转录组学和长期读取的基因组学的整合优先考虑了罕见疾病中的结构变异
Tanner D Jensen1, Bohan Ni2, Chloe M Reuter3,4
1Department of Genetics, Stanford University, Stanford, California 94305, USA.
Genome research
|March 20, 2025
概括
长读测序显著增加了罕见疾病患者罕见结构变异 (SV) 的检测. 将基因表达数据与一个新的模型 (Watershed-SV) 集成,可以更好地识别与疾病相关的功能性SV和并联重复扩展 (TREs).
科学领域:
- 基因组学和生物信息学
- 罕见疾病研究 罕见疾病研究
- 结构变量分析结构变量分析
背景情况:
- 罕见的结构变异 (SV),如插入,删除和复杂的重排,难以检测和解释,但可能导致门德尔病.
- 短读测序在准确识别和表征这些罕见的SVs方面存在局限性,留下了许多未被诊断的罕见疾病病例.
- 长读测序技术为检测复杂的基因组变化提供了更好的功能.
研究的目的:
- 通过使用长读测序来提高未被诊断的罕见疾病患者罕见结构变异 (SV) 的检测和功能解释.
- 为了研究罕见的SVs,并联重复扩展 (TREs) 和基因表达模式之间的关系.
- 开发和验证一种新的计算模型 (Watershed-SV),用于在罕见疾病环境中优先考虑功能性SV.
主要方法:
- 测序和分析未诊断疾病网络 (UDN) 中68个个体的长时间读取基因组和571个对照基因组.
- 优化SV检测管道以识别罕见的SV (MAF<0.01) 和并联重复扩展 (TREs).
- 整合基因表达数据 (来自血液或纤维细胞) 与使用水shed-SV概率模型的SV基因组注释.
主要成果:
- 长读序列测定平均每基因组发现716个罕见的SV等位基因,比短读序列测定增加了2.4倍.
- 发现罕见的SV重叠增强剂和罕见的TREs在基因表达异常值附近被丰富,这表明功能影响.
- 流域-SV模型确定了每个UDN基因组中位数为8个高保证度的功能性SV,其表现优于基线模型. 在FAM177A1中发现了一种导致神经发育障碍的复合异构缺失病例.
结论:
- 长读测序与优化分析管道相结合,显著改善了罕见疾病患者罕见结构变异的检测.
- 整合基因表达数据可以提高检测到的SV和TRE的功能优先级,有助于诊断罕见的遗传疾病.
- 水shed-SV模型显示,通过有效优先考虑候选功能变体,有望提高罕见疾病研究的诊断产量.
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