Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models.

Knowledge of lactation curves in dairy cattle is essential for understanding the animal production in milk production systems. Genomic prediction of lactation curves represents the genetic pattern of milk production of the animals in the herd. In this context, we made genomic predictions of lactation curves through genome-wide selection (GWS) to characterize the genetic pattern of lactation traits in Girolando cattle based on parameters estimated by nonlinear mixed effects (NLME) models. Data of 1,822 milk control records from 226 Girolando animals genotyped for 37,673 single nucleotide polymorphisms were analyzed. Nine NLME models were compared to identify the equation with the best fit. The lactation traits estimated by the best model were submitted to GWS analysis, using the Bayesian LASSO method. Then, based on the genomic estimated breeding values (GEBVs) obtained, genomic predictions of lactation curves were constructed, and the genetic parameters were calculated. Wood's equation showed the best fit among the evaluated models. Heritabilities ranged from 0.09 to 0.29 for the seven lactation variables (initial production, rates of increase and decline, lactation peak, time to peak yield, persistence and total production). The correlations among GEBVs ranged from -0.85 to 0.98. The concordances between the best animals selected according to the selected traits were greater when the correlations between GEBVs for these traits were also high. Consequently, the methodology allowed us to identify the best nonlinear model and to construct the genetic lactation curves of a Girolando cattle population, as well as to assess the differences between animals and the association between lactation variables.

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Main Authors: TEIXEIRA, F. R. F., NASCIMENTO, M., CECON, P. R., CRUZ, C. D., SILVA, F. F. e, NASCIMENTO, A. C. C., AZEVEDO, C. F., MARQUES, D. B. D., SILVA, M. V. G. B., CARNEIRO, A. P. S., PAIXAO, D. M.
Other Authors: Universidade Federal do Piauí; Universidade Federal de Viçosa; Universidade Federal de Viçosa; Universidade Federal de Viçosa; Universidade Federal de Viçosa; A.C.C. NASCIMENTO, Universidade Federal de Viçosa; Universidade Federal de Viçosa; D.B.D. MARQUES, Universidade Federal de Viçosa; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; A.P.S. CARNEIRO, Universidade Federal de Viçosa; Universidade de São Paulo.
Format: Artigo de periódico biblioteca
Language:Ingles
English
Published: 2021-08-13
Subjects:Previsão genômica, Bovino, Gado Leiteiro, Curva de Lactação, Heritability, Genome, Girolando,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/1133535
http://dx.doi.org/10.4238/gmr18691
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spelling dig-alice-doc-11335352021-08-13T17:00:32Z Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models. TEIXEIRA, F. R. F. NASCIMENTO, M. CECON, P. R. CRUZ, C. D. SILVA, F. F. e NASCIMENTO, A. C. C. AZEVEDO, C. F. MARQUES, D. B. D. SILVA, M. V. G. B. CARNEIRO, A. P. S. PAIXAO, D. M. Universidade Federal do Piauí; Universidade Federal de Viçosa; Universidade Federal de Viçosa; Universidade Federal de Viçosa; Universidade Federal de Viçosa; A.C.C. NASCIMENTO, Universidade Federal de Viçosa; Universidade Federal de Viçosa; D.B.D. MARQUES, Universidade Federal de Viçosa; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; A.P.S. CARNEIRO, Universidade Federal de Viçosa; Universidade de São Paulo. Previsão genômica Bovino Gado Leiteiro Curva de Lactação Heritability Genome Girolando Knowledge of lactation curves in dairy cattle is essential for understanding the animal production in milk production systems. Genomic prediction of lactation curves represents the genetic pattern of milk production of the animals in the herd. In this context, we made genomic predictions of lactation curves through genome-wide selection (GWS) to characterize the genetic pattern of lactation traits in Girolando cattle based on parameters estimated by nonlinear mixed effects (NLME) models. Data of 1,822 milk control records from 226 Girolando animals genotyped for 37,673 single nucleotide polymorphisms were analyzed. Nine NLME models were compared to identify the equation with the best fit. The lactation traits estimated by the best model were submitted to GWS analysis, using the Bayesian LASSO method. Then, based on the genomic estimated breeding values (GEBVs) obtained, genomic predictions of lactation curves were constructed, and the genetic parameters were calculated. Wood's equation showed the best fit among the evaluated models. Heritabilities ranged from 0.09 to 0.29 for the seven lactation variables (initial production, rates of increase and decline, lactation peak, time to peak yield, persistence and total production). The correlations among GEBVs ranged from -0.85 to 0.98. The concordances between the best animals selected according to the selected traits were greater when the correlations between GEBVs for these traits were also high. Consequently, the methodology allowed us to identify the best nonlinear model and to construct the genetic lactation curves of a Girolando cattle population, as well as to assess the differences between animals and the association between lactation variables. 2021-08-13T17:00:23Z 2021-08-13T17:00:23Z 2021-08-13 2021 Artigo de periódico Genetics and Molecular Research, v. 20, n. 1, gmr18691, 2021. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1133535 http://dx.doi.org/10.4238/gmr18691 Ingles en openAccess
institution EMBRAPA
collection DSpace
country Brasil
countrycode BR
component Bibliográfico
access En linea
databasecode dig-alice
tag biblioteca
region America del Sur
libraryname Sistema de bibliotecas de EMBRAPA
language Ingles
English
topic Previsão genômica
Bovino
Gado Leiteiro
Curva de Lactação
Heritability
Genome
Girolando
Previsão genômica
Bovino
Gado Leiteiro
Curva de Lactação
Heritability
Genome
Girolando
spellingShingle Previsão genômica
Bovino
Gado Leiteiro
Curva de Lactação
Heritability
Genome
Girolando
Previsão genômica
Bovino
Gado Leiteiro
Curva de Lactação
Heritability
Genome
Girolando
TEIXEIRA, F. R. F.
