Random regression models using B-splines functions provide more accurate genomic breeding values for milk yield and lactation persistence in Murrah buffaloes

Detalhes bibliográficos
Autor(a) principal: Silva, Alessandra A. [UNESP]
Data de Publicação: 2023
Outros Autores: Brito, Luiz F., Silva, Delvan A., Lazaro, Sirlene F. [UNESP], Silveira, Karina R. [UNESP], Stefani, Gabriela [UNESP], Tonhati, Humberto [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1111/jbg.12746
http://hdl.handle.net/11449/249341
Resumo: There is a great worldwide demand for cheese made with buffalo milk, due to its flavour and nutritional properties. In this context, there is a need for increasing the efficiency of buffalo milk production (including lactation persistence), which can be achieved through genomic selection. The most used methods for the genetic evaluation of longitudinal data, such as milk-related traits, are based on random regression models (RRM). The choice of the best covariance functions and polynomial order for modelling the random effects is an important step to properly fit RRM. To our best knowledge, there are no studies evaluating the impact of the order and covariance function (Legendre polynomials—LEG and B-splines—BSP) used to fit RRM for genomic prediction of breeding values in dairy buffaloes. Therefore, the main objectives of this study were to estimate variance components and evaluate the performance of LEG and BSP functions of different orders on the predictive ability of genomic breeding values for the first three lactations of milk yield (MY1, MY2, and MY3) and lactation persistence (LP1, LP2, and LP3) of Brazilian Murrah. Twenty-two models for each lactation were contrasted based on goodness of fit, genetic parameter estimates, and predictive ability. Overall, the models of higher orders of LEG or BSP had a better performance based on the deviance information criterion (DIC). The daily heritability estimates ranged from 0.01 to 0.30 for MY1, 0.08 to 0.42 for MY2, and from 0.05 to 0.47 for MY3. For lactation persistence (LP), the heritability estimates ranged from 0.09 to 0.32 for LP1, from 0.15 to 0.33 for LP2, and from 0.06 to 0.32 for LP3. In general, the curves plotted for variance components and heritability estimates based on BSP models presented lower oscillation along the lactation trajectory. Similar predictive ability was observed among the models. Considering a balance between the complexity of the model, goodness of fit, and credibility of the results, RRM using quadratic B-splines functions based on four or five segments to model the systematic, additive genetic, and permanent environment curves provide better fit with no significant differences between genetic variances estimates, heritabilities, and predictive ability for the genomic evaluation of dairy buffaloes.
id UNSP_522abb4f88c1d9c07c7ecedfa369532f
oai_identifier_str oai:repositorio.unesp.br:11449/249341
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Random regression models using B-splines functions provide more accurate genomic breeding values for milk yield and lactation persistence in Murrah buffaloesBubalus bubalislactation curveslongitudinal traitstest-day modelsThere is a great worldwide demand for cheese made with buffalo milk, due to its flavour and nutritional properties. In this context, there is a need for increasing the efficiency of buffalo milk production (including lactation persistence), which can be achieved through genomic selection. The most used methods for the genetic evaluation of longitudinal data, such as milk-related traits, are based on random regression models (RRM). The choice of the best covariance functions and polynomial order for modelling the random effects is an important step to properly fit RRM. To our best knowledge, there are no studies evaluating the impact of the order and covariance function (Legendre polynomials—LEG and B-splines—BSP) used to fit RRM for genomic prediction of breeding values in dairy buffaloes. Therefore, the main objectives of this study were to estimate variance components and evaluate the performance of LEG and BSP functions of different orders on the predictive ability of genomic breeding values for the first three lactations of milk yield (MY1, MY2, and MY3) and lactation persistence (LP1, LP2, and LP3) of Brazilian Murrah. Twenty-two models for each lactation were contrasted based on goodness of fit, genetic parameter estimates, and predictive ability. Overall, the models of higher orders of LEG or BSP had a better performance based on the deviance information criterion (DIC). The daily heritability estimates ranged from 0.01 to 0.30 for MY1, 0.08 to 0.42 for MY2, and from 0.05 to 0.47 for MY3. For lactation persistence (LP), the heritability estimates ranged from 0.09 to 0.32 for LP1, from 0.15 to 0.33 for LP2, and from 0.06 to 0.32 for LP3. In general, the curves plotted for variance components and heritability estimates based on BSP models presented lower oscillation along the lactation trajectory. Similar predictive ability was observed among the models. Considering a balance between the complexity of the model, goodness of fit, and credibility of the results, RRM using quadratic B-splines functions based on four or five segments to model the systematic, additive genetic, and permanent environment curves provide better fit with no significant differences between genetic variances estimates, heritabilities, and predictive ability for the genomic evaluation of dairy buffaloes.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Animal Science College of Agricultural and Veterinary Sciences São Paulo State University (UNESP)Department of Animal Sciences Purdue UniversityDepartment of Animal Science Universidade Federal de ViçosaDepartment of Animal Science College of Agricultural and Veterinary Sciences São Paulo State University (UNESP)Universidade Estadual Paulista (UNESP)Purdue UniversityUniversidade Federal de Viçosa (UFV)Silva, Alessandra A. [UNESP]Brito, Luiz F.Silva, Delvan A.Lazaro, Sirlene F. [UNESP]Silveira, Karina R. [UNESP]Stefani, Gabriela [UNESP]Tonhati, Humberto [UNESP]2023-07-29T15:13:27Z2023-07-29T15:13:27Z2023-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article167-184http://dx.doi.org/10.1111/jbg.12746Journal of Animal Breeding and Genetics, v. 140, n. 2, p. 167-184, 2023.1439-03880931-2668http://hdl.handle.net/11449/24934110.1111/jbg.127462-s2.0-85141389200Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Animal Breeding and Geneticsinfo:eu-repo/semantics/openAccess2024-06-07T18:43:35Zoai:repositorio.unesp.br:11449/249341Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:36:47.657641Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Random regression models using B-splines functions provide more accurate genomic breeding values for milk yield and lactation persistence in Murrah buffaloes
