ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2016
Epistasis and covariance: how gene interaction translates into genomic relationship.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Using genomic selection to examine subgenome dominance and epistasis in allopolyploid strawberry.The plant genome · 2026Article
- GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.Briefings in bioinformatics · 2026Article
- Comparing statistical 'phenomic prediction' models for remote-sensing-based phenotyping of maize susceptibility to common rust.Plant phenomics (Washington, D.C.) · 2025Article
- Integrating gene expression data via weighted multiple kernel ridge regression improved accuracy of genomic prediction.Genetics, selection, evolution : GSE · 2025Article
- Genomic Prediction of Semen Traits in Boars Incorporating Biological Interactions.International journal of molecular sciences · 2024Article
- Pleiotropy, epistasis and the genetic architecture of quantitative traits.Nature reviews. Genetics · 2024Review
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- Article
- Investigating the impact of non-additive genetic effects in the estimation of variance components and genomic predictions for heat tolerance and performance traits in crossbred and purebred pig populations.BMC genomic data · 2023Article
- deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle.Genetics, selection, evolution : GSE · 2023Article
- Comparison of accuracy of breeding value for cow from three methods in Hanwoo (Korean cattle) population.Journal of animal science and technology · 2023Article
- Modeling genotype × environment interaction for single and multitrait genomic prediction in potato (Solanum tuberosum L.).G3 (Bethesda, Md.) · 2023Article
- Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks.PloS one · 2023Article
- Choosing the right tool: Leveraging of plant genetic resources in wheat (Triticum aestivum L.) benefits from selection of a suitable genomic prediction model.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.Planta · 2022Review
- Incorporating kernelized multi-omics data improves the accuracy of genomic prediction.Journal of animal science and biotechnology · 2022Article
- Improvement of genomic prediction in advanced wheat breeding lines by including additive-by-additive epistasis.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Multifactorial methods integrating haplotype and epistasis effects for genomic estimation and prediction of quantitative traits.Frontiers in genetics · 2022Article
- Incorporating Omics Data in Genomic Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Genomic Prediction Methods Accounting for Nonadditive Genetic Effects.Methods in molecular biology (Clifton, N.J.) · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
key messageModels based on additive marker effects and on epistatic interactions can be translated into genomic relationship models. This equivalence allows to perform predictions based on complex gene interaction models and reduces computational effort significantly. In the theory of genome-assisted prediction, the equivalence of a linear model based on independent and identically normally distributed marker effects and a model based on multivariate Gaussian distributed breeding values with genomic relationship as covariance matrix is well known. In this work, we demonstrate equivalences of marker effect models incorporating epistatic interactions and corresponding mixed models based on relationship matrices and show how to exploit these equivalences computationally for genome-assisted prediction. In particular, we show how models with epistatic interactions of higher order (e.g., three-factor interactions) translate into linear models with certain covariance matrices and demonstrate how to construct epistatic relationship matrices for the linear mixed model, if we restrict the model to interactions defined a priori. We illustrate the practical relevance of our results with a publicly available data set on grain yield of wheat lines growing in four different environments. For this purpose, we select important interactions in one environment and use this knowledge on the network of interactions to increase predictive ability of grain yield under other environmental conditions. Our results provide a guide for building relationship matrices based on knowledge on the structure of trait-related gene networks.
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