NASCIMENTO, M.
CECON, P. R.
CRUZ, C. D.
SILVA, F. F. e
NASCIMENTO, A. C. C.
AZEVEDO, C. F.
MARQUES, D. B. D.
SILVA, M. V. G. B.
CARNEIRO, A. P. S.
PAIXAO, D. M.
Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models.
description Knowledge of lactation curves in dairy cattle is essential for understanding the animal production in milk production systems. Genomic prediction of lactation curves represents the genetic pattern of milk production of the animals in the herd. In this context, we made genomic predictions of lactation curves through genome-wide selection (GWS) to characterize the genetic pattern of lactation traits in Girolando cattle based on parameters estimated by nonlinear mixed effects (NLME) models. Data of 1,822 milk control records from 226 Girolando animals genotyped for 37,673 single nucleotide polymorphisms were analyzed. Nine NLME models were compared to identify the equation with the best fit. The lactation traits estimated by the best model were submitted to GWS analysis, using the Bayesian LASSO method. Then, based on the genomic estimated breeding values (GEBVs) obtained, genomic predictions of lactation curves were constructed, and the genetic parameters were calculated. Wood's equation showed the best fit among the evaluated models. Heritabilities ranged from 0.09 to 0.29 for the seven lactation variables (initial production, rates of increase and decline, lactation peak, time to peak yield, persistence and total production). The correlations among GEBVs ranged from -0.85 to 0.98. The concordances between the best animals selected according to the selected traits were greater when the correlations between GEBVs for these traits were also high. Consequently, the methodology allowed us to identify the best nonlinear model and to construct the genetic lactation curves of a Girolando cattle population, as well as to assess the differences between animals and the association between lactation variables.
author2 Universidade Federal do Piauí; Universidade Federal de Viçosa; Universidade Federal de Viçosa; Universidade Federal de Viçosa; Universidade Federal de Viçosa; A.C.C. NASCIMENTO, Universidade Federal de Viçosa; Universidade Federal de Viçosa; D.B.D. MARQUES, Universidade Federal de Viçosa; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; A.P.S. CARNEIRO, Universidade Federal de Viçosa; Universidade de São Paulo.
author_facet Universidade Federal do Piauí; Universidade Federal de Viçosa; Universidade Federal de Viçosa; Universidade Federal de Viçosa; Universidade Federal de Viçosa; A.C.C. NASCIMENTO, Universidade Federal de Viçosa; Universidade Federal de Viçosa; D.B.D. MARQUES, Universidade Federal de Viçosa; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; A.P.S. CARNEIRO, Universidade Federal de Viçosa; Universidade de São Paulo.
TEIXEIRA, F. R. F.
NASCIMENTO, M.
CECON, P. R.
CRUZ, C. D.
SILVA, F. F. e
NASCIMENTO, A. C. C.
AZEVEDO, C. F.
MARQUES, D. B. D.
SILVA, M. V. G. B.
CARNEIRO, A. P. S.
PAIXAO, D. M.
format Artigo de periódico
topic_facet Previsão genômica
Bovino
Gado Leiteiro
Curva de Lactação
Heritability
Genome
Girolando
author TEIXEIRA, F. R. F.
NASCIMENTO, M.
CECON, P. R.
CRUZ, C. D.
SILVA, F. F. e
NASCIMENTO, A. C. C.
AZEVEDO, C. F.
MARQUES, D. B. D.
SILVA, M. V. G. B.
CARNEIRO, A. P. S.
PAIXAO, D. M.
author_sort TEIXEIRA, F. R. F.
title Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models.
title_short Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models.
title_full Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models.
title_fullStr Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models.
title_full_unstemmed Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models.
title_sort genomic prediction of lactation curves of girolando cattle based on nonlinear mixed models.
publishDate 2021-08-13
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1133535
http://dx.doi.org/10.4238/gmr18691
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