14441 results for ""Pennsylvania+German"+ACS+study+'Socius'+open+access"

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• Whole Exome Sequencing of Chronic Lymphocytic Leukemia

  The somatic genetic basis of chronic lymphocytic leukemia (CLL), a common and clinically heterogenous adult leukemia, remains poorly understood. Massively parallel sequencing technology now provides a method for systematic discovery of genetic alterations that underlie disease, and for uncovering new therapeutic targets and biomarkers. In study version 2 we presented a dataset consisting of DNA sequencing from 169 CLL samples (with matched germline controls). Samples were collected from patients displaying a wide range of characteristics representing the broad clinical spectrum of CLL. Understanding the mutational landscape of CLL provides a starting point for systematic analyses to address fundamental questions in CLL, including how mutated genes alter cellular networks and phenotypes, and thereby contribute to disease heterogeneity. Intratumoral heterogeneity plays a critical role in tumor evolution. To define the contribution of DNA methylation to heterogeneity within tumors, we performed genome-scale bisulfite sequencing of >100 primary chronic lymphocytic leukemias (CLLs; data presented in study version 3). Compared with 26 normal B cell samples, CLLs consistently displayed higher intrasample variability of DNA methylation patterns across the genome, which appears to arise from stochastically disordered methylation in malignant cells. Transcriptome analysis of bulk and single CLL cells revealed that methylationdisorder was linked to low-level expression. Disordered methylation was further associated with adverse clinical outcome. We therefore propose that disordered methylation plays a similar role to that of genetic instability, enhancing the ability of cancer cells to search for superior evolutionary trajectories.

  Study phs000435

• VESPA: Vanderbilt Electronic Systems for Pharmacogenomic Assessment

  The Vanderbilt Electronic Systems for Pharmacogenomic Assessment (VESPA) Project is a large electronic medical record (EMR)- and biobank-based initiative for translational pharmacogenomics discoveries. Key research resources utilized in this effort include the BioVU DNA databank and associated Synthetic Derivative database of clinical information, and software tools developed to identify drugs and clinical events using EHR-derived structured and unstructured ("free text") data. Cohorts include subjects with primarily drug-response phenotypes, and most cases and controls identified include three data types: ICD-9 codes, medication regimens, and medical test results. Advanced informatics methods, such as natural language processing, were also used for some phenotypes. Algorithms used event-sequence analyses to establish temporal relationships between drugs and phenotypes. Both cases and control algorithms excluded records that contained specific comorbidities and were refined to achieve positive predictive value (PPV) > 90%. For automated algorithms failing to meet this threshold, manual review was coupled with algorithms to validate that the included cases were true positives. A total of 11,639 subjects met phenotyping criteria for a least one of the 28 phenotypes investigated. Across all phenotypes cases and controls, 90% were reused as either a case or control for a least one other phenotype. Data are deposited for individual phenotypes in the VESPA substudies. Substudies: VESPA: Genome Wide Association Study of Serum Creatinine during Vancomycin Therapy and Vancomycin Pharmacokinetics - phs000894 VESPA: Genome Wide Study of Clinically Diagnosed Ace Inhibitor Cough - phs000992

  Study phs000991

• Phase II Therapeutic Trial of Mexiletine in Non-Dystrophic Myotonia (IND #77,021)

  Non-dystrophic myotonias are a group of rare muscle disorders caused by abnormalities in different muscle cell membrane proteins. Patients experience delayed muscle relaxation that causes impaired physical activity, stiffness, and pain. Mexiletine has been shown to be beneficial for symptoms of myotonia in case reports of patients with non-dystrophic myotonia, however, current management of these patients primarily depends on individual practitioners' preference. This study is a placebo-controlled, double-blind, cross-over treatment trial using mexiletine in non-dystrophic myotonias. The results, if positive, will permit the identification of mexiletine as an effective therapy for myotonia, making it a first-line therapy for patients with non-dystrophic myotonia. The specific aim of this proposal is to perform a randomized, double-blind, placebo-controlled cross-over study to assess whether mexiletine improves both quantitative and qualitative measures of myotonia in patients with non-dystrophic myotonia. Given the rarity of non-dystrophic myotonia, large randomized controlled treatment trials in these disorders have not been feasible in the past. Primary Outcome Measure: Patient-assessed symptom: stiffness as measured by an Interactive Voice Response Diary (IVR). Secondary Outcome Measures: a) patient-assessed pain, weakness, and fatigue as measured by IVR; b) clinical myotonia assessment; c) quality of life as measured by INQoL, SF36; d) a quantitative measure of grip myotonia; e) measurement of CMAP after short and long exercise; f) grading of myotonia on needle EMG.

