The Undiagnosed Disease Program
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA243395 | ENA |
REPOSITORIES: ENA
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Project description:Clinical whole genome sequencing has enabled the discovery of potentially pathogenic noncoding variants in the genomes of rare disease patients with a prior history of negative genetic testing. However, interpreting the functional consequences of noncoding variants and distinguishing those that contribute to disease etiology remains a challenge. Here we address this challenge by experimentally profiling the functional consequences of rare noncoding variants detected in a cohort of undiagnosed rare disease patients at scale using a massively parallel reporter assay. We demonstrate that this approach successfully identifies rare noncoding variants that alter the regulatory capacity of genomic sequences. In addition, we describe an integrative analysis that utilizes genomic features alongside patient clinical data to further prioritize candidate variants with an increased likelihood of pathogenicity. This works represents an important step towards establishing a framework for the clinical interpretation of noncoding variants.
2022-05-11 | GSE185795 | GEO
Project description:The deposited data were collected from 148 patients and 133 family members accepted into the Undiagnosed Diseases Network (https://undiagnosed.hms.harvard.edu/). The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Here, we report and provide the mass spectrometry-based metabolomics (GC-MS) and lipidomics (LC-MS/MS) analyses of urine from 148 patients and 133 family members. We have deposited mass spectrometry-based metabolomics and lipidomics files including instrument files, normalized data processed files to allow for statistical analysis, and metabolomics and lipidomics results for each patient and associated relatives. In addition, as part of the mass spectrometry data made available, we have included mass spectrometry analyses and results from a reference population of individuals with no known metabolic diseases. UDN patients suffer from undiagnosed diseases and thus are typically represented as a sample size of one; therefore, understanding normal variation within a proband's condition needs to be measured against of dataset of normal individuals, which is included here.
2019-12-17 | MSV000084717 | MassIVE
Project description:The deposited data were collected from 148 patients and 133 family members accepted into the Undiagnosed Diseases Network (https://undiagnosed.hms.harvard.edu/). The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Here, we report and provide the mass spectrometry-based metabolomics (GC-MS) and lipidomics (LC-MS/MS) analyses of blood plasma from 148 patients and 133 family members. We have deposited mass spectrometry-based metabolomics and lipidomics files including instrument files, normalized data processed files to allow for statistical analysis, and metabolomics and lipidomics results for each patient and associated relatives. In addition, as part of the mass spectrometry data made available, we have included mass spectrometry analyses and results from a reference population of individuals with no known metabolic diseases. UDN patients suffer from undiagnosed diseases and thus are typically represented as a sample size of one; therefore, understanding normal variation within a proband's condition needs to be measured against of dataset of normal individuals, which is included here.
2019-12-17 | MSV000084716 | MassIVE
Project description:The deposited data were collected from 148 patients and 133 family members accepted into the Undiagnosed Diseases Network (https://undiagnosed.hms.harvard.edu/). The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Here, we report and provide the mass spectrometry-based metabolomics (GC-MS) and lipidomics (LC-MS/MS) analyses of cerebrospinal fluid from 148 patients and 133 family members. We have deposited mass spectrometry-based metabolomics and lipidomics files including instrument files, normalized data processed files to allow for statistical analysis, and metabolomics and lipidomics results for each patient and associated relatives. In addition, as part of the mass spectrometry data made available, we have included mass spectrometry analyses and results from a reference population of individuals with no known metabolic diseases. UDN patients suffer from undiagnosed diseases and thus are typically represented as a sample size of one; therefore, understanding normal variation within a proband's condition needs to be measured against of dataset of normal individuals, which is included here.
2019-12-17 | MSV000084715 | MassIVE
Project description:Clinical and Genetic Evaluations of Individuals with Undiagnosed Disorders Through the Undiagnosed Diseases Network
| PRJNA350184 | ENA
Project description:Pulmonary Vascular Disease Phenomics Program (PVDOMICS)
| PRJNA740364 | ENA
Project description:Massively parallel identification of functionally consequential noncoding genetic variants in undiagnosed rare disease patients
| PRJNA770771 | ENA
Project description:<p>The Undiagnosed Diseases Network (UDN) is an initiative to facilitate the diagnosis of conditions that have eluded diagnosis through the coordinated action of leading clinical and research centers. The purpose of this cooperative research network is to establish a national network added to and building upon the NIH Undiagnosed Diseases Program (NIH UDP). The objectives of this program are to: </p> <ol> <li>Improve the level of diagnosis and care for patients with undiagnosed diseases through the development of common protocols designed by a community of investigators; </li> <li>Facilitate research into the etiology of undiagnosed diseases, by collecting and sharing standardized, high-quality clinical and laboratory data including genotyping, phenotyping, and documentation of environmental exposures; and </li> <li>Create an integrated and collaborative research community across multiple clinical sites and among laboratory and clinical investigators prepared to investigate the pathophysiology of these new and rare diseases and share this understanding to identify improved options for optimal patient management.</li> </ol>
| phs001232 | dbGaP
Project description:NHLBI TOPMed: Pulmonary Vascular Disease Phenomics Program (PVDOMICS)
| PRJNA734976 | ENA