识别影响转录因子活动的基因调节变异
Xiaoting Li1, Tuuli Lappalainen2,3,4, Harmen J Bussemaker1,4
1Department of Biological Sciences, Columbia University, New York, NY 10027, USA.
Cell genomics
|September 18, 2023
概括
研究人员开发了一种新模型,使用人类遗传数据估计蛋白质水平转录因子 (TF) 活性. 该方法识别了影响TF活动的基因变异,推进了基于网络的多omics研究.
科学领域:
- 基因组学和生物信息学
- 系统生物学 系统生物学
- 人类遗传学 人类遗传学
背景情况:
- 基因型-组织表达 (GTEx) 项目绘制了影响人类基因表达的遗传变异.
- 通过共享转录因子 (TFs) 影响多个基因的转变作用变体的识别对于理解基因调节至关重要.
研究的目的:
- 从GTExRNA测序 (RNA-seq) 数据开发一个通用线性模型 (GLM) 来估计个体特异的蛋白级TF活性.
- 在各种人体组织中识别基因型特定的TF活动和相关的定量特征位点 (aQTL).
主要方法:
- 使用GLM推断TF活动,使用TF扰动后的差异基因表达作为预测器.
- 对邻近基因的差异表达的分析,控制基因染色体状态变异的混效应.
- 对推断的TF活动进行了全基因组关联分析,以发现aQTLs.
主要成果:
- 在49个人类组织中推断出55个TF的基因型特异性活动.
- 全基因组关联分析揭示了TF活动量化特征位点 (aQTLs).
- 识别的aQTL被丰富为功能性基因组特征.
结论:
- 开发的方法允许估计个体特异性TF活动,提供一种新的细胞内型.
- 这种方法促进了细胞表型的遗传关联研究,使用基于网络的多omics战略.
- 这些发现强调了将遗传数据与基因表达特征集成的潜力,以剖析复杂的生物网络.
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