The DOI matches its Crossref record; 118 of the 119 references it cites were found in a registry or an index.
Kernel-based whole-genome prediction of complex traits: a review
Open accessAbstract
Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.
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“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 Morota and Gianola 2014
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Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants
Xu, Y., Liu, X., Fu, J., Wang, H., Wang, J., Huang, C., Prasanna, B. M., Olsen, M. S., Wang, G., & Zhang, A. (2019) · Plant Communications · doi:10.1016/j.xplc.2019.100005 · Cited by 318
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Varona, L., Legarra, A., Toro, M. Á., & Vitezica, Z. G. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00078 · Cited by 242
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Metadata
- Authors
- Morota, G., & Gianola, D.
- Affiliations
- University of Nebraska–Lincoln (US)
- Corresponding author
- Morota, Gota
- ORCID
- Morota, Gota
- Venue
- Frontiers in Genetics
- Publisher
- Frontiers Media SA
- Published
- 2014-10-16
- Volume
- 5
- Pages
- 363-363
- ISSN
- 1664-8021 (ISSN-L)
- PMC
- PMC4199321
- OpenAlex
- W2061187129
- Cited by
- 162
- References
- 119
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 13 · 2023: 14 · 2022: 16 · 2021: 22 · 2020: 14 · 2019: 20 · 2018: 12 · 2017: 11 · 2016: 11 · 2015: 10
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lincoln (University of Nebraska) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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
- kernel methods, whole-genome prediction, complex traits, non-additive genetic effects, epistasis, quantitative genetics, animal breeding, plant breeding, molecular markers, genome annotation, predictive performance, high-dimensional genomic data
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