title Random regression models using B-splines functions provide more accurate genomic breeding values for milk yield and lactation persistence in Murrah buffaloes
spellingShingle Random regression models using B-splines functions provide more accurate genomic breeding values for milk yield and lactation persistence in Murrah buffaloes
Silva, Alessandra A. [UNESP]
Bubalus bubalis
lactation curves
longitudinal traits
test-day models
title_short Random regression models using B-splines functions provide more accurate genomic breeding values for milk yield and lactation persistence in Murrah buffaloes
title_full Random regression models using B-splines functions provide more accurate genomic breeding values for milk yield and lactation persistence in Murrah buffaloes
title_fullStr Random regression models using B-splines functions provide more accurate genomic breeding values for milk yield and lactation persistence in Murrah buffaloes
title_full_unstemmed Random regression models using B-splines functions provide more accurate genomic breeding values for milk yield and lactation persistence in Murrah buffaloes
title_sort Random regression models using B-splines functions provide more accurate genomic breeding values for milk yield and lactation persistence in Murrah buffaloes
author Silva, Alessandra A. [UNESP]
author_facet Silva, Alessandra A. [UNESP]
Brito, Luiz F.
Silva, Delvan A.
Lazaro, Sirlene F. [UNESP]
Silveira, Karina R. [UNESP]
Stefani, Gabriela [UNESP]
Tonhati, Humberto [UNESP]
author_role author
author2 Brito, Luiz F.
Silva, Delvan A.
Lazaro, Sirlene F. [UNESP]
Silveira, Karina R. [UNESP]
Stefani, Gabriela [UNESP]
Tonhati, Humberto [UNESP]
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Purdue University
Universidade Federal de Viçosa (UFV)
dc.contributor.author.fl_str_mv Silva, Alessandra A. [UNESP]
Brito, Luiz F.
Silva, Delvan A.
Lazaro, Sirlene F. [UNESP]
Silveira, Karina R. [UNESP]
Stefani, Gabriela [UNESP]
Tonhati, Humberto [UNESP]
dc.subject.por.fl_str_mv Bubalus bubalis
lactation curves
longitudinal traits
test-day models
topic Bubalus bubalis
lactation curves
longitudinal traits
test-day models
description There is a great worldwide demand for cheese made with buffalo milk, due to its flavour and nutritional properties. In this context, there is a need for increasing the efficiency of buffalo milk production (including lactation persistence), which can be achieved through genomic selection. The most used methods for the genetic evaluation of longitudinal data, such as milk-related traits, are based on random regression models (RRM). The choice of the best covariance functions and polynomial order for modelling the random effects is an important step to properly fit RRM. To our best knowledge, there are no studies evaluating the impact of the order and covariance function (Legendre polynomials—LEG and B-splines—BSP) used to fit RRM for genomic prediction of breeding values in dairy buffaloes. Therefore, the main objectives of this study were to estimate variance components and evaluate the performance of LEG and BSP functions of different orders on the predictive ability of genomic breeding values for the first three lactations of milk yield (MY1, MY2, and MY3) and lactation persistence (LP1, LP2, and LP3) of Brazilian Murrah. Twenty-two models for each lactation were contrasted based on goodness of fit, genetic parameter estimates, and predictive ability. Overall, the models of higher orders of LEG or BSP had a better performance based on the deviance information criterion (DIC). The daily heritability estimates ranged from 0.01 to 0.30 for MY1, 0.08 to 0.42 for MY2, and from 0.05 to 0.47 for MY3. For lactation persistence (LP), the heritability estimates ranged from 0.09 to 0.32 for LP1, from 0.15 to 0.33 for LP2, and from 0.06 to 0.32 for LP3. In general, the curves plotted for variance components and heritability estimates based on BSP models presented lower oscillation along the lactation trajectory. Similar predictive ability was observed among the models. Considering a balance between the complexity of the model, goodness of fit, and credibility of the results, RRM using quadratic B-splines functions based on four or five segments to model the systematic, additive genetic, and permanent environment curves provide better fit with no significant differences between genetic variances estimates, heritabilities, and predictive ability for the genomic evaluation of dairy buffaloes.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-29T15:13:27Z
2023-07-29T15:13:27Z
2023-03-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1111/jbg.12746
Journal of Animal Breeding and Genetics, v. 140, n. 2, p. 167-184, 2023.
1439-0388
0931-2668
http://hdl.handle.net/11449/249341
10.1111/jbg.12746
2-s2.0-85141389200
url http://dx.doi.org/10.1111/jbg.12746
http://hdl.handle.net/11449/249341
identifier_str_mv Journal of Animal Breeding and Genetics, v. 140, n. 2, p. 167-184, 2023.
1439-0388
0931-2668
10.1111/jbg.12746
2-s2.0-85141389200
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal of Animal Breeding and Genetics
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 167-184
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808129226930388992