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Implementation of genomic selection in the CIMMYT Global Wheat Program, findings from the past 10 years

Creator: Dreisigacker, S.
Creator: Crossa, J.
Creator: Pérez-Rodríguez, P.
Creator: Montesinos-Lopez, O.A.
Creator: Rosyara, U.
Creator: Juliana, P.
Creator: Mondal, S.
Creator: Crespo-Herrera, L.A.
Creator: Velu, G.
Creator: Singh, R.P.
Creator: Braun, H.J.
Year: 2021
Language: English
Publisher: Hapres
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: United Kingdom
Issue: 2
Volume: 3
DOI: 10.20900/cbgg20210005
Keywords: CIMMYT Global Wheat Program
Keywords: Genomic Selection
Keywords: Prediction Models
Description: Wheat is a fundamental crop for improving global food security and the International Maize and Wheat Improvement Center (CIMMYT) has been a central pillar for providing high yielding, nutritious, disease- and climate-resilient wheat varieties to target countries, which is the basis for establishing more resilient agri-food systems especially in the developing world. Increasing wheat yield potential through plant breeding will play a crucial role in fulfilling the projected future global demand of wheat. New emerging technologies and breeding strategies must be looked at to accelerate the rate of genetic gains in wheat. Genomic selection (GS) in one of these strategies that has already demonstrated higher rates of genetic gains in animal breeding and is becoming an essential component of many plant breeding programs including wheat. Throughout the last decades the CIMMYT Global Wheat Program has made significant contributions to promote the implementation of GS in wheat. Several new genome-wide prediction models (e.g., models accounting for genotype × environment interaction or, more recently, deep learning methods) were developed and tested on CIMMYT wheat datasets. GS is routinely implemented in the CIMMYT spring bread wheat program since 2013. Here we summarize the learnings from 10 years of experience with GS in the CIMMYT Global Wheat Program and give a brief outlook on future work.
Agrovoc: MODELS
Agrovoc: WHEAT
Elocator: 210005
ISSN: 2632-7309
Journal: Crop Breeding, Genetics and Genomics

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  • Wheat
    Wheat - breeding, phytopathology, physiology, quality, biotech

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