title: Strategies to improve the performance of rare variant association studies by optimizing the selection of controls.
abstract: BACKGROUND
  When analyzing a case group of patients with ultra-rare disorders the ethnicities are often diverse and the data quality might vary. The population substructure in the case group as well as the heterogeneous data quality can cause substantial inflation of test statistics and result in spurious associations in case-control studies if not properly adjusted for. Existing techniques to correct for confounding effects were especially developed for common variants and are not applicable to rare variants.
  
  
  RESULTS
  We analyzed strategies to select suitable controls for cases that are based on similarity metrics that vary in their weighting schemes. We simulated different disease entities on real exome data and show that a similarity-based selection scheme can help to reduce false positive associations and to optimize the performance of the statistical tests. Especially when data quality as well as ethnicities vary a lot in the case group, a matching approach that puts more weight on rare variants shows the best performance. We reanalyzed collections of unrelated patients with Kabuki make-up syndrome, Hyperphosphatasia with Mental Retardation syndrome and Catel-Manzke syndrome for which the disease genes were recently described. We show that rare variant association tests are more sensitive and specific in identifying the disease gene than intersection filters and should thus be considered as a favorable approach in analyzing even small patient cohorts.
  
  
  METHODS
  Datasets used in our analysis are available at ftp://ftp.1000genomes.ebi.ac.uk./vol1/ftp/
  
  
  BACKGROUND
  : peter.krawitz@charite.de
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, 13353 Berlin, Germany.
authors.forename: Na
authors.initials: N
authors.lastname: Zhu
authors.affiliation: Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, 13353 Berlin, Germany.
authors.forename: Verena
authors.initials: V
authors.lastname: Heinrich
authors.affiliation: Institute for Statistics, University of Bremen, 28344 Bremen, Germany.
authors.forename: Thorsten
authors.initials: T
authors.lastname: Dickhaus
authors.affiliation: Berlin-Brandenburg Center for Regenerative Therapies (BCRT), 13353 Berlin, Germany.
authors.forename: Jochen
authors.initials: J
authors.lastname: Hecht
authors.affiliation: Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, 13353 Berlin, Germany.
authors.forename: Peter N
authors.initials: PN
authors.lastname: Robinson
authors.affiliation: Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, 13353 Berlin, Germany, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany and.
authors.forename: Stefan
authors.initials: S
authors.lastname: Mundlos
authors.affiliation: GeneTalk, 13189 Berlin, Germany.
authors.forename: Tom
authors.initials: T
authors.lastname: Kamphans
authors.affiliation: Institute of Medical Genetics and Human Genetics, Charité Universitätsmedizin Berlin, 13353 Berlin, Germany, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany and.
authors.forename: Peter M
authors.initials: PM
authors.lastname: Krawitz
mesh_terms: D016022:Case-Control Studies; D000068598:Data Accuracy; D004194:Disease / Q000235:genetics; D005006:Ethnicity / Q000235:genetics; D056726:Genetic Association Studies; D014644:Genetic Variation; D006801:Humans; D012372:ROC Curve; D017422:Sequence Analysis, DNA