14667 results for "PLOS+ONE+Pennsylvania+German+ACS+language+code+pdc+open+access+study"

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• International Consortium to Identify Genes and Interactions Controlling Oral Clefts

  Oral clefts represent the most common group of craniofacial birth defects in humans, and include cleft lip with or without cleft palate (CL/P) and cleft palate (CP). Oral clefts have a complex and heterogeneous etiology, with strong evidence for both genetic and environmental causal factors. Candidate gene studies and genome wide linkage studies have yielded compelling but inconsistent evidence that multiple genes control risk, and several studies have shown evidence for interaction between genes and environmental exposures, especially maternal smoking and nutrient intake. This consortium pulls together a very large collection of cases and their parents from multiple populations, and offers a unique opportunity to expand the search for genes controlling risk to the genome wide level. The specific aims are: To conduct a genome wide analysis on 2000+ case-parent trios ascertained through a case with isolated, non-syndromic CL/P or CP to test for linkage and disequilibrium. Initial analysis will consist of individual tests for gene effects while simultaneously testing for GxE interaction with common maternal exposures including vitamin supplementation, cigarette smoking and alcohol consumption (which have all been implicated as environmental risk factors for oral clefts). To use haplotypes in tests for GxE interaction incorporating population specific estimates. Since haplotype frequencies vary among populations, trios will be assigned haplotypes in a stratified estimation and then a pooled test statistic will be constructed. To test for interaction between SNPs in different genes showing evidence of influencing risk in a test for GxG interaction. To test for interaction between genes and maternal biomarkers using trios from Utah where measures of plasma folate, vitamin B-6, homocysteine and zinc in mothers are available. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org), which was developed through the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to oral clefts through large-scale genome-wide association studies of well-characterized cases and their parents from multiple populations. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). The study was supported by the National Institute of Dental and Craniofacial Research (NIDCR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000094

• Characterization of Structural Variants in Acute Myeloid Leukemia Patients

  Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults and is expected to increase in frequency as the population ages. Most subtypes of AML are difficult to treat, in part due to a lack of understanding of possible molecular targets. Furthermore, the peak incidence of AML is in the sixth decade, and patients over the age of 65 do not tolerate chemotherapeutic regimens well. Moreover, AML has been highly studied as a model for other cancers. An RNA Biology Program is established aiming to develop comprehensive datasets through the profiling of all different classes of RNA expression, RNA editing, alternative splicing, and non-coding RNAs in CD34+ normal hematopoietic stem cells (HSC), their differentiated progeny, and AML clinical samples that could be utilized to understand leukemia. These datasets will allow us to computationally identify and characterize various RNA species and processes to enable the development of effective therapeutics to AML, leukemia and other types of cancers. Epigenetic factors such as methylation and histone modifications will also be characterized from these clinical samples allowing for integrated dataset analysis to look at the cross-talk between RNA and DNA/epigenomics in leukemia. The following is a subsidiary study of the above study description where whole genome sequencing was performed on two collected AML patients' samples using Illumina and Oxford Nanopore sequencing technologies. The aim is to characterize the roles of structural variants (SV) in RNA biology which are potentially important for AML development using our own established long-read SV characterization tool, NanoVar. The advent of Oxford Nanopore sequencing technologies has opened a new avenue for better SV characterization by having longer sequencing reads. NanoVar is an accurate and rapid SV characterization tool that utilizes low-depth Nanopore sequencing data. NanoVar makes use of split-reads and hard-clipped reads for SV discovery and employs a simulation-trained neural network classifier for true positive enrichment. NanoVar exhibited higher accuracy and faster computational speed amongst other long-read SV detection tools in simulated data. In AML patient data, NanoVar displayed excellent accuracy in SV characterization (16/16 SVs validated in two patients) by using only 4x depth sequencing data. In summary, NanoVar improves the accuracy and speed of SV characterization at a lower sequencing cost, an approach compatible with clinical studies.

  Study phs001847

• University of Michigan Clinical Sequencing Exploratory Research (CSER)

