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@ARTICLE{IonitaLaza:136182,
      author       = {Ionita-Laza, Iuliana and Buxbaum, Joseph D and Laird, Nan M
                      and Lange, Christoph},
      title        = {{A} new testing strategy to identify rare variants with
                      either risk or protective effect on disease.},
      journal      = {PLoS Genetics},
      volume       = {7},
      number       = {2},
      issn         = {1553-7404},
      address      = {San Francisco, Calif.},
      publisher    = {Public Library of Science},
      reportid     = {DZNE-2020-02504},
      pages        = {e1001289},
      year         = {2011},
      abstract     = {Rapid advances in sequencing technologies set the stage for
                      the large-scale medical sequencing efforts to be performed
                      in the near future, with the goal of assessing the
                      importance of rare variants in complex diseases. The
                      discovery of new disease susceptibility genes requires
                      powerful statistical methods for rare variant analysis. The
                      low frequency and the expected large number of such variants
                      pose great difficulties for the analysis of these data. We
                      propose here a robust and powerful testing strategy to study
                      the role rare variants may play in affecting susceptibility
                      to complex traits. The strategy is based on assessing
                      whether rare variants in a genetic region collectively occur
                      at significantly higher frequencies in cases compared with
                      controls (or vice versa). A main feature of the proposed
                      methodology is that, although it is an overall test
                      assessing a possibly large number of rare variants
                      simultaneously, the disease variants can be both protective
                      and risk variants, with moderate decreases in statistical
                      power when both types of variants are present. Using
                      simulations, we show that this approach can be powerful
                      under complex and general disease models, as well as in
                      larger genetic regions where the proportion of disease
                      susceptibility variants may be small. Comparisons with
                      previously published tests on simulated data show that the
                      proposed approach can have better power than the existing
                      methods. An application to a recently published study on
                      Type-1 Diabetes finds rare variants in gene IFIH1 to be
                      protective against Type-1 Diabetes.},
      keywords     = {Algorithms / Computer Simulation / DEAD-box RNA Helicases:
                      genetics / Data Interpretation, Statistical / Diabetes
                      Mellitus, Type 1: genetics / Genetic Predisposition to
                      Disease / Genetic Testing: statistics $\&$ numerical data /
                      Genetic Variation / Genome-Wide Association Study:
                      statistics $\&$ numerical data / Haplotypes: genetics /
                      Humans / Interferon-Induced Helicase, IFIH1 / Risk Factors /
                      Sequence Analysis, DNA / IFIH1 protein, human (NLM
                      Chemicals) / DEAD-box RNA Helicases (NLM Chemicals) /
                      Interferon-Induced Helicase, IFIH1 (NLM Chemicals)},
      cin          = {U T4 Researchers - Bonn},
      ddc          = {610},
      cid          = {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:21304886},
      pmc          = {pmc:PMC3033379},
      doi          = {10.1371/journal.pgen.1001289},
      url          = {https://pub.dzne.de/record/136182},
}