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Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation

Creator: Hickey, J.M.
Creator: Dreisigacker, S.
Creator: Crossa, J.
Creator: Hearne, S.
Creator: Babu, R.
Creator: Prasanna, B.M.
Creator: Grondona, M.
Creator: Zambelli, A.
Creator: Windhausen, V.S.
Creator: Mathews, K.
Creator: Gorjanc, G.
Year: 2014
Language: English
Publisher: CSSA
Publisher: Wiley
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
Place of Publication: Madison (USA)
Pages: 1476-1488
Issue: 4
Volume: 54
DOI: 10.2135/cropsci2013.03.0195
Description: Genomic selection offers great potential for increasing the rate of genetic improvement in plant breeding programs. This research used simulation to evaluate the effectiveness of different strategies for genotyping and phenotyping to enable genomic selection in early generation individuals (e.g., F2) in breeding programs involving biparental or similar (e.g., backcross or top cross) populations. By using phenotypes that were previously collected in other biparental populations, selection decisions could be made without waiting for phenotypes that pertain directly to the selection candidate to be collected, a process that would take at least three growing seasons. If these phenotypes were collected in biparental populations that were closely related to the selection candidates, only a small number of markers (e.g., 200–500) and a small number of phenotypes (e.g., 1000) were needed to achieve effective accuracy of estimated breeding values. If these phenotypes were collected in biparental populations that were not closely related to the selection candidates, as many as 10,000 markers and 5000 to 20,000 phenotypes were needed. Increasing marker density beyond 10,000 markers did not show benefit and in some scenarios reduced the accuracy of prediction. This study provides a guide, enabling resource allocation to be optimized between genotyping and phenotyping investment dependent on the population under development.
ISSN: 0011-183X
Journal: Crop Science

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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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