ArticleGenetics2011
Predicting genetic values: a kernel-based best linear unbiased prediction with genomic data.
Article in Genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Benchmarking the Mantel test and derived methods for testing association between distance matrices.Molecular ecology resources · 2025Article
- Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection.Scientific reports · 2024Article
- Comparing modeling methods of genomic prediction for growth traits of a tropical timber species,Frontiers in plant science · 2023Article
- On Hadamard and Kronecker products in covariance structures for genotype × environment interaction.The plant genome · 2020Article
- Multikernel linear mixed model with adaptive lasso for complex phenotype prediction.Statistics in medicine · 2020Article
- Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer.Scientific reports · 2019Article
- Lost in Translation: On the Problem of Data Coding in Penalized Whole Genome Regression with Interactions.G3 (Bethesda, Md.) · 2019Article
- Extensions of BLUP Models for Genomic Prediction in Heterogeneous Populations: Application in a Diverse Switchgrass Sample.G3 (Bethesda, Md.) · 2019Article
- Multikernel linear mixed models for complex phenotype prediction.Genome research · 2016Article
- Risk Prediction Modeling of Sequencing Data Using a Forward Random Field Method.Scientific reports · 2016Article
- Article
- Kernel methods for large-scale genomic data analysis.Briefings in bioinformatics · 2015Review
- Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2015Article
- Regularized machine learning in the genetic prediction of complex traits.PLoS genetics · 2014Article
- Establishing a multidisciplinary context for modeling 3D facial shape from DNA.PLoS genetics · 2014Article
- Genomic prediction based on data from three layer lines: a comparison between linear methods.Genetics, selection, evolution : GSE · 2014Article
- Poly-omic prediction of complex traits: OmicKriging.Genetic epidemiology · 2014Article
- Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.PloS one · 2014Article
- Kernel-based whole-genome prediction of complex traits: a review.Frontiers in genetics · 2014Review
- Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data.Genetics, selection, evolution : GSE · 2013Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic data provide a valuable source of information for modeling covariance structures, allowing a more accurate prediction of total genetic values (GVs). We apply the kriging concept, originally developed in the geostatistical context for predictions in the low-dimensional space, to the high-dimensional space spanned by genomic single nucleotide polymorphism (SNP) vectors and study its properties in different gene-action scenarios. Two different kriging methods ["universal kriging" (UK) and "simple kriging" (SK)] are presented. As a novelty, we suggest use of the family of Matérn covariance functions to model the covariance structure of SNP vectors. A genomic best linear unbiased prediction (GBLUP) is applied as a reference method. The three approaches are compared in a whole-genome simulation study considering additive, additive-dominance, and epistatic gene-action models. Predictive performance is measured in terms of correlation between true and predicted GVs and average true GVs of the individuals ranked best by prediction. We show that UK outperforms GBLUP in the presence of dominance and epistatic effects. In a limiting case, it is shown that the genomic covariance structure proposed by VanRaden (2008) can be considered as a covariance function with corresponding quadratic variogram. We also prove theoretically that if a specific linear relationship exists between covariance matrices for two linear mixed models, the GVs resulting from BLUP are linked by a scaling factor. Finally, the relation of kriging to other models is discussed and further options for modeling the covariance structure, which might be more appropriate in the genomic context, are suggested.
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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.