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@ARTICLE{Janouschek:845712,
      author       = {Janouschek, Hildegard and Eickhoff, Claudia and Mühleisen,
                      Thomas and Eickhoff, Simon and Nickl-Jockschat, Thomas},
      title        = {{U}sing coordinate-based meta-analyses to explore
                      structural imaging genetics},
      journal      = {Brain structure $\&$ function},
      volume       = {223},
      number       = {7},
      issn         = {1863-2661},
      address      = {Berlin},
      publisher    = {Springer},
      reportid     = {FZJ-2018-02924},
      pages        = {3045–3061},
      year         = {2018},
      abstract     = {Imaging genetics has become a highly popular approach in
                      the field of schizophrenia research. A frequently reported
                      finding is that effects from common genetic variation are
                      associated with a schizophrenia-related structural
                      endophenotype. Genetic contributions to a structural
                      endophenotype may be easier to delineate, when referring to
                      biological rather than diagnostic criteria. We used
                      coordinate-based meta-analyses, namely the anatomical
                      likelihood estimation (ALE) algorithm on 30
                      schizophrenia-related imaging genetics studies, representing
                      44 single-nucleotide polymorphisms at 26 gene loci
                      investigated in 4682 subjects. To test whether analyses
                      based on biological information would improve the
                      convergence of results, gene ontology (GO) terms were used
                      to group the findings from the published studies. We did not
                      find any significant results for the main contrast. However,
                      our analysis enrolling studies on genotype × diagnosis
                      interaction yielded two clusters in the left temporal lobe
                      and the medial orbitofrontal cortex. All other subanalyses
                      did not yield any significant results. To gain insight into
                      possible biological relationships between the genes
                      implicated by these clusters, we mapped five of them to GO
                      terms of the category “biological process” (AKT1, CNNM2,
                      DISC1, DTNBP1, VAV3), then five to “cellular component”
                      terms (AKT1, CNNM2, DISC1, DTNBP1, VAV3), and three to
                      “molecular function” terms (AKT1, VAV3, ZNF804A). A
                      subsequent cluster analysis identified representative,
                      non-redundant subsets of semantically similar terms that
                      aided a further interpretation. We regard this approach as a
                      new option to systematically explore the richness of the
                      literature in imaging genetics.},
      cin          = {INM-7 / INM-1},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-1-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29730826},
      UT           = {WOS:000443296300002},
      doi          = {10.1007/s00429-018-1670-9},
      url          = {https://juser.fz-juelich.de/record/845712},
}