  Overview. The personalization of therapy for cancer will require molecular characterization of unique and shared genetic aberrations. In particular, patients who have a sarcoma or other rare cancers and are candidates for clinical trials could potentially benefit by identifying eligibility for "targeted" drugs based on the "actionable" genes in their specific tumor. Growing technological advances in genomic sequencing has now made it possible to consider the use of sequence data in a clinical setting. For instance, comprehensive testing that includes whole exome and transcriptome sequencing may identify biomarkers for predictive or prognostic purposes and thereby inform treatment choices and prevention strategies. Thus, the translation of high throughput next generation sequencing would support a "personalized" strategy for cancer. However, the translation of clinical sequencing bears unique challenges including identifying patients who could benefit, developing informed consent and human subjects protections, outlining measurable outcomes, interpreting what results should be reported and validated, and how results should be reported. In addition, we know very little about how patients and clinicians will respond to the potentially confusing and overwhelming amount of information generated by genomic sequencing, and we lack model processes for clinically evaluating and presenting these data. For the promise of our innovative biotechnologies to be realized, "translational genomics" research that evaluates genomic applications within real-world clinical settings will be required. This proposal brings together expertise at the University of Michigan including clinical oncology, cancer genetics, genomic science/bioinformatics, clinical pathology, social and behavioral sciences, and bioethics in order to implement this clinical cancer sequencing project. Three integrated Projects have the following themes: Project 1) "Clinical Genomic Study" will identify patients with a rare cancer (i.e., 15 out of 100,000 individuals per year) who are eligible for clinical trials, consent them to the study, obtain biospecimens (tumor tissue, germline tissue), store clinical data, and assemble a multi-disciplinary Sequencing Tumor Board to deliberate on return of actionable or incidental genomic results; Project 2) "Sequencing & Analysis" will process biospecimens and perform comprehensive sequencing and analysis of tumors to identify point mutations, copy number changes, rearrangements/gene fusions, and aberrant gene expression; Project 3) "Ethics & Psychosocial Analysis" will observe the expert review process for evaluating sequence results and will examine the clinician and patient response to the informed consent process, delivery of genomic sequence results, and use of genomic results.

  Study phs000673

• Environmental Arsenic and Diabetes Mellitus (Chihuahua Cohort)

  The Chihuahua Cohort was established as part of a larger NIEHS-funded project that used laboratory models (tissue culture and mice) and human subjects to examine the association between exposure to arsenic and diabetes, including identification of mechanisms underlying this association. All procedures involving human subjects were approved by institutional review boards at the University of North Carolina at Chapel Hill and Cinvestav-IPN (Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico). All participants provided signed informed consent. A total of 1,160 adults (≥ 18 years old) with a minimum 5-year uninterrupted residency in the Chihuahua study area were recruited between 2008 and 2012. Samples of drinking water were obtained from the participants' households. Data on residency, occupation, drinking-water sources and use, smoking, alcohol consumption, and medical history were recorded by a study questionnaire. Spot urine and fasting venous blood were collected during medical examination that included an oral glucose tolerance test (OGTT) with blood drawn 2 hr after a 75-g glucose dose. Plasma from both fasting and 2-hr blood samples was collected. Body mass index (BMI) of each participant was calculated from body weight and height. BMI cutoffs of ≥ 25.0, ≥ 30, and < 18.5 kg/m2 were used to define overweight, obese, and underweight individuals, respectively. Participants' waist circumference was also measured. Blood pressure was assessed using a manual sphygmomanometer. Three measurements were taken at intervals of at least 1 min, with a 5-min rest before obtaining the first reading; the mean of the last two measurements was used. The concentrations of arsenic in drinking water and arsenic species in urine were measured by hydride generation-atomic absorption spectrometry. The limit of detection for As in water as well as for As species in urine was 0.01 µg As/L. Creatinine concentration in urine was determined using an established colorimetric assay. DNA was isolated from blood for genotyping analysis.The following measures were taken: glucose concentration in fasting blood (FBG) and in blood collected 2 hours into OGTT (2HBG); concentrations of triglycerides, HDL and total cholesterol (TC) in fasting plasma; and LDL was calculated using the Friedewald equation. Diabetes was classified by FPG ≥ 126 mg/dL, 2HPG ≥ 200 mg/dL, or self-reported diabetes diagnosis or medication use (WHO/International Diabetes Federation 2006). Prediabetes was defined as the absence of diabetes with FPG ≥ 110 mg/ dL or 2HPG ≥ 140 mg/dL. Elevated fasting levels of each lipid were defined as plasma TG ≥ 150 mg/dL, TC ≥ 200 mg/dL, and LDL ≥ 130 mg/dL. Fasting HDL < 40 mg/dL in men and < 50 mg/dL in women were designated as low. Hypertension was defined by systolic blood pressure (SBP) > 140 mm Hg, diastolic blood pressure (DBP) > 90 mmHg, or self-reported use of anti-hypertensive medication. Genotyping analysis, which will be available in version 2 of this study, was carried out in a subset of ~600 subjects with focus on enzymes involved in the metabolism of arsenic, including AS3MT, GSTs.All data collected during the medical exams or generated by the above-described chemical and biochemical analysis of water, urine and blood/plasma, as well as results of the genotyping analysis are included in dbGaP. Data generated by metabolomics and epigenetic analyses in small subsets of the Chihuahua cohort are not included, but are available in published articles.

