Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat

Genotype × environment (G × E) interaction can be studied through multienvironment trials used to select wheat (Triticum aestivum L.) lines. We used spring wheat yield data from 136 international environments to evaluate the predictive ability (PA) of different models in diverse environments by mode...

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Authors: Sukumaran, S., Crossa, J., Jarquin, D., Reynolds, M.P.
Format: article
Status:Published version
Publication Date:2017
Country:México
Institution:Centro Internacional de Mejoramiento de Maíz y Trigo
Repository:Repositorio Institucional de Publicaciones Multimedia del CIMMYT
OAI Identifier:oai:repository.cimmyt.org:10883/18619
Online Access:http://hdl.handle.net/10883/18619
Access Level:Open access
Keyword:AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
GENOTYPE ENVIRONMENT INTERACTION
WHEAT
YIELD POTENTIAL
CROP FORECASTING
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spelling Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheatSukumaran, S.Crossa, J.Jarquin, D.Reynolds, M.P.AGRICULTURAL SCIENCES AND BIOTECHNOLOGYGENOTYPE ENVIRONMENT INTERACTIONWHEATYIELD POTENTIALCROP FORECASTINGGenotype × environment (G × E) interaction can be studied through multienvironment trials used to select wheat (Triticum aestivum L.) lines. We used spring wheat yield data from 136 international environments to evaluate the predictive ability (PA) of different models in diverse environments by modeling G × E using the pedigree-derived additive relationship matrix (A matrix). These analyses focused on 109 wheat lines from three Wheat Yield Collaboration Yield Trials (WYCYTs) and 168 lines from four Stress Adapted Trait Yield Nurseries (SATYNs) developed by CIMMYT for yield potential conditions and stress conditions, respectively. The main objectives of this study were to use various pedigree-based reaction norm models to predict sites included in each of the three WYCYT nurseries and each of the four SATYN nurseries (individual population) and to predict environments (site-year combinations) when combining the three WYCYT and four SATYN trials (combined population). Results of the PA for the individual- and combined-population analyses indicated that best predictive Model 6 (E + L + A + AE + e) always included the G × E denoted as the interaction between the A matrix and environments. The most predictable sites in WYCYTs were Iran DZ (Dezful) and Pak I (Islamabad), whereas the most predictable sites in SATYNs were India I (Indore), Iran DZ, and Mex CM (Cd. Obregon). Heritability was correlated with PA for individual-population prediction analyses, but not for combined-population prediction analyses. Our results indicate pedigree-based reaction norm models with G × E can be useful for predicting the performance of lines and selecting good predictable key sites (or environments) to reduce phenotyping costs.1865-1880Crop Science Society of America (CSSA)2017-06-30T17:04:37Z2017-06-30T17:04:37Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlePDFapplication/pdfhttp://hdl.handle.net/10883/1861910.2135/cropsci2016.06.0558457Crop Sciencereponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYTinstname:Centro Internacional de Mejoramiento de Maíz y Trigoinstacron:CIMMYTEnglishhttp://hdl.handle.net/11529/10831https://dl.sciencesocieties.org/publications/cs/supplements/57/1865_supp-tables_supplement1.xlsxhttps://dl.sciencesocieties.org/publications/cs/supplements/57/1865_supp-figs_supplement2.tifhttps://dl.sciencesocieties.org/publications/cs/supplements/57/1865_supp-figs_supplement3.tifhttps://dl.sciencesocieties.org/publications/cs/supplements/57/1865_supp-figs_supplement4.tifhttps://dl.sciencesocieties.org/publications/cs/supplements/57/1865_supp-figs_supplement5.tifUSACIMMYT 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 Accessinfo:eu-repo/semantics/openAccessoai:repository.cimmyt.org:10883/186192024-10-11T19:55:13Z
dc.title.none.fl_str_mv Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat
title Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat
spellingShingle Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat
Sukumaran, S.
