ReviewFrontiers in genetics2014
Kernel-based whole-genome prediction of complex traits: a review.
Review in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 1 of them a synthesis that pooled it.
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Who cites it
102 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-Enabled Prediction Methods Based on Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2022Pooled it
- 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
- Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026Review
- CWAGS: multi-trait genomic selection using channel weighted attention convolutional network.BMC genomics · 2026Article
- Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction.G3 (Bethesda, Md.) · 2026Article
- Predicting productive, health, and reproductive traits in Mexican Holstein cattle using single nucleotide polymorphisms, haplotypes, and runs of homozygosity.JDS communications · 2026Article
- Nonlinear genomic selection index accelerates multi-trait crop improvement.Nature communications · 2026Article
- Genomic prediction using a cost-effective mid-density SNP platform in spring wheat.Frontiers in plant science · 2026Article
- Multi-marker GWAS and variant-specific genomic prediction for growth traits in Pacific white shrimp.Scientific reports · 2025Article
- Genomic prediction of stalk lodging resistance and the associated intermediate phenotypes in maize using whole-genome resequence and multi-environmental data.The plant genome · 2025Article
- Genomic prediction for heat and herbicide tolerance in faba bean.The plant genome · 2025Article
- Semi-parametric validation of genomic predictions and polygenic risk scores with the Blupf90 software suite.G3 (Bethesda, Md.) · 2025Article
- Leveraging historical trials to predict Fusarium head blight resistance in spring wheat breeding programs.The plant genome · 2025Article
- Article
- Optimizing fully-efficient two-stage models for genomic selection using open-source software.Plant methods · 2025Article
- Enhancing wheat genomic prediction by a hybrid kernel approach.Frontiers in plant science · 2025Article
- Genomic prediction of male fertility in Brown Swiss cattle.JDS communications · 2024Article
- Supervised Machine Learning Techniques for Breeding Value Prediction in Horses: An Example Using Gait Visual Scores.Animals : an open access journal from MDPI · 2024Article
- Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.bioRxiv : the preprint server for biology · 2024Article
- Modeling soybean growth: A mixed model approach.PLoS computational biology · 2024Article
42 more citing papers are in PubMed but not listed here.
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2 authors.
Funding
No grant is acknowledged in the PubMed record.
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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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.