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A guide for kernel generalized regression methods for genomic-enabled prediction

Creator: Montesinos-Lopez, A.
Creator: Montesinos-Lopez, O.A.
Creator: Montesinos-Lopez, J.C.
Creator: Flores-Cortes, C.A.
Creator: Rosa, R. de la
Creator: Crossa, J.
Year: 2021
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 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: 577-596
Volume: 126
DOI: 10.1038/s41437-021-00412-1
Description: The primary objective of this paper is to provide a guide on implementing Bayesian generalized kernel regression methods for genomic prediction in the statistical software R. Such methods are quite efficient for capturing complex non-linear patterns that conventional linear regression models cannot. Furthermore, these methods are also powerful for leveraging environmental covariates, such as genotype x environment (GxE) prediction, among others. In this study we provide the building process of seven kernel methods: linear, polynomial, sigmoid, Gaussian, Exponential, Arc-cosine 1 and Arc-cosine L. Additionally, we highlight illustrative examples for implementing exact kernel methods for genomic prediction under a single-environment, a multi-environment and multi-trait framework, as well as for the implementation of sparse kernel methods under a multi-environment framework. These examples are followed by a discussion on the strengths and limitations of kernel methods and, subsequently by conclusions about the main contributions of this paper.
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ISSN: 1365-2540
Journal: Heredity
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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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