Assessing genetic association in case-control studies with unmeasured population substructure

The case-control study design is one of the main tools for detecting associations between genetic markers and disease. It is well known population substructure (PS) can lead to spurious association between disease status and a genetic marker if the prevalence of disease and the marker allele frequency vary across subpopulations. In this paper, we proposed a novel statistical method to estimate the association in case-control studies with potential population substructure. The proposed method takes two steps. First, the information on genomic markers and disease status is used to infer population substructure; second, the association between disease and any one marker adjusting for the population substructure is then modeled and estimated parametrically through polytomous logistic regression. The performance of the proposed method, relative to others, on bias, coverage probability and computational time, is assessed through simulations. Finally, this method is applied to an asthma study and an end-stage renal disease study in African Americans populations.