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Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments

Author: Windhausen, V.S.
Author: Atlin, G.N.
Author: Hickey, J.M.
Author: Crossa, J.
Author: Jannink, J.L.
Author: Sorrells, M.E.
Author: Babu, R.
Author: Cairns, J.E.
Author: Amsal Tesfaye Tarekegne
Author: Fentaye Kassa Semagn
Author: Beyene, Y.
Author: Grudloyma, P.
Author: Technow, F.
Author: Riedelsheimer, C.
Author: Melchinger, A.E.
Year: 2012
Abstract: Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F2-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F2-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.
Format: PDF
Language: English
Publisher: Genetics 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, please contact indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
Type: Article
Region: Global
Pages: 1427-1436
Issue: 11
Volume: 2
DOI: 10.1534/g3.112.003699
Publisher URI:
Agrovoc: MAIZE
Agrovoc: HYBRIDS
Agrovoc: GENOME
Journal: G3: Genes, Genomes, Genetics

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

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