ArticlePloS one2014
Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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52 citing papers in PubMed.
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- Evaluating effect of different dominance genotype encodings on genome-wide association studies and genomic selection.Animal bioscience · 2025Article
- KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.Briefings in bioinformatics · 2024Article
- Molecular Mechanisms Associated with the Development of the Metritis Complex in Dairy Cattle.Genes · 2024Article
- Combining genotyping approaches improves resolution for association mapping: a case study in tropical maize under water stress conditions.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
- Identifying QTLs involved in hybrid performance and heterotic group complementarity: new GWAS models applied to factorial and admixed diallel maize hybrid panels.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Unraveling Prevalence and Effects of Deleterious Mutations in Maize Elite Lines across Decades of Modern Breeding.Molecular biology and evolution · 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
- Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks.PloS one · 2023Article
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- Multifactorial methods integrating haplotype and epistasis effects for genomic estimation and prediction of quantitative traits.Frontiers in genetics · 2022Article
- Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs.Genetics, selection, evolution : GSE · 2021Article
- Genomic inbreeding and population structure of northern pike (Ecology and evolution · 2021Article
- Inbreeding depression across the genome of Dutch Holstein Friesian dairy cattle.Genetics, selection, evolution : GSE · 2020Article
- Improving Prediction Accuracy Using Multi-allelic Haplotype Prediction and Training Population Optimization in Wheat.G3 (Bethesda, Md.) · 2020Article
- Estimating the inbreeding level and genetic relatedness in an isolated population of critically endangered Sichuan taimen (Ecology and evolution · 2020Article
- Using Local Convolutional Neural Networks for Genomic Prediction.Frontiers in genetics · 2020Article
- GVCHAP: A Computing Pipeline for Genomic Prediction and Variance Component Estimation Using Haplotypes and SNP Markers.Frontiers in genetics · 2020Article
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4 authors.
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Abstract
We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005-0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level.
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