The primary role of Core A ? ?Systems Biology and Gene Networks? ? is to explore in a predictive fashion the molecular and network-based similarities and differences between asthma and COPD. Networks provide frameworks for deriving information from a set of relationships among entities in the program project.
We aim to develop tools to identify the disease modules for asthma and COPD, and the molecular relationships between them. The work will initially rely on the data already collected by the PPG investigators, leading to a first round of mechanistic predictions. These predictions will drive the design of subsequent experimental work and data collection, iteratively improving the Core's predictive power. Core A has two specific aims:
Aim 1 : Construct the network infrastructure to predict the disease module relying on data collected by the PPG Projects and Cores. The goal is to develop and improve on the existing tools to identify the disease module for asthma and COPD, and to offer an initial set of experimentally testable predictions.
Aim 2 : Iteratively enhance the predictive value of the disease module by overlaying data collected by the PPG Projects and Cores. We plan to explore the network-based relationships between asthma and COPD by identifying common pathways, genes and potentially common disease mechanisms. Core A will apply network analysis support at different levels: (1) selection and prioritization of the variants and genes associated with airflow obstruction from Project 1; (2) integrating different levels of genomics and transcriptomics data associated with asthma and COPD with the molecular interaction networks (interactome) from Project 2; and (3) identifying and interpreting epigenetic changes (methylation and miRNA) associated with asthma and COPD from Project 3 by integration with protein interactome models. The networks of interactions will serve as a scaffold for information to extract global and local graph theory properties to inform about the initial seed gene choices and about the different ?omics? data in Projects 1, 2 and 3.
The interactome-based approach for network building in Core A is critical to our understanding of complex traits such as asthma and COPD. Core A will use genomic elements to build network models to explain the overlap of these diseases. If we can understand this overlap, it will lead to novel therapies and potential cures for these disorders.
|Dehmamy, Nima; Milanlouei, Soodabeh; Barabási, Albert-László (2018) A structural transition in physical networks. Nature 563:676-680|
|Santolini, Marc; Barabási, Albert-László (2018) Predicting perturbation patterns from the topology of biological networks. Proc Natl Acad Sci U S A 115:E6375-E6383|
|Zhou, Xuezhong; Lei, Lei; Liu, Jun et al. (2018) A Systems Approach to Refine Disease Taxonomy by Integrating Phenotypic and Molecular Networks. EBioMedicine 31:79-91|
|Hecker, Julian; Xu, Xin; Townes, F William et al. (2018) Family-based tests for associating haplotypes with general phenotype data: Improving the FBAT-haplotype algorithm. Genet Epidemiol 42:123-126|
|Yun, Jeong H; Lamb, Andrew; Chase, Robert et al. (2018) Blood eosinophil count thresholds and exacerbations in patients with chronic obstructive pulmonary disease. J Allergy Clin Immunol 141:2037-2047.e10|
|Peng, Cheng; Cardenas, Andres; Rifas-Shiman, Sheryl L et al. (2018) Epigenome-wide association study of total serum immunoglobulin E in children: a life course approach. Clin Epigenetics 10:55|
|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|
|Li, Xuan; Fu, Yuejiao; Wang, Xiaogang et al. (2018) Detecting Differentially Variable MicroRNAs via Model-Based Clustering. Int J Genomics 2018:6591634|
|Hecker, Julian; Prokopenko, Dmitry; Lange, Christoph et al. (2018) PolyGEE: a generalized estimating equation approach to the efficient and robust estimation of polygenic effects in large-scale association studies. Biostatistics 19:295-306|
|Lopes-Ramos, Camila M; Kuijjer, Marieke L; Ogino, Shuji et al. (2018) Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism. Cancer Res 78:5538-5547|
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