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@ARTICLE{Lai:277120,
      author       = {J. Lai and C. K. Wong and D. F. Schmidt and M. K.
                      Kapuscinski and K. Alpen and R. J. Maclnnis and D. D.
                      Buchanan and A. K. Win and J. C. Figueiredo and A. T. Chan
                      and T. A. Harrison and M. Hoffmeister$^*$ and E. White and
                      L. Le Marchand and R. K. Pai and U. Peters and J. L. Hopper
                      and M. A. Jenkins and E. Makalic},
      title        = {{U}sing {DEP}endency of association on the number of {T}op
                      {H}its ({DEPTH}) as a complementary tool to identify novel
                      colorectal cancer susceptibility loci.},
      journal      = {Cancer epidemiology, biomarkers $\&$ prevention},
      volume       = {32},
      number       = {9},
      issn         = {1055-9965},
      address      = {Philadelphia, Pa.},
      publisher    = {AACR},
      reportid     = {DKFZ-2023-01285},
      pages        = {1153-1159},
      year         = {2023},
      note         = {2023 Sep 1;32(9):1153-1159},
      abstract     = {DEPendency of association on the number of Top Hits (DEPTH)
                      is an approach to identify candidate susceptibility regions
                      by considering the risk signals from overlapping groups of
                      sequential variants across the genome.We conducted a DEPTH
                      analysis using a sliding window of 200 SNPs to colorectal
                      cancer (CRC) data from the Colon Cancer Family Registry
                      (CCFR) (5,735 cases and 3,688 controls), and GECCO (8,865
                      cases and 10,285 controls) studies. A DEPTH score >1 was
                      used to identify candidate susceptibility regions common to
                      both studies. We compared DEPTH results against those from
                      conventional GWAS analyses of these two studies as well as
                      against 132 published susceptibility regions.Initial DEPTH
                      analysis revealed 2,622 (CCFR) and 3,686 (GECCO) candidate
                      susceptibility regions, of which 569 were common to both
                      studies. Bootstrapping revealed 40 and 49 candidate
                      susceptibility regions in the CCFR and GECCO data sets,
                      respectively. Notably, DEPTH identified at least 82 regions
                      that would not be detected using conventional GWAS methods,
                      nor had they been identified by previous CRC GWASs. We found
                      four reproducible candidate susceptibility regions (2q22.2,
                      2q33.1, 6p21.32, 13q14.3). The highest DEPTH scores were in
                      the HLA locus at 6p21 where the strongest associated SNPs
                      were rs762216297, rs149490268, rs114741460, and rs199707618
                      for the CCFR data, and rs9270761 for the GECCO data.DEPTH
                      can identify candidate susceptibility regions for CRC not
                      identified using conventional analyses of larger
                      datasets.DEPTH has potential as a powerful complementary
                      tool to conventional GWAS analyses for discovering
                      susceptibility regions within the genome.},
      cin          = {C070},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37364297},
      doi          = {10.1158/1055-9965.EPI-22-1209},
      url          = {https://inrepo02.dkfz.de/record/277120},
}