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@ARTICLE{Bonaguro:165517,
      author       = {Bonaguro, Lorenzo and Schulte-Schrepping, Jonas and
                      Carraro, Caterina and Sun, Laura L and Reiz, Benedikt and
                      Gemünd, Ioanna and Saglam, Adem and Rahmouni, Souad and
                      Georges, Michel and Arts, Peer and Hoischen, Alexander and
                      Joosten, Leo A B and van de Veerdonk, Frank L and Netea,
                      Mihai G and Händler, Kristian and Mukherjee, Sach and Ulas,
                      Thomas and Schultze, Joachim L and Aschenbrenner, Anna
                      Christin},
      title        = {{H}uman variation in population-wide gene expression data
                      predicts gene perturbation phenotype.},
      journal      = {iScience},
      volume       = {25},
      number       = {11},
      issn         = {2589-0042},
      address      = {St. Louis},
      publisher    = {Elsevier},
      reportid     = {DZNE-2022-01665},
      pages        = {105328},
      year         = {2022},
      abstract     = {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.},
      keywords     = {Clinical genetics (Other) / Human genetics (Other) /
                      Pathophysiology (Other)},
      cin          = {PRECISE / AG Mukherjee / AG Halle},
      ddc          = {050},
      cid          = {I:(DE-2719)1013031 / I:(DE-2719)1013030 /
                      I:(DE-2719)1013034},
      pnm          = {352 - Disease Mechanisms (POF4-352) / 354 - Disease
                      Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-352 / G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36310583},
      pmc          = {pmc:PMC9614568},
      doi          = {10.1016/j.isci.2022.105328},
      url          = {https://pub.dzne.de/record/165517},
}