  Study phs001311

• Multimodal Immune Profiling to Determine Mechanisms of COVID-19 Clinical Trajectory in Uganda

  We conducted a prospective cohort study of adults (RESERVE-U-C19) admitted to Entebbe Regional Referral Hospital (ERRH), a national COVID-19 referral center, across three viral variant-driven phases of the pandemic in Uganda. In this cohort, which reflects the entire spectrum of SARS-CoV-2 infection, we integrated clinical data with serum proteomics (N=306) and whole-blood transcriptomics (N=100) to determine a range of host responses associated with COVID-19 severity, SARS-CoV-2/HIV co-infection, and circulating viral variants. Beyond the prognostic importance of myeloid cell-driven immune activation and lymphopenia, we show that multifaceted impairment of host protein synthesis and extensive redox imbalance define core metabolic signatures of severe COVID-19, with central roles for IL-7, IL-15, and lymphotoxin-α in COVID-19 respiratory failure. While prognostic signatures were generally consistent in SARS-CoV-2/HIV-coinfection, type I interferon responses uniquely scaled with COVID-19 severity in persons living with HIV. Throughout the pandemic, COVID-19 severity peaked during phases dominated by A.23/A.23.1 and Delta B.1.617.2/AY variants, with Delta phase COVID-19 distinguished by exaggerated pro-inflammatory myeloid cell and inflammasome activation, NK and CD8+ T-cell depletion, impaired host protein synthesis, and upregulation of viral cell entry and trafficking pathways. Data from this study available through dbGaP include raw RNA-Seq data from whole-blood of adult participants (N=100).

  Study phs003246

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Multimodal Developmental Neurogenetics of Females

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002043

• Michigan Polybrominated Biphenyl (PBB) Exposure Registry

  Humans are exposed to chemicals such as brominated flame retardants (BFRs) at unprecedented levels, and a wide variety of health effects are attributed to such exposures. However, the biological mechanisms that link exposures to its consequences remain unknown. An industrial accident in the 1970's exposed over 4000 individuals in rural Michigan to polybrominated biphenyl (PBB), a BFR that was present in the food supply for years prior to detection and management. Health effects are still evident in these individuals and their children. Because this population has been assessed regularly since the exposure, it is an ideal group in which to examine the molecular mechanisms that contribute to susceptibility, vulnerability and health effects. The goal of this project is to identify the genetic variants that associate with the rate of PBB elimination over time and integrate genetic and epigenetic datasets and identify complex gene-environment relationships in PBB exposed individuals. We will leverage data generated as part of past and ongoing studies in the Michigan Polybrominated Biphenyl Registry. We anticipate that this study will provide insight into how genetic variants and the environment interact in relation to PBB exposure and its health effects. We also anticipate the results of this study will be informative for studies of BFRs and other endocrine disrupting compounds with similar chemical properties. Note that methylation array data is available from NCBI GEO under GSE116339: Exposure to Polybrominated Biphenyl (PBB) Associates with DNA Methylation Differences Across the Genome

  Study phs001862

• Gabriella Miller Kids First Pediatric Research Program in Craniofacial Microsomia

  The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). All of the WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Craniofacial microsomia (CFM), also termed hemifacial microsomia or oculo-auricular-vertebral spectrum, is the third most common congenital craniofacial condition. CFM comprises a variable phenotype and the most common features include malformations of the ear (i.e. microtia) and lower jaw (i.e. mandibular hypoplasia) on one or both sides. Microtia in the absence of other anomalies is believed to represent the mildest form of CFM. The cause of CFM is unknown for most affected individuals. We have established a cohort through previous studies and collected DNA to identify the genetic contributions to CFM, which could facilitate diagnosis, tailored treatments and guide prevention strategies. Successful completion of this proposal will advance knowledge in the genetic architecture of susceptibility to CFM and will provide insight about the biological mechanisms underlying craniofacial development. The results from this study have the potential to further research on the etiology of other craniofacial conditions, and the pathogenesis of typical and atypical craniofacial development.

  Study phs002130

• Center for Common Disease Genomics [CCDG] - Neuropsychiatric: Genomics of Autism Spectrum Disorder (GASD)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of New York Genome Center (NYGC). WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal.An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002509

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Homozygosity Mapping Consortium for Autism (HMCA)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs001894

• Center for Common Disease Genomics [CCDG] - Neuropsychiatric: Simons Searchlight

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of New York Genome Center (NYGC). WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal.An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002512