ArticleBMC genetics2014
Comparison of single-trait and multiple-trait genomic prediction models.
Article in BMC genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers.
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Who cites it
90 citing papers in PubMed.
- Breeding for climate adaptation: genetic variation and genomic selection for drought response in Scots pine.BMC genomics · 2026Article
- Genomic Prediction Ability for Novel Profitability Traits Using Different Models in Nelore Cattle.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2026Article
- Multi-Breed Genomic Predictions for Average Daily Gain in Three Italian Beef Cattle Breeds.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2026Article
- Multivariate models improve accuracy of genomic prediction for spring frost tolerance in Norway spruce.The plant genome · 2025Article
- Advances in multi-trait genomic prediction approaches: classification, comparative analysis, and perspectives.Briefings in bioinformatics · 2025Review
- Random regression in comparison with finite-dimensional models for estimation of genetic parameters for growth traits in goats.Tropical animal health and production · 2025Article
- Integrating Significant SNPs Identified by GWAS for Genomic Prediction of the Number of Ribs and Carcass Length in Suhuai Pigs.Animals : an open access journal from MDPI · 2025Article
- Development of a prototype genetic evaluation for teat and udder score in American Angus cattle.Journal of animal science · 2025Article
- Multi-trait ridge regression BLUP withFrontiers in plant science · 2025Article
- Multi-trait phenotypic modeling through factor analysis and bayesian network learning to develop latent reproductive, body conformational, and carcass-associated traits in admixed beef heifers.Frontiers in genetics · 2025Article
- Principal component analysis revisited: fast multitrait genetic evaluations with smooth convergence.G3 (Bethesda, Md.) · 2024Article
- Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.).The plant genome · 2024Article
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- Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population.Animals : an open access journal from MDPI · 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
- Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes.Frontiers in plant science · 2023Article
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- Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea.The plant genome · 2022Article
- Using pooled data for genomic prediction in a bivariate framework with missing data.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2022Article
30 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn this study, a single-trait genomic model (STGM) is compared with a multiple-trait genomic model (MTGM) for genomic prediction using conventional estimated breeding values (EBVs) calculated using a conventional single-trait and multiple-trait linear mixed models as the response variables. Three scenarios with and without missing data were simulated; no missing data, 90% missing data in a trait with high heritability, and 90% missing data in a trait with low heritability. The simulated genome had a length of 500 cM with 5000 equally spaced single nucleotide polymorphism markers and 300 randomly distributed quantitative trait loci (QTL). The true breeding values of each trait were determined using 200 of the QTLs, and the remaining 100 QTLs were assumed to affect both the high (trait I with heritability of 0.3) and the low (trait II with heritability of 0.05) heritability traits. The genetic correlation between traits I and II was 0.5, and the residual correlation was zero.
resultsThe results showed that when there were no missing records, MTGM and STGM gave the same reliability for the genomic predictions for trait I while, for trait II, MTGM performed better that STGM. When there were missing records for one of the two traits, MTGM performed much better than STGM. In general, the difference in reliability of genomic EBVs predicted using the EBV response variables estimated from either the multiple-trait or single-trait models was relatively small for the trait without missing data. However, for the trait with missing data, the EBV response variable obtained from the multiple-trait model gave a more reliable genomic prediction than the EBV response variable from the single-trait model.
conclusionsThese results indicate that MTGM performed better than STGM for the trait with low heritability and for the trait with a limited number of records. Even when the EBV response variable was obtained using the multiple-trait model, the genomic prediction using MTGM was more reliable than the prediction using the STGM.
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