Cited by 162Genetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and AnimalsGenetic Associations and Epidemiology

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Kernel-based whole-genome prediction of complex traits: a review

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Morota, Gota, Gianola, Daniel·2014·Frontiers in Genetics

DOI 10.3389/fgene.2014.00363PMC PMC4199321

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Abstract

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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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)
DOI: 10.3389/fgene.2014.00363
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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Kernel-based whole-genome prediction of complex traits: a review

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