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Improving genomic prediction in cassava field experiments using spatial analysis

dc.contributor.authorElias, A.A.
dc.contributor.authorRabbi, Ismail Y
dc.contributor.authorKulakow, P.A.
dc.contributor.authorJannink, Jean-Luc
dc.date.accessioned2019-12-04T11:11:17Z
dc.date.available2019-12-04T11:11:17Z
dc.date.issued2017
dc.identifier.citationElias, 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.
dc.identifier.issn2160-1836
dc.identifier.urihttps://hdl.handle.net/20.500.12478/2394
dc.descriptionPublished online: 07 Nov 2017
dc.description.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.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.description.sponsorshipDepartment for International Development, United Kingdom
dc.format.extent1-14
dc.language.isoen
dc.subjectCassava
dc.subjectGenomics
dc.subjectFood Security
dc.subjectValue Chain
dc.subjectSpatial Kernel
dc.subjectPredictability
dc.subjectGenomic Selection
dc.subjectBreeding
dc.subjectGenotypes
dc.titleImproving genomic prediction in cassava field experiments using spatial analysis
dc.typeJournal Article
dc.description.versionPeer Review
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationCornell University
cg.contributor.affiliationUnited States Department of Agriculture
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.iitasubjectFood Security
cg.iitasubjectGenetic Improvement
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Genetic Resources
cg.journalG3: Genes Genomes Genetics
cg.howpublishedFormally Published
cg.accessibilitystatusLimited Access
local.dspaceid92345
cg.targetaudienceScientists
cg.identifier.doihttp://dx.doi.org/10.1534/g3.117.300323


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