Undiagnosed diseases are likely to be determined by genetic, environmental, and developmental factors. While some undiagnosed diseases will represent novel rare genetic syndromes with monogenic or oligogenic etiologies and others will reflect rare manifestations of known diseases, many are likely to result from a more analytically challenging combination of multiple genetic, environmental, and developmental factors. Whole exome and whole genome sequencing are powerful tools with which to ascertain the genetic contributions to undiagnosed diseases; however, these methods alone are unlikely to elucidate the basis for many, if not most, undiagnosed diseases. The central unifying hypothesis of this proposal is, therefore, that an integrated, network-based, systems biology approach that incorporates not only genetic variation data, but also gene expression, metabolomic, proteomic, and exposomic data, along with careful deep phenotyping, will prove to be the most effective way to identify the pathways and mechanisms responsible for many undiagnosed diseases. To address the underlying hypothesis, we propose to establish an integrated interdisciplinary research plan for the Harvard Undiagnosed Disease Program Clinical Site (Harvard UDP-CS) that involves three specific aims: 1) ascertainment and clinical characterization - we will perform case ascertainment and phenotypic characterization for selected rare and undiagnosed disease states in adults and children; 2) genetic assessments - we will use patient and family member-derived DNA sequence, transcriptomic data (when available), and clinical phenotype information to identify potentially causal DNA sequence variants, gene expression variation, and potentially causative pathway derangements; and 3) network approach to disease diagnosis - we will integrate other -omic data, including metabolomic, proteomic, and exposomic data, along with the candidate genetic variants into the comprehensive interactome, and thereby identify diseases or disease pathways in network proximity to the involved genes that may help identify potential pathobiological modules relevant to the etiology of the undiagnosed disease. We will work toward meeting these specific aims and addressing the overall hypothesis in close collaboration with the coordinating center and other clinical sites in the UDN in order to develop and implement assessment protocols of phenotype, environment, and genotype that will ultimately define the etiology and treatment of undiagnosed diseases.

Public Health Relevance

This proposal describes our plan to develop a state-of-the-art strategy for evaluating patients with previously undiagnosed diseases. Clinical assessments will be combined with patient and family member genetic data, and environmental exposure data, within a framework that includes the NIH-sponsored and led undiagnosed diseases network and its consortium member sites.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project--Cooperative Agreements (U01)
Project #
3U01HG007690-04S1
Application #
9635044
Study Section
Special Emphasis Panel (ZHG1)
Program Officer
Wise, Anastasia Leigh
Project Start
2014-07-01
Project End
2018-08-31
Budget Start
2018-04-01
Budget End
2018-08-31
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
Libby, Peter; Loscalzo, Joseph; Ridker, Paul M et al. (2018) Inflammation, Immunity, and Infection in Atherothrombosis: JACC Review Topic of the Week. J Am Coll Cardiol 72:2071-2081
Yang, Jia-Shu; Hsu, Jia-Wei; Park, Seung-Yeol et al. (2018) GAPDH inhibits intracellular pathways during starvation for cellular energy homeostasis. Nature 561:263-267
Splinter, Kimberly; Adams, David R; Bacino, Carlos A et al. (2018) Effect of Genetic Diagnosis on Patients with Previously Undiagnosed Disease. N Engl J Med 379:2131-2139
Cranford, Jonathan P; O'Hara, Thomas J; Villongco, Christopher T et al. (2018) Efficient Computational Modeling of Human Ventricular Activation and Its Electrocardiographic Representation: A Sensitivity Study. Cardiovasc Eng Technol 9:447-467
Musri, Melina M; Coll-Bonfill, NĂºria; Maron, Bradley A et al. (2018) MicroRNA Dysregulation in Pulmonary Arteries from Chronic Obstructive Pulmonary Disease. Relationships with Vascular Remodeling. Am J Respir Cell Mol Biol 59:490-499
Wang, Rui-Sheng; Loscalzo, Joseph (2018) Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications. J Mol Biol 430:2939-2950
Leopold, Jane A; Loscalzo, Joseph (2018) Emerging Role of Precision Medicine in Cardiovascular Disease. Circ Res 122:1302-1315
Cheng, Feixiong; Desai, Rishi J; Handy, Diane E et al. (2018) Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat Commun 9:2691
Oldham, William M; Oliveira, Rudolf K F; Wang, Rui-Sheng et al. (2018) Network Analysis to Risk Stratify Patients With Exercise Intolerance. Circ Res 122:864-876
Haghighi, Alireza; Krier, Joel B; Toth-Petroczy, Agnes et al. (2018) An integrated clinical program and crowdsourcing strategy for genomic sequencing and Mendelian disease gene discovery. NPJ Genom Med 3:21

Showing the most recent 10 out of 34 publications