title: Comparing family-based rare variant association tests for dichotomous phenotypes.
abstract: BACKGROUND
  It has been repeatedly stressed that family-based samples suffer less from genetic heterogeneity and that association analyses with family-based samples are expected to be powerful for detecting susceptibility loci for rare disease. Various approaches for rare-variant analysis with family-based samples have been proposed.
  
  
  METHODS
  In this report, performances of the existing methods were compared with the simulated data set provided as part of Genetic Analysis Workshop 19 (GAW19). We considered the rare variant transmission disequilibrium test (RV-TDT), generalized estimating equations-based kernel association (GEE-KM) test, an extended combined multivariate and collapsing test for pedigree data (known as Pedigree Combined Multivariate and Collapsing [PedCMC]), gene-level kernel and burden association tests with disease status for pedigree data (PedGene), and the family-based rare variant association test (FARVAT).
  
  
  RESULTS
  The results show that PedGene and FARVAT are usually the most efficient, and the optimal test statistic provided by FARVAT is robust under different disease models. Furthermore, FARVAT was implemented with C++, which is more computationally faster than other methods.
  
  
  CONCLUSIONS
  Considering both statistical and computational efficiency, we conclude that FARVAT is a good choice for rare-variant analysis with extended families.
authors.affiliation: Interdisciplinary Program in bioinformatics, Seoul National University, Seoul, 151-742 Korea.
authors.forename: Longfei
authors.initials: L
authors.lastname: Wang
authors.affiliation: Interdisciplinary Program in bioinformatics, Seoul National University, Seoul, 151-742 Korea.
authors.forename: Sungkyoung
authors.initials: S
authors.lastname: Choi
authors.affiliation: Interdisciplinary Program in bioinformatics, Seoul National University, Seoul, 151-742 Korea.
authors.forename: Sungyoung
authors.initials: S
authors.lastname: Lee
authors.affiliation: Interdisciplinary Program in bioinformatics, Seoul National University, Seoul, 151-742 Korea ; Department of Statistics, Seoul National University, Seoul, 151-742 Korea.
authors.forename: Taesung
authors.initials: T
authors.lastname: Park
authors.affiliation: Interdisciplinary Program in bioinformatics, Seoul National University, Seoul, 151-742 Korea ; Department of Public Health Science, Seoul National University, Seoul, 151-742 Korea ; Institute of Health Environment, Seoul National University, Seoul, 151-742 Korea.
authors.forename: Sungho
authors.initials: S
authors.lastname: Won