Genome-enabled prediction models for yield related traits in chickpea

Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped...

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Autores: Roorkiwal, M., Abhishek Rathore, Das, R.R., Muneendra K. Singh, Ankit Jain, Samineni Srinivasan, Gaur, P., Chellapilla Bharadwaj, Tripathi, S., Yongle Li, Hickey, J., Lorenz, A.J., Sutton, T., Crossa, J., Jannink, J.L., Varshney, R.K.
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2016
País:México
Recursos:Centro Internacional de Mejoramiento de Maíz y Trigo
Repositório:Repositorio Institucional de Publicaciones Multimedia del CIMMYT
OAI Identifier:oai:repository.cimmyt.org:10883/21349
Acesso em linha:https://hdl.handle.net/10883/21349
Access Level:Acceso aberto
Palavra-chave:AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Genomic Prediction Accuracy
Genomic Selection
Training Population
Prediction Models
GENOMICS
GENETIC GAIN
MARKER-ASSISTED SELECTION
CHICKPEAS
POPULATION STRUCTURE
MODELS
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spelling Genome-enabled prediction models for yield related traits in chickpeaRoorkiwal, M.Abhishek RathoreDas, R.R.Muneendra K. SinghAnkit JainSamineni SrinivasanGaur, P.Chellapilla BharadwajTripathi, S.Yongle LiHickey, J.Lorenz, A.J.Sutton, T.Crossa, J.Jannink, J.L.Varshney, R.K.AGRICULTURAL SCIENCES AND BIOTECHNOLOGYGenomic Prediction AccuracyGenomic SelectionTraining PopulationPrediction ModelsGENOMICSGENETIC GAINMARKER-ASSISTED SELECTIONCHICKPEASPOPULATION STRUCTUREMODELSGenomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped extensively for yield and yield related traits at two different locations (Delhi and Patancheru, India) during the crop seasons 2011-12 and 2012-13 under rainfed and irrigated conditions. In parallel, these lines were also genotyped using DArTseq platform to generate genotyping data for 3000 polymorphic markers. Phenotyping and genotyping data were used with six statistical GS models to estimate the prediction accuracies. GS models were tested for four yield related traits viz. seed yield, 100 seed weight, days to 50% flowering and days to maturity. Prediction accuracy for the models tested varied from 0.138 (seed yield) to 0.912 (100 seed weight), whereas performance of models did not show any significant difference for estimating prediction accuracy within traits. Kinship matrix calculated using genotyping data reaffirmed existence of two different groups within selected lines. There was not much effect of population structure on prediction accuracy. In brief, present study establishes the necessary resources for deployment of GS in chickpea breeding.Frontiers2021-04-10T00:10:17Z2021-04-10T00:10:17Z2016Published Versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10883/2134910.3389/fpls.2016.0166671664-462XFrontiers in Plant Science1666reponame:Repositorio Institucional de Publicaciones Multimedia del CIMMYTinstname:Centro Internacional de Mejoramiento de Maíz y Trigoinstacron:CIMMYTEnglishSwitzerlandCIMMYT 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 purposeOpen Accessinfo:eu-repo/semantics/openAccessoai:repository.cimmyt.org:10883/213492024-10-11T19:57:31Z
dc.title.none.fl_str_mv Genome-enabled prediction models for yield related traits in chickpea
title Genome-enabled prediction models for yield related traits in chickpea
spellingShingle Genome-enabled prediction models for yield related traits in chickpea
Roorkiwal, M.
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Genomic Prediction Accuracy
Genomic Selection
Training Population
Prediction Models
GENOMICS
GENETIC GAIN
MARKER-ASSISTED SELECTION
CHICKPEAS
POPULATION STRUCTURE
MODELS
title_short Genome-enabled prediction models for yield related traits in chickpea
title_full Genome-enabled prediction models for yield related traits in chickpea
title_fullStr Genome-enabled prediction models for yield related traits in chickpea
title_full_unstemmed Genome-enabled prediction models for yield related traits in chickpea
title_sort Genome-enabled prediction models for yield related traits in chickpea
dc.creator.none.fl_str_mv Roorkiwal, M.
Abhishek Rathore
Das, R.R.
Muneendra K. Singh
Ankit Jain
Samineni Srinivasan
Gaur, P.
Chellapilla Bharadwaj
Tripathi, S.
Yongle Li
Hickey, J.
Lorenz, A.J.
Sutton, T.
Crossa, J.
Jannink, J.L.
Varshney, R.K.
author Roorkiwal, M.
author_facet Roorkiwal, M.
Abhishek Rathore
Das, R.R.
Muneendra K. Singh
Ankit Jain
Samineni Srinivasan
Gaur, P.
Chellapilla Bharadwaj
Tripathi, S.
Yongle Li
Hickey, J.
Lorenz, A.J.
Sutton, T.
Crossa, J.
Jannink, J.L.
Varshney, R.K.
author_role author
author2 Abhishek Rathore
Das, R.R.
Muneendra K. Singh
Ankit Jain
Samineni Srinivasan
Gaur, P.
Chellapilla Bharadwaj
Tripathi, S.
Yongle Li
Hickey, J.
Lorenz, A.J.
Sutton, T.
Crossa, J.
Jannink, J.L.
Varshney, R.K.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Genomic Prediction Accuracy
Genomic Selection
Training Population
Prediction Models
GENOMICS
GENETIC GAIN
MARKER-ASSISTED SELECTION
CHICKPEAS
POPULATION STRUCTURE
MODELS
topic AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Genomic Prediction Accuracy
Genomic Selection
Training Population
Prediction Models
GENOMICS
GENETIC GAIN
MARKER-ASSISTED SELECTION
CHICKPEAS
POPULATION STRUCTURE
MODELS
description Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped extensively for yield and yield related traits at two different locations (Delhi and Patancheru, India) during the crop seasons 2011-12 and 2012-13 under rainfed and irrigated conditions. In parallel, these lines were also genotyped using DArTseq platform to generate genotyping data for 3000 polymorphic markers. Phenotyping and genotyping data were used with six statistical GS models to estimate the prediction accuracies. GS models were tested for four yield related traits viz. seed yield, 100 seed weight, days to 50% flowering and days to maturity. Prediction accuracy for the models tested varied from 0.138 (seed yield) to 0.912 (100 seed weight), whereas performance of models did not show any significant difference for estimating prediction accuracy within traits. Kinship matrix calculated using genotyping data reaffirmed existence of two different groups within selected lines. There was not much effect of population structure on prediction accuracy. In brief, present study establishes the necessary resources for deployment of GS in chickpea breeding.
publishDate 2016
dc.date.none.fl_str_mv 2016
2021-04-10T00:10:17Z
2021-04-10T00:10:17Z
dc.type.none.fl_str_mv Published Version
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10883/21349
10.3389/fpls.2016.01666
url https://hdl.handle.net/10883/21349
identifier_str_mv 10.3389/fpls.2016.01666
dc.language.none.fl_str_mv English
language_invalid_str_mv English
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 application/pdf
dc.coverage.none.fl_str_mv Switzerland
dc.publisher.none.fl_str_mv Frontiers
publisher.none.fl_str_mv Frontiers
dc.source.none.fl_str_mv 7
1664-462X
Frontiers in Plant Science
1666
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
institution CIMMYT
reponame_str Repositorio Institucional de Publicaciones Multimedia del CIMMYT
collection Repositorio Institucional de Publicaciones Multimedia del CIMMYT
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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