对可能良性内基和同名变异的综合生物信息和拼接分析揭示了致病性证据
Owen R Hirschi1,2, Stephanie A Felker3, Surya P Rednam1,2
1Baylor College of Medicine, Houston, TX.
Genetics in medicine open
|December 13, 2024
概括
拼接AI提高了罕见拼接变体的检测,重新分类良性变体,增加了罕见疾病的诊断产量. 这种工具通过识别有影响力的内部和同义变体来增强变体分析.
科学领域:
- 基因组学就是基因组学.
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 目前的变异分析管道对从外子-内子边界超过10-20个基的拼接效应的灵敏度有限.
- 由于对拼接影响的分析不足,被归类为良性/可能良性 (B/LB) 的罕见变异可能被错误分类.
研究的目的:
- 为了证明SpliceAI在改善罕见变体治愈方面的实用性.
- 为了确定以前被归类为B/LB.的具有影响力的内部和同义变体.
- 通过增强的变体分析,提高罕见疾病的诊断产量.
主要方法:
- 来自576名儿科癌症患者 (KidsCanSeq研究) 的外体序列数据被过以寻找内部/同义变异.
- 预测通过SpliceAI (>0.20) 改变拼接的变体,并通过联合注释依赖枯竭 (CAD) 获得>10分,被优先考虑.
- 对选定的变体进行了RNA分析 (信使RNA和体外拼接报告员测定).
主要成果:
- 斯普利斯AI确定了一种DICER1内基变异,导致一个患有多发性肺母细胞瘤的患者的错误分离.
- 对34,188个B/LB ClinVar变体的分析显示,预计18个会导致阅读的中断.
- 在体外拼接试验证实了8种变体 (DICER1,CDH1,PALB2) 的异常拼接产品,与家族中的相关瘤表型有关.
结论:
- 将SpliceAI纳入变异选管道可以改善B/LB内基和同义变异的分类.
- 在功能测定和RNA分析中,SpliceAI突出显示了假定致病变体,从而增加了诊断产量.
- 这种方法可以更好地检测导致疾病的变种,而目前的方法无法检测到.
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