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Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Creator: Lopez-Cruz, M.
Creator: Beyene, Y.
Creator: Gowda, M.
Creator: Crossa, J.
Creator: Perez-Rodriguez, P.
Creator: De los Campos, G.
Year: 2021
URI: https://hdl.handle.net/10883/21694
Language: English
Publisher: Springer Nature
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: United Kingdom
Pages: 423-432
Issue: 5
Volume: 127
DOI: 10.1038/s41437-021-00474-1
Description: Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5–17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.
Agrovoc: GENOMICS
Agrovoc: MODELS
Agrovoc: GENETICS
Related Datasets: https://doi.org/10.5061/dryad.qjq2bvqgz
Related Datasets: https://www.nature.com/articles/s41437-021-00474-1#Sec19
ISSN: 0018-067X
Journal: Heredity


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This item appears in the following Collection(s)

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

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