Show simple item record

dc.contributorRathore, Abhisheken_US
dc.contributorDas, Roma R.en_US
dc.contributorSingh, Muneendra K.en_US
dc.contributorJain, Ankiten_US
dc.contributorSamineni, Srinivasanen_US
dc.contributorGaur, Pooranen_US
dc.contributorChellapilla, Bharadwajen_US
dc.contributorTripathi, Shaileshen_US
dc.contributorLi, Yongleen_US
dc.contributorHickey, Johnen_US
dc.contributorLorenz, Aaronen_US
dc.contributorSutton, Timen_US
dc.contributorCrossa, Joseen_US
dc.contributorJannink, Jean-Lucen_US
dc.contributorVarshney, Rajeeven_US
dc.creatorRoorkiwal, Manishen_US
dc.date2016-11-22en_US
dc.date.accessioned2017-04-12T11:45:02Z
dc.date.available2017-04-12T11:45:02Z
dc.identifierhttp://oar.icrisat.org/id/eprint/9797en_US
dc.identifierhttps://mel.cgiar.org/reporting/download/hash/9evQgAwFen_US
dc.identifier.citationManish Roorkiwal, Abhishek Rathore, Roma R. Das, Muneendra K. Singh, Ankit Jain, Srinivasan Samineni, Pooran Gaur, Bharadwaj Chellapilla, Shailesh Tripathi, Yongle Li, John Hickey, Aaron Lorenz, Tim Sutton, Jose Crossa, Jean-Luc Jannink, Rajeev Varshney. (22/11/2016). Genome-Enabled Prediction Models for Yield Related Traits in Chickpea. Frontiers in Plant Science, 7: 1666.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/6744
dc.description.abstractGenomic 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.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherFrontiers Mediaen_US
dc.rightsCC-BY-4.0en_US
dc.sourceFrontiers in Plant Science; 7:1666,(2016)en_US
dc.subjectgenomic prediction accuracyen_US
dc.subjecttraining populationen_US
dc.subjectprediction modelsen_US
dc.subjectmodelingen_US
dc.subjectChickpeaen_US
dc.titleGenome-Enabled Prediction Models for Yield Related Traits in Chickpeaen_US
dc.typeJournal Articleen_US
cg.creator.idRoorkiwal, Manish: 0000-0001-6595-281Xen_US
cg.creator.idRathore, Abhishek: 0000-0001-6887-4095en_US
cg.creator.idSamineni, Srinivasan: 0000-0001-9350-8847en_US
cg.creator.idJannink, Jean-Luc: 0000-0003-4849-628Xen_US
cg.creator.ID-typeORCIDen_US
cg.creator.ID-typeORCIDen_US
cg.creator.ID-typeORCIDen_US
cg.creator.ID-typeORCIDen_US
cg.subject.agrovocpopulation structureen_US
cg.subject.agrovocgenomic selectionen_US
cg.subject.agrovocchickpeasen_US
cg.subject.agrovocgenetic gainen_US
cg.contributor.centerInternational Crops Research Institute for the Semi-Arid Tropics - ICRISATen_US
cg.contributor.centerIndian Council of Agricultural Research, Indian Agricultural Research Institute - ICAR-IARIen_US
cg.contributor.centerUniversity of Adelaide, Australian Centre for Plant Functional Genomics - Adelaide - ACPFGen_US
cg.contributor.centerUniversity of Edinburgh, College of Medicine & Veterinary Medicine, Royal (Dick) School of Veterinary Studies - ED - CMVM - VETen_US
cg.contributor.centerUniversity of Nebraska-Lincoln - UNLen_US
cg.contributor.centerSouth Australian Research and Development Institute - SARDIen_US
cg.contributor.centerInternational Maize and Wheat Improvement Center - CIMMYTen_US
cg.contributor.centerCornell University - CORNELLen_US
cg.contributor.centerThe University of Western Australia, Faculty of Science, School of Plant Biology - UWA - FoS - SoPBen_US
cg.contributor.crpCGIAR Research Program on Grain Legumes - GLen_US
cg.contributor.funderCGIAR System Organization - CGIARen_US
cg.coverage.regionWestern Africaen_US
cg.coverage.regionSouthern Asiaen_US
cg.coverage.regionEastern Africaen_US
cg.coverage.countryBFen_US
cg.coverage.countryINen_US
cg.coverage.countryKEen_US
cg.coverage.countryNGen_US
cg.coverage.countryTZen_US
cg.contactM.Roorkiwal@cgiar.orgen_US
cg.identifier.doihttps://dx.doi.org/10.3389/fpls.2016.01666en_US
dc.identifier.statusOpen accessen_US
mel.impact-factor3.678en_US


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record


DSpace software copyright © 2002-2016  DuraSpace
Disclaimer:
MELSpace content providers and partners accept no liability to any consequence resulting from use of the content or data made available in this repository. Users of this content assume full responsibility for compliance with all relevant national or international regulations and legislation.
Theme by 
Atmire NV