Reaction norm for yearling weight in beef cattle using single-step genomic evaluation
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1093/jas/skx006 http://hdl.handle.net/11449/166021 |
Resumo: | When the environment on which the animals are raised is very diverse, selecting the best sires for different environments may require the use of models that account for genotype by environment interaction (G x E). The main objective of this study was to evaluate the existence of G x E for yearling weight (YW) in Nellore cattle using reaction norm models with only pedigree and pedigree combined with genomic relationships. Additionally, genomic regions associated with each environment gradient were identified. A total of 67,996 YW records were used in reaction norm models to calculate EBV and genomic EBV. The method of choice for genomic evaluations was single-step genomic BLUP (ssGBLUP). Traditional and genomic models were tested on the ability to predict future animal performance. Genetic parameters for YW were obtained with the average information restricted maximum likelihood method, with and without adding genomic information for 5,091 animals. Additive genetic variances explained by windows of 200 adjacent SNP were used to identify genomic regions associated with the environmental gradient. Estimated variance components for the intercept and the slope in traditional and genomic models were similar. In both models, the observed changes in heritabilities and genetic correlations for YW across environments indicate the occurrence of genotype by environment interactions. Both traditional and genomic models were capable of identifying the genotype by environment interaction; however, the inclusion of genomic information in reaction norm models improved the ability to predict animals' future performance by 7.9% on average. The proportion of genetic variance explained by the top SNP window was 0.77% for the regression intercept (BTA5) and 0.82% for the slope (BTA14). Single-step GBLUP seems to be a suitable model to predict genetic values for YW in different production environments. |
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Reaction norm for yearling weight in beef cattle using single-step genomic evaluationBos taurus indicusgenomic predictiongenotype by environmentperformance traitsWhen the environment on which the animals are raised is very diverse, selecting the best sires for different environments may require the use of models that account for genotype by environment interaction (G x E). The main objective of this study was to evaluate the existence of G x E for yearling weight (YW) in Nellore cattle using reaction norm models with only pedigree and pedigree combined with genomic relationships. Additionally, genomic regions associated with each environment gradient were identified. A total of 67,996 YW records were used in reaction norm models to calculate EBV and genomic EBV. The method of choice for genomic evaluations was single-step genomic BLUP (ssGBLUP). Traditional and genomic models were tested on the ability to predict future animal performance. Genetic parameters for YW were obtained with the average information restricted maximum likelihood method, with and without adding genomic information for 5,091 animals. Additive genetic variances explained by windows of 200 adjacent SNP were used to identify genomic regions associated with the environmental gradient. Estimated variance components for the intercept and the slope in traditional and genomic models were similar. In both models, the observed changes in heritabilities and genetic correlations for YW across environments indicate the occurrence of genotype by environment interactions. Both traditional and genomic models were capable of identifying the genotype by environment interaction; however, the inclusion of genomic information in reaction norm models improved the ability to predict animals' future performance by 7.9% on average. The proportion of genetic variance explained by the top SNP window was 0.77% for the regression intercept (BTA5) and 0.82% for the slope (BTA14). Single-step GBLUP seems to be a suitable model to predict genetic values for YW in different production environments.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Sao Paulo State Univ, Dept Anim Sci, FCAV, BR-14884900 Jaboticabal, BrazilUniv Georgia, Dept Anim & Dairy Sci, Athens, GA 30602 USAFed Inst Sci & Technol Goiano, Campus Rio Verde, BR-75901970 Rio Verde, Go, BrazilFed Univ Grande Dourados UFGD, Coll Agr Sci, Dourados, MS, BrazilSao Paulo State Univ, Dept Anim Sci, FCAV, BR-14884900 Jaboticabal, BrazilCAPES: PDSE 012708/2013-05FAPESP: 2009/16118-5Oxford Univ Press IncUniversidade Estadual Paulista (Unesp)Univ GeorgiaFed Inst Sci & Technol GoianoFed Univ Grande Dourados UFGDOliveira, D. P. [UNESP]Lourenco, D. A. L.Tsuruta, S.Misztal, I.Santos, D. J. A. [UNESP]Araujo Neto, F. R. deAspilcueta-Borquis, R. R.Baldi, F. [UNESP]Carvalheiro, R. [UNESP]Camargo, G. M. F. de [UNESP]Albuquerque, L. G. [UNESP]Tonhati, H. [UNESP]2018-11-29T08:13:36Z2018-11-29T08:13:36Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article27-34application/pdfhttp://dx.doi.org/10.1093/jas/skx006Journal Of Animal Science. Cary: Oxford Univ Press Inc, v. 96, n. 1, p. 27-34, 2018.0021-8812http://hdl.handle.net/11449/16602110.1093/jas/skx006WOS:000425841600004WOS000425841600004.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal Of Animal Scienceinfo:eu-repo/semantics/openAccess2024-06-07T18:45:08Zoai:repositorio.unesp.br:11449/166021Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-06T00:08:51.929858Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Reaction norm for yearling weight in beef cattle using single-step genomic evaluation |
