Abstract

BackgroundAsthma is one of the leading chronic illnesses among children in the United States. Asthma prevalence is higher among African Americans (11.2%) compared to European Americans (7.7%). Bronchodilator medications are part of the first-line therapy, and the rescue medication, for acute asthma symptoms. Bronchodilator drug response (BDR) varies substantially among different racial/ethnic groups. Asthma prevalence in African Americans is only 3.5% higher than that of European Americans, however, asthma mortality among African Americans is four times that of European Americans; variation in BDR may play an important role in explaining this health disparity. To improve our understanding of disparate health outcomes in complex phenotypes such as BDR, it is important to consider interactions between environmental and biological variables. ResultsWe evaluated the impact of pairwise and three-variable interactions between environmental, social, and biological variables on BDR in 617 African American youth with asthma using Visualization of Statistical Epistasis Networks (ViSEN). ViSEN is a non-parametric entropy-based approach able to identify interaction effects. We performed analyses in the full dataset and in sex-stratified subsets. Analysis in the full dataset identified six significant interactions associated with BDR, the strongest of which was an interaction between prenatal smoke exposure, age, and global African ancestry (IG: 1.09%, p=0.005). Sex-stratified analyses yielded additional significant, but divergent, results for females and males, indicating the presence of sex-specific effects. ConclusionsOur study identified novel interaction effects significantly influencing BDR in African American children with asthma. Notably, we found that the impact of higher-order interactions was greater than that of pairwise or main effects on BDR highlighting the complexity of the network of genetic and environmental factors impacting this phenotype. Several associations uncovered by ViSEN would not have been detected using regression-based methods emphasizing the importance of employing statistical methods optimized to detect both linear and non-linear interaction effects when studying complex phenotypes such as BDR. The information gained in this study increases our understanding and appreciation of the complex nature of the interactions between environmental and health-related factors that influence BDR and will be invaluable to biomedical researchers designing future studies.