Improving root characterisation for genomic prediction in cassava

Authors: YONIS, BILAN - Montpellier Supagro – International Center For High Education In Agricultural Sciences; PINO DEL CARPIO, DUNIA - Agribiotech; WOLFE, MARNIN - Cornell University; Jannink, Jean-Luc; KULAKOW, PETER - International Institute Of Tropical Agriculture (IITA); ISMAIL, RABBI - International Institute Of Tropical Agriculture (IITA)

Submitted to: Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/23/2020
Publication Date: 5/14/2020
Citation: Yonis, B., Pino Del Carpio, D., Wolfe, M., Jannink, J., Kulakow, P., Ismail, R. 2020. Improving root characterisation for genomic prediction in cassava. Scientific Reports. https://doi.org/10.1038/s41598-020-64963-9.
DOI: https://doi.org/10.1038/s41598-020-64963-9

Interpretive Summary: Cassava is grown for its starchy storage roots. The lack of uniformity and irregular shape of storage roots make harvest and processing difficult. To study genetic variation in root size and shape we analyzed images of storage root photographs taken in the field from the International Institute of Tropical Agriculture (IITA) breeding program. We detected DNA markers that explained variation for most shape and size-related traits. We measured root uniformity using the standard deviation (SD) of individual root measurements per clone. With SD measurements we identified new significant marker for root shape. Using this data, we could predict root size and shape more accurately than yield. This study aimed to evaluate the feasibility of the image phenotyping protocol and determine whether markers could be associated with size and shape from image-extracted traits. The methods and results are promising and open up new directions for improving cassava.

Technical Abstract: Cassava 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.