在罕见基因组项目中,对罕见疾病诊断的变异优先级方法进行批判性评估
Sarah L Stenton1,2,3, Melanie C O'Leary2, Gabrielle Lemire1,2
1Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Human genomics
|April 29, 2024
概括
通过优先考虑遗传变异,计算模型可以显著改善罕见疾病诊断. 一项社区挑战显示,顶级模型在大多数已解决的病例中确定了因果变异,有助于诊断和基因发现.
科学领域:
- 基因组学就是基因组学.
- 罕见疾病 罕见疾病
- 计算生物学 计算生物学
背景情况:
- 获得罕见疾病的遗传诊断具有挑战性,具有漫长的诊断旅程和低的变异识别率.
- 变量解释和优先级的计算方法正在增加,但它们的有效性尚不清楚.
- 基因组解释的批判性评估 (CAGI) 挑战旨在评估这些计算工具.
研究的目的:
- 在现实世界的临床诊断环境中评估计算变异优先级模型的性能.
- 鼓励创新和开发用于基因诊断的新计算方法.
主要方法:
- 在罕见基因组项目中利用了175个个体 (65个家庭) 的基因组测序数据.
- 为16个参与团队提供了35个已解决的培训家庭和30个测试家庭的变异调用和表型数据.
- 团队提交了变体预测,估计了因果关系概率 (EPCR) 值;性能根据变体排名和F-测量进行评估.
主要成果:
- 性能最好的模型在14个解决的家族中,在排名前5个变异中,在最多13个解决的家族中确定了因果变异.
- 新发现的诊断变异被返回两个以前未解决的家庭,导致诊断.
- 两种新型疾病基因候选人被确定并提交给匹配商交易所; 一个案例涉及深层内在变异和异常拼接.
结论:
- 模型的性能差异很大,但那些包含呼叫质量,等位基因频率,预测有害性,分离和表型数据的模型是有效的.
- 考虑表型扩展和非编码变体的模型改善了诊断产量,并确定了新的疾病基因.
- 计算模型在很大程度上有助于确定变异优先级,但临床使用需要仔细审查和谨慎评估.
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