Приказ основних података о документу

dc.creatorJankulovska, Mirjana
dc.creatorIvanovska, Sonja
dc.creatorMarjanović-Jeromela, Ana
dc.creatorBolarić, Snjezana
dc.creatorJankuloski, Ljupčo
dc.creatorDimov, Zoran
dc.creatorBosev, Dane
dc.creatorKuzmanovska, Biljana
dc.date.accessioned2021-04-26T19:09:53Z
dc.date.available2021-04-26T19:09:53Z
dc.date.issued2014
dc.identifier.issn0534-0012
dc.identifier.urihttp://fiver.ifvcns.rs/handle/123456789/1310
dc.description.abstractIn this study, the use of different multivariate approaches to classify rapeseed genotypes based on quantitative traits has been presented. Tree regression analysis, PCA analysis and two-way cluster analysis were applied in order todescribe and understand the extent of genetic variability in spring rapeseed genotype by trait data. The traits which highly influenced seed and oil yield in rapeseed were successfully identified by the tree regression analysis. Principal predictor for both response variables was number of pods per plant (NP). NP and 1000 seed weight could help in the selection of high yielding genotypes. High values for both traits and oil content could lead to high oil yielding genotypes. These traits may serve as indirect selection criteria and can lead to improvement of seed and oil yield in rapeseed. Quantitative traits that explained most of the variability in the studied germplasm were classified using principal component analysis. In this data set, five PCs were identified, out of which the first three PCs explained 63% of the total variance. It helped in facilitating the choice of variables based on which the genotypes' clustering could be performed. The two-way cluster analysissimultaneously clustered genotypes and quantitative traits. The final number of clusters was determined using bootstrapping technique. This approach provided clear overview on the variability of the analyzed genotypes. The genotypes that have similar performance regarding the traits included in this study can be easily detected on the heatmap. Genotypes grouped in the clusters 1 and 8 had high values for seed and oil yield, and relatively short vegetative growth duration period and those in cluster 9, combined moderate to low values for vegetative growth duration and moderate to high seed and oil yield. These genotypes should be further exploited and implemented in the rapeseed breeding program. The combined application of these multivariate methods can assist in deciding how, and based on which traits to select the genotypes, especially in early generations, at the beginning of a breeding program.en
dc.publisherDruštvo genetičara Srbije, Beograd
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceGenetika-Belgrade
dc.subjectmultivariate techniquesen
dc.subjectprincipal component analysisen
dc.subjectrapeseed tree regression analysisen
dc.subjecttwo-way cluster analysisen
dc.titleMultivariate analysis of quantitative traits can effectively classify rapeseed germplasmen
dc.typearticle
dc.rights.licenseBY-NC-ND
dc.citation.epage559
dc.citation.issue2
dc.citation.other46(2): 545-559
dc.citation.rankM23
dc.citation.spage545
dc.citation.volume46
dc.identifier.doi10.2298/GENSR1402545J
dc.identifier.fulltexthttp://fiver.ifvcns.rs/bitstream/id/251/1307.pdf
dc.identifier.scopus2-s2.0-84968830403
dc.identifier.wos000342659500021
dc.type.versionpublishedVersion


Документи

Thumbnail

Овај документ се појављује у следећим колекцијама

Приказ основних података о документу