The impact of self-identified race on epidemiologic studies of gene expression

Genet Epidemiol. 2011 Feb;35(2):93-101. doi: 10.1002/gepi.20560.

Abstract

Although population differences in gene expression have been established, the impact on differential gene expression studies in large populations is not well understood. We describe the effect of self-reported race on a gene expression study of lung function in asthma. We generated gene expression profiles for 254 young adults (205 non-Hispanic whites and 49 African Americans) with asthma on whom concurrent total RNA derived from peripheral blood CD4(+) lymphocytes and lung function measurements were obtained. We identified four principal components that explained 62% of the variance in gene expression. The dominant principal component, which explained 29% of the total variance in gene expression, was strongly associated with self-identified race (P<10(-16)). The impact of these racial differences was observed when we performed differential gene expression analysis of lung function. Using multivariate linear models, we tested whether gene expression was associated with a quantitative measure of lung function: pre-bronchodilator forced expiratory volume in one second (FEV(1)). Though unadjusted linear models of FEV(1) identified several genes strongly correlated with lung function, these correlations were due to racial differences in the distribution of both FEV(1) and gene expression, and were no longer statistically significant following adjustment for self-identified race. These results suggest that self-identified race is a critical confounding covariate in epidemiologic studies of gene expression and that, similar to genetic studies, careful consideration of self-identified race in gene expression profiling studies is needed to avoid spurious association.

Publication types

  • Clinical Trial
  • Multicenter Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Adolescent
  • CD4-Positive T-Lymphocytes / cytology
  • Epidemiologic Studies*
  • Ethnicity
  • Female
  • Gene Expression Profiling / methods*
  • Humans
  • Male
  • Multivariate Analysis
  • Phenotype
  • Principal Component Analysis
  • Respiratory Function Tests