title |
Reaction norm for yearling weight in beef cattle using single-step genomic evaluation |
spellingShingle |
Reaction norm for yearling weight in beef cattle using single-step genomic evaluation Oliveira, D. P. [UNESP] Bos taurus indicus genomic prediction genotype by environment performance traits |
title_short |
Reaction norm for yearling weight in beef cattle using single-step genomic evaluation |
title_full |
Reaction norm for yearling weight in beef cattle using single-step genomic evaluation |
title_fullStr |
Reaction norm for yearling weight in beef cattle using single-step genomic evaluation |
title_full_unstemmed |
Reaction norm for yearling weight in beef cattle using single-step genomic evaluation |
title_sort |
Reaction norm for yearling weight in beef cattle using single-step genomic evaluation |
author |
Oliveira, D. P. [UNESP] |
author_facet |
Oliveira, D. P. [UNESP] Lourenco, D. A. L. Tsuruta, S. Misztal, I. Santos, D. J. A. [UNESP] Araujo Neto, F. R. de Aspilcueta-Borquis, R. R. Baldi, F. [UNESP] Carvalheiro, R. [UNESP] Camargo, G. M. F. de [UNESP] Albuquerque, L. G. [UNESP] Tonhati, H. [UNESP] |
author_role |
author |
author2 |
Lourenco, D. A. L. Tsuruta, S. Misztal, I. Santos, D. J. A. [UNESP] Araujo Neto, F. R. de Aspilcueta-Borquis, R. R. Baldi, F. [UNESP] Carvalheiro, R. [UNESP] Camargo, G. M. F. de [UNESP] Albuquerque, L. G. [UNESP] Tonhati, H. [UNESP] |
author2_role |
author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Univ Georgia Fed Inst Sci & Technol Goiano Fed Univ Grande Dourados UFGD |
dc.contributor.author.fl_str_mv |
Oliveira, D. P. [UNESP] Lourenco, D. A. L. Tsuruta, S. Misztal, I. Santos, D. J. A. [UNESP] Araujo Neto, F. R. de Aspilcueta-Borquis, R. R. Baldi, F. [UNESP] Carvalheiro, R. [UNESP] Camargo, G. M. F. de [UNESP] Albuquerque, L. G. [UNESP] Tonhati, H. [UNESP] |
dc.subject.por.fl_str_mv |
Bos taurus indicus genomic prediction genotype by environment performance traits |
topic |
Bos taurus indicus genomic prediction genotype by environment performance traits |
description |
When the environment on which the animals are raised is very diverse, selecting the best sires for different environments may require the use of models that account for genotype by environment interaction (G x E). The main objective of this study was to evaluate the existence of G x E for yearling weight (YW) in Nellore cattle using reaction norm models with only pedigree and pedigree combined with genomic relationships. Additionally, genomic regions associated with each environment gradient were identified. A total of 67,996 YW records were used in reaction norm models to calculate EBV and genomic EBV. The method of choice for genomic evaluations was single-step genomic BLUP (ssGBLUP). Traditional and genomic models were tested on the ability to predict future animal performance. Genetic parameters for YW were obtained with the average information restricted maximum likelihood method, with and without adding genomic information for 5,091 animals. Additive genetic variances explained by windows of 200 adjacent SNP were used to identify genomic regions associated with the environmental gradient. Estimated variance components for the intercept and the slope in traditional and genomic models were similar. In both models, the observed changes in heritabilities and genetic correlations for YW across environments indicate the occurrence of genotype by environment interactions. Both traditional and genomic models were capable of identifying the genotype by environment interaction; however, the inclusion of genomic information in reaction norm models improved the ability to predict animals' future performance by 7.9% on average. The proportion of genetic variance explained by the top SNP window was 0.77% for the regression intercept (BTA5) and 0.82% for the slope (BTA14). Single-step GBLUP seems to be a suitable model to predict genetic values for YW in different production environments. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-11-29T08:13:36Z 2018-11-29T08:13:36Z 2018-01-01 |
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/10.1093/jas/skx006 Journal Of Animal Science. Cary: Oxford Univ Press Inc, v. 96, n. 1, p. 27-34, 2018. 0021-8812 http://hdl.handle.net/11449/166021 10.1093/jas/skx006 WOS:000425841600004 WOS000425841600004.pdf |
url |
http://dx.doi.org/10.1093/jas/skx006 http://hdl.handle.net/11449/166021 |
identifier_str_mv |
Journal Of Animal Science. Cary: Oxford Univ Press Inc, v. 96, n. 1, p. 27-34, 2018. 0021-8812 10.1093/jas/skx006 WOS:000425841600004 WOS000425841600004.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal Of Animal Science |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
27-34 application/pdf |
dc.publisher.none.fl_str_mv |
Oxford Univ Press Inc |
publisher.none.fl_str_mv |
Oxford Univ Press Inc |
dc.source.none.fl_str_mv |
Web of Science 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_ |
1808129589849882624 |