ArticlePlant biotechnology journal2021
Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice.
Article in Plant biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 42 citations in OpenAlex.
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- Machine learning-based prediction of dynamic heterosis for plant height with pathway biomarkers in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Additive and Partially Dominant Effects from Genomic Variation Contribute to Rice Heterosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Beyond QTL and GWAS: how deep learning, graph models, and multi-omics are reshaping plant genomic prediction analysis.Frontiers in genetics · 2026Review
- Climate-Resilient Crops: Integrating AI, Multi-Omics, and Advanced Phenotyping to Address Global Agricultural and Societal Challenges.Plants (Basel, Switzerland) · 2025Review
- Rice3KGS: A powerful web platform and database for large-scale genome selection.Plant communications · 2025Article
- GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection.Plant communications · 2025Article
- SPDC-HG: An accelerator of genomic hybrid breeding in maize.Plant biotechnology journal · 2025Article
- Metabolic marker-assisted genomic prediction improves hybrid breeding.Plant communications · 2025Article
- Enhancing Across-Population Genomic Prediction for Maize Hybrids.Plants (Basel, Switzerland) · 2024Article
- Genetic analysis of yield components in buckwheat using high-throughput sequencing analysis and wild resource populations.Physiology and molecular biology of plants : an international journal of functional plant biology · 2024Article
- GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection.Briefings in bioinformatics · 2024Article
- Integrated Transcriptomic and Proteomic Characterization of a Chromosome Segment Substitution Line Reveals the Regulatory Mechanism Controlling the Seed Weight in Soybean.Plants (Basel, Switzerland) · 2024Article
- Multi-omics assists genomic prediction of maize yield with machine learning approaches.Molecular breeding : new strategies in plant improvement · 2024Article
- Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Molecular breeding : new strategies in plant improvement · 2023Article
- Multivariate Genomic Hybrid Prediction with Kernels and Parental Information.International journal of molecular sciences · 2023Article
- Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice.G3 (Bethesda, Md.) · 2023Article
- Integrating Parental Phenotypic Data Enhances Prediction Accuracy of Hybrids in Wheat Traits.Genes · 2023Article
- Underutilized legumes: nutrient status and advanced breeding approaches for qualitative and quantitative enhancement.Frontiers in nutrition · 2023Review
- Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP.Frontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hybrid breeding has been shown to effectively increase rice productivity. However, identifying desirable hybrids out of numerous potential combinations is a daunting challenge. Genomic selection holds great promise for accelerating hybrid breeding by enabling early selection before phenotypes are measured. With the recent advances in multi-omic technologies, hybrid prediction based on transcriptomic and metabolomic data has received increasing attention. However, the current omic-based hybrid prediction has ignored parental phenotypic information, which is of fundamental importance in plant breeding. In this study, we integrated parental phenotypic information into various multi-omic prediction models applied in hybrid breeding of rice and compared the predictabilities of 15 combinations from four sets of predictors from the parents, that is genome, transcriptome, metabolome and phenome. The predictability for each combination was evaluated using the best linear unbiased prediction and a modified fast HAT method. We found significant interactions between predictors and traits in predictability, but joint prediction with various combinations of the predictors significantly improved predictability relative to prediction of any single source omic data for each trait investigated. Incorporation of parental phenotypic data into various omic predictors increased the predictability, averagely by 13.6%, 54.5%, 19.9% and 8.3%, for grain yield, number of tillers per plant, number of grains per panicle and 1000 grain weight, respectively. Among nine models of incorporating parental traits, the AD-All model was the most effective one. This novel strategy of incorporating parental phenotypic data into multi-omic prediction is expected to improve hybrid breeding progress, especially with the development of high-throughput phenotyping technologies.
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