- Contreras, María G;
- Keys, Kevin;
- Magaña, Joaquin;
- Goddard, Pagé C;
- Risse-Adams, Oona;
- Zeiger, Andrew M;
- Mak, Angel CY;
- Samedy-Bates, Lesly-Anne;
- Neophytou, Andreas M;
- Lee, Eunice;
- Thakur, Neeta;
- Elhawary, Jennifer R;
- Hu, Donglei;
- Huntsman, Scott;
- Eng, Celeste;
- Hu, Ting;
- Burchard, Esteban G;
- White, Marquitta J
Objective
Asthma is the most common chronic disease in children. Short-acting bronchodilator medications are the most commonly prescribed asthma treatment worldwide, regardless of disease severity. Puerto Rican children display the highest asthma morbidity and mortality of any US population. Alarmingly, Puerto Rican children with asthma display poor bronchodilator drug response (BDR). Reduced BDR may explain, in part, the increased asthma morbidity and mortality observed in Puerto Rican children with asthma. Gene-environment interactions may explain a portion of the heritability of BDR. We aimed to identify gene-environment interactions associated with BDR in Puerto Rican children with asthma.Setting
Genetic, environmental, and psycho-social data from the Genes-environments and Admixture in Latino Americans (GALA II) case-control study.Participants
Our discovery dataset consisted of 658 Puerto Rican children with asthma; our replication dataset consisted of 514 Mexican American children with asthma.Main outcome measures
We assessed the association of pairwise interaction models with BDR using ViSEN (Visualization of Statistical Epistasis Networks).Results
We identified a non-linear interaction between Native American genetic ancestry and air pollution significantly associated with BDR in Puerto Rican children with asthma. This interaction was robust to adjustment for age and sex but was not significantly associated with BDR in our replication population.Conclusions
Decreased Native American ancestry coupled with increased air pollution exposure was associated with increased BDR in Puerto Rican children with asthma. Our study acknowledges BDR's phenotypic complexity, and emphasizes the importance of integrating social, environmental, and biological data to further our understanding of complex disease.