在6224个未解决的罕见疾病外源中,结构变异调用和临床解释
German Demidov1, Steven Laurie2, Annalaura Torella3,4
1Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany. German.Demidov@med.uni-tuebingen.de.
European journal of human genetics : EJHG
|May 31, 2024
概括
结构变异 (SV) 调用外体序列 (ES) 改善了罕见疾病的诊断. 这种方法确定了其他分析遗漏的致病性SVs,增加了未解决患者队列的诊断产量.
科学领域:
- 基因组学就是基因组学.
- 医学遗传学 医学遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 结构变异 (SV) 可以通过破坏基因功能来引起罕见疾病.
- 当前的外体序列 (ES) 分析通常优先考虑小变体 (SNV,indels) 和副本数变体 (CNV).
- 在ES数据中检测非CNV SVs是具有挑战性的,因为在非编码区域中难以识别断点.
研究的目的:
- 为了评估结构变异 (SV) 的诊断实用性,调用外基因组测序 (ES) 数据.
- 评估SV检测在未解决患者队列中诊断罕见疾病的贡献.
主要方法:
- 应用 SV 调用算法对来自大量未解决的罕见病患者队列的外体序列 (ES) 数据进行调用.
- 将SV检测结果与现有的复制数变异 (CNV) 分析进行了比较.
- 分析了已识别的SVs的病原性和诊断影响.
主要成果:
- 在ES中调用SV的诊断率为0.4% (23/5825 probands).
- 八种致病性SV仅通过SV调用被确定,CNV分析错过了.
- 这意味着诊断产量增加了0.13%,突出显示了全面的SV检测的价值.
结论:
- 结构变异 (SV) 调用是用于罕见疾病诊断的外基因组测序 (ES) 分析的宝贵补充.
- 综合性SV检测可以发现传统CNV分析遗漏的致病变体.
- 整合SV调用在具有挑战性的,未解决的罕见疾病病例中提高了诊断产量.
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