Identifying genetic variants associated with complex diseases via genome-wide SNP and whole genome sequencing (WGS) studies has outpaced our ability to translate these findings into actionable biologic and clinical insights. We need to use in silico methods that integrate multiple layers of data, including transcriptomic, epigenetic, social, and environmental, to focus experimental validation on the most impactful targets. Asthma-related deaths are 4-fold higher in minority children than white children. Moreover, minority children with asthma have markedly decreased drug response to albuterol, a bronchodilator rescue medication that is the most commonly prescribed asthma medication in the world, and to glucocorticoids, anti-inflammatory medications that decrease symptoms and exacerbations. Our goal is to understand the biological basis of differential drug response that leads to observed racial/ethnic asthma disparities. In this proposal, we use two cloud-based apps we developed to identify functional biologic mechanisms of genes that are associated with racial/ethnic variation in asthma therapies. Specifically, our apps 1) provide gene-centric WGS association findings in the context of integrated multi-tissue omic results, and 2) reprioritize WGS association results using machine-learned tissue-specific networks constructed from gene expression, known protein-protein interactions, and established functional pathways. Our results will increase knowledge about the biological role of genes associated with asthma therapy and facilitate design of experiments to understand their function.

Public Health Relevance

Asthma-related deaths are 4-fold higher in minority children than white children. Moreover, minority children with asthma have markedly decreased drug response to albuterol, a bronchodilator rescue medication that is the most commonly prescribed asthma medication in the world, and to glucocorticoids, anti-inflammatory medications that decrease symptoms and exacerbations. We will use two free and user-friendly apps we developed to perform integrative analyses with TOPMed whole genome sequencing data to identify biologic mechanisms via which genes modify asthma drug response.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL141992-02
Application #
9673771
Study Section
Special Emphasis Panel (ZHL1)
Program Officer
Gan, Weiniu
Project Start
2018-05-01
Project End
2021-04-30
Budget Start
2019-05-01
Budget End
2021-04-30
Support Year
2
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
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Park, Danny S; Eskin, Itamar; Kang, Eun Yong et al. (2018) An ancestry-based approach for detecting interactions. Genet Epidemiol 42:49-63