomic REML (ssGREML). Prediction reliability (R) measures the precision of a genetic evaluation.

Results and Findings

  • The heritability estimates, showing the proportion of observed variation in a particular trait that can be attributed to inherited genetic factors, remained similar for both REML and ssGREML methods. They ranged from 0.07 for IF12 to 0.349 for ALF.
  • However, an increase in prediction reliability was evident with the ssGREML method compared to the REML for every trait tested.
  • In genotyped animals, prediction reliability values ranged from 17.81% to 24.04%. However, substantially lower values (0.80% to 2.34%) were found in non-genotyped animals.
  • Significant improvements in prediction reliability, using the ssGREML approach, were observed in horses that initially had low reliability values under the REML method. This was particularly evident in stallions with fewer than 40 controlled offspring.

Conclusion

  • The results demonstrated that integrating genomic data using the single-step BLUP method can enhance the reliability of fertility trait evaluations within the PRE horse breed.

Cite This Article

APA
Ziadi C, Valera M, Laseca N, Perdomo-González D, Demyda-Peyrás S, de Los Terreros AR, Molina A. (2025). Enhanced Reliability of the Evaluation of Fertility Traits in Pura Raza Española Horses Using Single-Step Genomic Best Linear Unbiased Prediction. Genes (Basel), 16(5), 562. https://doi.org/10.3390/genes16050562

Publication

Genes
ISSN: 2073-4425
NlmUniqueID: 101551097
Country: Switzerland
Language: English
Volume: 16
Issue: 5
PII: 562

Researcher Affiliations

Ziadi, Chiraz
  • Departamento de Genética, Universidad de Córdoba, 14071 Córdoba, Spain.
Valera, Mercedes
  • Departamento de Agronomía, Universidad de Sevilla, 41013 Sevilla, Spain.
Laseca, Nora
  • Departamento de Agronomía, Universidad de Sevilla, 41013 Sevilla, Spain.
  • Real Asociación Nacional de Criadores de Caballos de Pura Raza Española (ANCCE), 41014 Sevilla, Spain.
Perdomo-González, Davinia
  • Departamento de Producción Animal, Universidad Complutense de Madrid, 28040 Madrid, Spain.
Demyda-Peyrás, Sebastián
  • Departamento de Genética, Universidad de Córdoba, 14071 Córdoba, Spain.
de Los Terreros, Arancha Rodríguez-Sainz
  • Real Asociación Nacional de Criadores de Caballos de Pura Raza Española (ANCCE), 41014 Sevilla, Spain.
Molina, Antonio
  • Departamento de Genética, Universidad de Córdoba, 14071 Córdoba, Spain.

MeSH Terms

  • Animals
  • Horses / genetics
  • Fertility / genetics
  • Female
  • Polymorphism, Single Nucleotide
  • Breeding / methods
  • Genotype
  • Genomics / methods
  • Pedigree
  • Reproducibility of Results

Grant Funding

  • No REGAGE22e00014966312 / European Union Recuperation Instrument (Next Generation funds)

Conflict of Interest Statement

The authors declare no conflicts of interest.

