Genomic-enabled prediction in maize using kernel models with genotype x environment interaction
Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.
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Genetics Society of America
2017
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Genomic Best Linear Unbiased Predictor, Linear Kernel, Gaussian Nonlinear Kernel, Genomic Selection, GenPred, Shared Data Resources, GENOTYPE ENVIRONMENT INTERACTION, STATISTICAL METHODS, ARTIFICIAL SELECTION, GENOMICS, DATA ANALYSIS, FORECASTING, |
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dig-cimmyt-10883-193282023-12-08T15:05:56Z Genomic-enabled prediction in maize using kernel models with genotype x environment interaction Bandeira e Sousa, M. Cuevas, J. De Oliveira Couto, E.G. Perez-Rodriguez, P. Jarquín, D. Fritsche-Neto, R. Burgueño, J. Crossa, J. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Best Linear Unbiased Predictor Linear Kernel Gaussian Nonlinear Kernel Genomic Selection GenPred Shared Data Resources GENOTYPE ENVIRONMENT INTERACTION STATISTICAL METHODS ARTIFICIAL SELECTION GENOMICS DATA ANALYSIS FORECASTING Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. 1995-2014 2018-03-23T21:26:22Z 2018-03-23T21:26:22Z 2017 Article 2160-1836 https://hdl.handle.net/10883/19328 10.1534/g3.117.042341 English http://hdl.handle.net/11529/10887 CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose. Open Access PDF Bethesda, Maryland, U.S. Genetics Society of America 6 7 G3: Genes, Genomes, Genetics |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Best Linear Unbiased Predictor Linear Kernel Gaussian Nonlinear Kernel Genomic Selection GenPred Shared Data Resources GENOTYPE ENVIRONMENT INTERACTION STATISTICAL METHODS ARTIFICIAL SELECTION GENOMICS DATA ANALYSIS FORECASTING AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Best Linear Unbiased Predictor Linear Kernel Gaussian Nonlinear Kernel Genomic Selection GenPred Shared Data Resources GENOTYPE ENVIRONMENT INTERACTION STATISTICAL METHODS ARTIFICIAL SELECTION GENOMICS DATA ANALYSIS FORECASTING |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Best Linear Unbiased Predictor Linear Kernel Gaussian Nonlinear Kernel Genomic Selection GenPred Shared Data Resources GENOTYPE ENVIRONMENT INTERACTION STATISTICAL METHODS ARTIFICIAL SELECTION GENOMICS DATA ANALYSIS FORECASTING AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Best Linear Unbiased Predictor Linear Kernel Gaussian Nonlinear Kernel Genomic Selection GenPred Shared Data Resources GENOTYPE ENVIRONMENT INTERACTION STATISTICAL METHODS ARTIFICIAL SELECTION GENOMICS DATA ANALYSIS FORECASTING Bandeira e Sousa, M. Cuevas, J. De Oliveira Couto, E.G. Perez-Rodriguez, P. Jarquín, D. Fritsche-Neto, R. Burgueño, J. Crossa, J. Genomic-enabled prediction in maize using kernel models with genotype x environment interaction |
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Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. |
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topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Best Linear Unbiased Predictor Linear Kernel Gaussian Nonlinear Kernel Genomic Selection GenPred Shared Data Resources GENOTYPE ENVIRONMENT INTERACTION STATISTICAL METHODS ARTIFICIAL SELECTION GENOMICS DATA ANALYSIS FORECASTING |
author |
Bandeira e Sousa, M. Cuevas, J. De Oliveira Couto, E.G. Perez-Rodriguez, P. Jarquín, D. Fritsche-Neto, R. Burgueño, J. Crossa, J. |
author_facet |
Bandeira e Sousa, M. Cuevas, J. De Oliveira Couto, E.G. Perez-Rodriguez, P. Jarquín, D. Fritsche-Neto, R. Burgueño, J. Crossa, J. |
author_sort |
Bandeira e Sousa, M. |
title |
Genomic-enabled prediction in maize using kernel models with genotype x environment interaction |
title_short |
Genomic-enabled prediction in maize using kernel models with genotype x environment interaction |
title_full |
Genomic-enabled prediction in maize using kernel models with genotype x environment interaction |
title_fullStr |
Genomic-enabled prediction in maize using kernel models with genotype x environment interaction |
title_full_unstemmed |
Genomic-enabled prediction in maize using kernel models with genotype x environment interaction |
title_sort |
genomic-enabled prediction in maize using kernel models with genotype x environment interaction |
publisher |
Genetics Society of America |
publishDate |
2017 |
url |
https://hdl.handle.net/10883/19328 |
work_keys_str_mv |
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_version_ |
1787232938560061440 |