  Study phs002139

• Asthma in the Lives of Families Today (ALOFT)

  Social interactions and the overall psychosocial environment have a demonstrated impact on health, particularly for people living in disadvantaged urban areas. Here we investigated the effect of psychosocial experiences on gene expression in peripheral blood immune cells of 251 children with asthma in Metro Detroit. Participants were included from an ongoing longitudinal study Asthma in the Lives of Families Today (ALOFT; recruited from November 2010-July 2018. The ALOFT project was established to identify the behavioral and biological pathways through which family social environments impact youth with asthma.In version 1 we used RNA-sequencing and a new machine learning approach to identify transcriptional signatures of 19 variables including psychosocial factors, blood cell composition and asthma symptoms. Using longitudinal data collected from a subset of the participants we showed that transcriptional signatures track the longitudinal changes in measured phenotypes. Importantly, we found 169 genes associated with asthma that are regulated by psychosocial factors, and 344 significant gene-environment interactions for gene expression levels. These results demonstrate that immune gene expression mediates the link between negative psychosocial experiences and asthma risk.In version 2, we focused on pubertal development. We identified substantial gene expression changes associated with age and pubertal development. We showed that genetic effects on gene expression change dynamically during pubertal development. Gene expression changes during puberty are correlated with gene expression changes associated with asthma and may explain sex differences in prevalence. Our results show that molecular data used to study the genetics of early onset diseases should consider pubertal development as an important factor that modifies the transcriptome.Version 3 uses single cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated Peripheral Blood Mononuclear Cells from 96 African American children. We employed novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we demonstrated that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and demonstrated widespread genetic regulation of the transcriptional dynamics of the gene expression response.Version 4 adds scATAC-seq in activated peripheral blood mononuclear cells (PBMC) from 16 children with asthma with phytohemagglutinin (PHA) or lipopolysaccharide (LPS), and treated with dexamethasone (DEX), an anti-inflammatory glucocorticoid. We analyzed changes in chromatin accessibility, measured transcription factor motif activity, and identified treatment and cell-type specific transcription factors that drive changes in both gene expression mean and variability. We observed strong positive linear dependence between motif response and their target gene expression changes, but negative in variability changes. This result suggests that an increase of transcription factor binding tightens the variability of gene expression around the mean. We then annotated genetic variants in chromatin accessibility peaks and response motifs followed by computational fine-mapping of eQTL signals from a pediatric asthma cohort. We found that eQTLs were 5-fold enriched in peaks with response motifs and refined the credible set for 410 asthma risk genes, with 191 having the causal variant in response motifs. These results enhance the understanding of molecular mechanisms for asthma risk variants mediated by gene expression.

  Study phs002182

• Cambridge_INTERVAL_SomaLogic_pQTLs

  This dataset contains four data files relating to the Cambridge Interval SomaLogic pQTL study to go with the corresponding genetic data: (1) Genome-wide pQTL summary associations for each analyte. (2) Mapping table for the genetic variants analysed - containing rsID, position and allele information. (3) Normalised quantitative readouts for each analyte, along with covariates used for the pQTL analysis. (4) Table of SOMAmer analytes mapped to their protein targets. Please see the readme file in the dataset for more information.

  Dataset EGAD00001004080

• Nurminen et al ("GP2Men") Study Primary and Metastatic Prostate Cancer Whole Genome Sequence Data

  We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones.

  Dataset EGAD50000000005

• IBD dataset

  We performed multi-omics profiling of 38 Crohn's disease and Ulcerative colitis patients across several stimulations (RPMI, LPS, Salmonella, in total 80 samples). Nuclei were profiled using the 10X Multiome protocol which offers paired RNA+ATAC from the same nucleus (e.g. shared barcodes). Per library, the ATAC (I1, R1, R2, R3 reads) and RNA (I1, I2, R1, R2 reads) are provided. Pools are genetically multiplexed across donors. Genotype files are provided to allow genetic demultiplexing.

  Dataset EGAD50000000198

• Heterogeneity and evolution of DNA mutation rates in microsatellite stable colorectal cancer. Higher mutation rates (MR) in metastatic tumours

  Building on the observation that MR appears to be a unique and inheritable feature of each cancer cell, we explored whether MR is subject to selective pressure during cancer progression. These data suggest that a higher MR is selected during CRC metastatic progression.

  Study EGAS50000000147

• Whole exome sequencing of preneoplasia lung adenocarcinoma

  Multi-region exome sequencing of 271 pulmonary nodules and matched adjacent lung tissue including lung preneoplasia atypical adenomatous hyperplasia (AAH, N=49), adenocarcinoma in situ (AIS, N=42), minimally invasive adenocarcinoma (MIA, N=37), invasive lung adenocarcinoma (ADC, N=57) and adjacent lung tissues (Normal, N=88).

  Study EGAS50000000270