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Maximizing efficiency of genomic selection in CIMMYT's tropical maize breeding program

Creator: Atanda, A.S.
Creator: Olsen, M.
Creator: Burgueño, J.
Creator: Crossa, J.
Creator: Dzidzienyo, D.
Creator: Beyene, Y.
Creator: Gowda, M.
Creator: Dreher, K.A.
Creator: Zhang, X.
Creator: Prasanna, B.M.
Creator: Tongoona, P.
Creator: Danquah, E.
Creator: Olaoye, G.
Creator: Robbins, K.
Year: 2020
URI: https://hdl.handle.net/10883/21003
Format: PDF
Language: English
Publisher: Springer
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 CIMMYT-Knowledge-Center@cgiar.org 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: Berlin (Germany)
Volume: In press
DOI: 10.1007/s00122-020-03696-9
Description: Key message Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a "test-half-predict-half approach." Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT's maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or "test-half-predict-half" can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.
Agrovoc: MARKER-ASSISTED SELECTION
Agrovoc: PLANT BREEDING
Agrovoc: MAIZE
Related Datasets: https://link.springer.com/article/10.1007%2Fs00122-020-03696-9#Sec20
ISSN: 0040-5752
Journal: Theoretical and Applied Genetics


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

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