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Trait variation and genetic diversity in a banana genomic selection training population

dc.contributor.authorNyine, M.
dc.contributor.authorUwimana, B.
dc.contributor.authorSwennen, R.L.
dc.contributor.authorBatte, M.
dc.contributor.authorBrown, A.
dc.contributor.authorChristelova, P.
dc.contributor.authorHribova, E.
dc.contributor.authorLorenzen, J.H.
dc.contributor.authorDolezel, Jaroslav
dc.date.accessioned2019-12-04T11:08:54Z
dc.date.available2019-12-04T11:08:54Z
dc.date.issued2017-06-06
dc.identifier.citationNyine, 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.
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/20.500.12478/1935
dc.descriptionOpen Access Journal
dc.description.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.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.format.extent1-23
dc.language.isoen
dc.subjectGenetic Variation
dc.subjectGenetic Variation
dc.subjectGenomic Selection
dc.subjectEast African Highland Banana
dc.subjectTraits Variation
dc.titleTrait variation and genetic diversity in a banana genomic selection training population
dc.typeJournal Article
dc.description.versionPeer Review
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationPalacky University
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationInstitute of Experimental Botany, Czech Republic
cg.coverage.regionAfrica
cg.coverage.regionEast Africa
cg.coverage.countryUganda
cg.isijournalISI Journal
cg.authorship.typesCGIAR and advanced research institute
cg.iitasubjectBanana
cg.iitasubjectFood Security
cg.iitasubjectPlant Genetic Resources
cg.journalPloS ONE
cg.howpublishedFormally Published
cg.accessibilitystatusOpen Access
local.dspaceid85148
cg.targetaudienceScientists
cg.identifier.doihttps://doi.org/10.1371/journal.pone.0178734


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