Reproducing Kernel Hilbert Spaces Regression Methods for Genomic Assisted Prediction of Quantitative Traits
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Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components. Models for capturing different forms of interaction, e.g., chromosome-specific, are presented. Implementations can be carried out using software for likelihood-based or Bayesian inference.
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Some of the statistical complexities can be addressed by using semiparametric genomic regression methods to account for nonadditive variation.
“Some of the statistical complexities can be addressed by using semiparametric genomic regression methods to account for nonadditive variation (Gianola et al. 2006, 2011; Gianola and Van Kaam 2008; Morota and Gianola 2014).”
From Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials (2021) · cites this paper as Gianola and Van Kaam 2008
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- Authors
- Gianola, D., & van Kaam, J. B. C. H. M.
- Affiliations
- Department of Animal Sciences, University of Wisconsin, Madison, Wisconsin 53706
- Istituto Zooprofilattico Sperimentale della Sicilia “A. Mirri,” 90129 Palermo, Italy
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-04-01
- Volume
- 178
- Issue
- 4
- Pages
- 2289-2303
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- PMC
- PMC2323816
- OpenAlex
- W2154562310
- Semantic Scholar
- 062d27341f70443d5bfa52b4e1ca7ab6accece9b
- Cited by
- 457
- References
- 55
- Citations by year
- 2026: 31 · 2025: 34 · 2024: 29 · 2023: 24 · 2022: 27 · 2021: 35 · 2020: 32 · 2019: 28 · 2018: 26 · 2017: 30 · 2016: 29 · 2015: 23 · 2014: 28 · 2013: 14 · 2012: 22
- FWCI
- 8.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- 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 › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- reproducing kernel Hilbert space, quantitative traits, nonparametric regression, linear mixed models, variance components, smoothing parameter, single nucleotide polymorphisms, interaction model, likelihood-based inference, Bayesian inference
- MeSH terms
- Animals, Bayes Theorem, Chickens, Chromosomes, Models, Genetic, Regression Analysis, Genome, Quantitative Trait, Heritable
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