Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle

Detalhes bibliográficos
Autor(a) principal: Somavilla, Adriana Luiza [UNESP]
Data de Publicação: 2015
Tipo de documento: Tese
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/128158
http://www.athena.biblioteca.unesp.br/exlibris/bd/cathedra/18-09-2015/000848601.pdf
Resumo: Selection for fast growth rates using number of days to achieve specific weights or average weight gain would result in shorter production periods. Maintaining the rate of productivity increasing will demand, among other factors, genetically improved animals in both pasture and feedlot systems. Besides, genomic information could be used to predict genomic-enabled breeding values (GEBVs) earlier in animals' life, which would reduce generation intervals and increase productivity gains. Numerous studies have been conducted in order to identify appropriate methodologies to specific breeds and traits, which will result in more accurate GEBVs. The aim of this study was to compare the prediction accuracy of GEBVs and the ability to identify genomic regions and genes related to average weight daily gain in Nelore cattle, by applying different regression models and genotypes densities datasets. Genomic and phenotypic information of 804 steers born in three season, offspring of 34 bulls, were used to predict GEBVs through three models (Bayesian GBLUP, BayesA and BayesC ), four genotypic densities (Illumina BovineHD BeadChip, TagSNPs, GeneSeek Genomic Profiler High (HDi) and Low (LDi) density indicus) and two adjusted phenotypes. Family structure was accounted by using principal component analysis. Animals were assigned either to training (seasons 1 and 2) or testing (season 3) subsets to perform the cross-validation analysis. Estimates of Pearson correlation, regression coefficients and mean squared errors were used to access accuracy, inflation and bias of the estimated GEBVs, respectively. Genome-wide association study (GWAS) was also performed on above datasets, however, results were compared based on ...
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spelling Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattleBovinoTeoria bayesiana de decisão estatisticaNelore (Zebu)CrescimentoGenômicaGenetica animalGenomicsSelection for fast growth rates using number of days to achieve specific weights or average weight gain would result in shorter production periods. Maintaining the rate of productivity increasing will demand, among other factors, genetically improved animals in both pasture and feedlot systems. Besides, genomic information could be used to predict genomic-enabled breeding values (GEBVs) earlier in animals' life, which would reduce generation intervals and increase productivity gains. Numerous studies have been conducted in order to identify appropriate methodologies to specific breeds and traits, which will result in more accurate GEBVs. The aim of this study was to compare the prediction accuracy of GEBVs and the ability to identify genomic regions and genes related to average weight daily gain in Nelore cattle, by applying different regression models and genotypes densities datasets. Genomic and phenotypic information of 804 steers born in three season, offspring of 34 bulls, were used to predict GEBVs through three models (Bayesian GBLUP, BayesA and BayesC ), four genotypic densities (Illumina BovineHD BeadChip, TagSNPs, GeneSeek Genomic Profiler High (HDi) and Low (LDi) density indicus) and two adjusted phenotypes. Family structure was accounted by using principal component analysis. Animals were assigned either to training (seasons 1 and 2) or testing (season 3) subsets to perform the cross-validation analysis. Estimates of Pearson correlation, regression coefficients and mean squared errors were used to access accuracy, inflation and bias of the estimated GEBVs, respectively. Genome-wide association study (GWAS) was also performed on above datasets, however, results were compared based on ...A seleção para taxa de crescimento utilizando o número de dias para atingir determinado peso ou ganho de peso médio resultaria em menores ciclos de produção. Manter o aumento da produtividade exige, entre outros fatores, a utilização de animais melhorados, tanto nos sistemas de pastagem quanto de confinamento. Além disso, as informações genômicas podem ser usadas para predizer os valores genéticos genômicos (GEBVs) mais cedo na vida dos animais, o que reduziria os intervalos de geração e aumentaria os ganhos de produtividade. Inúmeros trabalhos tem sido conduzidos para identificar metodologias apropriadas à determinadas raças e características, o que irá resultar em GEBVs mais acurados. Os objetivos deste estudo foram comparar a acurácia de predição dos GEBVs e a habilidade de identificar regiões genômicas e genes relacionados ao ganho de peso médio diário em bovinos da raça Nelore, pela aplicação de diferentes modelos de regressão e densidades genotípicas. Informações genômica e fenotípica de 804 novilhos nascidos em três safras, filhos de 34 touros, foram utilizadas para predizer GEBVs por meio de três modelos (Bayesian GBLUP, BayesA e BayesC ), quatro densidades genotípicas (Illumina BovineHD BeadChip, TagSNPs, GeneSeek indicus de alta (HDi) e baixa (LDi) densidades) e dois fenótipos ajustados. A estrutura de família foi considerada por meio da análise de componentes principais. Os animais foram distribuídos em subconjunto de treinamento (safras 1 e 2) ou validação (safra 3) para realização da análise de validação cruzada. Estimativas de correlação de Pearson, coeficientes de regressão e erro quadrado médio foram usados para avaliar acurácia, inflação e viés dos GEBVs estimados, respectivamente. O estudo de associação ampla do genoma (GWAS) também foi realizado nos mesmos conjuntos de dados, entretanto, os resultados foram comparados com...Universidade Estadual Paulista (Unesp)Munari, Danísio Prado [UNESP]Regitano, Luciana Correia de Almeida [UNESP]Mokry, Fabiana Barichello [UNESP]Universidade Estadual Paulista (Unesp)Somavilla, Adriana Luiza [UNESP]2015-10-06T13:03:36Z2015-10-06T13:03:36Z2015-06-26info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisv, 71 p. : il.application/pdfSOMAVILLA, Adriana Luiza. Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle. 2015. v, 71 p. Tese (doutorado) - Universidade Estadual Paulista Júlio de Mesquita Filho, Faculdade de Ciências Agrárias e Veterinárias, 2015.http://hdl.handle.net/11449/128158000848601http://www.athena.biblioteca.unesp.br/exlibris/bd/cathedra/18-09-2015/000848601.pdf33004102030P46064277731903249Alephreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPenginfo:eu-repo/semantics/openAccess2024-06-05T18:32:21Zoai:repositorio.unesp.br:11449/128158Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:48:03.132928Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle
title Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle
spellingShingle Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle
Somavilla, Adriana Luiza [UNESP]
Bovino
Teoria bayesiana de decisão estatistica
Nelore (Zebu)
Crescimento
Genômica
Genetica animal
Genomics
title_short Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle
title_full Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle
title_fullStr Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle
title_full_unstemmed Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle
title_sort Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle
author Somavilla, Adriana Luiza [UNESP]
author_facet Somavilla, Adriana Luiza [UNESP]
author_role author
dc.contributor.none.fl_str_mv Munari, Danísio Prado [UNESP]
Regitano, Luciana Correia de Almeida [UNESP]
Mokry, Fabiana Barichello [UNESP]
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Somavilla, Adriana Luiza [UNESP]
dc.subject.por.fl_str_mv Bovino
Teoria bayesiana de decisão estatistica
Nelore (Zebu)
Crescimento
Genômica
Genetica animal
Genomics
topic Bovino
Teoria bayesiana de decisão estatistica
Nelore (Zebu)
Crescimento
Genômica
Genetica animal
Genomics
description Selection for fast growth rates using number of days to achieve specific weights or average weight gain would result in shorter production periods. Maintaining the rate of productivity increasing will demand, among other factors, genetically improved animals in both pasture and feedlot systems. Besides, genomic information could be used to predict genomic-enabled breeding values (GEBVs) earlier in animals' life, which would reduce generation intervals and increase productivity gains. Numerous studies have been conducted in order to identify appropriate methodologies to specific breeds and traits, which will result in more accurate GEBVs. The aim of this study was to compare the prediction accuracy of GEBVs and the ability to identify genomic regions and genes related to average weight daily gain in Nelore cattle, by applying different regression models and genotypes densities datasets. Genomic and phenotypic information of 804 steers born in three season, offspring of 34 bulls, were used to predict GEBVs through three models (Bayesian GBLUP, BayesA and BayesC ), four genotypic densities (Illumina BovineHD BeadChip, TagSNPs, GeneSeek Genomic Profiler High (HDi) and Low (LDi) density indicus) and two adjusted phenotypes. Family structure was accounted by using principal component analysis. Animals were assigned either to training (seasons 1 and 2) or testing (season 3) subsets to perform the cross-validation analysis. Estimates of Pearson correlation, regression coefficients and mean squared errors were used to access accuracy, inflation and bias of the estimated GEBVs, respectively. Genome-wide association study (GWAS) was also performed on above datasets, however, results were compared based on ...
publishDate 2015
dc.date.none.fl_str_mv 2015-10-06T13:03:36Z
2015-10-06T13:03:36Z
2015-06-26
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv SOMAVILLA, Adriana Luiza. Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle. 2015. v, 71 p. Tese (doutorado) - Universidade Estadual Paulista Júlio de Mesquita Filho, Faculdade de Ciências Agrárias e Veterinárias, 2015.
http://hdl.handle.net/11449/128158
000848601
http://www.athena.biblioteca.unesp.br/exlibris/bd/cathedra/18-09-2015/000848601.pdf
33004102030P4
6064277731903249
identifier_str_mv SOMAVILLA, Adriana Luiza. Prediction of genomic-enabled breeding values and genome-wide association study for feedlot average daily weight gain in Nelore cattle. 2015. v, 71 p. Tese (doutorado) - Universidade Estadual Paulista Júlio de Mesquita Filho, Faculdade de Ciências Agrárias e Veterinárias, 2015.
000848601
33004102030P4
6064277731903249
url http://hdl.handle.net/11449/128158
http://www.athena.biblioteca.unesp.br/exlibris/bd/cathedra/18-09-2015/000848601.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv v, 71 p. : il.
application/pdf
dc.publisher.none.fl_str_mv Universidade Estadual Paulista (Unesp)
publisher.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.source.none.fl_str_mv Aleph
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
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