Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials
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...
| Autores principales: | , , , , , |
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| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
| Publicado: |
Springer Nature
2022
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| Materias: | |
| Acceso en línea: | 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 |
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| author | Angelini, Julia Bortolotto, Eugenia Belén Faviere, Gabriela Soledad Pairoba, Claudio Fabián Valentini, Gabriel Hugo Cervigni, Gerardo Domingo Lucio |
| author_browse | Angelini, Julia Bortolotto, Eugenia Belén Cervigni, Gerardo Domingo Lucio Faviere, Gabriela Soledad Pairoba, Claudio Fabián Valentini, Gabriel Hugo |
| author_facet | Angelini, Julia Bortolotto, Eugenia Belén Faviere, Gabriela Soledad Pairoba, Claudio Fabián Valentini, Gabriel Hugo Cervigni, Gerardo Domingo Lucio |
| author_sort | Angelini, Julia |
| collection | INTA Digital |
| 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA12355 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Springer Nature |
| publisherStr | Springer Nature |
| record_format | dspace |
| spelling | INTA123552022-07-19T19:05:48Z Parameter estimation and selection efficiency under Bayesian and frequentist approaches in peach trials Angelini, Julia Bortolotto, Eugenia Belén Faviere, Gabriela Soledad Pairoba, Claudio Fabián Valentini, Gabriel Hugo Cervigni, Gerardo Domingo Lucio 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 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 2022-07-19T18:57:42Z 2022-07-19T18:57:42Z 2022-07 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng info:eu-repo/semantics/restrictedAccess application/pdf Springer Nature Euphytica 218 (8) : 107. (jul. 2022) |
| spellingShingle | 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 Angelini, Julia Bortolotto, Eugenia Belén Faviere, Gabriela Soledad Pairoba, Claudio Fabián Valentini, Gabriel Hugo Cervigni, Gerardo Domingo Lucio 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 |
| 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_short | 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 |
| 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 |
| 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 |
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