Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.

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  • Additional Information
    • Source:
      Publisher: Oxford University Press Country of Publication: England NLM ID: 101566598 Publication Model: Electronic Cited Medium: Internet ISSN: 2160-1836 (Electronic) Linking ISSN: 21601836 NLM ISO Abbreviation: G3 (Bethesda) Subsets: MEDLINE
    • Publication Information:
      Publication: 2021- : [Oxford] : Oxford University Press
      Original Publication: Bethesda, MD : Genetics Society of America, 2011-
    • Subject Terms:
      Gene-Environment Interaction* ; Genome, Plant* ; Genomics*/methods ; Models, Genetic*; Zea mays/*genetics; Algorithms ; Environment ; Genotype ; Models, Statistical ; Phenotype ; Reproducibility of Results ; Selection, Genetic
    • Abstract:
      Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.
      (Copyright © 2017 Bandeira e Sousa et al.)
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    • Contributed Indexing:
      Keywords: Gaussian nonlinear kernel; GenPred; Genomic Best Linear Unbiased Predictor (GBLUP) linear kernel; Genomic Selection; Genotype× Environment interaction (G×E); Shared Data Resources
    • Publication Date:
      Date Created: 20170430 Date Completed: 20180326 Latest Revision: 20220331
    • Publication Date:
      20240829
    • Accession Number:
      PMC5473775
    • Accession Number:
      10.1534/g3.117.042341
    • Accession Number:
      28455415