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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
13/01/2020 |
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Data da última atualização: |
06/02/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
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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Afiliação: |
CLAUDIO NAPOLIS COSTA, CNPGL. |
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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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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Journal of Animal Breeding and Genetics, v. 137, n. 3, p. 305-315, 2020. |
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DOI: |
https://doi.org/10.1111/jbg.12459 |
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Idioma: |
Inglês |
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Conteúdo: |
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. |
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Palavras-Chave: |
Autoregression; Legendre polynomials; Random regression. |
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Thesaurus Nal: |
Dairy cattle. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02289naa a2200289 a 4500
001 2118639
005 2024-02-06
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1111/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
520 $aAutoregressive (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.
650 $aDairy cattle
653 $aAutoregression
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 $tJournal of Animal Breeding and Genetics$gv. 137, n. 3, p. 305-315, 2020.
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