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@MISC{Bonaguro:280034,
      author       = {Bonaguro, Lorenzo},
      title        = {{S}oftware: huva, v0.1.4},
      address      = {Zenodo},
      reportid     = {DZNE-2025-00878},
      year         = {2022},
      abstract     = {Population-scale multi-layered datasets assemble extensive
                      experimental data of different types on single healthy
                      individuals in large cohorts, capturing genetic variation
                      and environmental factors influencing gene expression with
                      no clinical evidence of pathology. Variance of gene
                      expression can be exploited to set up a conditional quasi
                      loss- and gain-of-function “in population” experiment if
                      expression values for the gene of interest (GOI) are
                      available. We describe here a novel approach, called huva
                      (human variation), that takes advantage of population-scale
                      multi-layered data to infer gene function and relationships
                      between phenotypes and gene expression. Within a reference
                      dataset, huva derives two experimental groups, i.e.
                      individuals 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 and efficiently identifies the phenotypic relevance
                      of a GOI, allows the stratification of genes according to
                      shared biological functions, and we further generalized this
                      concept to almost 16,000 genes in the human blood
                      transcriptome. Additionally, we describe how huva predicts
                      the phenotype of naturally occurring activating mutations in
                      humans. Here, huva predicts monocytes rather than
                      lymphocytes to be the major cell type in the pathophysiology
                      of STAT1 activating mutations, evidence which was validated
                      in a cohort of clinically characterized patients. This
                      repository contains the huva package (v 0.1.4) used in the
                      original manuscript, Bonaguro et al. iScience 2022, together
                      with the R enviroment of the analysis shown in the
                      manuscript (
                      $https://github.com/lorenzobonaguro/huva_reproducibility$ )},
      cin          = {AG Schultze},
      cid          = {I:(DE-2719)1013038},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)33},
      doi          = {10.5281/ZENODO.7071267},
      url          = {https://pub.dzne.de/record/280034},
}