Asthma is the most common chronic respiratory disease, affecting over 300 million people worldwide. In the United States alone, asthma creates a financial burden of over $50 billion annually. This burden is not distributed equally; asthma is more prevalent and severe among African Americans and Hispanics, as compared to Caucasians. Asthma is known to be caused by a combination of environmental and genetic factors, but our knowledge of the genetic causes of asthma is still incomplete.
A small number of common genetic variants have been identified through genome wide association studies (GWAS), but these explain only a small proportion of asthma heritability. Furthermore, many GWASs have focused on Caucasian cohorts, leaving variants specific to other populations largely unknown.
In this project, we explore new ground in the genetic landscape of asthma by 1) probing rare variants, which could have larger effect sizes and may be population-specific, and 2) analyzing a cohort of traditionally under-studied populations. We leverage WGS data from the Asthma Translational Genomic Collaborative (ATGC) in the NHLBI Tran-Omics for Precision Medicine (TOPMed) program. In total, we analyze over 15,000 African American and racially admixed Hispanic patients.
The analysis is performed on NHLBI BioData Catalyst powered by Terra. The NHLBI BioData Catalyst facilitates this research by allowing TOPMed data to be easily imported to a scalable, high-performance cloud workspace, and to be analyzed using custom and community-developed workflows.
Hail is used to perform quality control on variant calls and filter for rare variants at two different frequency thresholds (MAF < 1% and 0.1%). Variants are annotated using ANNOVAR, SnpEff and VEP in order to predict the functional results of the variants and to generate quantitative functional scores. Only coding variants and coding regions are included in the analysis. Burden tests are then performed using the STAAR Rare Variant Pipeline workflow. The variant-Set Test for Association using Annotation infoRmation (STAAR) method incorporates multiple annotations, both quantitative and functional, to perform variant set association testing. In addition to STAAR, the workflow runs SKAT, burden and ACAT tests. Both stratified and pooled analyses will be performed.
Our approach is focused on identifying novel genes harboring rare variants which are associated with asthma in under-studied populations. We anticipate our results will improve our understanding of the genetics of asthma, contribute to efforts to precisely tailor treatments to each patient's unique genetic background, and ultimately help reduce healthcare disparities.