Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials

Autores
Angelini, Julia; Bortolotto, Eugenia Belén; Faviere, Gabriela Soledad; Pairoba, Claudio Fabián; Valentini, Gabriel Hugo; Cervigni, Gerardo Domingo Lucio
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Identification of stable and high-yielding genotypes is a real challenge in peach breeding, since genotype-by-environment interaction (GE) masks the performance of the materials. The aim of this work was to evaluate the effectiveness of parameter estimation and genotype selection solving the linear mixed models (LMM) under frequentist and Bayesian approaches. Fruit yield of 308 peach genotypes were assessed under different seasons and replication numbers arranged in a completely randomized design. Under the frequentist framework the restricted maximum likelihood method to estimate variance component and genotypic prediction was used. Different models considering environment, genotype and GE effects according to the likelihood ratio test and Akaike information criteria were compared. In the Bayesian approach, the mean and the variance components were assumed to be random variables having a priori non-informative distributions with known parameters. According the deviance information criteria the most suitable Bayesian model was selected. The full model was the most appropriate to calculate parameters and genotypic predictions, which were very similar in both approaches. Due to imbalance data, Cullis’s method was the most appropriate to estimate heritability. It was calculated at 0.80, and selecting above 5% of the genotypes, the realized gain of 14.80 kg tree1 was attained. Genotypic frequentist and Bayesian predictions showed a positive correlation (r = 0.9991; P = 0.0001). Since the Bayesian method incorporates the credible interval for genetic parameters, genotypic Bayesian prediction would be a more useful tool than the frequentist approach and allowed the selection of 17 high-yielding and stable genotypes.
EEA San Pedro
Fil: Angelini, Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Angelini, Julia. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Bortolotto, Eugenia Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico de Rosario.Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Bortolotto, Eugenia Belén. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Faviere, Gabriela Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Faviere, Gabriela Soledad. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Pairoba, Claudio Fabián. Universidad Nacional de Rosario. Secretaria de Ciencia y Tecnología; Argentina
Fil: Valentini, Gabriel Hugo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Pedro; Argentina
Fil: Cervigni, Gerardo Domingo Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Cervigni, Gerardo Domingo Lucio. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fuente
Euphytica 218 (8) : 107. (jul. 2022)
Materia
Durazno
Prunus persica
Modelos Lineales
Modelos Estadísticos
Fitomejoramiento
Interacción Genotipo Ambiente
Peaches
Best Linear Unbiased Predictor
Linear Models
Statistical Models
Plant Breeding
Genetic Gain
Genotype Environment Interaction
Mejora Genética
BLUP
Linear Mixed Model
Multienvironment Trials
Modelo Lineal Mixto
Ganancia Genética
Ensayos Multiambientales
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/12355
Acceder
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oai_identifier_str oai:localhost:20.500.12123/12355
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trialsAngelini, JuliaBortolotto, Eugenia BelénFaviere, Gabriela SoledadPairoba, Claudio FabiánValentini, Gabriel HugoCervigni, Gerardo Domingo LucioDuraznoPrunus persicaModelos LinealesModelos EstadísticosFitomejoramientoInteracción Genotipo AmbientePeachesBest Linear Unbiased PredictorLinear ModelsStatistical ModelsPlant BreedingGenetic GainGenotype Environment InteractionMejora GenéticaBLUPLinear Mixed ModelMultienvironment TrialsModelo Lineal MixtoGanancia GenéticaEnsayos MultiambientalesIdentification of stable and high-yielding genotypes is a real challenge in peach breeding, since genotype-by-environment interaction (GE) masks the performance of the materials. The aim of this work was to evaluate the effectiveness of parameter estimation and genotype selection solving the linear mixed models (LMM) under frequentist and Bayesian approaches. Fruit yield of 308 peach genotypes were assessed under different seasons and replication numbers arranged in a completely randomized design. Under the frequentist framework the restricted maximum likelihood method to estimate variance component and genotypic prediction was used. Different models considering environment, genotype and GE effects according to the likelihood ratio test and Akaike information criteria were compared. In the Bayesian