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dc.contributor.authorNyine, M.en_US
dc.contributor.authorUwimana, B.en_US
dc.contributor.authorSwennen, Rony L.en_US
dc.contributor.authorBatte, M.en_US
dc.contributor.authorBrown, A.en_US
dc.contributor.authorChristelova, P.en_US
dc.contributor.authorHribova, E.en_US
dc.contributor.authorLorenzen, J.H.en_US
dc.contributor.authorDoležel, Jaroslaven_US
dc.date.accessioned2017-07-13T08:57:25Zen_US
dc.date.available2017-07-13T08:57:25Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/82760en_US
dc.titleTrait variation and genetic diversity in a banana genomic selection training populationen_US
cg.authorship.typesCGIAR and advanced research instituteen_US
cg.subject.iitaBANANAen_US
cg.subject.iitaFOOD SECURITYen_US
cg.subject.iitaPLANT GENETIC RESOURCESen_US
dcterms.abstractBanana (Musa spp.) is an important crop in the African Great Lakes region in terms of income and food security, with the highest per capita consumption worldwide. Pests, diseases and climate change hamper sustainable production of bananas. New breeding tools with increased crossbreeding efficiency are being investigated to breed for resistant, high yielding hybrids of East African Highland banana (EAHB). These include genomic selection (GS), which will benefit breeding through increased genetic gain per unit time. Understanding trait variation and the correlation among economically important traits is an essential first step in the development and selection of suitable GS models for banana. In this study, we tested the hypothesis that trait variations in bananas are not affected by cross combination, cycle, field management and their interaction with genotype. A training population created using EAHB breeding material and its progeny was phenotyped in two contrasting conditions. A high level of correlation among vegetative and yield related traits was observed. Therefore, genomic selection models could be developed for traits that are easily measured. It is likely that the predictive ability of traits that are difficult to phenotype will be similar to less difficult traits they are highly correlated with. Genotype response to cycle and field management practices varied greatly with respect to traits. Yield related traits accounted for 31–35% of principal component variation under low and high input field management conditions. Resistance to Black Sigatoka was stable across cycles but varied under different field management depending on the genotype. The best cross combination was 1201K-1xSH3217 based on selection response (R) of hybrids. Genotyping using simple sequence repeat (SSR) markers revealed that the training population was genetically diverse, reflecting a complex pedigree background, which was mostly influenced by the male parents.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.audienceScientistsen_US
dcterms.bibliographicCitationNyine, M., Uwimana, B., Swennen, R., Batte, M., Brown, A., Christelová, P. ... & Doležel, J. (2017). Trait variation and genetic diversity in a banana genomic selection training population. PLoS One, 12(6), e0178734. 1-23.en_US
dcterms.descriptionOpen Access Journalen_US
dcterms.extent1-23en_US
dcterms.issued2017-06-06en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.publisherPublic Library of Science (PLoS)en_US
dcterms.subjectgenetic variationen_US
dcterms.subjectgenomic selectionen_US
dcterms.subjecteast african highland bananaen_US
dcterms.subjecttraits variationen_US
dcterms.typeJournal Articleen_US
cg.contributor.affiliationPalacky Universityen_US
cg.contributor.affiliationInternational Institute of Tropical Agricultureen_US
cg.contributor.affiliationCzech Academy of Sciencesen_US
cg.identifier.doihttps://doi.org/10.1371/journal.pone.0178734en_US
cg.isijournalISI Journalen_US
cg.coverage.regionAfricaen_US
cg.coverage.regionEastern Africaen_US
cg.coverage.countryUgandaen_US
cg.contributor.crpRoots, Tubers and Bananasen_US
cg.coverage.iso3166-alpha2UGen_US
cg.contributor.donorBill & Melinda Gates Foundationen_US
cg.reviewStatusPeer Reviewen_US
cg.howPublishedFormally Publisheden_US
cg.journalPLOS ONEen_US
cg.issn1932-6203en_US
cg.volume12en_US
cg.issue6en_US


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