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| 187. | | CARDOSO, F. F.; SOLLERO, B. P.; COMIN, H. B.; GOMES, C. G.; ROSO, V. M.; HIGA, R. H.; CAETANO, A. R.; YOKOO, M. J.; AGUILAR, I. Accuracy of genomic prediction for tick resistance in Braford and Hereford cattle. Volume Species Breeding: Beef cattle (Posters), 713. In: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.713. Acknowledgments: Research supported by CNPq - National Council for Scientific and Technological Development grant 478992/2012-2, Embrapa - Brazilian Agricultural Research Corporation grants 02.09.07.004 and 01.11.07.002.07, and CAPES -...| Biblioteca(s): INIA Las Brujas. |
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| 198. | | ROMAN, L.; LA MANNA, A.; ACOSTA, Y.; MENDOZA, A.; AGUILAR, I.; MORALES-PIÑEYRUA, J.; PLA, M.; LAURA ASTIGARRAGA, L.; SARAVIA, C. Evaluación de medidas de mitigación del estrés por calor sobre las respuestas productivas de vacas lecheras de alta producción. In: Día de Campo: producción de forraje y leche en verano. La Estanzuela, Colonia, (Uruguay): INIA, 2013. p. 15. (Serie Actividades de Difusión; 705).| Biblioteca(s): INIA La Estanzuela. |
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| 199. | | NAVAJAS, E.; MACEDO, F.; RAVAGNOLO, O.; AGUILAR, I.; CLARIGET, J.; LEMA, O.M.; PERAZA, P.; PRAVIA, M.I.; DALLA RIZZA, M.; CIAPPESONI, G. Herramientas genómicas para mejorar la eficiencia de alimentación y la calidad de canal de la raza Hereford. 3 - SIMPOSIOS "MEJORA GENÉTICA EN PRODUCCIÓN Y CALIDAD DE CARNE EN ESPECIES DE INTERÉS ECONÓMICO" In: JOURNAL OF BASIC & APPLIED GENETICS, 2016, Vol.27, Iss. 1 (Supp.). XVI LATIN AMERICAN CONGRESS OF GENETICS, IV CONGRESS OF THE URUGUAYAN SOCIETY OF GENETICS, XLIX ANNUAL MEETING OF THE GENETICS SOCIETY OF CHILE, XLV ARGENTINE CONGRESS OF GENETICS, 9-12 October 2016. PROCEEDINGS. Montevideo (Uruguay): SAG, 2016. p. 28| Biblioteca(s): INIA Las Brujas. |
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| 200. | | LOURENCO, D. A. L.; TSURUTA, S.; FRAGOMENI, B. O.; MASUDA, Y.; AGUILAR, I.; LEGARRA, A.; BERTRAND, J. K.; AMEN, T. S.; WANG. L.; MOSER, D. W.; MISZTAL, I. Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.(*) Journal of Animal Science, 2015, v. 93, p. 2653-2662. Published June 25, 2015. OPEN ACCESS. (*) This study was partially funded by the American Angus Association (St. Joseph, MO), Zoetis (Kalamazoo, MI), and Agriculture and Food Research Initiative Competitive Grants no. 2015-67015-22936 from the U.S. Department of Agriculture?s...| Biblioteca(s): INIA Las Brujas. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
27/01/2020 |
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Actualizado : |
29/05/2020 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
SILVA, D.A.; COSTA, C.N.; SILVA, A.A.; SILVA, H.T.; LOPES, P.S.; SILVA, F.F.; VERONEZE, R.; THOMPSON, G.; AGUILAR, I.; CARVALHEIRA, J. |
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Afiliación : |
DELVAN ALVES SILVA, Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil; CLAUDIO NÁPOLIS COSTA, Embrapa Dairy Cattle, Juiz de Fora, Brazil; ALESSANDRA ALVES SILVA, Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil; HUGO TEIXEIRA SILVA, Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil; PAULO SÁVIO LOPES, Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil; FABYANO FONSECA SILVA, Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil; RENATA VERONEZE, Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Brazil; GERTRUDE THOMPSON, Research Center in Biodiversity and Genetic Resources (CIBIO-InBio), University of Porto, Vairão, Portugal; Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, Vairão, Portugal; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JÚLIO CARVALHEIRA, Research Center in Biodiversity and Genetic Resources (CIBIO-InBio), University of Porto, Vairão, Portugal; Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, Vairão, Portugal. |
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Título : |
Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Journal of Animal Breeding and Genetics, 1 May 2020, Volume 137, Issue 3, Pages 305-315. Doi: https://doi.org/10.1111/jbg.12459 |
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ISSN : |
0931-2668 |
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DOI : |
10.1111/jbg.12459 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 10 July 2019 / Revised: 31 October 2019 / Accepted: 3 November 2019 / First published: 08 December 2019.
Funding information: The authors acknowledge the Brazilian Holstein Cattle Breeders Association (ABCBRH) for providing data for this study. This study was partially financed by Coordination for the Improvement of Higher Education Personnel and Portuguese National Funding Agency for Science, Research and Technology (CAPES/FCT, nº 99999.008462/2014‐03 and 88887.125450/2016‐00), and National Council of Technological and Scientific Development (CNPq 465377/2014‐9 ‐ PROGRAMA INCT and CNPq 142467/20154). |
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Contenido : |
ABSTRACT.
Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and −0.019 (−0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and −0.022 (−0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.
© 2019 Blackwell Verlag GmbH |
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Palabras claves : |
Autoregression; Dairy cattle; Legendre polynomials; Random regression. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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Marc : |
LEADER 03092naa a2200313 a 4500
001 1060695
005 2020-05-29
008 2020 bl uuuu u00u1 u #d
022 $a0931-2668
024 7 $a10.1111/jbg.12459$2DOI
100 1 $aSILVA, D.A.
245 $aAutoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle.$h[electronic resource]
260 $c2020
500 $aArticle history: Received: 10 July 2019 / Revised: 31 October 2019 / Accepted: 3 November 2019 / First published: 08 December 2019. Funding information: The authors acknowledge the Brazilian Holstein Cattle Breeders Association (ABCBRH) for providing data for this study. This study was partially financed by Coordination for the Improvement of Higher Education Personnel and Portuguese National Funding Agency for Science, Research and Technology (CAPES/FCT, nº 99999.008462/2014‐03 and 88887.125450/2016‐00), and National Council of Technological and Scientific Development (CNPq 465377/2014‐9 ‐ PROGRAMA INCT and CNPq 142467/20154).
520 $aABSTRACT. Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and −0.019 (−0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and −0.022 (−0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder. © 2019 Blackwell Verlag GmbH
653 $aAutoregression
653 $aDairy cattle
653 $aLegendre polynomials
653 $aRandom regression
700 1 $aCOSTA, C.N.
700 1 $aSILVA, A.A.
700 1 $aSILVA, H.T.
700 1 $aLOPES, P.S.
700 1 $aSILVA, F.F.
700 1 $aVERONEZE, R.
700 1 $aTHOMPSON, G.
700 1 $aAGUILAR, I.
700 1 $aCARVALHEIRA, J.
773 $tJournal of Animal Breeding and Genetics, 1 May 2020, Volume 137, Issue 3, Pages 305-315. Doi: https://doi.org/10.1111/jbg.12459
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