approach, the mean and the variance components were assumed to be random variables having a priori non-informative distributions with known parameters. According the deviance information criteria the most suitable Bayesian model was selected. The full model was the most appropriate to calculate parameters and genotypic predictions, which were very similar in both approaches. Due to imbalance data, Cullis’s method was the most appropriate to estimate heritability. It was calculated at 0.80, and selecting above 5% of the genotypes, the realized gain of 14.80 kg tree1 was attained. Genotypic frequentist and Bayesian predictions showed a positive correlation (r = 0.9991; P = 0.0001). Since the Bayesian method incorporates the credible interval for genetic parameters, genotypic Bayesian prediction would be a more useful tool than the frequentist approach and allowed the selection of 17 high-yielding and stable genotypes.EEA San PedroFil: Angelini, Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); ArgentinaFil: Angelini, Julia. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); ArgentinaFil: Bortolotto, Eugenia Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico de Rosario.Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); ArgentinaFil: Bortolotto, Eugenia Belén. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); ArgentinaFil: Faviere, Gabriela Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); ArgentinaFil: Faviere, Gabriela Soledad. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); ArgentinaFil: Pairoba, Claudio Fabián. Universidad Nacional de Rosario. Secretaria de Ciencia y Tecnología; ArgentinaFil: Valentini, Gabriel Hugo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Pedro; ArgentinaFil: Cervigni, Gerardo Domingo Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); ArgentinaFil: Cervigni, Gerardo Domingo Lucio. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); ArgentinaSpringer Nature2022-07-19T18:57:42Z2022-07-19T18:57:42Z2022-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/12355https://link.springer.com/article/10.1007/s10681-022-03063-31573-50600014-2336https://doi.org/10.1007/s10681-022-03063-3Euphytica 218 (8) : 107. (jul. 2022)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-09-29T13:45:37Zoai:localhost:20.500.12123/12355instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:45:37.839INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials
title Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials
spellingShingle Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials
Angelini, Julia
Durazno
Prunus persica
Modelos Lineales
Modelos Estadísticos
Fitomejoramiento
Interacción Genotipo Ambiente
Peaches
Best Linear Unbiased Predictor
Linear Models
Statistical Models
Plant Breeding
Genetic Gain
Genotype Environment Interaction
Mejora Genética
BLUP
Linear Mixed Model
Multienvironment Trials
Modelo Lineal Mixto
Ganancia Genética
Ensayos Multiambientales
title_short Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials
title_full Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials
title_fullStr Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials
title_full_unstemmed Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials
title_sort Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials
dc.creator.none.fl_str_mv Angelini, Julia
Bortolotto, Eugenia Belén
Faviere, Gabriela Soledad
Pairoba, Claudio Fabián
Valentini, Gabriel Hugo
Cervigni, Gerardo Domingo Lucio
author Angelini, Julia
author_facet Angelini, Julia
Bortolotto, Eugenia Belén
Faviere, Gabriela Soledad
Pairoba, Claudio Fabián
Valentini, Gabriel Hugo
Cervigni, Gerardo Domingo Lucio
author_role author
author2 Bortolotto, Eugenia Belén
Faviere, Gabriela Soledad
Pairoba, Claudio Fabián
Valentini, Gabriel Hugo
Cervigni, Gerardo Domingo Lucio
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Durazno
Prunus persica
Modelos Lineales
Modelos Estadísticos
Fitomejoramiento
Interacción Genotipo Ambiente
Peaches
Best Linear Unbiased Predictor
Linear Models
Statistical Models
Plant Breeding
Genetic Gain
Genotype Environment Interaction
Mejora Genética
BLUP
Linear Mixed Model
Multienvironment Trials
Modelo Lineal Mixto
Ganancia Genética
Ensayos Multiambientales
topic Durazno
Prunus persica
Modelos Lineales
Modelos Estadísticos
Fitomejoramiento
Interacción Genotipo Ambiente
Peaches
Best Linear Unbiased Predictor
Linear Models
Statistical Models
Plant Breeding
Genetic Gain
Genotype Environment Interaction
Mejora Genética
BLUP
Linear Mixed Model
Multienvironment Trials
Modelo Lineal Mixto
Ganancia Genética
Ensayos Multiambientales
