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.
Fil: Angelini, Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina
Fil: Bortolotto, Eugenia Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina
Fil: Faviere, Gabriela Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina
Fil: Pairoba, Claudio Fabián. Universidad Nacional de Rosario; Argentina
Fil: Valentini, Gabriel Hugo. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Norte. Estacion Experimental Agropecuaria San Pedro. Agencia de Extension Rural San Pedro.; Argentina
Fil: Cervigni, Gerardo Domingo Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina
Materia
BLUP
GENETIC GAIN
GENOTYPE-BY-ENVIRONMENT INTERACTION
LINEAR MIXED MODEL
MULTIENVIRONMENT TRIALS
PEACH BREEDING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/211616
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/211616
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
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 LucioBLUPGENETIC GAINGENOTYPE-BY-ENVIRONMENT INTERACTIONLINEAR MIXED MODELMULTIENVIRONMENT TRIALSPEACH BREEDINGhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Identification 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.Fil: Angelini, Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; ArgentinaFil: Bortolotto, Eugenia Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; ArgentinaFil: Faviere, Gabriela Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; ArgentinaFil: Pairoba, Claudio Fabián. Universidad Nacional de Rosario; ArgentinaFil: Valentini, Gabriel Hugo. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Norte. Estacion Experimental Agropecuaria San Pedro. Agencia de Extension Rural San Pedro.; ArgentinaFil: Cervigni, Gerardo Domingo Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; ArgentinaSpringer2022-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/211616Angelini, Julia; Bortolotto, Eugenia Belén; Faviere, Gabriela Soledad; Pairoba, Claudio Fabián; Valentini, Gabriel Hugo; et al.; Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials; Springer; Euphytica; 218; 8; 8-2022; 1-130014-2336CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s10681-022-03063-3info:eu-repo/semantics/altIdentifier/doi/10.1007/s10681-022-03063-3info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:34:30Zoai:ri.conicet.gov.ar:11336/211616instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:34:30.434CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
BLUP
GENETIC GAIN
GENOTYPE-BY-ENVIRONMENT INTERACTION
LINEAR MIXED MODEL
MULTIENVIRONMENT TRIALS
PEACH BREEDING
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 BLUP
GENETIC GAIN
GENOTYPE-BY-ENVIRONMENT INTERACTION
LINEAR MIXED MODEL
MULTIENVIRONMENT TRIALS
PEACH BREEDING
topic BLUP
GENETIC GAIN
GENOTYPE-BY-ENVIRONMENT INTERACTION
LINEAR MIXED MODEL
MULTIENVIRONMENT TRIALS
PEACH BREEDING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
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.
Fil: Angelini, Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina
Fil: Bortolotto, Eugenia Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina
Fil: Faviere, Gabriela Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; Argentina
Fil: Pairoba, Claudio Fabián. Universidad Nacional de Rosario; Argentina
Fil: Valentini, Gabriel Hugo. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Norte. Estacion Experimental Agropecuaria San Pedro. Agencia de Extension Rural San Pedro.; Argentina
Fil: Cervigni, Gerardo Domingo Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro de Estudios Fotosintéticos y Bioquímicos. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Centro de Estudios Fotosintéticos y Bioquímicos; 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 2022-08
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/11336/211616
Angelini, Julia; Bortolotto, Eugenia Belén; Faviere, Gabriela Soledad; Pairoba, Claudio Fabián; Valentini, Gabriel Hugo; et al.; Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials; Springer; Euphytica; 218; 8; 8-2022; 1-13
0014-2336
CONICET Digital
CONICET
url http://hdl.handle.net/11336/211616
identifier_str_mv Angelini, Julia; Bortolotto, Eugenia Belén; Faviere, Gabriela Soledad; Pairoba, Claudio Fabián; Valentini, Gabriel Hugo; et al.; Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials; Springer; Euphytica; 218; 8; 8-2022; 1-13
0014-2336
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s10681-022-03063-3
info:eu-repo/semantics/altIdentifier/doi/10.1007/s10681-022-03063-3
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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