利用全基因组对基因表达的影响来识别具有转基因组件的疾病关键基因
medRxiv : the preprint server for health sciences
|March 11, 2026
概括
EGRET模拟全基因组基因表达,显著改善疾病基因发现和调控网络分析. 这种方法捕获了比cis-eQTLs更多的表达变异,增强了复杂的特征相关基因的识别.
科学领域:
- 遗传学 遗传学 是一个
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 全基因组关联研究 (GWAS) 确定与复杂的特征和疾病相关的遗传变异.
- 表达量的特征位点 (eQTL) 映射将遗传变异与基因表达联系起来,有助于识别疾病关键基因.
- 目前的方法主要关注cis-eQTLs,可能低估了trans-eQTLs在基因调节中的作用.
研究的目的:
- 引入EGRET (对转录组的全基因组监管效应的估计),这是一个用于联合建模cis-和 trans-eQTLs的整体框架.
- 量化跨-eQTLs解释的差异,并将其与cis-eQTLs进行比较.
- 通过全基因组表达模型增强疾病关键基因和基因调控网络的发现.
主要方法:
- EGRET将cis-eQTL与三种跨eQTL映射方法 (矩阵eQTL,跨PCO,GBAT) 集成在一起.
- 该框架应用于用于预测基因表达模型的GTEx数据.
- EGRET模型被用于转录组范围的关联研究 (TWAS) 和构建基因调控网络.
主要成果:
- 在49个组织中,EGRET产生了超过353,000个预测基因表达模型,其中12317个显示出显著的转基因遗传成分.
- EGRET模型解释了33%的基因表达变异比单独的cis-eQTL模型更多.
- 在模拟中,EGRET显著增加了功率 (1.2x-3.1x),并且与现有方法相比,在真实数据分析中发现了更多的基因疾病关联.
结论:
- 建模基因表达的全基因组遗传成分,包括跨eQTLs,大大提高了疾病关键基因的检测.
- EGRET改善了GWAS变异的特征,并有助于定义复杂的基因调节网络.
- EGRET框架为了解基因表达的遗传结构及其在复杂特征和疾病中的作用提供了一种强有力的方法.
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