ArticleThe plant genome2021
Experimental evaluation of genomic selection prediction for rust resistance in sugarcane.
Article in The plant genome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Revisiting the Molecular Roadmap for Sugar Crops: Genome Reading, Trait Writing and Variety Redesigning.Plant biotechnology journal · 2026Review
- Genomic prediction of wild-derived powdery mildew resistance for strawberry (Fragaria × ananassa) pre-breeding.Heredity · 2026Article
- Participant-Aware Model Validation for Repeated-Measures Data: Comparative Cross-Validation Study.JMIR AI · 2026Article
- Sugarcane Breeding in the Genomic Era: Integrative Strategies and Emerging Technologies.Plants (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025Review
- Exploiting historical agronomic data to develop genomic prediction strategies for early clonal selection in the Louisiana sugarcane variety development program.The plant genome · 2025Article
- Machine learning for genomic and pedigree prediction in sugarcane.The plant genome · 2024Article
- Unveiling the predominance of Saccharum spontaneum alleles for resistance to orange rust in sugarcane using genome-wide association.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Sugarcane breeding: a fantastic past and promising future driven by technology and methods.Frontiers in plant science · 2024Article
- Molecular evaluation and phenotypic screening of brown and orange rust in Saccharum germplasm.PloS one · 2024Article
- Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non-additive variation for key traits.The plant genome · 2023Article
- A Genome-Wide Association Study and Genomic Prediction for Fiber and Sucrose Contents in a Mapping Population of LCP 85-384 Sugarcane.Plants (Basel, Switzerland) · 2023Article
- Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inFrontiers in plant science · 2023Article
- A first look at the ability to use genomic prediction for improving the ratooning ability of sugarcane.Frontiers in plant science · 2023Article
- Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane.Plants (Basel, Switzerland) · 2022Review
- Recent Advances in Sugarcane Genomics, Physiology, and Phenomics for Superior Agronomic Traits.Frontiers in genetics · 2022Review
- Combining genomic selection with genome-wide association analysis identified a large-effect QTL and improved selection for red rot resistance in sugarcane.Frontiers in plant science · 2022Article
- The Plant Genome special issue: Advances in genomic selection and application of machine learning in genomic prediction for crop improvement.The plant genome · 2021Article
- Experimental evaluation of genomic selection prediction for rust resistance in sugarcane.The plant genome · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The total sugarcane (Saccharum L.) production has increased worldwide; however, the rate of growth is lower compared with other major crops, mainly due to a plateauing of genetic gain. Genomic selection (GS) has proven to substantially increase the rate of genetic gain in many crops. To investigate the utility of GS in future sugarcane breeding, a field trial was conducted using 432 sugarcane clones using an augmented design with two replications. Two major diseases in sugarcane, brown and orange rust (BR and OR), were screened artificially using whorl inoculation method in the field over two crop cycles. The genotypic data were generated through target enrichment sequencing technologies. After filtering, a set of 8,825 single nucleotide polymorphic markers were used to assess the prediction accuracy of multiple GS models. Using fivefold cross-validation, we observed GS prediction accuracies for BR and OR that ranged from 0.28 to 0.43 and 0.13 to 0.29, respectively, across two crop cycles and combined cycles. The prediction ability further improved by including a known major gene for resistance to BR as a fixed effect in the GS model. It also substantially reduced the minimum number of markers and training population size required for GS. The nonparametric GS models outperformed the parametric GS suggesting that nonadditive genetic effects could contribute genomic sources underlying BR and OR. This study demonstrated that GS could potentially predict the genomic estimated breeding value for selecting the desired germplasm for sugarcane breeding for disease resistance.
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