ArticleHeredity2019
Identification of optimal prediction models using multi-omic data for selecting hybrid rice.
Article in Heredity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-omics revolution to promote plant breeding efficiency.Frontiers in plant science · 2022Pooled it
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- Integrating genomic selection into potato breeding: A comparison of genotyping platforms and cross-environmental predictions.The plant genome · 2026Article
- Climate change and crop resilience: harnessing metabolomics for predicting stress tolerance.The New phytologist · 2026Review
- Sugarcane Breeding in the Genomic Era: Integrative Strategies and Emerging Technologies.Plants (Basel, Switzerland) · 2026Review
- Multi-trait and multi-environment genomic prediction enhances yield components improvement in durum wheat.Frontiers in plant science · 2026Article
- Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment.Plant methods · 2025Article
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Molecular and physiological basis of heterosis in hybrid rice performance.Molecular breeding : new strategies in plant improvement · 2025Review
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Questions and Consequences of Omics in Genetically Engineered Crop Regulation.Plant-environment interactions (Hoboken, N.J.) · 2025Review
- Incorporating gene expression and environment for genomic prediction in wheat.Frontiers in plant science · 2025Article
- Molecular mechanisms controlling grain size and weight and their biotechnological breeding applications in maize and other cereal crops.Journal of advanced research · 2024Review
- Multi-omics assists genomic prediction of maize yield with machine learning approaches.Molecular breeding : new strategies in plant improvement · 2024Article
- Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice.G3 (Bethesda, Md.) · 2023Article
- Identification of Drought Tolerant Rice (Plants (Basel, Switzerland) · 2023Article
- Genomic prediction of rice mesocotyl length indicative of directing seeding suitability using a half-sib hybrid population.PloS one · 2023Article
- Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass.The plant genome · 2022Article
- Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.Planta · 2022Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available for predicting breeding values for agronomically important traits. In this study, the best prediction strategies were determined for yield, 1000 grain weight, number of grains per panicle, and number of tillers per plant of hybrid rice (derived from recombinant inbred lines) by comprehensively evaluating all possible combinations of omic datasets with different prediction methods. It was demonstrated that, in rice, the predictions using a combination of genomic and metabolomic data generally produce better results than single-omics predictions or predictions based on other combined omic data. Best linear unbiased prediction (BLUP) appears to be the most efficient prediction method compared to the other commonly used approaches, including least absolute shrinkage and selection operator (LASSO), stochastic search variable selection (SSVS), support vector machines with radial basis function and epsilon regression (SVM-R(EPS)), support vector machines with radial basis function and nu regression (SVM-R(NU)), support vector machines with polynomial kernel and epsilon regression (SVM-P(EPS)), support vector machines with polynomial kernel and nu regression (SVM-P(NU)) and partial least squares regression (PLS). This study has provided guidelines for selection of hybrid rice in terms of which types of omic datasets and which method should be used to achieve higher trait predictability. The answer to these questions will benefit academic research and will also greatly reduce the operative cost for the industry which specializes in breeding and selection.
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