title: Population stratification in epidemiologic studies of common genetic variants and cancer: quantification of bias.
abstract: BACKGROUND
  Some critics argue that bias from population stratification (the mixture of individuals from heterogeneous genetic backgrounds) undermines the credibility of epidemiologic studies designed to estimate the association between a genotype and the risk of disease. We investigated the degree of bias likely from population stratification in U.S. studies of cancer among non-Hispanic Caucasians of European origin.
  
  
  METHODS
  An expression of the confounding risk ratio-the ratio of the effect of the genetic factor on risk of disease with and without adjustment for ethnicity-is used to measure the potential relative bias from population stratification. We first use empirical data on the frequency of the N-acetyltransferase (NAT2) slow acetylation genotype and incidence rates of male bladder cancer and female breast cancer in non-Hispanic U.S. Caucasians with ancestries from eight European countries to assess the bias in a hypothetical population-based U.S. study that does not take ethnicity into consideration. Then, we provide theoretical calculations of the bias over a large range of allele frequencies and disease rates.
  
  
  RESULTS
  Ignoring ethnicity leads to a bias of 1% or less in our empirical studies of NAT2. Furthermore, evaluation of a wide range of allele frequencies and representative ranges of cancer rates that exist across European populations shows that the risk ratio is biased by less than 10% in U.S. studies except under extreme conditions. We note that the bias decreases as the number of ethnic strata increases.
  
  
  CONCLUSIONS
  There will be only a small bias from population stratification in a well-designed case-control study of genetic factors that ignores ethnicity among non-Hispanic U.S. Caucasians of European origin. Further work is needed to estimate the effect of population stratification within other populations.
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892-7244, USA. Wacholder@nih.gov
authors.forename: S
authors.initials: S
authors.lastname: Wacholder
authors.forename: N
authors.initials: N
authors.lastname: Rothman
authors.forename: N
authors.initials: N
authors.lastname: Caporaso
mesh_terms: D000483:Alleles; D001191:Arylamine N-Acetyltransferase / Q000235:genetics; D015982:Bias; D001943:Breast Neoplasms / Q000235:genetics; D015986:Confounding Factors, Epidemiologic; D005060:Europe / Q000208:ethnology; D005260:Female; D005838:Genotype; D006801:Humans; D015994:Incidence; D008297:Male; D015233:Models, Statistical; D017720:Molecular Epidemiology; D009369:Neoplasms / Q000201:enzymology / Q000208:ethnology* / Q000235:genetics*; D016017:Odds Ratio; D012306:Risk; D012494:Sampling Studies; D014481:United States / Q000453:epidemiology; D001749:Urinary Bladder Neoplasms / Q000235:genetics; D044465:White People / Q000235:genetics*
chemical_list: D001191:Arylamine N-Acetyltransferase