Improving genomic prediction in cassava field experiments using spatial analysis

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cg.authorship.typesCGIAR and advanced research instituteen
cg.contributor.affiliationCornell Universityen
cg.contributor.affiliationUnited States Department of Agricultureen
cg.contributor.affiliationInternational Institute of Tropical Agricultureen
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.donorBill & Melinda Gates Foundationen
cg.contributor.donorDepartment for International Development, United Kingdomen
cg.coverage.countryNigeria
cg.coverage.iso3166-alpha2NG
cg.coverage.regionAfrica
cg.coverage.regionWestern Africa
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1534/g3.117.300323en
cg.identifier.iitathemeBIOTECH & PLANT BREEDING
cg.isijournalISI Journalen
cg.issn2160-1836en
cg.issue1en
cg.journalG3: Genes, Genomes, Geneticsen
cg.reviewStatusPeer Reviewen
cg.subject.iitaCASSAVAen
cg.subject.iitaFOOD SECURITYen
cg.subject.iitaGENETIC IMPROVEMENTen
cg.subject.iitaPLANT BREEDINGen
cg.subject.iitaPLANT GENETIC RESOURCESen
cg.volume8en
dc.contributor.authorElias, A.A.en
dc.contributor.authorRabbi, Ismail Y.en
dc.contributor.authorKulakow, Peter A.en
dc.contributor.authorJannink, Jean-Lucen
dc.date.accessioned2017-12-20T10:27:26Zen
dc.date.available2017-12-20T10:27:26Zen
dc.identifier.urihttps://hdl.handle.net/10568/89814
dc.titleImproving genomic prediction in cassava field experiments using spatial analysisen
dcterms.abstractCassava (Manihot esculenta Crantz) is an important staple food in sub-Saharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the heterogeneity in the field while evaluating these trials can increase the accuracy in estimation of breeding values. We used an exploratory approach using the parametric spatial kernels Power, Spherical, and Gaussian to determine the best kernel for a given scenario. The spatial kernel was fit simultaneously with a genomic kernel in a genomic selection model. Predictability of these models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error compared to that of the base model having no spatial kernel. Results from our real and simulated data studies indicated that predictability can be increased by accounting for spatial variation irrespective of the heritability of the trait. In real data scenarios we observed that the accuracy can be increased by a median value of 3.4%. Through simulations we showed that a 21% increase in accuracy can be achieved. We also found that Range (row) directional spatial kernels, mostly Gaussian, explained the spatial variance in 71% of the scenarios when spatial correlation was significant.en
dcterms.accessRightsOpen Access
dcterms.audienceScientistsen
dcterms.available2018-01-01
dcterms.bibliographicCitationElias, A.A., Rabbi, I., Kulakow, P. & Jannink, J.L. (2017). Improving genomic prediction in cassava field experiments using spatial analysis. G3: Genes, Genomes, Genetics, 1-14.en
dcterms.extentp. 53-62en
dcterms.issued2018-01-01
dcterms.languageen
dcterms.publisherOxford University Pressen
dcterms.subjectcassavaen
dcterms.subjectgenomicsen
dcterms.subjectfood securityen
dcterms.subjectvalue chainen
dcterms.subjectspatial kernelen
dcterms.subjectpredictabilityen
dcterms.subjectgenomic selectionen
dcterms.subjectbreedingen
dcterms.subjectgenotypesen
dcterms.typeJournal Article

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