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Training population optimization for prediction of cassava brown streak disease resistance in west African clones

dc.contributor.authorOzimati, A.
dc.contributor.authorKawuki, R.
dc.contributor.authorEsuma, W.
dc.contributor.authorKayondo, I.S.
dc.contributor.authorWolfe, M.
dc.contributor.authorLozano, R.
dc.contributor.authorRabbi, I.
dc.contributor.authorKulakow, P.
dc.contributor.authorJannink, J.L
dc.date.accessioned2019-12-04T11:30:04Z
dc.date.available2019-12-04T11:30:04Z
dc.date.issued2018
dc.identifier.citationOzimati, A., Kawuki, R., Esuma, W., Kayondo, I.S., Wolfe, M., Lozano, R., ... & Jannink, J.L. (2018). Training population optimization for prediction of cassava brown streak disease resistance in west African clones. G3: Genes, Genomes, Genetics, 8(12), 3903-3913.
dc.identifier.issn2160-1836
dc.identifier.urihttps://hdl.handle.net/20.500.12478/5177
dc.descriptionPublished online: 29 Oct 2018; Open Access Journal
dc.description.abstractCassava production in the central, southern and eastern parts of Africa is under threat by cassava brown streak virus (CBSV). Yield losses of up to 100% occur in cases of severe infections of edible roots. Easy illegal movement of planting materials across African countries, and long-range movement of the virus vector (Bemisia tabaci) may facilitate spread of CBSV to West Africa. Thus, effort to pre-emptively breed for CBSD resistance in W. Africa is critical. Genomic selection (GS) has become the main approach for cassava breeding, as costs of genotyping per sample have declined. Using phenotypic and genotypic data (genotyping-by-sequencing), followed by imputation to whole genome sequence (WGS) for 922 clones from National Crops Resources Research Institute, Namulonge, Uganda as a training population (TP), we predicted CBSD symptoms for 35 genotyped W. African clones, evaluated in Uganda. The highest prediction accuracy (r = 0.44) was observed for cassava brown streak disease severity scored at three months (CBSD3s) in the W. African clones using WGS-imputed markers. Optimized TPs gave higher prediction accuracies for CBSD3s and CBSD6s than random TPs of the same size. Inclusion of CBSD QTL chromosome markers as kernels, increased prediction accuracies for CBSD3s and CBSD6s. Similarly, WGS imputation of markers increased prediction accuracies for CBSD3s and for cassava brown streak disease root severity (CBSDRs), but not for CBSD6s. Based on these results we recommend TP optimization, inclusion of CBSD QTL markers in genomic prediction models, and the use of high-density (WGS-imputed) markers for CBSD predictions across population.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.description.sponsorshipDepartment for International Development, United Kingdom
dc.format.extent3903-3913
dc.language.isoen
dc.rightsCC-BY-4.0
dc.subjectCassava
dc.subjectGenomics
dc.subjectPlant Diseases
dc.subjectUganda
dc.subjectTanzania
dc.subjectGenetics
dc.titleTraining population optimization for prediction of cassava brown streak disease resistance in west African clones
dc.typeJournal Article
dc.description.versionPeer Review
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationNational Crops Resources Research Institute, Uganda
cg.contributor.affiliationCornell University
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationUnited States Department of Agriculture-Agricultural Research Service
cg.coverage.regionAfrica
cg.coverage.regionEast Africa
cg.coverage.countryTanzania
cg.coverage.countryUganda
cg.creator.identifierIsmail Rabbi: 0000-0001-9966-2941
cg.creator.identifierPeter Kulakow: 0000-0002-7574-2645
cg.creator.identifierJean-Luc Jannink: 0000-0003-4849-628X
cg.researchthemeBIOTECH & PLANT BREEDING
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectCassava
cg.iitasubjectGenetic Improvement
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Genetic Resources
cg.journalG3-Genes Genomes Genetics
cg.howpublishedFormally Published
cg.accessibilitystatusOpen Access
local.dspaceid102574
cg.targetaudienceScientists
cg.identifier.doihttp://dx.doi.org/10.1534/g3.118.200710


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