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表观基因组增强的eQTL热点揭示了36个人类组织中的全基因组转录程序

Huanhuan Liu^1,2,3, Qinwei Chen^1,2, Jintao Guo^1,2,3

¹Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361102, China.

Briefings in bioinformatics

|March 28, 2024

概括

这项研究引入了一种新方法,用于找到表达量特征位点 (eQTL) 热点,从而改善了人类组织中基因调节机制的发现. 这种方法增强了对复杂的cis-regulatory网络的理解.

科学领域:

基因组学就是基因组学.
系统生物学系统生物学
计算生物学计算生物学

背景情况:

表达量的特征位点 (eQTLs) 对于理解转录调节至关重要.
鉴定全基因组的eQTL面临由于严格的错误发现率控制的限制.
发现组织特异性的调节机制需要先进的分析方法.

研究的目的:

开发一种用于识别eQTL热点的新方法,定义为频繁,多个eQTL关联的区域.
创建一个机器学习模型 (E-SpotFinder) 用于增强发现特定于组织或细胞类型的eQTL热点.
重建综合性全基因组的CIS调节网络,并确定转录程序.

主要方法:

利用一种不均的Poisson过程,从59种人类组织/细胞类型的公共数据中识别出125,489个eQTL热点.
基于序列和表观遗传特征的分层eQTL热点.
开发并将E-SpotFinder机器学习模型应用于36种组织/细胞类型,以增强eQTL热点发现.

主要成果:

确定了125,489个eQTL热点,并将它们分为两个不同的组.
增强式发现产生了655,402个eSNP,并重建了2,725,380个cis相互作用的网络.
确定了52,012个模块,代表了独特的转录程序.

结论:

这项研究为表观基因组增强的eQTL分析提供了一个框架.
在各种各样的人体组织中构建了全基因组的综合性cis-regulatory网络.
这些发现为转录调节机制和功能基因组学提供了新的见解.

关键词:

网络 cis 监管网络.在eQTL热点.用表观基因组增强的eQTL映射不同质的Poisson过程不同质的Poisson过程转录程序是转录的程序.

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