Li Xiaoting, Lappalainen Tuuli, Bussemaker Harmen J
Department of Biological Sciences, Columbia University, New York, NY 10027, USA.
New York Genome Center, New York, NY 10013, USA.
Cell Genom. 2023 Aug 18;3(9):100382. doi: 10.1016/j.xgen.2023.100382. eCollection 2023 Sep 13.
Genetic variants affecting gene expression levels in humans have been mapped in the Genotype-Tissue Expression (GTEx) project. -acting variants impacting many genes simultaneously through a shared transcription factor (TF) are of particular interest. Here, we developed a generalized linear model (GLM) to estimate protein-level TF activity levels in an individual-specific manner from GTEx RNA sequencing (RNA-seq) profiles. It uses observed differential gene expression after TF perturbation as a predictor and, by analyzing differential expression within pairs of neighboring genes, controls for the confounding effect of variation in chromatin state along the genome. We inferred genotype-specific activities for 55 TFs across 49 tissues. Subsequently performing genome-wide association analysis on this virtual trait revealed TF activity quantitative trait loci (aQTLs) that, as a set, are enriched for functional features. Altogether, the set of tools we introduce here highlights the potential of genetic association studies for cellular endophenotypes based on a network-based multi-omics approach. The transparent peer review record is available.
在基因型-组织表达(GTEx)项目中,已绘制出影响人类基因表达水平的遗传变异图谱。通过共享转录因子(TF)同时影响多个基因的顺式作用变异尤其令人关注。在此,我们开发了一种广义线性模型(GLM),以个体特异性方式从GTEx RNA测序(RNA-seq)数据中估计蛋白质水平的TF活性水平。该模型将TF扰动后观察到的差异基因表达用作预测指标,并通过分析相邻基因对中的差异表达,控制沿基因组染色质状态变化的混杂效应。我们推断了49个组织中55种TF的基因型特异性活性。随后对这一虚拟性状进行全基因组关联分析,揭示了TF活性定量性状位点(aQTLs),这些位点作为一个整体,富含功能特征。总之,我们在此介绍的这套工具突出了基于网络多组学方法的细胞内表型遗传关联研究的潜力。透明的同行评审记录可供查询。