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.

Public Health Relevance

Numerous studies have probed the genetics of lung disease, analyzing variation in the genome across thousands of subjects. These studies have identified many variants within genes and other genomic regions that are strongly associated with the disease state, but often these are of unknown functional significance. Here, using COPD as a model, we propose to use systems biology methods to map these genes to pathways and to use these pathway-based associations to assign putative functions to these genes.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Infectious Diseases, Reproductive Health, Asthma and Pulmonary Conditions Study Section (IRAP)
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Punturieri, Antonello
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Dana-Farber Cancer Institute
United States
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Padi, Megha; Quackenbush, John (2018) Detecting phenotype-driven transitions in regulatory network structure. NPJ Syst Biol Appl 4:16
Hicks, Stephanie C; Okrah, Kwame; Paulson, Joseph N et al. (2018) Smooth quantile normalization. Biostatistics 19:185-198
Sharma, Amitabh; Halu, Arda; Decano, Julius L et al. (2018) Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes. NPJ Syst Biol Appl 4:25
Morrow, Jarrett D; Cho, Michael H; Platig, John et al. (2018) Ensemble genomic analysis in human lung tissue identifies novel genes for chronic obstructive pulmonary disease. Hum Genomics 12:1
Barry, Joseph D; Fagny, Maud; Paulson, Joseph N et al. (2018) Histopathological Image QTL Discovery of Immune Infiltration Variants. iScience 5:80-89
Morrow, Jarrett D; Glass, Kimberly; Cho, Michael H et al. (2018) Human Lung DNA Methylation Quantitative Trait Loci Colocalize with Chronic Obstructive Pulmonary Disease Genome-Wide Association Loci. Am J Respir Crit Care Med 197:1275-1284
Sharma, Amitabh; Kitsak, Maksim; Cho, Michael H et al. (2018) Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module. Sci Rep 8:14439
Qiu, Weiliang; Guo, Feng; Glass, Kimberly et al. (2018) Differential connectivity of gene regulatory networks distinguishes corticosteroid response in asthma. J Allergy Clin Immunol 141:1250-1258
Nishihara, Reiko; Glass, Kimberly; Mima, Kosuke et al. (2017) Biomarker correlation network in colorectal carcinoma by tumor anatomic location. BMC Bioinformatics 18:304
Yuan, Guo-Cheng; Cai, Long; Elowitz, Michael et al. (2017) Challenges and emerging directions in single-cell analysis. Genome Biol 18:84

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