S Wacholder1, N Rothman, N Caporaso. 1. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892-7244, USA. Wacholder@nih.gov
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.
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: Bradford Burke Worrall; Devin L Brown; Thomas G Brott; Robert D Brown; Scott L Silliman; James F Meschia Journal: Neuroepidemiology Date: 2003 Jul-Aug Impact factor: 3.282
Authors: Jun J Yang; Cheng Cheng; Wenjian Yang; Deqing Pei; Xueyuan Cao; Yiping Fan; Stanley B Pounds; Geoffrey Neale; Lisa R Treviño; Deborah French; Dario Campana; James R Downing; William E Evans; Ching-Hon Pui; Meenakshi Devidas; W P Bowman; Bruce M Camitta; Cheryl L Willman; Stella M Davies; Michael J Borowitz; William L Carroll; Stephen P Hunger; Mary V Relling Journal: JAMA Date: 2009-01-28 Impact factor: 56.272