ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2021
Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Optimization of recurrent rapid cycle breeding in maize for sustained long-term genetic improvement via stochastic simulations.G3 (Bethesda, Md.) · 2025Article
- Genomic Prediction and Heritability Estimation for Daughter Pregnancy Rate in U.S. Holstein Cows Using SNP, Epistasis and Haplotype Effects.International journal of molecular sciences · 2025Article
- Historic rewiring of grass flowering time pathways and implications for crop improvement under climate change.The New phytologist · 2025Review
- Epistasis and pleiotropy-induced variation for plant breeding.Plant biotechnology journal · 2024Review
- Pleiotropy, epistasis and the genetic architecture of quantitative traits.Nature reviews. Genetics · 2024Review
- Genomic prediction within and across maize landrace derived populations using haplotypes.Frontiers in plant science · 2024Article
- 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
- Salinity stress tolerance prediction for biomass-related traits in maize (Zea mays L.) using genome-wide markers.The plant genome · 2023Article
- Comparison of the Accuracy of Epistasis and Haplotype Models for Genomic Prediction of Seven Human Phenotypes.Biomolecules · 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
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Authors and funding
7 authors.
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Abstract
key messageThe accuracy of genomic prediction of phenotypes can be increased by including the top-ranked pairwise SNP interactions into the prediction model. We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from two European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested at six locations in Germany and Spain. The compared models were Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) accounting for all pairwise SNP interactions, and selective Epistatic Random Regression BLUP (sERRBLUP) accounting for a selected subset of pairwise SNP interactions. These models have been compared in both univariate and bivariate statistical settings for predictions within and across environments. Our results indicate that modeling all pairwise SNP interactions into the univariate/bivariate model (ERRBLUP) is not superior in predictive ability to the respective additive model (GBLUP). However, incorporating only a selected subset of interactions with the highest effect variances in univariate/bivariate sERRBLUP can increase predictive ability significantly compared to the univariate/bivariate GBLUP. Overall, bivariate models consistently outperform univariate models in predictive ability. Across all studied traits, locations and landraces, the increase in prediction accuracy from univariate GBLUP to univariate sERRBLUP ranged from 5.9 to 112.4 percent, with an average increase of 47 percent. For bivariate models, the change ranged from -0.3 to + 27.9 percent comparing the bivariate sERRBLUP to the bivariate GBLUP, with an average increase of 11 percent. This considerable increase in predictive ability achieved by sERRBLUP may be of interest for "sparse testing" approaches in which only a subset of the lines/hybrids of interest is observed at each location.
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