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
GENOTYPE ENVIRONMENT INTERACTION
WHEAT
YIELD POTENTIAL
CROP FORECASTING
title_short Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat
title_full Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat
title_fullStr Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat
title_full_unstemmed Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat
title_sort Pedigree-based prediction models with genotype × environment interaction in multi-environment trials of CIMMYT wheat
dc.creator.none.fl_str_mv Sukumaran, S.
Crossa, J.
Jarquin, D.
Reynolds, M.P.
author Sukumaran, S.
author_facet Sukumaran, S.
Crossa, J.
Jarquin, D.
Reynolds, M.P.
author_role author
author2 Crossa, J.
Jarquin, D.
Reynolds, M.P.
author2_role author
author
author
dc.subject.none.fl_str_mv AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
GENOTYPE ENVIRONMENT INTERACTION
WHEAT
YIELD POTENTIAL
CROP FORECASTING
topic AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
GENOTYPE ENVIRONMENT INTERACTION
WHEAT
YIELD POTENTIAL
CROP FORECASTING
description Genotype × environment (G × E) interaction can be studied through multienvironment trials used to select wheat (Triticum aestivum L.) lines. We used spring wheat yield data from 136 international environments to evaluate the predictive ability (PA) of different models in diverse environments by modeling G × E using the pedigree-derived additive relationship matrix (A matrix). These analyses focused on 109 wheat lines from three Wheat Yield Collaboration Yield Trials (WYCYTs) and 168 lines from four Stress Adapted Trait Yield Nurseries (SATYNs) developed by CIMMYT for yield potential conditions and stress conditions, respectively. The main objectives of this study were to use various pedigree-based reaction norm models to predict sites included in each of the three WYCYT nurseries and each of the four SATYN nurseries (individual population) and to predict environments (site-year combinations) when combining the three WYCYT and four SATYN trials (combined population). Results of the PA for the individual- and combined-population analyses indicated that best predictive Model 6 (E + L + A + AE + e) always included the G × E denoted as the interaction between the A matrix and environments. The most predictable sites in WYCYTs were Iran DZ (Dezful) and Pak I (Islamabad), whereas the most predictable sites in SATYNs were India I (Indore), Iran DZ, and Mex CM (Cd. Obregon). Heritability was correlated with PA for individual-population prediction analyses, but not for combined-population prediction analyses. Our results indicate pedigree-based reaction norm models with G × E can be useful for predicting the performance of lines and selecting good predictable key sites (or environments) to reduce phenotyping costs.
publishDate 2017
dc.date.none.fl_str_mv 2017-06-30T17:04:37Z
2017-06-30T17:04:37Z
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10883/18619
10.2135/cropsci2016.06.0558
url http://hdl.handle.net/10883/18619
identifier_str_mv 10.2135/cropsci2016.06.0558
dc.language.none.fl_str_mv English
language_invalid_str_mv English
dc.relation.none.fl_str_mv http://hdl.handle.net/11529/10831
https://dl.sciencesocieties.org/publications/cs/supplements/57/1865_supp-tables_supplement1.xlsx
https://dl.sciencesocieties.org/publications/cs/supplements/57/1865_supp-figs_supplement2.tif
https://dl.sciencesocieties.org/publications/cs/supplements/57/1865_supp-figs_supplement3.tif
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https://dl.sciencesocieties.org/publications/cs/supplements/57/1865_supp-figs_supplement5.tif
dc.rights.none.fl_str_mv Open Access
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Open Access
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv PDF
application/pdf
dc.coverage.none.fl_str_mv USA
dc.publisher.none.fl_str_mv Crop Science Society of America (CSSA)
publisher.none.fl_str_mv Crop Science Society of America (CSSA)
dc.source.none.fl_str_mv 4
57
Crop Science
reponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYT
instname:Centro Internacional de Mejoramiento de Maíz y Trigo
instacron:CIMMYT
instname_str Centro Internacional de Mejoramiento de Maíz y Trigo
instacron_str CIMMYT
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