ArticleScientific reports2020
Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 37 citations in OpenAlex.
- Multiomic insights into sucrose accumulation in sugarcane.Plant molecular biology · 2025Article
- AI and Biotechnology to Combat Aflatoxins: Future Directions for Modern Technologies in Reducing Aflatoxin Risk.Toxins · 2025Review
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large-scale soybean dataset.The plant genome · 2025Article
- Machine learning for genomic and pedigree prediction in sugarcane.The plant genome · 2024Article
- QTL mapping and identification of candidate genes linked to red rot resistance in sugarcane.3 Biotech · 2023Article
- Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inFrontiers in plant science · 2023Article
- A divide-and-conquer approach for genomic prediction in rubber tree using machine learning.Scientific reports · 2022Article
- Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane.Plants (Basel, Switzerland) · 2022Review
- Genome-wide approaches for the identification of markers and genes associated with sugarcane yellow leaf virus resistance.Scientific reports · 2021Article
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Authors and funding
11 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% when using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. As a result of this approach, we achieved an accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.
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