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@ARTICLE{Choi:137632,
      author       = {Choi, Sungkyoung and Lee, Sungyoung and Cichon, Sven and
                      Nöthen, Markus M and Lange, Christoph and Park, Taesung and
                      Won, Sungho},
      title        = {{FARVAT}: a family-based rare variant association test.},
      journal      = {Bioinformatics},
      volume       = {30},
      number       = {22},
      issn         = {1460-2059},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DZNE-2020-03954},
      pages        = {3197-3205},
      year         = {2014},
      abstract     = {Individuals in each family are genetically more homogeneous
                      than unrelated individuals, and family-based designs are
                      often recommended for the analysis of rare variants.
                      However, despite the importance of family-based samples
                      analysis, few statistical methods for rare variant
                      association analysis are available.In this report, we
                      propose a FAmily-based Rare Variant Association Test
                      (FARVAT). FARVAT is based on the quasi-likelihood of whole
                      families, and is statistically and computationally efficient
                      for the extended families. FARVAT assumed that families were
                      ascertained with the disease status of family members, and
                      incorporation of the estimated genetic relationship matrix
                      to the proposed method provided robustness under the
                      presence of the population substructure. Depending on the
                      choice of working matrix, our method could be a burden test
                      or a variance component test, and could be extended to the
                      SKAT-O-type statistic. FARVAT was implemented in C++, and
                      application of the proposed method to schizophrenia data and
                      simulated data for GAW17 illustrated its practical
                      importance.The software calculates various statistics for
                      the analysis of related samples, and it is freely
                      downloadable from
                      http://healthstats.snu.ac.kr/software/farvat.won1@snu.ac.kr
                      or tspark@stats.snu.ac.krsupplementary data are available at
                      Bioinformatics online.},
      keywords     = {Family / Genetic Association Studies: methods / Genetic
                      Variation / Humans / Schizophrenia: genetics / Software},
      cin          = {U T4 Researchers - Bonn},
      ddc          = {570},
      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:25075118},
      doi          = {10.1093/bioinformatics/btu496},
      url          = {https://pub.dzne.de/record/137632},
}