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@ARTICLE{Lacour:137838,
      author       = {Lacour, Andre and Schüller, Vitalia and Drichel, Dmitriy
                      and Herold, Christine and Jessen, Frank and Leber, Markus
                      and Maier, Wolfgang and Noethen, Markus M and Ramirez,
                      Alfredo and Vaitsiakhovich, Tatsiana and Becker, Tim},
      title        = {{N}ovel genetic matching methods for handling population
                      stratification in genome-wide association studies.},
      journal      = {BMC bioinformatics},
      volume       = {16},
      number       = {1},
      issn         = {1471-2105},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DZNE-2020-04160},
      pages        = {84},
      year         = {2015},
      abstract     = {A usually confronted problem in association studies is the
                      occurrence of population stratification. In this work, we
                      propose a novel framework to consider population matchings
                      in the contexts of genome-wide and sequencing association
                      studies. We employ pairwise and groupwise optimal
                      case-control matchings and present an agglomerative
                      hierarchical clustering, both based on a genetic similarity
                      score matrix. In order to ensure that the resulting matches
                      obtained from the matching algorithm capture correctly the
                      population structure, we propose and discuss two stratum
                      validation methods. We also invent a decisive extension to
                      the Cochran-Armitage Trend test to explicitly take into
                      account the particular population structure.We assess our
                      framework by simulations of genotype data under the null
                      hypothesis, to affirm that it correctly controls for the
                      type-1 error rate. By a power study we evaluate that
                      structured association testing using our framework displays
                      reasonable power. We compare our result with those obtained
                      from a logistic regression model with principal component
                      covariates. Using the principal components approaches we
                      also find a possible false-positive association to
                      Alzheimer's disease, which is neither supported by our new
                      methods, nor by the results of a most recent large meta
                      analysis or by a mixed model approach.Matching methods
                      provide an alternative handling of confounding due to
                      population stratification for statistical tests for which
                      covariates are hard to model. As a benchmark, we show that
                      our matching framework performs equally well to state of the
                      art models on common variants.},
      keywords     = {Alzheimer Disease: genetics / Case-Control Studies /
                      Cluster Analysis / Genetics, Population / Genome-Wide
                      Association Study: methods / Genotype / Humans / Logistic
                      Models / Population Groups},
      cin          = {GenomMathematik / AG Roes / AG Jessen},
      ddc          = {610},
      cid          = {I:(DE-2719)1013007 / I:(DE-2719)1610003 /
                      I:(DE-2719)1011102},
      pnm          = {345 - Population Studies and Genetics (POF3-345) / 344 -
                      Clinical and Health Care Research (POF3-344)},
      pid          = {G:(DE-HGF)POF3-345 / G:(DE-HGF)POF3-344},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:25880419},
      pmc          = {pmc:PMC4367953},
      doi          = {10.1186/s12859-015-0521-4},
      url          = {https://pub.dzne.de/record/137838},
}