Development and Application of an LDR-Based SNP Panel for High-Resolution Genotyping and Variety Identification in Sugarcane

Sugarcane (Saccharum spp. L.) is a globally vital sugar and energy crop whose genetic improvement has been constrained by its complex polyploid–allopolyploid genome. To address this limitation, we developed a practical, high-throughput single-nucleotide polymorphism (SNP) genotyping system. Using specific-locus amplified fragment sequencing (SLAF-seq) on 107 diverse accessions, we identified 2,420,550 high-quality SNPs anchored to the Saccharum officinarum LA-Purple genome. Stringent filtering yielded 55,750 SNPs for population analysis, which revealed three distinct genetic groups consistent with breeding history and adaptation. From these resources, 329 SNPs were converted into PCR-based ligase detection reaction (PCR-LDR) markers, resulting in a validated panel of 177 highly reliable SNPs (151 core and 26 extended) organized into an efficient multiplex typing system. The panel exhibited exceptional discriminatory power, successfully distinguishing all 303 tested sugarcane varieties and clearly resolving 186 individuals from three segregated hybrid populations. Compared to existing SSR and SNaPshot platforms, this SNP system offers superior experimental reproducibility, enhanced varietal clustering, and broader genome coverage. This work provides a robust, efficient genotyping tool to advance sugarcane variety identification, germplasm management, pedigree analysis, and marker-assisted breeding, with potential applicability to other complex polyploid crops.

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Publication Details

Journal
Agronomy
Published
2026-01-30
DOI
https://doi.org/10.3390/agronomy16030343
Primary Topic
Sugarcane Cultivation and Processing
Type
article
Field-Weighted Citation Impact
0.00

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article

Development and Application of an LDR-Based SNP Panel for High-Resolution Genotyping and Variety Identification in Sugarcane

Zuhu Deng, Chaohua Huang, Junjie Zhao, J. Liu et al.
Agronomy
Sugarcane Cultivation and Processing
article

Development and Application of an LDR-Based SNP Panel for High-Resolution Genotyping and Variety Identification in Sugarcane

Zuhu Deng, Chaohua Huang, Junjie Zhao, J. Liu, Weitong Zhao, Yue Wang, Liangnian Xu, Zhiwei Yang, Xinwang Zhao, Yuebin Zhang, Guoqiang Huang, Yong Zhao
article en

Abstract

Sugarcane (Saccharum spp. L.) is a globally vital sugar and energy crop whose genetic improvement has been constrained by its complex polyploid–allopolyploid genome. To address this limitation, we developed a practical, high-throughput single-nucleotide polymorphism (SNP) genotyping system. Using specific-locus amplified fragment sequencing (SLAF-seq) on 107 diverse accessions, we identified 2,420,550 high-quality SNPs anchored to the Saccharum officinarum LA-Purple genome. Stringent filtering yielded 55,750 SNPs for population analysis, which revealed three distinct genetic groups consistent with breeding history and adaptation. From these resources, 329 SNPs were converted into PCR-based ligase detection reaction (PCR-LDR) markers, resulting in a validated panel of 177 highly reliable SNPs (151 core and 26 extended) organized into an efficient multiplex typing system. The panel exhibited exceptional discriminatory power, successfully distinguishing all 303 tested sugarcane varieties and clearly resolving 186 individuals from three segregated hybrid populations. Compared to existing SSR and SNaPshot platforms, this SNP system offers superior experimental reproducibility, enhanced varietal clustering, and broader genome coverage. This work provides a robust, efficient genotyping tool to advance sugarcane variety identification, germplasm management, pedigree analysis, and marker-assisted breeding, with potential applicability to other complex polyploid crops.

AgronomyVol. 16(3)
Guangxi University (CN), Yunnan Academy of Agricultural Sciences (CN), Fujian Agriculture and Forestry University (CN)
Fujian Agriculture and Forestry University
Reduced inequalities
Openalex Percentile: Top 71%
Sugarcane Cultivation and Processing
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