ArticleFrontiers in genetics2022
Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Review
- Performance of multi-trait and single-trait genomic selection for grain Fe and Zn concentrations in sorghum under different breeding constraints.Scientific reports · 2025Article
- Multivariate models improve accuracy of genomic prediction for spring frost tolerance in Norway spruce.The plant genome · 2025Article
- Implementing marker covariates and multi-trait genomic selection models to improve grain milling, appearance, cooking, and edible quality in rice (Oryza sativa L.).The plant genome · 2025Article
- GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection.Plant communications · 2025Article
- Advances in Detection Technologies for Pesticide Residues and Heavy Metals in Rice: A Comprehensive Review of Spectroscopy, Chromatography, and Biosensors.Foods (Basel, Switzerland) · 2025Review
- Comparative Study of Single-Trait and Multi-Trait Genomic Prediction Models.Animals : an open access journal from MDPI · 2024Article
- Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.).The plant genome · 2024Article
- Genomic prediction and QTL analysis for grain Zn content and yield inJournal of plant biochemistry and biotechnology · 2024Article
- Single- and multi-trait genomic prediction and genome-wide association analysis of grain yield and micronutrient-related traits in ICARDA wheat under drought environment.Molecular genetics and genomics : MGG · 2023Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (
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