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Improving root characterisation for genomic prediction in cassava

dc.contributor.authorYonis, B.O.
dc.contributor.authordel Carpio, D.P.
dc.contributor.authorWolfe, M.
dc.contributor.authorJannink, J.L.
dc.contributor.authorKulakow, P.
dc.contributor.authorRabbi, I.
dc.date.accessioned2022-08-16T09:20:33Z
dc.date.available2022-08-16T09:20:33Z
dc.date.issued2020-05-14
dc.identifier.citationYonis, B.O., del Carpio, D.P., Wolfe, M., Jannink, J.L., Kulakow, P. & Rabbi, I. (2020). Improving root characterisation for genomic prediction in cassava. Scientific Reports, 10(1):8003. 1-12.
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7646
dc.description.abstractCassava is cultivated due to its drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots poses constraints on harvesting and post-harvest processing. Here, we phenotyped the Genetic gain and offspring (C1) populations from the International Institute of Tropical Agriculture (IITA) breeding program using image analysis of storage root photographs taken in the field. In the genome-wide association analysis (GWAS), we detected for most shape and size-related traits, QTL on chromosomes 1 and 12. In a previous study, we found the QTL on chromosome 12 to be associated with cassava mosaic disease (CMD) resistance. Because the root uniformity is important for breeding, we calculated the standard deviation (SD) of individual root measurements per clone. With SD measurements we identified new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes 6, 9 and 16. Predictive accuracies of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies. This study aimed to evaluate the feasibility of the image phenotyping protocol and assess GWAS and genomic prediction for size and shape image-extracted traits. The methodology described and the results are promising and open up the opportunity to apply high-throughput methods in cassava.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.description.sponsorshipDepartment for International Development, United Kingdom
dc.format.extent1-12
dc.language.isoen
dc.subjectCassava
dc.subjectAfrican Cassava Mosaic Virus
dc.subjectProcessing
dc.subjectPlant Diseases
dc.subjectNigeria
dc.titleImproving root characterisation for genomic prediction in cassava
dc.typeJournal Article
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationMontpellier SupAgro
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.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeBiotech and Plant Breeding
cg.identifier.bibtexciteidYONIS:2020
cg.isijournalISI Journal
cg.authorship.typesCGIAR and advanced research institute
cg.iitasubjectAgronomy
cg.iitasubjectCassava
cg.iitasubjectDisease Control
cg.iitasubjectFood Security
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Diseases
cg.iitasubjectPlant Production
cg.journalScientific Reports
cg.notesPublished online: 14 May 2020
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.1038/s41598-020-64963-9
cg.iitaauthor.identifierJean-Luc Jannink: 0000-0003-4849-628X
cg.iitaauthor.identifierPeter Kulakow: 0000-0002-7574-2645
cg.iitaauthor.identifierIsmail Rabbi: 0000-0001-9966-2941
cg.futureupdate.requiredNo
cg.identifier.issue1
cg.identifier.volume10


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