A guide for kernel generalized regression methods for genomic-enabled prediction.
Journal
Heredity
ISSN: 1365-2540
Titre abrégé: Heredity (Edinb)
Pays: England
ID NLM: 0373007
Informations de publication
Date de publication:
04 2021
04 2021
Historique:
received:
24
10
2020
accepted:
24
01
2021
revised:
23
01
2021
pubmed:
3
3
2021
medline:
26
10
2021
entrez:
2
3
2021
Statut:
ppublish
Résumé
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 × environment (G×E) 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.
Identifiants
pubmed: 33649571
doi: 10.1038/s41437-021-00412-1
pii: 10.1038/s41437-021-00412-1
pmc: PMC8115678
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
577-596Références
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