title: Case-control association studies in mixed populations: correcting using genomic control.
abstract: OBJECTIVE
  Case-control association studies in mixed populations can result in spurious disease-marker associations if subpopulation disease prevalence and marker frequencies both differ. Genomic control (GC) uses neutral loci to correct for spurious association (due to population stratification), but how well this works remains undetermined.
  
  
  METHODS
  We simulated and mixed populations with different disease and marker frequencies but without marker-disease association. We generated case-control datasets, calculated the chi2 for disease association with each marker, and applied two GC procedures, dividing by the mean chi2 or median-chi2/0.456.
  
  
  RESULTS
  Corrections became conservative (false positive rate [FPR] <5%) with increasing subpopulation prevalence and marker differences. The mean correction resulted in FPRs close to 5% at average subpopulation allele frequency differences <0.26, but inclusion of just a few markers with large frequency differences resulted in conservative FPRs. FPRs from the median correction were mostly conservative but became anticonservative when a few markers with large frequency differences were included.
  
  
  CONCLUSIONS
  GC can both lead to a notable loss of power to detect a true association (conservative) in many circumstances or may fail to eliminate the spurious associations (anticonservative). The mean correction factor is useful in certain situations to correct population stratification, but it is difficult to know when those situations exist.
authors.affiliation: Division of Statistical Genetics, Department of Biostatistics, Columbia University, New York, NY, USA.
authors.forename: Dvora
authors.initials: D
authors.lastname: Shmulewitz
authors.forename: Junying
authors.initials: J
authors.lastname: Zhang
authors.forename: David A
authors.initials: DA
authors.lastname: Greenberg
mesh_terms: D000483:Alleles; D016022:Case-Control Studies; D003198:Computer Simulation; D030541:Databases, Genetic; D005189:False Positive Reactions; D005787:Gene Frequency; D005819:Genetic Markers; D005826:Genetics, Medical; D023281:Genomics; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D012107:Research Design; D013997:Time Factors
chemical_list: D005819:Genetic Markers