dc.description.none.fl_txt_mv Identification of stable and high-yielding genotypes is a real challenge in peach breeding, since genotype-by-environment interaction (GE) masks the performance of the materials. The aim of this work was to evaluate the effectiveness of parameter estimation and genotype selection solving the linear mixed models (LMM) under frequentist and Bayesian approaches. Fruit yield of 308 peach genotypes were assessed under different seasons and replication numbers arranged in a completely randomized design. Under the frequentist framework the restricted maximum likelihood method to estimate variance component and genotypic prediction was used. Different models considering environment, genotype and GE effects according to the likelihood ratio test and Akaike information criteria were compared. In the Bayesian approach, the mean and the variance components were assumed to be random variables having a priori non-informative distributions with known parameters. According the deviance information criteria the most suitable Bayesian model was selected. The full model was the most appropriate to calculate parameters and genotypic predictions, which were very similar in both approaches. Due to imbalance data, Cullis’s method was the most appropriate to estimate heritability. It was calculated at 0.80, and selecting above 5% of the genotypes, the realized gain of 14.80 kg tree1 was attained. Genotypic frequentist and Bayesian predictions showed a positive correlation (r = 0.9991; P = 0.0001). Since the Bayesian method incorporates the credible interval for genetic parameters, genotypic Bayesian prediction would be a more useful tool than the frequentist approach and allowed the selection of 17 high-yielding and stable genotypes.
EEA San Pedro
Fil: Angelini, Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Angelini, Julia. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Bortolotto, Eugenia Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico de Rosario.Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Bortolotto, Eugenia Belén. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Faviere, Gabriela Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Faviere, Gabriela Soledad. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Pairoba, Claudio Fabián. Universidad Nacional de Rosario. Secretaria de Ciencia y Tecnología; Argentina
Fil: Valentini, Gabriel Hugo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Pedro; Argentina
Fil: Cervigni, Gerardo Domingo Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
Fil: Cervigni, Gerardo Domingo Lucio. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina
description Identification of stable and high-yielding genotypes is a real challenge in peach breeding, since genotype-by-environment interaction (GE) masks the performance of the materials. The aim of this work was to evaluate the effectiveness of parameter estimation and genotype selection solving the linear mixed models (LMM) under frequentist and Bayesian approaches. Fruit yield of 308 peach genotypes were assessed under different seasons and replication numbers arranged in a completely randomized design. Under the frequentist framework the restricted maximum likelihood method to estimate variance component and genotypic prediction was used. Different models considering environment, genotype and GE effects according to the likelihood ratio test and Akaike information criteria were compared. In the Bayesian approach, the mean and the variance components were assumed to be random variables having a priori non-informative distributions with known parameters. According the deviance information criteria the most suitable Bayesian model was selected. The full model was the most appropriate to calculate parameters and genotypic predictions, which were very similar in both approaches. Due to imbalance data, Cullis’s method was the most appropriate to estimate heritability. It was calculated at 0.80, and selecting above 5% of the genotypes, the realized gain of 14.80 kg tree1 was attained. Genotypic frequentist and Bayesian predictions showed a positive correlation (r = 0.9991; P = 0.0001). Since the Bayesian method incorporates the credible interval for genetic parameters, genotypic Bayesian prediction would be a more useful tool than the frequentist approach and allowed the selection of 17 high-yielding and stable genotypes.
publishDate 2022
dc.date.none.fl_str_mv 2022-07-19T18:57:42Z
2022-07-19T18:57:42Z
2022-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/12355
https://link.springer.com/article/10.1007/s10681-022-03063-3
1573-5060
0014-2336
https://doi.org/10.1007/s10681-022-03063-3
url http://hdl.handle.net/20.500.12123/12355
https://link.springer.com/article/10.1007/s10681-022-03063-3
https://doi.org/10.1007/s10681-022-03063-3
identifier_str_mv 1573-5060
0014-2336
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv Euphytica 218 (8) : 107. (jul. 2022)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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