ArticleHeredity2017
Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II.
Article in Heredity, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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Who cites it
66 citing papers in PubMed.
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- GS-Impute: A neural network framework for accurate imputation of low-density markers in across-population genomic selection.Plant communications · 2026Article
- Review
- Enhancing genomic prediction ability of blast resistance using genome-wide association study-derived marker weights in two rice (Oryza sativa L.) populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Multi-trait multi-environment genomic prediction strategies forFrontiers in plant science · 2026Article
- Comprehensive quality profiling and multivariate analysis of rice (Oryza sativa L.) cultivars: integrating physical, cooking, nutritional, and micronutrient characteristics for enhanced varietal selection.BMC plant biology · 2025Article
- Genomic Prediction for Germplasm Improvement Through Inter-Heterotic-Group Line Crossing in Maize.International journal of molecular sciences · 2025Article
- MtCro: multi-task deep learning framework improves multi-trait genomic prediction of crops.Plant methods · 2025Article
- Genomic selection shows improved expected genetic gain over phenotypic selection of agronomic traits in allotetraploid white clover.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Impact of different genomic relationship matrix construction methods on the accuracy of genomic prediction in different species.Frontiers in genetics · 2025Article
- Molecular mechanisms controlling grain size and weight and their biotechnological breeding applications in maize and other cereal crops.Journal of advanced research · 2024Review
- Review of applications of artificial intelligence (AI) methods in crop research.Journal of applied genetics · 2024Review
- Univariate and multivariate genomic prediction for agronomic traits in durum wheat under two field conditions.PloS one · 2024Article
- Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population.Rice (New York, N.Y.) · 2023Article
- A statistical package for evaluation of hybrid performance in plant breeding via genomic selection.Scientific reports · 2023Article
- Sparse Phenotyping and Haplotype-Based Models for Genomic Prediction in Rice.Rice (New York, N.Y.) · 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
- Enhancing genetic gain through the application of genomic selection in developing irrigated rice for the favorable ecosystem in Bangladesh.Frontiers in genetics · 2023Article
- Time-series multispectral imaging in soybean for improving biomass and genomic prediction accuracy.The plant genome · 2022Article
6 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
Genomic selection (GS) is more efficient than traditional phenotype-based methods in hybrid breeding. The present study investigated the predictive ability of genomic best linear unbiased prediction models for rice hybrids based on the North Carolina mating design II, in which a total of 115 inbred rice lines were crossed with 5 male sterile lines. Using 8 traits of the 575 (115 × 5) hybrids from two environments, both univariate (UV) and multivariate (MV) prediction analyses, including additive and dominance effects, were performed. Using UV models, the prediction results of cross-validation indicated that including dominance effects could improve the predictive ability for some traits in rice hybrids. Additionally, we could take advantage of GS even for a low-heritability trait, such as grain yield per plant (GY), because a modest increase in the number of top selection could generate a higher, more stable mean phenotypic value for rice hybrids. Thus this strategy was used to select superior potential crosses between the 115 inbred lines and those between the 5 male sterile lines and other genotyped varieties. In our MV research, an MV model (MV-ADV) was developed utilizing a MV relationship matrix constructed with auxiliary variates. Based on joint analysis with multi-trait (MT) or with multi-environment, the prediction results confirmed the superiority of MV-ADV over an UV model, particularly in the MT scenario for a low-heritability target trait (such as GY), with highly correlated auxiliary traits. For a high-heritability trait (such as thousand-grain weight), MT prediction is unnecessary, and UV prediction is sufficient.
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