References

This article includes 55 references
  1. Stott AW, Veerkamp RF, Wassell TR. The Economics of Fertility in the Dairy Herd. Anim. Sci. 1999;68:49–57.
    doi: 10.1017/S1357729800050074google scholar: lookup
  2. MAPA Datos Censales (PURA RAZA ESPANOLA) 2023. [(accessed on 10 April 2025)]. Available online: https://servicio.mapa.gob.es/arca/flujos.html?_flowId=datosCensalesRaza-flow&tipoOperacion=CONSULTA&formatoPagina=0&id=50157.
  3. ANCCE Purebred Spanish Horse Breeding Program. 2020. [(accessed on 10 April 2025)]. Available online: https://www.lgancce.com/Documentacion/Normativa/Nacional/programa_cria_en.pdf.
  4. Perdomo-González DI, Molina A, Sánchez-Guerrero MJ, Bartolomé E, Varona L, Valera M. Genetic Inbreeding Depression Load for Fertility Traits in Pura Raza Española Mares. J. Anim. Sci. 2021;99:skab316.
    doi: 10.1093/jas/skab316pmc: PMC8645228pubmed: 34718615google scholar: lookup
  5. Mahon GAT, Cunningham EP. Inbreeding and Infertility in the Thoroughbred Mare. Annales de Génétique et de Sélection Animale Volume 14. EDP Sciences; Les Ulis, France: 1982. p. 115.
    doi: 10.1186/1297-9686-14-1-115Bgoogle scholar: lookup
  6. Khan IU, Khairullah AR, Khan AY, Ur Rehman A, Mustofa I. Strategic Approaches to Improve Equine Breeding and Stud Farm Outcomes. Vet. World. 2025;18:311–328.
    doi: 10.14202/vetworld.2025.311-328pmc: PMC11963589pubmed: 40182817google scholar: lookup
  7. Gómez MD, Sánchez MJ, Bartolomé E, Cervantes I, Poyato-Bonilla J, Demyda-Peyrás S, Valera M. Phenotypic and Genetic Analysis of Reproductive Traits in Horse Populations with Different Breeding Purposes. Animal 2020;14:1351–1361.
    doi: 10.1017/S1751731120000087pubmed: 32026801google scholar: lookup
  8. Laseca N, Demyda-Peyrás S, Valera M, Ramón M, Escribano B, Perdomo-González DI, Molina A. A Genome-Wide Association Study of Mare Fertility in the Pura Raza Español Horse. Animal 2022;16:100476.
    doi: 10.1016/j.animal.2022.100476pubmed: 35247706google scholar: lookup
  9. Perdomo-González DI, Sánchez-Guerrero MJ, Bartolomé E, Guedes dos Santos R, Molina A, Valera M. Designing an Early Selection Morphological Traits Index for Reproductive Efficiency in Pura Raza Española Mares. J. Anim. Sci. 2024;102:skad409.
    doi: 10.1093/jas/skad409pmc: PMC10762892pubmed: 38118055google scholar: lookup
  10. Meuwissen TH, Hayes BJ, Goddard M. 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
  11. VanRaden PM. Efficient Methods to Compute Genomic Predictions. J. Dairy Sci. 2008;91:4414–4423.
    doi: 10.3168/jds.2007-0980pubmed: 18946147google scholar: lookup
  12. Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S, Lawlor TJ. 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
  13. Misztal I, Legarra A, Aguilar I. Computing Procedures for Genetic Evaluation Including Phenotypic, Full Pedigree, and Genomic Information. J. Dairy Sci. 2009;92:4648–4655.
    doi: 10.3168/jds.2009-2064pubmed: 19700728google scholar: lookup
  14. Legarra A, Christensen OF, Aguilar I, Misztal I. Single Step, a General Approach for Genomic Selection. Livest. Sci. 2014;166:54–65.
    doi: 10.1016/j.livsci.2014.04.029google scholar: lookup
  15. Christensen OF. Compatibility of Pedigree-Based and Marker-Based Relationship Matrices for Single-Step Genetic Evaluation. Genet. Sel. Evol. 2012;44:37.
    doi: 10.1186/1297-9686-44-37pmc: PMC3549765pubmed: 23206367google scholar: lookup
  16. 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
  17. Lourenco DAL, Misztal I, Tsuruta S, Aguilar I, Ezra E, Ron M, Shirak A, Weller JI. Methods for Genomic Evaluation of a Relatively Small Genotyped Dairy Population and Effect of Genotyped Cow Information in Multiparity Analyses. J. Dairy Sci. 2014;97:1742–1752.
    doi: 10.3168/jds.2013-6916pubmed: 24472123google scholar: lookup
  18. Ziadi C, Demyda-Peyrás S, Valera M, Perdomo-González D, Laseca N, Rodríguez-Sainz de los Terreros A, Encina A, Azor P, Molina A. Comparative Analysis of Genomic and Pedigree-Based Approaches for the Genetic Evaluation of Morphological Traits in Pura Raza Española Horses. Genes 2025;16:131.
