ArticleNature communications2024
Prediction of plant complex traits via integration of multi-omics data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.The New phytologist · 2026Article
- Dissecting genetic architecture and improving machine learning‑based genomic prediction of flowering time in Osmanthus fragrans by integrating structural variants.The plant genome · 2026Article
- Predictive models of the genetic bases underlying budding yeast fitness in multiple environments.NAR genomics and bioinformatics · 2026Article
- Adaptive Responses of Tropical Crops: A Multi-Scale Omics Integrated Perspective.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Review
- Advances in mutant characterization for detecting causal mutations in crop plants.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Robustly enhancing crop genomic prediction accuracy through ensemble learning and iterative optimization.Nature communications · 2026Article
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Mapping structural variants in Populus tomentosa reveals adaptive signatures and improves prediction of wood properties.Nature communications · 2026Article
- Omics-driven plant breeding through phenomics-enviromics crosstalk.Nature communications · 2026Review
- Special Issue "Functional and Structural Genomics Studies for Plant Breeding".International journal of molecular sciences · 2026Article
- Predicting complex phenotypes using multi-omics data in maize.The Plant cell · 2026Article
- Article
- Leveraging AI and integrated genomic-enviromic prediction for intelligent sugarcane breeding.Plant communications · 2026Review
- Identification of biogeographically informative microssatelite markers for Brazilian Cannabis sativa samples: a machine learning approach for forensic origin prediction.International journal of legal medicine · 2026Article
- Improving genomic prediction accuracy of complex traits by integrating massive types of functional annotation information.Nature communications · 2026Article
- Interpretable multi-omics machine learning reveals drought-driven shifts in plant-microbe interactions.Environmental microbiome · 2026Article
- Next-Generation Genotyping: Innovations Driving Plant Genomic Improvement.Life (Basel, Switzerland) · 2026Review
- Integration of proxy intermediate omics traits into a nonlinear two-step model for accurate phenotypic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- An omnigenic interactome model to chart the genetic architecture of individual plants.Horticulture research · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The formation of complex traits is the consequence of genotype and activities at multiple molecular levels. However, connecting genotypes and these activities to complex traits remains challenging. Here, we investigate whether integrating genomic, transcriptomic, and methylomic data can improve prediction for six Arabidopsis traits. We find that transcriptome- and methylome-based models have performances comparable to those of genome-based models. However, models built for flowering time using different omics data identify different benchmark genes. Nine additional genes identified as important for flowering time from our models are experimentally validated as regulating flowering. Gene contributions to flowering time prediction are accession-dependent and distinct genes contribute to trait prediction in different genotypes. Models integrating multi-omics data perform best and reveal known and additional gene interactions, extending knowledge about existing regulatory networks underlying flowering time determination. These results demonstrate the feasibility of revealing molecular mechanisms underlying complex traits through multi-omics data integration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.