社区驱动的复制数变异发现规模:罕见疾病基因组哈卡通的结果
medRxiv : the preprint server for health sciences
|August 20, 2025
概括
开发了可扩展拷贝数变异 (CNV) 分析工作流程,以加快罕见疾病诊断. 这些方法有效地从短读基因组测序数据中检测和解释CNV,有助于识别疾病的遗传原因.
科学领域:
- 基因组学
- 罕见疾病
- 生物信息学
背景情况:
- 复制数变异 (CNV) 是罕见遗传疾病的重要贡献者.
- 从短读基因组测序 (srGS) 数据中检测和解释CNV具有挑战性,特别是在规模上.
- 目前大量的 srGS 数据仍未得到充分利用,以确定临床相关的 CNV.
研究的目的:
- 为srGS数据开发和应用可扩展的CNV分析工作流程.
- 在罕见疾病群体中加速诊断和遗传发现.
- 应对大型数据集中CNV检测和解释的挑战.
主要方法:
- 使用Parliament2进行结构变体 (SV) 调用和Mosdepth/SLMSuite进行基于读取深度的质量控制和CNV检测.
- 应用R Shiny进行可视化,并开发了带有注释的SV/CNV变体数据库.
- 采用DBSCAN集群来估计等位基因的频率,并使用三向注释策略进行解释.
主要成果:
- 开发并将可扩展的CNV分析管道应用于三个罕见疾病队列的srGS数据.
- 确定了高可信度的CNV和简化解释,在2天内产生了39个潜在的致病性SV.
- 证明了临床相关变体的快速过,优先考虑和可视化.
结论:
- 一场由社区主导的黑客马拉松成功展示了可扩展的CNV分析的可行性.
- 开发的工具和工作流程显著加快了罕见疾病研究的诊断和发现.
- 这种方法提高了现有的srGS数据用于识别罕见疾病的遗传原因的实用性.
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