Despite the availability of three major classes of medications for asthma, the response to therapy is highly variable, with as many one-half of all patients being non-responders. Pharmacogenetics provides the promise of """"""""personalized medicine"""""""", whereby an individual's response to therapy will be guided a priori by his or her genetic make-up. While we have made substantial progress in identifying candidate genes influencing the response to asthma medications, none of these associations accounts for more than a small proportion of the variability in the response. It is clear that to accomplish the goal of """"""""personalized medicine"""""""" in asthma, we need to identify multiple additional pharmacogenetics loci. Recent advances in technology offer the unprecedented ability to rapidly identify the genetic variants that influence drug treatment response on a genome-wide scale. The major goal of this project is to enhance our prior work in the field of asthma pharmacogenetics via the efficient, but detailed, identification of novel genes associated with the asthmatic response to medications. To accomplish this, we have structured our specific aims as follows: 1. We will analyze GWAS data from 3493 subjects in asthma clinical trials of B2-adrenergic and glucocorticoid treatment response, and perform association testing to determine which genetic variants are associated with an altered therapeutic response. Machine learning approaches will be utilized to model the genetic association data. 2. Robust and replicated pharmacogenetic associations will be explored at the molecular, cellular, and integrative genomic levels to establish functional variants and pharmacogenetic mechanisms. Functional methods will involve bioinformatics tools, mRNA profiling, systems genetics, and cellular and animal models of asthma therapeutic responsiveness. 3. We will develop and validate a set of predictive tests of asthma therapeutic response to short acting B2-adrenergic and inhaled glucocorticoid drugs using clinical and functional knowledge and high-resolution analysis of genetic associations, including epistasis and gene-drug interaction. Additional aims relate to our collaboration with PharmGKB and the PGRN and helping investigators interested in pharmacogenetics. In addition to the GWAS samples, nearly 7000 DNA samples and matching asthma phenotypic data are available for replication testing to validate the initial GWAS results and predictive tests. We believe that these findings will uncover sufficient new information that working models of prognostic tests combining genetic and clinical traits in the prediction of drug response in asthma will result.

Public Health Relevance

Asthma affects an estimated 300 million individuals worldwide and accounts for approximately $20 billion in direct health care costs in the United States annually, with the greatest proportion of those costs allocated to medication and hospitalization costs. The identification of genetic variants that can be used as the basis of a prognostic test to predict which individuals will or will not respond to therapy, thereby minimizing both need for trial and error medication dispensing and risk of hospitalizations due to inadequate therapy, has the potential to substantially decrease both morbidity and financial burden related to asthma.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project--Cooperative Agreements (U01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Gan, Weiniu
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Brigham and Women's Hospital
United States
Zip Code
McGeachie, Michael J; Davis, Joshua S; Kho, Alvin T et al. (2017) Asthma remission: Predicting future airways responsiveness using an miRNA network. J Allergy Clin Immunol 140:598-600.e8
Park, H-W; Tse, S; Yang, W et al. (2017) A genetic factor associated with low final bone mineral density in children after a long-term glucocorticoids treatment. Pharmacogenomics J 17:180-185
Qiu, Weiliang; Guo, Feng; Glass, Kimberly et al. (2017) Differential connectivity of gene regulatory networks distinguishes corticosteroid response in asthma. J Allergy Clin Immunol :
McGeachie, M J; Yates, K P; Zhou, X et al. (2016) Patterns of Growth and Decline in Lung Function in Persistent Childhood Asthma. N Engl J Med 374:1842-1852
McGeachie, Michael J; Yates, Katherine P; Zhou, Xiaobo et al. (2016) Genetics and Genomics of Longitudinal Lung Function Patterns in Individuals with Asthma. Am J Respir Crit Care Med 194:1465-1474
Dahlin, Amber; Weiss, Scott T (2016) Genetic and Epigenetic Components of Aspirin-Exacerbated Respiratory Disease. Immunol Allergy Clin North Am 36:765-789
Kho, Alvin T; Chhabra, Divya; Sharma, Sunita et al. (2016) Age, Sexual Dimorphism, and Disease Associations in the Developing Human Fetal Lung Transcriptome. Am J Respir Cell Mol Biol 54:814-21
Ierodiakonou, Despo; Zanobetti, Antonella; Coull, Brent A et al. (2016) Ambient air pollution, lung function, and airway responsiveness in asthmatic children. J Allergy Clin Immunol 137:390-9
Howrylak, Judie A; Moll, Matthew; Weiss, Scott T et al. (2016) Gene expression profiling of asthma phenotypes demonstrates molecular signatures of atopy and asthma control. J Allergy Clin Immunol 137:1390-1397.e6
Kho, Alvin T; Sharma, Sunita; Davis, Joshua S et al. (2016) Circulating MicroRNAs: Association with Lung Function in Asthma. PLoS One 11:e0157998

Showing the most recent 10 out of 227 publications