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@ARTICLE{Guan:147458,
      author       = {Z. Guan$^*$ and J. R. Raut$^*$ and K. Weigl$^*$ and B.
                      Schöttker$^*$ and B. Holleczek and Y. Zhang$^*$ and H.
                      Brenner$^*$},
      title        = {{I}ndividual and joint performance of {DNA} methylation
                      profiles, genetic risk scores and environmental risk scores
                      for predicting breast cancer risk.},
      journal      = {Molecular oncology},
      volume       = {14},
      number       = {1},
      issn         = {1878-0261},
      address      = {Hoboken, NJ},
      publisher    = {John Wiley $\&$ Sons, Inc.},
      reportid     = {DKFZ-2019-02540},
      pages        = {42-53},
      year         = {2020},
      note         = {2020 Jan;14(1):42-53#EA:C070#LA:C070#},
      abstract     = {DNA methylation patterns in the blood, genetic risk scores
                      (GRSs) and environmental risk factors can potentially
                      improve breast cancer (BC) risk prediction. We assessed the
                      individual and joint predictive performance of methylation,
                      GRS and environmental risk factors for BC incidence in a
                      prospective cohort study. In a cohort of 5462 women aged
                      50-75 from Germany, 101 BC cases were identified during 14
                      years of follow-up and were compared to 263 BC-free controls
                      in a nested case-control design. Three previously suggested
                      methylation risk scores (MRSs) based on methylation of 423,
                      248 and 131 cytosine-phosphate-guanine (CpG) loci, and a GRS
                      based on the risk alleles from 269 recently identified
                      single-nucleotide polymorphisms were constructed.
                      Additionally, multiple previously proposed environmental
                      risk scores (ERSs) were built based on environmental
                      variables. Areas under the receiver operating characteristic
                      curves (AUCs) were estimated for evaluating BC risk
                      prediction performance. MRS and ERS showed limited accuracy
                      in predicting BC incidence, with AUCs ranging from 0.52 to
                      0.56 and from 0.52 to 0.59, respectively. The GRS predicted
                      BC incidence with a higher accuracy (AUC=0.61). Adjusted
                      odds ratios per standard deviation increase $(95\%$
                      confidence interval) were 1.07 (0.84-1.36) and 1.40
                      (1.09-1.80) for the best performing MRS and ERS,
                      respectively, and 1.48 (1.16-1.90) for the GRS. A full risk
                      model combining the MRS, GRS and ERS predicted BC incidence
                      with the highest accuracy (AUC=0.64), and might be useful
                      for identifying high-risk populations for BC screening.},
      cin          = {C070 / C120 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)C120-20160331 /
                      I:(DE-He78)HD01-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31677238},
      doi          = {10.1002/1878-0261.12594},
      url          = {https://inrepo02.dkfz.de/record/147458},
}