Random regression in comparison with finite-dimensional models for estimation of genetic parameters for growth traits in goats

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cg.authorship.typesCGIAR and developing country instituteen
cg.contributor.affiliationDebre Birhan Agricultural Research Center, Ethiopiaen
cg.contributor.affiliationSirinka Agricultural Research Center, Ethiopiaen
cg.contributor.affiliationInternational Center for Agricultural Research in the Dry Areasen
cg.contributor.affiliationInternational Livestock Research Instituteen
cg.creator.identifierTesfaye Getachew Mengistu: 0000-0002-0544-6314
cg.creator.identifierSelam Meseret: 0000-0002-1178-1821
cg.creator.identifierSolomon Gizaw: 0000-0002-0600-7188
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1007/s11250-025-04366-yen
cg.isijournalISI Journalen
cg.issn0049-4747en
cg.issue2en
cg.journalTropical Animal Health and Productionen
cg.reviewStatusPeer Reviewen
cg.subject.ilriANIMAL BREEDINGen
cg.subject.ilriGENETICSen
cg.subject.ilriGOATSen
cg.subject.ilriSMALL RUMINANTSen
cg.subject.impactAreaNutrition, health and food security
cg.subject.sdgSDG 2 - Zero hungeren
cg.volume57en
dc.contributor.authorTesema, Z.en
dc.contributor.authorDeribe, B.en
dc.contributor.authorGetachew, Tesfayeen
dc.contributor.authorMeseret, Selamen
dc.contributor.authorGizaw, Solomonen
dc.date.accessioned2025-03-18T05:25:36Zen
dc.date.available2025-03-18T05:25:36Zen
dc.identifier.urihttps://hdl.handle.net/10568/173664
dc.titleRandom regression in comparison with finite-dimensional models for estimation of genetic parameters for growth traits in goatsen
dcterms.abstractThe application of the random regression model in comparison with finite-dimensional models (univariate and multivariate animal models) for genetic parameter estimation of growth traits in goats was evaluated in this study. A total of 2888 body weight records from 875 animals, recorded from birth to yearling age were used. All models included direct additive genetic and maternal genetic effects as a random effect in addition to fixed effects. Random regression model (RRM) was fitted with different orders (1st – 3rd) of Legendre polynomials and accounted for both homogeneous and heterogeneous residual variance. The best-fitting RRM had a polynomial of three orders for both random effects. The direct heritability estimate obtained via RRM was moderate to high, while it varied from 0.00 ± 0.08 to 0.36 ± 0.10 in finite dimensional models. A lower standard error of heritability and genetic correlation estimates was observed with RRM compared to multivariate (MUV) and univariate (UNI) analysis. Likewise, high accuracy and reliability of breeding value estimates are obtained via RRM, whereas the accuracy for MUV and UNI animal models were moderate and low to moderate, respectively. Based on standard errors, accuracy, and reliability of estimates, RRM seems versatile for genetic evaluation of growth traits of goats. However, the MUV animal model is the best-fitting model, according to the information criteria values. Thus, for small and less frequently measured data set, multivariate animal model seems good. Further studies with large and frequently measured body weight data sets may help ensure random regression’s applicability and differentiate it from finite-dimensional models.en
dcterms.accessRightsLimited Access
dcterms.audienceAcademicsen
dcterms.audienceScientistsen
dcterms.available2025-03-15
dcterms.bibliographicCitationTesema, Z., Deribe, B., Getachew, T., Meseret, S. and Gizaw, S. 2025. Random regression in comparison with finite-dimensional models for estimation of genetic parameters for growth traits in goats. Tropical Animal Health and Production 57(2): 121.en
dcterms.extent121en
dcterms.issued2025-03-15
dcterms.languageen
dcterms.licenseOther
dcterms.publisherSpringeren
dcterms.subjectanimal breedingen
dcterms.subjectbreeding valueen
dcterms.subjectgeneticsen
dcterms.subjectgoatsen
dcterms.subjectsmall ruminantsen
dcterms.typeJournal Article

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