Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods
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Summary: Prediction of genetic values is a central problem in quantitative genetics. Over many decades, such predictions have been successfully accomplished using information on phenotypic records and family structure usually represented with a pedigree. Dense molecular markers are now available in the genome of humans, plants and animals, and this information can be used to enhance the prediction of genetic values. However, the incorporation of dense molecular marker data into models poses many statistical and computational challenges, such as how models can cope with the genetic complexity of multi-factorial traits and with the curse of dimensionality that arises when the number of markers exceeds the number of data points. Reproducing kernel Hilbert spaces regressions can be used to address some of these challenges. The methodology allows regressions on almost any type of prediction sets (covariates, graphs, strings, images, etc.) and has important computational advantages relative to many parametric approaches. Moreover, some parametric models appear as special cases. This article provides an overview of the methodology, a discussion of the problem of kernel choice with a focus on genetic applications, algorithms for kernel selection and an assessment of the proposed methods using a collection of 599 wheat lines evaluated for grain yield in four mega environments.
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The strategy for selecting an appropriate bandwidth parameter for the Gaussian kernel often relies on cross-validation, Bayesian methods, or a technique known as kernel averaging.
“For example, the strategy for selecting an appropriate bandwidth parameter for the Gaussian kernel often relies on cross-validation, Bayesian methods, or a technique known as kernel averaging (P´ erez-Elizalde et al. , 2015; de Los Campos et al. , 2010).”
From REML implementations of kernel-based genomic prediction models for genotype x environment x management interactions (2025) · cites this paper as de Los Campos et al. , 2010
de Los Campos et al. (2010) proposed kernel averaging which involves modeling multiple random effects with different bandwidths.
“de Los Campos et al. (2010) proposed kernel averaging which involves modeling multiple random effects with different bandwidths.”
From REML implementations of kernel-based genomic prediction models for genotype x environment x management interactions (2025) · cites this paper as de Los Campos et al. (2010)
The eigen-decomposition reparametrization and orthogonal transformation of variance-covariance matrices are employed to increase the computational efficiency of the subsequent steps.
“Both processes are employed to increase the computational efficiency of the subsequent steps.”
From Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials (2021) · cites this paper as De Los Campos et al. (2010)
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“There have been several approaches to choosing a bandwidth value. de Los Campos et al. (2010) proposed kernel averaging which involves modeling multiple random effects with different bandwidths.”
Influential
- Authors
- DE LOS CAMPOS, G., GIANOLA, D., ROSA, G. J. M., WEIGEL, K. A., & CROSSA, J.
- Affiliations
- University of Wisconsin–Madison (US)
- Centro Internacional de Mejoramiento de Maíz Y Trigo (MX)
- Venue
- Genetics Research
- Publisher
- Wiley
- Published
- 2010-08
- Volume
- 92
- Issue
- 4
- Pages
- 295-308
- ISSN
- 0016-6723 (ISSN-L)
- 1469-5073
- OpenAlex
- W2109349581
- Cited by
- 361
- References
- 51
- Citations by year
- 2026: 21 · 2025: 28 · 2024: 21 · 2023: 21 · 2022: 35 · 2021: 36 · 2020: 41 · 2019: 27 · 2018: 24 · 2017: 23 · 2016: 20 · 2015: 12 · 2014: 21 · 2013: 11 · 2012: 12
- FWCI
- 5.57
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genetics Research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- University of Queensland
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Wheat and Barley Genetics and Pathology
- Keywords
- reproducing kernel Hilbert space, semiparametric regression, kernel selection, genetic value prediction, curse of dimensionality, wheat grain yield, mega-environments, nonparametric regression
- MeSH terms
- Bayes Theorem, Breeding, Genetic Markers, Models, Genetic, Regression Analysis, Triticum, Models, Statistical
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