Joint modeling of genetic association and population stratification using latent class models

We show how latent class log-linear models can be used to test for an association between a candidate gene and a disease phenotype in a stratified population when the stratification is unobserved. The stratification may arise because of several ethnic groups or immigration and may lead to spurious associations between several loci and the disease. The information about the stratification is drawn from additional markers that are chosen to be independent of the disease and unlinked to the candidate gene and to each other within each population stratum. We use the EM algorithm to simultaneously estimate all the model parameters, including proportions of individuals in the latent population strata. The latent class model is used to test the phenotype association of single nucleotide polymorphism markers in four candidate regions in population-based case-control data selected from simulated Genetic Analysis Workshop (GAW) 12 population isolate 30. The analysis clearly demonstrates how the number of false positive associations can be reduced when the model accounts for population stratification.