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Genomic prediction in a large African maize population

Author: Edriss, V.
Author: Yanxin Gao
Author: Zhang, X.
Author: Jumbo, M.B.
Author: Makumbi, D.
Author: Olsen, M.
Author: Crossa, J.
Author: Packard, K.C.
Author: Jannink, J.L.
Year: 2017
Year: 2017
URI: http://hdl.handle.net/10883/19104
Descriptors: Maize
Descriptors: Genomics
Abstract: Genomic prediction (GP) combines genomewide marker data with phenotypic data in a training population to predict the genomic estimated breeding values of untested individuals in a relevant testing population. Our objective was to evaluate the effects of p
Abstract: Genomic prediction (GP) combines genomewide marker data with phenotypic data in a training population to predict the genomic estimated breeding values of untested individuals in a relevant testing population. Our objective was to evaluate the effects of population structure, genotype × trial, tester, and management interactions, and imputation methods on the accuracy of GP for grain yield in the CIMMYT’s African maize (Zea mays L.) program. The dataset included 2022 diverse breeding lines in 156 Stage 1 yield trials and 66,000 single-nucleotide polymorphism markers. The first two principal components from principal component analysis explained 10.5% of the variance in marker data. Based on marker data, five clusters were detected, but cluster of origin explained only 2% of the phenotypic variation. Prediction accuracy, assessed by cross validation, ranged from 0.20 to 0.36 within clusters and from 0.04 to 0.26 across clusters. Mean GP accuracy within clusters (0.27) outperformed pedigree-based prediction (0.03). Imputation methods did not strongly affect prediction accuracy. Testers and management had large effects. To achieve acceptable GP accuracy within such a diverse population, one can employ (i) a very large training population size, (ii) carefully planned and relevant testers, and (iii) common trial environments and management between the training and validation populations and related genetic materials.
Language: English
Publisher: Crop Science Society of America
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, p
Type: Article
Place: USA
Pages: p. 2361-2371
Journal: Crop Science
Journal volume: v. 57
DOI: 10.2135/cropsci2016.08.0715
Audicence: Researchers


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  • Maize
    Maize breeding, phytopathology, entomology, physiology, quality, and biotech

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