title: Association tests for X-chromosomal markers--a comparison of different test statistics.
abstract: OBJECTIVE
  Genome-wide association studies have successfully elucidated the genetic background of complex diseases, but X chromosomal data have usually not been analyzed. A reason for this is that there is no consensus approach for the analysis taking into account the specific features of X chromosomal data. This contribution evaluates test statistics proposed for X chromosomal markers regarding type I error frequencies and power.
  
  
  METHODS
  We performed extensive simulation studies covering a wide range of different settings. Besides characteristics of the general population, we investigated sex-balanced or unbalanced sampling procedures as well as sex-specific effect sizes, allele frequencies and prevalence. Finally, we applied the test statistics to an association data set on Crohn's disease.
  
  
  RESULTS
  Simulation results imply that in addition to standard quality control, sex-specific allele frequencies should be checked to control for type I errors. Furthermore, we observed distinct differences in power between test statistics which are determined by sampling design and sex specificity of effect sizes. Analysis of the Crohn's disease data detects two previously unknown genetic regions on the X chromosome.
  
  
  CONCLUSIONS
  Although no test is uniformly most powerful under all settings, recommendations are offered as to which test performs best under certain conditions.
authors.affiliation: Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Deutschland.
authors.forename: Christina
authors.initials: C
authors.lastname: Loley
authors.forename: Andreas
authors.initials: A
authors.lastname: Ziegler
authors.forename: Inke R
authors.initials: IR
authors.lastname: König
mesh_terms: D000465:Algorithms; D000483:Alleles; D016022:Case-Control Studies; D041321:Chromosomes, Human, X / Q000235:genetics*; D003198:Computer Simulation; D003424:Crohn Disease / Q000235:genetics; D003627:Data Interpretation, Statistical; D005260:Female; D005787:Gene Frequency; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium; D008297:Male; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012737:Sex Factors
chemical_list: D005819:Genetic Markers