Bonaguro Lorenzo, Schulte-Schrepping Jonas, Carraro Caterina, Sun Laura L, Reiz Benedikt, Gemünd Ioanna, Saglam Adem, Rahmouni Souad, Georges Michel, Arts Peer, Hoischen Alexander, Joosten Leo A B, van de Veerdonk Frank L, Netea Mihai G, Händler Kristian, Mukherjee Sach, Ulas Thomas, Schultze Joachim L, Aschenbrenner Anna C
Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany.
Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany.
iScience. 2022 Oct 12;25(11):105328. doi: 10.1016/j.isci.2022.105328. eCollection 2022 Nov 18.
Population-scale datasets of healthy individuals capture genetic and environmental factors influencing gene expression. The expression variance of a gene of interest (GOI) can be exploited to set up a quasi loss- or gain-of-function "" experiment. We describe here an approach, , taking advantage of population-scale multi-layered data to infer gene function and relationships between phenotypes and expression. Within a reference dataset, derives two experimental groups with LOW or HIGH expression of the GOI, enabling the subsequent comparison of their transcriptional profile and functional parameters. We demonstrate that this approach robustly identifies the phenotypic relevance of a GOI allowing the stratification of genes according to biological functions, and we generalize this concept to almost 16,000 genes in the human transcriptome. Additionally, we describe how predicts monocytes to be the major cell type in the pathophysiology of STAT1 mutations, evidence validated in a clinical cohort.
健康个体的群体规模数据集捕捉到了影响基因表达的遗传和环境因素。感兴趣基因（GOI）的表达变异可用于建立一个准功能丧失或功能获得“实验”。我们在此描述一种方法，利用群体规模的多层数据来推断基因功能以及表型与表达之间的关系。在一个参考数据集中，该方法衍生出GOI表达低或高的两个实验组，从而能够随后比较它们的转录谱和功能参数。我们证明，这种方法能够可靠地识别GOI的表型相关性，允许根据生物学功能对基因进行分层，并且我们将这一概念推广到人类转录组中近16000个基因。此外，我们描述了该方法如何预测单核细胞是STAT1突变病理生理学中的主要细胞类型，这一证据在一个临床队列中得到了验证。
