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@ARTICLE{Freytag:120041,
      author       = {S. Freytag and J. Manitz and M. Schlather and T. Kneib and
                      C. I. Amos and A. Risch$^*$ and J. Chang$^*$ and J. Heinrich
                      and H. Bickeböller},
      title        = {{A} network-based kernel machine test for the
                      identification of risk pathways in genome-wide association
                      studies.},
      journal      = {Human heredity},
      volume       = {76},
      number       = {2},
      issn         = {1423-0062},
      address      = {Basel},
      publisher    = {Karger},
      reportid     = {DKFZ-2017-00628},
      pages        = {64 - 75},
      year         = {2013},
      abstract     = {Biological pathways provide rich information and biological
                      context on the genetic causes of complex diseases. The
                      logistic kernel machine test integrates prior knowledge on
                      pathways in order to analyze data from genome-wide
                      association studies (GWAS). In this study, the kernel
                      converts the genomic information of 2 individuals into a
                      quantitative value reflecting their genetic similarity. With
                      the selection of the kernel, one implicitly chooses a
                      genetic effect model. Like many other pathway methods, none
                      of the available kernels accounts for the topological
                      structure of the pathway or gene-gene interaction types.
                      However, evidence indicates that connectivity and
                      neighborhood of genes are crucial in the context of GWAS,
                      because genes associated with a disease often interact.
                      Thus, we propose a novel kernel that incorporates the
                      topology of pathways and information on interactions. Using
                      simulation studies, we demonstrate that the proposed method
                      maintains the type I error correctly and can be more
                      effective in the identification of pathways associated with
                      a disease than non-network-based methods. We apply our
                      approach to genome-wide association case-control data on
                      lung cancer and rheumatoid arthritis. We identify some
                      promising new pathways associated with these diseases, which
                      may improve our current understanding of the genetic
                      mechanisms.},
      cin          = {C010 / C020},
      ddc          = {570},
      cid          = {I:(DE-He78)C010-20160331 / I:(DE-He78)C020-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:24434848},
      pmc          = {pmc:PMC4026009},
      doi          = {10.1159/000357567},
      url          = {https://inrepo02.dkfz.de/record/120041},
}