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Genomic prediction in a multiploid crop: genotype by environment interaction and allele dosage effects on predictive ability in banana

dc.contributor.authorNyine, M.
dc.contributor.authorUwimana, B.
dc.contributor.authorBlavet, N.
dc.contributor.authorHřibová, E.
dc.contributor.authorVanrespaille, H.
dc.contributor.authorBatte, M.
dc.contributor.authorAkech, V.
dc.contributor.authorBrown, A.
dc.contributor.authorLorenzen, J.H.
dc.contributor.authorSwennen, R.L.
dc.contributor.authorDoležel, J.
dc.date.accessioned2019-12-04T11:18:25Z
dc.date.available2019-12-04T11:18:25Z
dc.date.issued2018
dc.identifier.citationNyine, M., Uwimana, B., Blavet, N., Hřibová, E., Vanrespaille, H., Batte, M., ... & Doležel, J. (2018). Genomic prediction in a multiploid crop: genotype by environment interaction and allele dosage effects on predictive ability in banana. Plant Genome, 1-16.
dc.identifier.issn1940-3372
dc.identifier.urihttps://hdl.handle.net/20.500.12478/3199
dc.descriptionOpen Access Journal; Published online: 2 March 2018
dc.description.abstractImproving the efficiency of selection in conventional crossbreeding is a major priority in banana (Musa spp.) breeding. Routine application of classical marker assisted selection (MAS) is lagging in banana due to limitations in MAS tools. Genomic selection (GS) based on genomic prediction models can address some limitations of classical MAS, but the use of GS in banana has not been reported to date. The aim of this study was to evaluate the predictive ability of six genomic prediction models for 15 traits in a multi-ploidy training population. The population consisted of 307 banana genotypes phenotyped under low and high input field management conditions for two crop cycles. The single nucleotide polymorphism (SNP) markers used to fit the models were obtained from genotyping by sequencing (GBS) data. Models that account for additive genetic effects provided better predictions with 12 out of 15 traits. The performance of BayesB model was superior to other models particularly on fruit filling and fruit bunch traits. Models that included averaged environment data were more robust in trait prediction even with a reduced number of markers. Accounting for allele dosage in SNP markers (AD-SNP) reduced predictive ability relative to traditional bi-allelic SNP (BA-SNP), but the prediction trend remained the same across traits. The high predictive values (0.47– 0.75) of fruit filling and fruit bunch traits show the potential of genomic prediction to increase selection efficiency in banana breeding.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.format.extent1-16
dc.language.isoen
dc.subjectBananas
dc.subjectMusa
dc.subjectGenotypes
dc.subjectGenomic Prediction
dc.subjectGenotype By Environment Interaction
dc.subjectAllele Dosage
dc.titleGenomic prediction in a multiploid crop: genotype by environment interaction and allele dosage effects on predictive ability in banana
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.contributor.affiliationKatholieke Universiteit Leuven
cg.coverage.regionAfrica
cg.coverage.regionEast Africa
cg.coverage.countryUganda
cg.creator.identifierMoses Nyine: 0000-0002-8409-7588
cg.creator.identifierBrigitte Uwimana: 0000-0001-7460-9001
cg.creator.identifierMichael Batte: 0000-0002-6793-2967
cg.creator.identifierAllen Brown: 0000-0002-4468-5932
cg.creator.identifierJim Lorenzen: 0000-0001-7930-5752
cg.creator.identifierRony Swennen: 0000-0002-5258-9043
cg.creator.identifierJaroslav Dolezel: 0000-0002-6263-0492
cg.researchthemeBIOTECH & PLANT BREEDING
cg.isijournalISI Journal
cg.authorship.typesCGIAR and advanced research institute
cg.iitasubjectBanana
cg.iitasubjectGenetic Improvement
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Genetic Resources
cg.iitasubjectPlant Production
cg.journalPlant Genome
cg.howpublishedFormally Published
cg.accessibilitystatusOpen Access
local.dspaceid94830
cg.targetaudienceScientists
cg.identifier.doihttp://dx.doi.org/10.3835/plantgenome2017.10.0090


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