    doi: 10.3390/genes16020131pmc: PMC11855142pubmed: 40004460google scholar: lookup
  19. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, De Bakker PI, Daly MJ. 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
  20. Marschner IC. Glm2: Fitting Generalized Linear Models with Convergence Problems. R J. 2011;3:12–15.
    doi: 10.32614/RJ-2011-012google scholar: lookup
  21. R-Core-Team. R: A Language and Environment for Statistical Computing V4.4.2 “Pile of Leaves”. 2024.
  22. Legarra A, Aguilar I, Misztal I. A Relationship Matrix Including Full Pedigree and Genomic Information. J. Dairy Sci. 2009;92:4656–4663.
    doi: 10.3168/jds.2009-2061pubmed: 19700729google scholar: lookup
  23. Lourenco D, Tsuruta S, Aguilar I, Masuda Y, Bermann M, Legarra A, Misztal I. Recent Updates in the BLUPF90 Software Suite. Proceedings of the 12th World Congress of Genetics Applied to Livestock Production; Rotterdam, The Netherlands. 3–8 July 2022; Wageningen, The Netherlands: Wageningen Academic Publishers; 2022. pp. 1530–1533.
  24. Laseca N, Ziadi C, Perdomo-Gonzalez DI, Valera M, Demyda-Peyras S, Molina A. Reproductive Traits in Pura Raza Española Mares Manifest Inbreeding Depression from Low Levels of Homozygosity. J. Anim. Breed. Genet. 2024;141:453–464.
    doi: 10.1111/jbg.12856pubmed: 38299872google scholar: lookup
  25. Karlau A, Molina A, Antonini A, Demyda-Peyrás S. The Influence of Foreign Lineages in the Genetic Component of Reproductive Traits in Criollo Argentino Mares: A 30-Year Study. Livest. Sci. 2023;267:105153.
    doi: 10.1016/j.livsci.2022.105153google scholar: lookup
  26. Clément V, Bibé B, Verrier É, Elsen JM, Manfredi E, Bouix J, Hanocq É. 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.
    doi: 10.1186/1297-9686-33-4-369pmc: PMC2705412pubmed: 11563370google scholar: lookup
  27. Laseca N, Molina A, Ramón M, Valera M, Azcona F, Encina A, Demyda-Peyrás S. Fine-Scale Analysis of Runs of Homozygosity Islands Affecting Fertility in Mares. Front. Vet. Sci. 2022;9:754028.
    doi: 10.3389/fvets.2022.754028pmc: PMC8891756pubmed: 35252415google scholar: lookup
  28. Saura M, Fernández A, Varona L, Fernández AI, De Cara M, Barragán C, Villanueva B. Detecting Inbreeding Depression for Reproductive Traits in Iberian Pigs Using Genome-Wide Data. Genet. Sel. Evol. 2015;47:1.
    doi: 10.1186/s12711-014-0081-5pmc: PMC4297446pubmed: 25595431google scholar: lookup
  29. Sairanen J, Nivola K, Katila T, Virtala AM, Ojala M. Effects of Inbreeding and Other Genetic Components on Equine Fertility. Animal 2009;3:1662–1672.
    doi: 10.1017/S1751731109990553pubmed: 22443550google scholar: lookup
  30. Vosgerau S, Krattenmacher N, Falker-Gieske C, Seidel A, Tetens J, Stock KF, 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
  31. Haberland AM, von Borstel UK, Simianer H, König 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
  32. Guinan FL, Wiggans GR, Norman HD, Dürr JW, Cole JB, Van Tassell CP, Misztal I, Lourenco D. Changes in Genetic Trends in US Dairy Cattle since the Implementation of Genomic Selection. J. Dairy Sci. 2023;106:1110–1129.
    doi: 10.3168/jds.2022-22205pubmed: 36494224google scholar: lookup
  33. Baloche G, Legarra A, Sallé G, Larroque H, Astruc JM, Robert-Granié 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
  34. Teissier M, Larroque H, Robert-Granié 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
  35. Hayes BJ, Visscher PM, Goddard ME. Increased Accuracy of Artificial Selection by Using the Realized Relationship Matrix. Genet. Res. 2009;91:47–60.
    doi: 10.1017/S0016672308009981pubmed: 19220931google scholar: lookup
  36. Daetwyler HD, Villanueva B, Woolliams JA. Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach. PLoS ONE 2008;3:e3395.
    doi: 10.1371/journal.pone.0003395pmc: PMC2561058pubmed: 18852893google scholar: lookup
  37. 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
