在SNP扰动下学习基因网络,使用SNP和基因特异表达数据
1Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States of America.
bioRxiv : the preprint server for biology
|November 14, 2023
概括
我们开发了CiTruss,这是一种通过分析影响基因表达的遗传变异 (eQTL) 来重建基因网络的新方法. 这种方法揭示了本地和全球网络结构,为基因调节和遗传多样性提供了洞察力.
科学领域:
- 基因组学就是基因组学.
- 系统生物学 系统生物学
- 统计遗传学 统计遗传学
背景情况:
- 使用RNA-seq数据的基因特异表达 (ASE) 分析有助于理解受遗传变异影响的基因调控网络.
- 现有的方法往往侧重于单基因/SNP关联来表达定量特征位置 (eQTL),限制了网络层面的解释.
研究的目的:
- 引入CiTruss,这是一个统计框架,用于同时学习基因网络及其扰乱 cis-和 trans-acting eQTL.
- 通过将eQTL数据视为基因调控系统的干扰来重建基因网络.
主要方法:
- CiTruss采用多级条件高斯图形模型来表示跨作用和 cis作用的eQTL.
- 一个模型转换能够从大规模的人类和小鼠遗传数据 (GTEx,LG×SM) 进行高效的学习.
主要成果:
- CiTruss的分析显示,基因网络包括局部子网络 (近邻基因) 和全球子网络 (分散基因).
- 该框架解释了各种eQTL效应,包括遗传多样性的影响,多样性,组织特异性调节和链接不平衡.
结论:
- CiTruss提供了一种新的方法,可以从eQTL数据中推断基因调节网络.
- 确定了本地和全球网络结构,为复杂的基因调节遗传学提供了新的见解.
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