Identification of quantitative trait loci for yield and yield related traits in groundnut (Arachis hypogaea L.) under different water regimes in Niger and Senegal

cg.contactM.Pandey@cgiar.orgen_US
cg.contributor.centerInternational Crops Research Institute for the Semi-Arid Tropics - ICRISATen_US
cg.contributor.crpCGIAR Research Program on Grain Legumes - GLen_US
cg.contributor.funderNot Applicableen_US
cg.coverage.countryNEen_US
cg.coverage.countrySNen_US
cg.coverage.regionWestern Africaen_US
cg.creator.idPandey, Manish K: 0000-0002-4101-6530en_US
cg.creator.idRathore, Abhishek: 0000-0001-6887-4095en_US
cg.creator.idVadez, Vincent: 0000-0003-2014-0281en_US
cg.identifier.doihttps://dx.doi.org/10.1007/s10681-015-1472-6en_US
cg.isijournalISI journalen_US
cg.issn1573-5060en_US
cg.issue3en_US
cg.journalEuphyticaen_US
cg.subject.agrovocplant genetic resourcesen_US
cg.subject.agrovocdrought toleranceen_US
cg.volume206en_US
dc.contributorFalalou, Hamidouen_US
dc.contributorRathore, Abhisheken_US
dc.contributorVadez, Vincenten_US
dc.contributorVarshney, Rajeeven_US
dc.creatorPandey, Manish Ken_US
dc.date.accessioned2017-08-16T11:37:43Z
dc.date.available2017-08-16T11:37:43Z
dc.description.abstractYield under drought stress is a highly complex trait with large influence to even a minor fluctuation in the environmental conditions. Genomics-assisted breeding holds great promise for improving such complex traits more efficiently in less time, but requires markers associated with the trait of interest. In this context, a recombinant inbred line mapping population (TAG 24 × ICGV 86031) was used to identify markers associated with quantitative trait loci (QTLs) for yield and yield related traits at two important locations of West Africa under well watered and water stress conditions. Among the traits analyzed under WS condition, the harvest index (HI) and the haulm yield (HYLD) were positively correlated with the pod yield (PYLD) and showed intermediate broad sense heritability. QTL analysis using phenotyping and genotyping data resulted in identification of 52 QTLs. These QTLs had low phenotypic variance (<12 %) for all the nine traits namely plant height, primary branching, SPAD chlorophyll meter reading, percentage of sound mature kernels, 100 kernel weight, shelling percentage, HI, HYLD and PYLD. Interestingly, few QTLs identified in this study were also overlapped with previously reported QTLs detected for drought tolerance related traits identified earlier in Indian environmental conditions using the same mapping population. Accumulating these many small-effect QTLs into a single genetic background is nearly impossible through marker-assisted backcrossing and even marker-assisted recurrent selection. Under such circumstances, the deployment of genomic selection is the most appropriate approach for improving such complex traits with more precision and in less time.en_US
dc.formatPDFen_US
dc.identifierhttps://link.springer.com/article/10.1007/s10681-015-1472-6/fulltext.htmlen_US
dc.identifierhttps://mel.cgiar.org/reporting/downloadmelspace/hash/NcSVlfLk/v/21d9b12a987a38f8bf61c9f3194474e3en_US
dc.identifier.citationManish K Pandey, Hamidou Falalou, Abhishek Rathore, Vincent Vadez, Rajeev Varshney. (5/6/2015). Identification of quantitative trait loci for yield and yield related traits in groundnut (Arachis hypogaea L. ) under different water regimes in Niger and Senegal. Euphytica, 206 (3), pp. 631-647.en_US
dc.identifier.statusOpen accessen_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/7432
dc.languageenen_US
dc.publisherSpringeren_US
dc.rightsCC-BY-NC-4.0en_US
dc.sourceEuphytica;206,(2015) Pagination 631,647en_US
dc.subjectpod yielden_US
dc.subjectquantitative trait locuen_US
dc.subjectepistatic qtlsen_US
dc.subjectmain-effect qtlsen_US
dc.subjectGroundnuten_US
dc.titleIdentification of quantitative trait loci for yield and yield related traits in groundnut (Arachis hypogaea L.) under different water regimes in Niger and Senegalen_US
dc.typeJournal Articleen_US
dcterms.available2015-06-05en_US
dcterms.extent631-647en_US
mel.impact-factor1.618en_US

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