优先考虑者的精细偏好改善了孟德尔病的智能诊断
Xiao Yuan1, Jieqiong Su1, Jing Wang1
1Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China.
Scientific reports
|February 3, 2024
概括
优化基因优先级工具,如Exomiser和LIRICAL显著提高了门德尔病的诊断产量. 这些增强的工具准确地对因果基因进行排名,有助于诊断遗传疾病.
科学领域:
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 以表型为指导的基因优先级对识别在门德尔疾病中引起疾病的基因至关重要.
- 之前对基因优先级软件的评估使用了默认设置和单元模式,限制了其现实世界的诊断潜力.
研究的目的:
- 通过使用优化的参数和三重模式,重新评估四种表现最好的基因优先级工具 (Exomiser,PhenIX,AMELIE,LIRICAL) 的性能.
- 评估诊断产量,并确定这些工具在大型家庭和三组数据集中的局限性.
主要方法:
- 利用了来自解密发育障碍 (DDD) 项目的大规模家庭数据 (N=305) 和内部三组队列 (N=152).
- 进行了参数优化,并为四个选定的基因优先级工具使用了三重模式:Exomiser,PhenIX,AMELIE和LIRICAL.
- 在每个工具的输出列表中分析了因果基因的排名.
主要成果:
- 实现了高的诊断产量,83.3-91.8%的因果基因被列为前十名候选人.
- 超过97.7%的因果基因在前50名中被至少一个四个工具中确定.
- 在排名第一个原因基因方面,Exomiser表现出色,而LIRICAL则表现出优越的整体检测能力. 错误判断发生在低透率,高频变体.
结论:
- 优化的基因优先级工具,特别是Exomiser和LIRICAL,显著提高了孟德尔病的诊断准确性.
- 需要进一步开发以解决优先考虑低透率和高频率病原体变异病例的局限性.
- 结合使用或进一步改进这些工具有望改善复杂病例的遗传诊断.
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