三种生物信息学工具的比较,用于从整个外基因组测序数据中检测ASD候选变体
Apurba Shil1,2,3, Liron Levin4, Hava Golan2,3,5
1Department of Epidemiology, Biostatistics, and Health Community Sciences, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Scientific reports
|November 2, 2023
概括
整合多种生物信息学工具可以从整个外因组测序数据中更好地检测自闭症谱系障碍 (ASD) 候选变体. 结合解释方法可以提高与ASD相关的遗传变异的诊断产量.
科学领域:
- 遗传学 遗传学 是一个
- 神经发育障碍 神经发育障碍
- 生物信息学是一种生物信息学.
背景情况:
- 自闭症谱系障碍 (ASD) 是一种复杂的神经发育状况,具有重要的遗传成分.
- 鉴定ASD的致病性遗传变异是具有挑战性的,因为它的异质性.
- 整体外基因组测序 (WES) 对于检测罕见变异至关重要,但解释需要集成的基因组数据.
研究的目的:
- 从WES数据中比较三个生物信息学工具的一致性和有效性,以检测自闭症谱系障碍 (ASD) 候选变体.
- 评估在ASD中解释单核酸变异 (SNV) 和短插入/删除 (INDEL) 的不同方法.
- 确定变异解释的最佳策略,以最大限度地提高ASD的诊断产量.
主要方法:
- 通过使用InterVar,TAPES和内部工具Psi-Variant,分析了220个以色列ASD家族三重组的WES数据.
- 专注于罕见的 (种群频率<1%) 试验特异型 (SNV和短INDEL).
- 使用美国医学遗传学院 (ACMG) 的指导方针和集成的in-silico预测来评估变异性病原性.
主要成果:
- 在168个试验中,共检测到311个基因中的372个变异.
- InterVar和Psi-Variant的交叉显示出在检测已知的ASD基因 (PPV=0.274) 中变异的最高有效性.
- InterVar和Psi-Variant的结合产生了20.5%的最高诊断产量.
结论:
- 整合多种生物信息学工具和WES数据的解释方法可以显著改善ASD候选变体的检测.
- 结合策略,特别是InterVar和Psi-Variant的结合,与单个工具相比,提供了更高的诊断产量.
- 通过包括额外的标准来进一步细化,可以加强与自闭症谱系障碍相关的遗传变异的识别.
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