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dc.contributorButler, Daviden_US
dc.contributorOgbonnaya, Francis Chuksen_US
dc.contributorDreccer, Fernandaen_US
dc.creatorBorgognone, Gabrielaen_US
dc.date2016-10-01en_US
dc.date.accessioned2021-01-13T23:11:54Z
dc.date.available2021-01-13T23:11:54Z
dc.identifierhttps://mel.cgiar.org/reporting/download/hash/a7bf46336898e96c998aae04ea318fa2en_US
dc.identifier.citationGabriela Borgognone, David Butler, Francis Chuks Ogbonnaya, Fernanda Dreccer. (1/10/2016). Molecular Marker Information in the Analysis of Multi‐Environment Trials Helps Differentiate Superior Genotypes from Promising Parents. Crop Science, 56 (5), pp. 2612-2628.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/12344
dc.description.abstractThe statistical analysis of multi-environment trial data aims to provide reliable and accurate predictions of genotype performance across the target environments and information on specific performance from the interaction of genotypes with the environments. Genetic gain can be achieved faster when selections are based on predictions from a model that accounts for the relationships among genotypes rather than from a model that assumes unrelated genotypes. Yield and plant height data from 37 international wheat trials were analyzed using a linear mixed model that accounted for relationships among the genotypes via a genomic relationship matrix G derived from 2487 polymorphic DArT molecular markers for 197 genotypes. The elements of this matrix reflect the actual proportion of the parts of the genome surveyed that is identical by state between pairs of individuals, and including it into the model resulted in generally lower average prediction error variances of individual trials in the analyses. Partitioning the total genetic effects into additive and residual non-additive genetic effects has familiar interpretations for plant breeders and facilitates exploring genotype by environment interactions for additive and total effects. This interpretation is still possible with the form of G used in this paper. This method of analysis could be readily implemented to accelerate genetic gain by plant breeding programs that have molecular markers for the genotypes under study.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherCrop Science Society of Americaen_US
dc.rightsCC-BY-NC-ND-4.0en_US
dc.sourceCrop Science;56,(2016) Pagination 2612,2628en_US
dc.subjectmolecular markeren_US
dc.subjecttrailsen_US
dc.titleMolecular Marker Information in the Analysis of Multi‐Environment Trials Helps Differentiate Superior Genotypes from Promising Parentsen_US
dc.typeJournal Articleen_US
cg.subject.agrovocgenotypesen_US
cg.subject.agrovocparentsen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerDepartment of Agriculture and Fisheries - dafen_US
cg.contributor.centerGrains Research and Development Corporation - GRDCen_US
cg.contributor.centerCommonwealth Science and Industrial Research Organisation - CSIROen_US
cg.contributor.funderInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.funderGeneration Challenge Program** - GCPen_US
cg.contributor.funderGrains Research and Development Corporation - GRDCen_US
cg.contributor.projectCommunication and Documentation Information Services (CODIS)en_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.regionGlobalen_US
cg.contactGabriela.Borgognone@daf.qld.gov.auen_US
dc.identifier.statusOpen accessen_US
mel.impact-factor1.878en_US


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