  38. Lourenco DAL, Tsuruta S, Fragomeni BO, Masuda Y, Aguilar I, Legarra A, Bertrand JK, Amen TS, Wang L, Moser DW. Genetic Evaluation Using Single-Step Genomic Best Linear Unbiased Predictor in American Angus. J. Anim. Sci. 2015;93:2653–2662.
    doi: 10.2527/jas.2014-8836pubmed: 26115253google scholar: lookup
  39. Muir WM. Comparison of Genomic and Traditional BLUP-estimated Breeding Value Accuracy and Selection Response under Alternative Trait and Genomic Parameters. J. Anim. Breed. Genet. 2007;124:342–355.
    doi: 10.1111/j.1439-0388.2007.00700.xpubmed: 18076471google scholar: lookup
  40. Van den Berg I, Meuwissen THE, MacLeod IM, Goddard ME. 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
  41. Baby S, Hyeong KE, Lee YM, Jung JH, Oh DY, Nam KC, Kim TH, Lee HK, Kim JJ. Evaluation of Genome Based Estimated Breeding Values for Meat Quality in a Berkshire Population Using High Density Single Nucleotide Polymorphism Chips. Asian-Australas. J. Anim. Sci. 2014;27:1540.
    doi: 10.5713/ajas.2014.14371pmc: PMC4213697pubmed: 25358312google scholar: lookup
  42. Hidalgo J, Lourenco D, Tsuruta S, Masuda Y, Breen V, Hawken R, Bermann M, Misztal I. Investigating the Persistence of Accuracy of Genomic Predictions over Time in Broilers. J. Anim. Sci. 2021;99:skab239.
    doi: 10.1093/jas/skab239pmc: PMC8420680pubmed: 34378776google scholar: lookup
  43. Scott BA, Haile-Mariam M, Cocks BG, Pryce JE. How Genomic Selection Has Increased Rates of Genetic Gain and Inbreeding in the Australian National Herd, Genomic Information Nucleus, and Bulls. J. Dairy Sci. 2021;104:11832–11849.
    doi: 10.3168/jds.2021-20326pubmed: 34454757google scholar: lookup
  44. Guarini AR, Lourenco DAL, Brito LF, Sargolzaei M, Baes CF, Miglior F, Misztal I, Schenkel FS. 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
  45. VanRaden PM, Tooker ME, Wright JR, Sun C, Hutchison JL. 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
  46. 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
  47. Vostrỳ L, Čapková Z, Přibyl J, Hofmanová B, Vostrá Vydrová H, Mach K. Population Structure of Czech Cold-Blooded Breeds of Horses. Arch. Anim. Breed. 2011;54:1–9.
    doi: 10.5194/aab-54-1-2011google scholar: lookup
  48. Stock KF, 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
  49. König S, Simianer H, Willam A. Economic Evaluation of Genomic Breeding Programs. J. Dairy Sci. 2009;92:382–391.
    doi: 10.3168/jds.2008-1310pubmed: 19109296google scholar: lookup
  50. Weigel K, Chasco A, Pacheco H, Sigdel A, Guinan F, Lauber M, Fricke P, Peñagaricano F. Genomic Selection in Dairy Cattle: Impact and Contribution to the Improvement of Bovine Fertility. Clin. Theriogenol. 2024;16:10399.
    doi: 10.58292/ct.v16.10399google scholar: lookup
  51. De Roos APW, Schrooten C, Veerkamp RF, Van Arendonk JAM. Effects of Genomic Selection on Genetic Improvement, Inbreeding, and Merit of Young versus Proven Bulls. J. Dairy Sci. 2011;94:1559–1567.
    doi: 10.3168/jds.2010-3354pubmed: 21338821google scholar: lookup
  52. Doublet AC, Croiseau P, Fritz S, Michenet A, Hozé C, Danchin-Burge C, Laloë D, Restoux G. The Impact of Genomic Selection on Genetic Diversity and Genetic Gain in Three French Dairy Cattle Breeds. Genet. Sel. Evol. 2019;51:52.
    doi: 10.1186/s12711-019-0495-1pmc: PMC6757367pubmed: 31547802google scholar: lookup
  53. Forutan M, Ansari Mahyari S, Baes C, Melzer N, Schenkel FS, Sargolzaei M. Inbreeding and Runs of Homozygosity before and after Genomic Selection in North American Holstein Cattle. BMC Genom. 2018;19:98.
    doi: 10.1186/s12864-018-4453-zpmc: PMC5787230pubmed: 29374456google scholar: lookup
  54. Sonesson AK, Woolliams JA, Meuwissen TH. Genomic Selection Requires Genomic Control of Inbreeding. Genet. Sel. Evol. 2012;44:27.
    doi: 10.1186/1297-9686-44-27pmc: PMC3522025pubmed: 22898324google scholar: lookup
  55. Daetwyler HD, Villanueva B, Bijma P, Woolliams JA. Inbreeding in Genome-wide Selection. J. Anim. Breed. Genet. 2007;124:369–376.
    doi: 10.1111/j.1439-0388.2007.00693.xpubmed: 18076474google scholar: lookup

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