Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common controls

Abstract

ABSTRACTGenome-wide association studies (GWAS) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common controls from biobanks and extensive consortiums is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the controls are not well-characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of cases to population-based common controls regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen exposed cases and population-based common controls, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well characterized controls, and population-based common controls from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance-associations. These findings suggest that the choice of controls is especially important for infectious diseases or outcomes that are conditional upon environmental exposures.