ArticleNature communications2020
Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 48 citations in OpenAlex.
- Plasticity of metabolism in plants.The Plant journal : for cell and molecular biology · 2026Review
- KineticGP: A computational framework for genomic prediction of leaf photosynthetic traits.Plant communications · 2026Article
- 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
- Cropformer: An interpretable deep learning framework for crop genomic prediction.Plant communications · 2025Article
- Study on the Genetic Diversity Characteristics of the Endemic PlantEcology and evolution · 2025Article
- Importance of phenotypic plasticity in crop resilience.Journal of experimental botany · 2024Article
- Development and applications of metabolic models in plant multi-omics research.Frontiers in plant science · 2024Review
- Identification of gene function based on models capturing natural variability of Arabidopsis thaliana lipid metabolism.Nature communications · 2023Article
- Association between Polymorphism in the Janus Kinase 2 (Animals : an open access journal from MDPI · 2023Article
- Toward mechanistic modeling and rational engineering of plant respiration.Plant physiology · 2023Article
- Article
- A Bayesian model for genomic prediction using metabolic networks.Bioinformatics advances · 2023Article
- Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization.Biology · 2022Article
- Target-oriented prioritization: targeted selection strategy by integrating organismal and molecular traits through predictive analytics in breeding.Genome biology · 2022Article
- Bayesian multitrait kernel methods improve multienvironment genome-based prediction.G3 (Bethesda, Md.) · 2022Article
- Identification of flux trade-offs in metabolic networks.Scientific reports · 2021Article
- Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle.Metabolites · 2021Review
- Using precision phenotyping to inform de novo domestication.Plant physiology · 2021Article
- Characterization of effects of genetic variants via genome-scale metabolic modelling.Cellular and molecular life sciences : CMLS · 2021Review
- Data Management and Modeling in Plant Biology.Frontiers in plant science · 2021Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current trends of crop yield improvements are not expected to meet the projected rise in demand. Genomic selection uses molecular markers and machine learning to identify superior genotypes with improved traits, such as growth. Plant growth directly depends on rates of metabolic reactions which transform nutrients into the building blocks of biomass. Here, we predict growth of Arabidopsis thaliana accessions by employing genomic prediction of reaction rates estimated from accession-specific metabolic models. We demonstrate that, comparing to classical genomic selection on the available data sets for 67 accessions, our approach improves the prediction accuracy for growth within and across nitrogen environments by 32.6% and 51.4%, respectively, and from optimal nitrogen to low carbon environment by 50.4%. Therefore, integration of molecular markers into metabolic models offers an approach to predict traits directly related to metabolism, and its usefulness in breeding can be examined by gathering matching datasets in crops.
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