ArticleG3 (Bethesda, Md.)2020
Phantom Epistasis in Genomic Selection: On the Predictive Ability of Epistatic Models.
Article in G3 (Bethesda, Md.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 40 citations in OpenAlex.
- Integration of physiological and remote sensing traits for improved genomic prediction of wheat yield.The plant genome · 2025Article
- Harnessing big data for enhanced genome-wide prediction in winter wheat breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Article
- Impact of inclusion non-additive effects on genome-wide association and variance's components in Scottish black sheep.Animal biotechnology · 2023Article
- Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits.American journal of human genetics · 2023Article
- (Quasi) multitask support vector regression with heuristic hyperparameter optimization for whole-genome prediction of complex traits: a case study with carcass traits in broilers.G3 (Bethesda, Md.) · 2023Article
- Accurate prediction of quantitative traits with failed SNP calls in canola and maize.Frontiers in plant science · 2023Article
- Prediction of additive, epistatic, and dominance effects using models accounting for incomplete inbreeding in parental lines of hybrid rye and sugar beet.Frontiers in plant science · 2023Article
- Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets.Frontiers in plant science · 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
- Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass.The plant genome · 2022Article
- High accuracy of genome-enabled prediction of belowground and physiological traits in barley seedlings.G3 (Bethesda, Md.) · 2022Article
- Genomic Prediction: Progress and Perspectives for Rice Improvement.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- The utility of genomic prediction models in evolutionary genetics.Proceedings. Biological sciences · 2021Article
- Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size.BMC genomics · 2021Article
- Emerging issues in genomic selection.Journal of animal science · 2021Article
- Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals.American journal of human genetics · 2021Article
- Neighbor GWAS: incorporating neighbor genotypic identity into genome-wide association studies of field herbivory.Heredity · 2021Article
- Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat.Genes · 2021Article
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Authors and funding
8 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic selection uses whole-genome marker models to predict phenotypes or genetic values for complex traits. Some of these models fit interaction terms between markers, and are therefore called epistatic. The biological interpretation of the corresponding fitted effects is not straightforward and there is the threat of overinterpreting their functional meaning. Here we show that the predictive ability of epistatic models relative to additive models can change with the density of the marker panel. In more detail, we show that for publicly available Arabidopsis and rice datasets, an initial superiority of epistatic models over additive models, which can be observed at a lower marker density, vanishes when the number of markers increases. We relate these observations to earlier results reported in the context of association studies which showed that detecting statistical epistatic effects may not only be related to interactions in the underlying genetic architecture, but also to incomplete linkage disequilibrium at low marker density ("Phantom Epistasis"). Finally, we illustrate in a simulation study that due to phantom epistasis, epistatic models may also predict the genetic value of an underlying purely additive genetic architecture better than additive models, when the marker density is low. Our observations can encourage the use of genomic epistatic models with low density panels, and discourage their biological over-interpretation.
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Registered trials
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.