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@ARTICLE{Park:138071,
      author       = {Park, Suyeon and Lee, Sungyoung and Lee, Young and Herold,
                      Christine and Hooli, Basavaraj and Mullin, Kristina and
                      Park, Taesung and Park, Changsoon and Bertram, Lars and
                      Lange, Christoph and Tanzi, Rudolph and Won, Sungho},
      title        = {{A}djusting heterogeneous ascertainment bias for genetic
                      association analysis with extended families.},
      journal      = {BMC medical genetics},
      volume       = {16},
      number       = {1},
      issn         = {1471-2350},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DZNE-2020-04393},
      pages        = {62},
      year         = {2015},
      abstract     = {In family-based association analysis, each family is
                      typically ascertained from a single proband, which renders
                      the effects of ascertainment bias heterogeneous among family
                      members. This is contrary to case-control studies, and may
                      introduce sample or ascertainment bias. Statistical
                      efficiency is affected by ascertainment bias, and careful
                      adjustment can lead to substantial improvements in
                      statistical power. However, genetic association analysis has
                      often been conducted using family-based designs, without
                      addressing the fact that each proband in a family has had a
                      great influence on the probability for each family member to
                      be affected.We propose a powerful and efficient statistic
                      for genetic association analysis that considered the
                      heterogeneity of ascertainment bias among family members,
                      under the assumption that both prevalence and heritability
                      of disease are available. With extensive simulation studies,
                      we showed that the proposed method performed better than the
                      existing methods, particularly for diseases with large
                      heritability.We applied the proposed method to the
                      genome-wide association analysis of Alzheimer's disease.
                      Four significant associations with the proposed method were
                      found.Our significant findings illustrated the practical
                      importance of this new analysis method.},
      keywords     = {Alzheimer Disease: genetics / Computer Simulation / Data
                      Interpretation, Statistical / Family / Gene Frequency /
                      Genetic Association Studies: methods / Genetic Heterogeneity
                      / Humans / Selection Bias},
      cin          = {GenomMathematik / U T4 Researchers - Bonn},
      ddc          = {610},
      cid          = {I:(DE-2719)1013007 / I:(DE-2719)7000008},
      pnm          = {345 - Population Studies and Genetics (POF3-345)},
      pid          = {G:(DE-HGF)POF3-345},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:26286599},
      pmc          = {pmc:PMC4593209},
      doi          = {10.1186/s12881-015-0198-6},
      url          = {https://pub.dzne.de/record/138071},
}