ArticleGenetics2014
Unraveling additive from nonadditive effects using genomic relationship matrices.
Article in Genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers.
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Who cites it
99 citing papers in PubMed.
- From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Advancing genetic evaluation of milk yield and composition using a genomic-polygenic model in smallholder dairy cattle farms in Thailand.Tropical animal health and production · 2026Article
- Dissecting genetic variance structure and evaluating genomic prediction models for single-cross hybrids derived from Stiff Stalk and Non-Stiff Stalk maize heterotic groups.G3 (Bethesda, Md.) · 2026Article
- Genomic prediction of agronomic traits in a switchgrass (Panicum virgatum L.) half-sib progeny panel evaluated across multiple environments.The plant genome · 2026Article
- Unveiling the interactions between design optimization, modeling strategies, and spatial analysis in genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Integrating genomic additive relationship matrices improves the efficiency in diploid banana breeding.Horticulture research · 2026Article
- Derivation of prediction error variance for non-genotyped individuals in genomic selection.Frontiers in genetics · 2026Article
- The effect of haplotype size on genomic selection accuracy and epistasis: An empirical study in rice.The plant genome · 2025Article
- Multivariate models improve accuracy of genomic prediction for spring frost tolerance in Norway spruce.The plant genome · 2025Article
- Quantifying genetic and genotypic gain gaps in Eucalyptus: the hidden cost of ignoring inbreeding and dominance.Heredity · 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
- Using genomic selection to correct pedigree errors in kiwiberry breeding.Molecular breeding : new strategies in plant improvement · 2025Article
- Genomic prediction accounting for dominance and epistatic genetic effects on litter size traits in Large White pigs.Journal of animal science · 2025Article
- Prediction of resistance, virulence, and host-by-pathogen interactions using dual-genome prediction models.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Article
- Genomic-assisted breeding for climate-smart coffee.The plant genome · 2024Article
- Article
- Genomic dissection of additive and non-additive genetic effects and genomic prediction in an open-pollinated family test of Japanese larch.BMC genomics · 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
- Comparison of the Accuracy of Epistasis and Haplotype Models for Genomic Prediction of Seven Human Phenotypes.Biomolecules · 2023Article
39 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies.
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