Improving root characterisation for genomic prediction in cassava

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dc.creatorYonis, B.O.
dc.creatorCarpio, D.P. del
dc.creatorWolfe, M.
dc.creatorJannink, Jean-Luc
dc.creatorKulakow, Peter A.
dc.creatorRabbi, Ismail Y.
dc.date2020
dc.date2020-09-18T14:36:21Z
dc.date2020-09-18T14:36:21Z
dc.date.accessioned2026-06-27T16:08:20Z
dc.descriptionCassava 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.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/109544
dc.identifier.urihttp://hdl.handle.net/123456789/123588
dc.languageen
dc.publisherSpringer
dc.rightsOpen Access
dc.sourceYonis, 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), 1-12.
dc.subjectcassava
dc.subjectafrican cassava mosaic virus
dc.subjectprocessing
dc.subjectplant diseases
dc.subjectnigeria
dc.subjectgenomics
dc.subjectphenotypes
dc.subjectpostharvest technology
dc.titleImproving root characterisation for genomic prediction in cassava
dc.typeJournal Article

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Instituto Internacional de Agricultura Tropical (IITA)