ArticleThe Plant journal : for cell and molecular biology2025
Machine learning-enhanced multi-trait genomic prediction for optimizing cannabinoid profiles in cannabis.
Article in The Plant journal : for cell and molecular biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Identifying the interactive pathways shaping health satisfaction among Chinese older adults with tourism participation.Scientific reports · 2026Article
- Genetic Advances inPlants (Basel, Switzerland) · 2026Review
- Advancing anthracnose resistance in dry beans through the transition from traditional to computational breeding efforts.Frontiers in plant science · 2026Review
- Merging traditional practices and modern technology through computational plant breeding.Plant physiology · 2025Review
- Nutrients and Bioactive Compounds fromInternational journal of molecular sciences · 2025Review
- Advances in multi-trait genomic prediction approaches: classification, comparative analysis, and perspectives.Briefings in bioinformatics · 2025Review
- Editorial: Harnessing machine learning to decode plant-microbiome dynamics for sustainable agriculture.Frontiers in microbiomes · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Cannabis sativa L., known for its medicinal and psychoactive properties, has recently experienced rapid market expansion but remains understudied in terms of its fundamental biology due to historical prohibitions. This pioneering study implements GS and ML to optimize cannabinoid profiles in cannabis breeding. We analyzed a representative population of drug-type cannabis accessions, quantifying major cannabinoids and utilizing high-density genotyping with 250K SNPs for GS. Our evaluations of various models-including ML algorithms, statistical methods, and Bayesian approaches-highlighted Random Forest's superior predictive accuracy for single and multi-trait genomic predictions, particularly for THC, CBD, and their precursors. Multi-trait analyses elucidated complex genetic interdependencies and identified key loci crucial to cannabinoid biosynthesis. These results demonstrate the efficacy of integrating GS and ML in developing cannabis varieties with tailored cannabinoid profiles.
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Registered trials
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