因果拼接变异通过深度学习整合在流感感染下的单细胞sQTL映射来揭示
Research square
|January 16, 2026
概括
这项研究确定了76种影响RNA拼接的因果遗传变异,将它们与自身免疫性疾病等疾病联系起来. 在PARP2的一个变异影响蛋白质异型和流感A病毒水平.
科学领域:
- 人类遗传学 人类遗传学
- 分子生物学分子生物学
- 计算生物学是一种计算生物学.
背景情况:
- 识别因果遗传变异对于理解疾病机制至关重要.
- 分子定量特征位点 (molQTL) 分析,如表达QTL (eQTL) 和拼接QTL (sQTL),将遗传变异与分子表型联系起来,但难以确定因果变异.
- 将sQTL分析与深度学习相结合,可以更好地识别因果变异及其对人类表型的功能影响.
研究的目的:
- 为了确定影响RNA拼接的因果遗传变异.
- 阐明这些变异影响人类表型的功能机制.
- 将拼接QTL与全基因组关联研究 (GWAS) 的特征和疾病易感性联系起来.
主要方法:
- 采用单细胞全基因组关联研究 (scGWAS) 方法 (scHi-HOST) 在有或没有流感A病毒 (IAV) 感染的淋巴细胞细胞系 (LCL) 上.
- 集成拼接QTL (sQTL) 分析与基于深度学习的拼接效应预测.
- 使用CRISPR编辑实验验验证的因果变异.
主要成果:
- 在IAV感染后发现了大约43,000个与217个基因相关的sQTLs.
- 确定了76种可能影响拼接组件的因果变异,包括在PARP2中验证的变异 (rs2297616),该变异改变了拼接位,产生蛋白质异型,并影响了IAV水平.
- 将这些因果性sQTL与超过一百个GWAS特征联系起来,包括与自身免疫性疾病相关的变异.
结论:
- 开发了对RNA剪接有直接影响的因果性sQTLs目录.
- 提供了对基因型-表型关系的机制性见解,将遗传变异与疾病易感性联系起来.
- 证明了将sQTL分析与人工智能驱动的拼接预测集成为功能基因组学的实用性.
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