在罕见疾病中的移动元素插入:一个比较的基准和重新分析6万个外体样本
Robin Wijngaard1,2, German Demidov3, Luke O'Gorman1
1Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands.
European journal of human genetics : EJHG
|October 18, 2023
概括
移动元素插入 (MEI) 导致遗传疾病,并且在外基因组测序 (ES) 中经常错过. 这项研究发现,MELT和SCRAMble工具可靠地检测ES数据中的MEI,改善了以前通过标准遗传测试错过的患者的诊断.
科学领域:
- 遗传学 遗传学 是一个
- 生物信息学是一种生物信息学.
- 基因组医学是基因组医学.
背景情况:
- 移动元素插入 (MEI) 是遗传疾病的重要原因,但它们的检测受到当前遗传检测技术的限制.
- 现有的MEI识别生物信息工具主要是为基因组测序 (GS) 数据而设计的,而不是外基因组测序 (ES) 数据,这是诊断中更常见的.
研究的目的:
- 在ES和GS数据上对六个MEI检测工具的性能进行基准测试.
- 评估ES数据中MEI检测的各种过策略的灵敏度和精度.
- 评估MEI检测工具在大型患者队列中诊断遗传疾病的临床实用性.
主要方法:
- 使用公共ES和GS数据集 (HG002,NA12878) 对六种MEI检测工具 (ERVcaller,MELT,Mobster,SCRAMble,TEMP2,xTea) 的比较分析.
- 用不同的过方法评估工具的灵敏度和精度.
- 将高性能工具 (MELT和SCRAMble) 应用于大规模的临床ES数据集 (Solve-RD和Radboudumc).
主要成果:
- 在评估的MEI检测工具中,ES和GS数据之间观察到显著的性能差异.
- MELT在ES数据上表现最好,其与SCRAMble的结合大大提高了MEI检测率.
- 将MELT和SCRAMble应用于超过63,000个ES样本导致了10名以前未被诊断的患者的诊断.
结论:
- MELT和SCRAMble是用于在常规外基因组测序数据中识别临床相关MEI的可靠工具.
- 在临床诊断中实施这些工具可以提高诊断产量,有可能在3000至4000名患者中1人中识别引起的MEI.
- 这一进步为诊断以前无法通过传统的外体分析检测到的遗传疾病提供了新的途径.
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