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Regularized selection indices for breeding value prediction using hyper-spectral image data

Author: Lopez-Cruz, M.
Author: Olson, E.
Author: Rovere, G.
Author: Crossa, J.
Author: Dreisigacker, S.
Author: Mondal, S.
Author: Singh, R.P.
Author: De los Campos, G.
Year: 2020
ISSN: 2045-2322 (Print)
URI: https://hdl.handle.net/10883/20890
Format: PDF
Language: English
Publisher: Nature Publishing Group
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: London (United Kingdom)
Issue: 1
Issue: art. 8195
Volume: 10
DOI: 10.1038/s41598-020-65011-2
Description: High-throughput phenotyping (HTP) technologies can produce data on thousands of phenotypes per unit being monitored. These data can be used to breed for economically and environmentally relevant traits (e.g., drought tolerance); however, incorporating high-dimensional phenotypes in genetic analyses and in breeding schemes poses important statistical and computational challenges. To address this problem, we developed regularized selection indices; the methodology integrates techniques commonly used in high-dimensional phenotypic regressions (including penalization and rank-reduction approaches) into the selection index (SI) framework. Using extensive data from CIMMYT?s (International Maize and Wheat Improvement Center) wheat breeding program we show that regularized SIs derived from hyper-spectral data offer consistently higher accuracy for grain yield than those achieved by standard SIs, and by vegetation indices commonly used to predict agronomic traits. Regularized SIs offer an effective approach to leverage HTP data that is routinely generated in agriculture; the methodology can also be used to conduct genetic studies using high-dimensional phenotypes that are often collected in humans and model organisms including body images and whole-genome gene expression profiles.
Agrovoc: QUANTITATIVE TRAIT LOCI
Agrovoc: STATISTICAL METHODS
Agrovoc: PHENOTYPES
Related Datasets: https://www.nature.com/articles/s41598-020-65011-2#Sec30
Journal: Nature Scientific Reports


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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP
  • Wheat
    Wheat - breeding, phytopathology, physiology, quality, biotech

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