TY  - JOUR
AU  - Bonaguro, Lorenzo
AU  - Schulte-Schrepping, Jonas
AU  - Carraro, Caterina
AU  - Sun, Laura L
AU  - Reiz, Benedikt
AU  - Gemünd, Ioanna
AU  - Saglam, Adem
AU  - Rahmouni, Souad
AU  - Georges, Michel
AU  - Arts, Peer
AU  - Hoischen, Alexander
AU  - Joosten, Leo A B
AU  - van de Veerdonk, Frank L
AU  - Netea, Mihai G
AU  - Händler, Kristian
AU  - Mukherjee, Sach
AU  - Ulas, Thomas
AU  - Schultze, Joachim L
AU  - Aschenbrenner, Anna Christin
TI  - Human variation in population-wide gene expression data predicts gene perturbation phenotype.
JO  - iScience
VL  - 25
IS  - 11
SN  - 2589-0042
CY  - St. Louis
PB  - Elsevier
M1  - DZNE-2022-01665
SP  - 105328
PY  - 2022
AB  - 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 'in population' experiment. We describe here an approach, huva (human variation), taking advantage of population-scale multi-layered data to infer gene function and relationships between phenotypes and expression. Within a reference dataset, huva 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 huva predicts monocytes to be the major cell type in the pathophysiology of STAT1 mutations, evidence validated in a clinical cohort.
KW  - Clinical genetics (Other)
KW  - Human genetics (Other)
KW  - Pathophysiology (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:36310583
C2  - pmc:PMC9614568
DO  - DOI:10.1016/j.isci.2022.105328
UR  - https://pub.dzne.de/record/165517
ER  -