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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #346636

Research Project: Enhancing Breeding of Small Grains through Improved Bioinformatics

Location: Plant, Soil and Nutrition Research

Title: Prospects for genomic selection in cassava breeding

Author
item Wolfe, Marnin - Cornell University - New York
item Carpio, Dunia Pino Del - Cornell University - New York
item Alabi, Olumide - International Institute Of Tropical Agriculture (IITA)
item Ezenwaka, Lydia - National Root Crops Research Institute (NRCRI)
item Ikeogu, Ugochukwu - National Root Crops Research Institute (NRCRI)
item Kayondo, Ismail - Cornell University - New York
item Lozano, Roberto - Cornell University - New York
item Okeke, Uche - Cornell University - New York
item Ozimati, Alfred - Cornell University - New York
item Williams, Esuma - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - Nigeria
item Egesi, Chiedozie - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - Nigeria
item Kawuki, Robert - International Crops Research Institute For Semi-Arid Tropics (ICRISAT) - Nigeria
item Kulakow, Peter - International Institute Of Tropical Agriculture (IITA)
item Rabbi, Ismail - International Institute Of Tropical Agriculture (IITA)
item Jannink, Jean-luc

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/28/2017
Publication Date: 6/28/2017
Citation: Wolfe, M., Carpio, D., Alabi, O., Ezenwaka, L., Ikeogu, U., Kayondo, I., Lozano, R., Okeke, U., Ozimati, A., Williams, E., Egesi, C., Kawuki, R., Kulakow, P., Rabbi, I., Jannink, J. 2017. Prospects for genomic selection in cassava breeding. The Plant Genome. 10(3):1-19. doi: 10.3835/plantgenome2017.03.0015
DOI: https://doi.org/10.3835/plantgenome2017.03.0015

Interpretive Summary: Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) allows new clones to be selected on the basis of a prediction derived from DNA marker data, and has been implemented at three breeding institutions in Africa. In this report, we expanded on previous analyses by assessing the accuracy of seven prediction models for seven traits in predictions both within and across populations and within and across generations. We tested additive and non-additive models, with non-additive models predicting an average of 10% better. Cross-population accuracy was generally low (mean 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when combining data from another related population. Accuracy across-generation was poorer than within, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling GS. A method to select of 1/3rd of clones for phenotyping could achieve accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop, but results are promising for some traits. General guidelines are that we need more phenotypic and genomic data but phenotyping can be done on cleverly selected subsets of individuals, reducing the overall phenotyping burden.

Technical Abstract: Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa in order to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross-validation within populations, cross-population prediction and cross-generation prediction. We also evaluated the impact of increasing training population (TP) size by phenotyping progenies selected either at random or using a genetic algorithm. Cross-validation results were mostly consistent across programs, with non-additive models predicting an average of 10% better. Cross-population accuracy was generally low (mean 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when combining data from another related population. Accuracy across-generation was poorer than within as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid- cycling GS. Selection of prediction model made some difference across generations, but increasing TP size was more important. Using a genetic algorithm, selection of 1/3rd of progeny could achieve accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop, but results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden.