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高通量基因型分型技术在优良玉米品种的精准鉴定与高效培育过程中发挥着关键作用。博瑞迪自主研发的靶向测序基因分型技术(GBTS)整合了芯片技术与高通量测序的双重优势,在标记定制化、检测通量及成本控制方面形成了独特的技术优势。

基于GBTS技术,我们已开发出覆盖不同标记密度的对外推广型玉米系列检测液相芯片。这些芯片具备广泛的样本适用性,可同时满足植物组织、种子等多种样本类型的基因分型需求。

除对外推广的产品外,我们还提供高度灵活的定制化服务——通过接收客户指定的SNP标记列表 ,能够快速设计符合其研究或育种目标的液相检测组合,覆盖从低密度到高密度的全范围需求,且兼具经济高效特性。

借助博瑞迪玉米基因分型解决方案,可有效赋能育种计划优化,助力实现玉米品种开发的高品质目标。

基因型分型检测业务
*参考基因组: AGPv3

Law M, Childs KL, Campbell MS, et al. Automated update, revision, and quality control of the maize genome annotations using MAKER-P improves the B73 Ref_v3 gene models and identifies new genes. Plant Physiol. 2015;167:25-39.

注:我们所有的即用型 GBTS 面板都可根据特定性状或基因座定制附加标记。如需加入您感兴趣的基因位点,请联系我们

产品亮点
应用
科学研究
· 遗传图谱构建
· QTL定位
· 全基因组关联分析
育种应用
· 种质资源鉴定
· 分子标记辅助选择
· 全基因组选择育种
· 品种保护、品种认证
服务流程
样品制备和提交指南
结果展示
GenoBaits® 玉米 45K 组合
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GenoBaits® 玉米 10K 组合
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GenoBaits® 玉米 1K 组合
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参考文献
  1. Gao J, Wang S, Zhou Z, et al. Linkage mapping and genome-wide association reveal candidate genes conferring thermotolerance of seed-set in maize. J Exp Bot. 2019;70(18):4849-4864.    
  2. Guo Z, Wang H, Tao J, et al. Development of multiple SNP marker panels affordable to breeders through genotyping by target sequencing (GBTS) in maize. Mol Breeding. 2019;39(37).    
  3. Liu HJ, Jian L, Xu J, et al. High-Throughput CRISPR/Cas9 Mutagenesis Streamlines Trait Gene Identification in Maize. Plant Cell. 2020;32(5):1397-1413. DOI: 10.1105/tpc.19.00934    
  4. Wen J, Shen Y, Xing Y, et al. QTL Mapping of Fusarium Ear Rot Resistance in Maize. Plant Dis. 2021;105(3):558-565.
  5. Guo Z, Yang Q, Huang F, et al. Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip. Plant Commun. 2021;2(6):100230.  
  6. Han L, Jiang C, Zhang W, et al. Morphological Characterization and Transcriptome Analysis of New Dwarf and Narrow-Leaf (dnl2) Mutant in Maize. Int J Mol Sci. 2022;23(2):795.  
  7. Zhang X, Wang M, Zhang C, et al. Genetic dissection of QTLs for starch content in four maize DH populations. Front Plant Sci. 2022;13:950664.
  8. Huang J, Li Y, Ma Y, et al. The rhizospheric microbiome becomes more diverse with maize domestication and genetic improvement. J Integr Agric. 2022;4:1188-1202.
  9. Liu R, Cui Y, Kong L, et al. Evaluating the Genetic Background Effect on Dissecting the Genetic Basis of Kernel Traits in Reciprocal Maize Introgression Lines. Genes (Basel). 2023;14(5):1044.    
  10. Gao J, Feng P, Zhang J, et al. Enhancing maize's nitrogen-fixing potential through ZmSBT3, a gene suppressing mucilage secretion. J Integr Plant Biol. 2023;65(12):2645-2659.  
  11. Yu G, Cui Y, Jiao Y, et al. Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance. Crop J. 2023;11(2):490-498.  
  12. Luo P, Wang H, Ni Z, et al. Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design. Crop J. 2023;11(6):1884-92.    
  13. Xu F, Liu S, Zhao A, et al. iFLAS: positive-unlabeled learning facilitates full-length transcriptome-based identification and functional exploration of alternatively spliced isoforms in maize. New Phytol. 2024;241(6):2606-2620.
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