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Background: Cross-validation techniques in genetic evaluations encounter limitations due to the unobservable nature of breeding values and the challenge of validating estimated breeding values (EBVs) against pre-corrected phenotypes, challenges which the Linear Regression (LR) method addresses as an alternative. Furthermore, beef cattle genetic evaluation programs confront challenges with connectedness among herds and pedigree errors. The objective of [ver mas...]
dc.contributor.authorPardo, Alan Maxs
dc.contributor.authorLegarra, Andres
dc.contributor.authorVitezica, Zulma G.
dc.contributor.authorForneris, Natalia S.
dc.contributor.authorMaizon, Daniel Omar
dc.contributor.authorMunilla, Sebastian
dc.date.accessioned2025-03-17T12:45:49Z
dc.date.available2025-03-17T12:45:49Z
dc.date.issued2024-11
dc.identifier.issn1297-9686
dc.identifier.otherhttps://doi.org/10.1186/s12711-024-00943-1
dc.identifier.urihttp://hdl.handle.net/20.500.12123/21684
dc.identifier.urihttps://gsejournal.biomedcentral.com/articles/10.1186/s12711-024-00943-1
dc.description.abstractBackground: Cross-validation techniques in genetic evaluations encounter limitations due to the unobservable nature of breeding values and the challenge of validating estimated breeding values (EBVs) against pre-corrected phenotypes, challenges which the Linear Regression (LR) method addresses as an alternative. Furthermore, beef cattle genetic evaluation programs confront challenges with connectedness among herds and pedigree errors. The objective of this work was to evaluate the LR method's performance under pedigree errors and weak connectedness typical in beef cattle genetic evaluations, through simulation. Methods: We simulated a beef cattle population resembling the Argentinean Brangus, including a quantitative trait selected over six pseudo-generations with a heritability of 0.4. This study considered various scenarios, including: 25% and 40% pedigree errors (PE-25 and PE-40), weak and strong connectedness among herds (WCO and SCO, respectively), and a benchmark scenario (BEN) with complete pedigree and optimal herd connections. Results: Over six pseudo-generations of selection, genetic gain was simulated to be under- and over-estimated in PE-40 and WCO, respectively, contrary to the BEN scenario which was unbiased. In genetic evaluations with PE-25 and PE-40, true biases of − 0.13 and − 0.18 genetic standard deviations were simulated, respectively. In the BEN scenario, the LR method accurately estimated bias, however, in PE-25 and PE-40 scenarios, it overestimated biases by 0.17 and 0.25 genetic standard deviations, respectively. In herds facing WCO, significant true bias due to confounding environmental and genetic effects was simulated, and the corresponding LR statistic failed to accurately estimate the magnitude and direction of this bias. On average, true dispersion values were close to one for BEN, PE-40, SCO and WCO, showing no significant inflation or deflation, and the values were accurately estimated by LR. However, PE-25 exhibited inflation of EBVs and was slightly underestimated by LR. Accuracies and reliabilities showed good agreement between true and LR estimated values for the scenarios evaluated. Conclusions: The LR method demonstrated limitations in identifying biases induced by incomplete pedigrees, including scenarios with as much as 40% pedigree errors, or lack of connectedness, but it was effective in assessing dispersion, and population accuracies and reliabilities even in the challenging scenarios addressed.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherBMCes_AR
dc.relationinfo:eu-repograntAgreement/INTA/2019-PE-E6-I145-001, Mejora genética objetiva para aumentar la eficiencia de los sistemas de producción animales_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/es_AR
dc.sourceGenetics Selection Evolution 56 : Article number: 74. (2024)es_AR
dc.subjectAnálisis de la Regresiónes_AR
dc.subjectRegression Analysiseng
dc.subjectPedigríes_AR
dc.subjectPedigreeseng
dc.subjectFenotiposes_AR
dc.subjectPhenotypeseng
dc.subjectGanado Bovinoes_AR
dc.subjectCattleeng
dc.titleOn the ability of the LR method to detect bias when there is pedigree misspecification and lack of connectednesses_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.rights.licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)es_AR
dc.description.origenEEA Balcarcees_AR
dc.description.filFil: Pardo, Alan Maxs. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.es_AR
dc.description.filFil: Pardo, Alan Maxs. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentinaes_AR
dc.description.filFil: Legarra, Andres. Council On Dairy Cattle Breeding; Estados Unidoses_AR
dc.description.filFil: Vitezica, Zulma G. INPT/INRAE-UMR; Franciaes_AR
dc.description.filFil: Forneris, Natalia S. Universidad de Buenos Aires. Facultad de Agronomía; Argentinaes_AR
dc.description.filFil: Forneris, Natalia S. Universidad de Buenos Aires. Instituto de Investigaciones en Producción Animal; Argentinaes_AR
dc.description.filFil: Forneris, Natalia S. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Producción Animal; Argentinaes_AR
dc.description.filFil: Maizon, Daniel Omar. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil; Argentinaes_AR
dc.description.filFil: Munilla, Sebastian. Universidad de Buenos Aires. Facultad de Agronomía; Argentinaes_AR
dc.description.filFil: Munilla, Sebastian. Universidad de Buenos Aires. Instituto de Investigaciones en Producción Animal; Argentinaes_AR
dc.description.filFil: Munilla, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Producción Animal; Argentinaes_AR
dc.subtypecientifico


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