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<p>Genetically modified (GM) crops hold substantial potential to increase agricultural productivity, enhance resistance to pest and disease, and reduce reliance on chemical pesticides. However, the rapid proliferation of GM organisms and transgenic crop varieties has posed new challenges for regulatory oversight and traceability. To address these challenges, we developed a targeted detection strategy for identifying transformation events in maize using low-depth next-generation sequencing (NGS). We systematically evaluated key parameters, including tissue or organ selection, DNA extraction protocols, high-throughput Library preparation methods, sequencing depth, and bioinformatics analysis. Our results showed that detection accuracy is improved with high-quality DNA input but unaffected by plant tissue type. PCR-free Library preparation outperformed amplification-based methods, and a sequencing depth of 5× reliably detected transgenic sequences in uncharacterized samples. This methodology provides a high-efficiency, multi-target framework for cost-effective identification of maize transformants, facilitating standardized and scalable surveillance of genetically modified crops.</p>

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Efficient transgenic maize (Zea Mays L.) detection using low-depth next-generation sequencing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月28日

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  • Xin Qi,
  • Xin Zhao,
  • Mingjie Lyu,
  • Ke Wu,
  • Na Liu,
  • Ruihuan Li,
  • Chao Xu,
  • Rongyan Shen,
  • Zehui Zhou,
  • Cheng Wang,
  • Chen Zhang,
  • Ya-Si Wang,
  • Qingkuo Lan,
  • Yong Wang

摘要

Genetically modified (GM) crops hold substantial potential to increase agricultural productivity, enhance resistance to pest and disease, and reduce reliance on chemical pesticides. However, the rapid proliferation of GM organisms and transgenic crop varieties has posed new challenges for regulatory oversight and traceability. To address these challenges, we developed a targeted detection strategy for identifying transformation events in maize using low-depth next-generation sequencing (NGS). We systematically evaluated key parameters, including tissue or organ selection, DNA extraction protocols, high-throughput Library preparation methods, sequencing depth, and bioinformatics analysis. Our results showed that detection accuracy is improved with high-quality DNA input but unaffected by plant tissue type. PCR-free Library preparation outperformed amplification-based methods, and a sequencing depth of 5× reliably detected transgenic sequences in uncharacterized samples. This methodology provides a high-efficiency, multi-target framework for cost-effective identification of maize transformants, facilitating standardized and scalable surveillance of genetically modified crops.