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Accuracies of univariate and multivariate genomic prediction models in African cassava

dc.contributor.authorOkeke, U.G.
dc.contributor.authorAkdemir, D.
dc.contributor.authorRabbi, Ismail Y
dc.contributor.authorKulakow, P.A.
dc.contributor.authorJannink, Jean-Luc
dc.date.accessioned2019-12-04T11:11:29Z
dc.date.available2019-12-04T11:11:29Z
dc.date.issued2017
dc.identifier.citationOkeke, U.G., Akdemir, D., Rabbi, I., Kulakow, P. & Jannink, J.L. (2017). Accuracies of univariate and multivariate genomic prediction models in African Cassava. Genetics Selection Evolution, 1-10.
dc.identifier.issn0999-193X
dc.identifier.urihttps://hdl.handle.net/20.500.12478/2438
dc.descriptionOpen Access Journal; Published online:15 March 2017
dc.description.abstractBackground: Genomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for crop species such as cassava that have long breeding cycles. Practically, to implement GS in cassava breeding, it is necessary to evaluate different GS models and to develop suitable models for an optimized breeding pipeline. In this paper, we compared (1) prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for a singleenvironment genetic evaluation (Scenario 1), and (2) accuracies from a compound symmetric multi-environment model (uE) parameterized as a univariate multi-kernel model to a multivariate (ME) multi-environment mixed model that accounts for genotype-by-environment interaction for multi-environment genetic evaluation (Scenario 2). For these analyses, we used 16 years of public cassava breeding data for six target cassava traits and a fivefold cross-validation scheme with 10-repeat cycles to assess model prediction accuracies. Results: In Scenario 1, the MT models had higher prediction accuracies than the uT models for all traits and locations analyzed, which amounted to on average a 40% improved prediction accuracy. For Scenario 2, we observed that the ME model had on average (across all locations and traits) a 12% improved prediction accuracy compared to the uE model. Conclusions: We recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.description.sponsorshipDepartment for International Development, United Kingdom
dc.format.extent1-10
dc.language.isoen
dc.subjectGenomics
dc.subjectPlant Breeding
dc.subjectCassava
dc.subjectGenotypes
dc.subjectPlant Genetic Resources
dc.titleAccuracies of univariate and multivariate genomic prediction models in African cassava
dc.typeJournal Article
dc.description.versionPeer Review
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationCornell University
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryNigeria
cg.researchthemeBIOTECH & PLANT BREEDING
cg.isijournalISI Journal
cg.authorship.typesCGIAR and advanced research institute
cg.iitasubjectCassava
cg.iitasubjectGenetic Improvement
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Genetic Resources
cg.journalGenetics Selection Evolution
cg.howpublishedFormally Published
cg.accessibilitystatusOpen Access
local.dspaceid93016
cg.targetaudienceScientists
cg.identifier.doihttp://dx.doi.org/10.1186/s12711-017-0361-y


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