ReviewHeredity2021
A guide for kernel generalized regression methods for genomic-enabled prediction.
Review in Heredity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Evaluating reaction norm and fixed effect models for genomic selection of end-use quality in wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Non-linear kernel methods for genomic prediction of soybean yield and quality in multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Enhancing genomic prediction ability of blast resistance using genome-wide association study-derived marker weights in two rice (Oryza sativa L.) populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Climate-resilientFrontiers in plant science · 2026Article
- Disentangling soybean GxE effects in an integrated genomic prediction and machine learning-GWAS workflow.Plant methods · 2025Article
- Article
- Enhancing wheat genomic prediction by a hybrid kernel approach.Frontiers in plant science · 2025Article
- Efficient large-scale genomic prediction in approximate genome-based kernel model.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle.Scientific reports · 2024Article
- Use of continuous genotypes for genomic prediction in sugarcane.The plant genome · 2024Article
- Stacked generalization as a computational method for the genomic selection.Frontiers in genetics · 2024Article
- Maintenance of UK bread baking quality: Trends in wheat quality traits over 50 years of breeding and potential for future application of genomic-assisted selection.The plant genome · 2023Article
- (Quasi) multitask support vector regression with heuristic hyperparameter optimization for whole-genome prediction of complex traits: a case study with carcass traits in broilers.G3 (Bethesda, Md.) · 2023Article
- Article
- Article
- Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine.Journal of animal science · 2022Article
- A new approach fits multivariate genomic prediction models efficiently.Genetics, selection, evolution : GSE · 2022Article
- Bayesian multitrait kernel methods improve multienvironment genome-based prediction.G3 (Bethesda, Md.) · 2022Article
- A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.