在全基因组数据上对变异效应预测者的校准可以掩盖跨基因的异质性表现
Malvika Tejura1, Shawn Fayer1, Abbye E McEwen2
1Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
American journal of human genetics
|August 22, 2024
概括
在 silico 变异效应预测显示基因的性能各不相同. 基因特异性分析揭示了许多不一致的预测,强调了临床变异分类中需要量身定制的校准.
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:
- 在 silico 变体效应预测是广泛可用的,但由于证据不足,临床使用是有限的.
- 克林基因序列变异解释 (SVI) 工作组更新了使用这些预测的建议,使得证据强度计算成为可能.
- 对预测器性能的全基因组分析可能会掩盖基因特异性的变异.
研究的目的:
- 量化REVEL和BayesDel预测器的基因对基因性能.
- 为了识别与基因组范围趋势背道而的不一致的变体预测.
- 评估基因特异性预测器性能对临床变异分类的影响.
主要方法:
- 分析了对REVEL和BayesDel.的3,668个疾病相关基因的病原和良性变异的控制.
- 在基因对基因的基础上,在特定得分区间内量化预测器性能.
- 开发了一个网络应用程序,可视化基因特异性预测和间隔对应.
主要成果:
- 大约10%的预测得分区间有足够的对照变异来进行分析.
- 大约70%的分析间隔超过了SVI隐含的不正确预测极限,表明基因特异差异.
- 超过22%的不确定意义的ClinVar变异陷入不一致的间隔,可能导致错误分类.
结论:
- 对变异效应预测因子的全基因组校准可能对许多基因不合适.
- 基因特异性校准是必要的,以准确评估预测器性能和证据强度.
- 开发的网络应用程序有助于审查SVI校准和理解基因特异性预测器行为.
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