Using Networks to Assign Gene Function in Lung Disease

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DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) have analyzed patterns of genomic variation in thousands of subjects and identified a large number of genes and chromosomal regions that are associated with various lung diseases. Since many of these genes remain poorly characterized, a major challenge facing pulmonary research is to systematically investigate their biological functions, the pathways in which they are involved, and the effects of genetic mutations on both. Fortunately, there is a large and growing body of genomic, transcriptomic, and epigenomic data that can be used to help shed light on gene function. Here we propose to leverage these data, using advanced computational methods to systematically characterize the functions of genes identified through GWAS studies for lung diseases with an emphasis on Chronic Obstructive Pulmonary Disease (COPD). Several research groups, including investigators involved in this proposed project, have conducted detailed genetic, genomic, and epigenomic studies on COPD and identified multiple genetic loci that are associated with disease. On the other hand, the multiple data-types generated from these studies have provided a uniquely exciting opportunity to systematically formulate testable hypotheses by using data-integration computational methods. To this end, we have assembled an interdisciplinary team including experts in GWAS, medicine, laboratory biology, and bioinformatics. We will apply recently developed systems biology-based approaches to integrate multiple data-types and construct gene regulatory networks (GRN) centered on GWAS candidates and from these, use the local network to predict functions of the uncharacterized genes. These new functional assignments will then be experimentally validated and, if necessary, further refined through additional rounds of network inference and experimental assessment. Finally, we will create a publicly accessible website to make the functional predictions and network models accessible to the pulmonary community.