Genomic and Transcriptomic Analysis of Emphysema and Subclinical ILD

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 DESCRIPTION (provided by applicant): Together, pulmonary emphysema and chronic obstructive pulmonary disease (COPD) are the third leading cause of death in the United States. Although often considered one disease, emphysema on computed tomography (CT) represents a distinct entity that is absent in some patients with COPD and present in some without COPD. The interstitial lung diseases (ILDs) are a class of non-infectious, non-malignant lung diseases characterized by alveolar injury, inflammation, and fibrosis. There are currently few medications available to stop the progression of these diseases, reflecting a limited understanding of the underlying molecular mechanisms. We recently completed genome-wide association studies (GWASs) of percent emphysema and subclinical ILD on CT scan in ~7,600 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). The overall success of our GWAS efforts in identifying SNPs at genome-wide significance in or near SNRPF and PPT2 for percent emphysema, and ANRIL and D21S2088E for subclinical ILD traits reflect the notable quality of the pulmonary phenotypes in MESA. Still, GWAS approaches continue to face multiple limitations including (a) large multiple testing burden from considering millions of SNPs, and (b) lack of functional annotation to connect identified SNPs with specific genes. To address these and other limitations, we propose to apply a new gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates the "imputed" gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The framework of PrediXcan opens the possibility to "impute" tissue-specific genome-wide gene expression levels for the MESA participants, thereby creating a population-based data set that combines both high quality phenotypes for percent emphysema on CT scan with whole blood and lung-specific gene expression levels. We expect that reduced multiple testing burden and increased functional relevance of gene expression traits obtained through PrediXcan will allow us to identify novel genes and pathways related to emphysema and ILD. Therefore, we propose to carry out transcriptome-wide studies of gene expression predictors for percent emphysema (Aim 1a) and subclinical ILD (Aim 1b) traits in MESA. We further use a model selection framework to identify combinations of genes underlying pathogenesis of emphysema and subclinical ILD in the general population (Aim 2). We have assembled a highly collaborative and interdisciplinary team of investigators representing expertise in statistical genetics (Manichaikul and Im), genetic epidemiology (Manichaikul and Rich), pulmonary epidemiology (Lederer and Barr), lung pathology (Borczuk) and systems genetics (Farber). Completion of the proposed Aims would result in improved understanding of predicted gene expression traits in relation to emphysema and ILD, leading to improved targeted prevention and treatment of these diseases.