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• HTAN MCL Pre-Cancer Atlas Pilot Project - Targeted Sequencing Development Study

  The HTAN-MCL Pre-Cancer Atlas Pilot Project (PCAPP) is the result of a collaboration between the seven members of the MCL consortium. Across four organ types, PCAPP's goal is to collect and profile pre-malignant lesions for gene expression, DNA mutations, single-cell gene expression and immune-environment. Most PML are small in size and only available come from formalin fixed paraffin embedded archived tissue. The primary goal of PCAPP is to 1) understand the logistical challenges of PML specimen collection, 2) document technical limitations of the assays that are specific to the PML and 3) overcome them to support the generation of a more comprehensive Pre-Cancer Atlas in the future. The current upload provides RNA and DNA sequencing from participants with DCIS who were studied at the University of San Diego and the University of Vermont. Description of the overall study: A. Background/Significance One of the critical barriers to developing new approaches for cancer detection and prevention is the lack of understanding of the key molecular and cellular changes that cause cancer initiation and progression. Unlike the extensive work that has been done profiling advanced stage tumors, few studies have comprehensively profiled the molecular alterations found in precancerous tissues. Premalignant lesions are currently characterized by histologic changes that precede the development of invasive carcinoma1,2.These lesions can often be identified in regions surrounding an invasive tumor or in biopsies taken from patients undergoing diagnostic evaluation for suspicion of cancer. Currently, limited metrics exist to identify lesions that will likely progress to carcinoma and require intervention from those that will naturally regress or remain stable3,4. Characterization of the molecular alterations in premalignant lesions and the corresponding changes in the microenvironment would hasten the development of biomarkers for early detection and risk stratification as well as suggest preventive interventions to reverse or delay the development of cancer. Our pilot study will establish the feasibility of transcriptomic, genomic and immune profiling of FFPE premalignant lesions from multiple organ sites, collected and profiled with uniform SOPs across multiple institutions within the MCL consortium. We will characterize the molecular alterations in precancerous lesions and the corresponding microenvironment in four major organ sites, in order to uncover the molecular and cellular determinants of premalignancy, and establish standardized sequencing and immunohistochemistry protocols on FFPE precancerous tissue. We will also evaluate the technical feasibility of single nuclei sequencing of small FFPE pre-cancer lesions. Successful completion of the proposed pilot study will set the stage for expansion and development of a comprehensive Pre-Cancer Atlas (PCA) as part of the NCI's moonshot.B. Specific Aims Aim 1: Collect premalignant lesions (PML) and their associated microenvironment via LCM from FFPE tissue across four organ sites (breast, lung, pancreas & prostate). Aim 2: Perform bulk RNA and DNA seq on premalignant FFPE samples (and flash frozen tissue where available) and compare the genomic/transcriptomic alterations within and across organ sites. C. ApproachAim 1: Collect premalignant lesions (PML) and their associated microenvironment via LCM from FFPE tissue across four organ sites (breast, lung, pancreas & prostate). MethodsI. Patient Population/Sample Collection: Overview of the sites collecting PML tissue from the respective organs is provided in Table 1 and a full description of the biospecimens to be obtained is described in detail for each organ type below. Table 1. Breakdown of cohort by tissue type and collection site.Organ siteBreastLungPancreasProstateType of PMLDCISAAH, Squamous Dysplasia/CISIPMNsPINCollection of PatientsUCSF/UCSDUVMBU*/UCLAVanderbilt/MoffittMDACC*JHUStanford*# of Patients201920 (10 of each type)20 (10 of each type)242020Total patients per Organ39402440Note: single nuclei/cell RNA-Seq will be performed on 4-5 FFPE samples from each of the organ types 1. DCIS lesions from breast tissue: DCIS lesions will be collected from 39 patients (20 from UCSF/UCSD & 19 from UVM) with primary low or high-grade DCIS diagnosed from a breast core biopsy. Subsequent resected lumpectomy or mastectomy tissues will be prospectively sampled in the vicinity of the prior biopsy site using multiple approaches: 1) Live cells (heterogeneous mix) will be obtained as a cell scrape slurry from the lesion surface or by fine needle aspirate (FNA); 2) For a subset of specimens where size is sufficient, a block of breast tissue with DCIS will be fresh-frozen; 3) The remainder of the specimen will be taken for routine formalin-fixation and paraffin-embedding (FFPE). The FFPE sample will be annotated to identify the matched FFPE tissue block adjacent to the fresh-frozen sample and will be sectioned for use in bulk and single nuclei sequencing . We will dissect DCIS, adjacent normal and when available, associated carcinoma. In addition, when possible, normal tissue will be collected from a tissue block lacking lesions as well as collection of blood. A subset of patients (n = 5 | FFPE, flash frozen and fresh) will be sent to the Broad Institute for single nuclei/cell sequencing.2. AAH and squamous dysplastic/CIS lesions from airway and lung tissue: For squamous cell lung cancer, we will collect endobronchial biopsies from abnormal airway regions identified on autofluroscence bronchoscopy or identify PMLs in the margins of resected lung tissue. We will study 20 patients (5 each from BU/UCLA/Vanderbilt/Moffitt) with pre-invasive squamous lesions (moderate-severe dysplasia or carcinoma in situ (CIS)) identified on pathologic examination. LCM of the premalignant region and adjacent normal epithelium will be performed as well as the invasive tumor for those collected from the resection margin (n=5 from UCLA). On a subset of lesions collected at bronchoscopy (n=5), we will collect additional biopsies that will be flash frozen and fresh for single nuclei and cell sequencing, respectively, performed at the Broad Institute. In parallel to the work at the Broad, BU will perform single cell RNA-seq on these freshly cell sorted tissues (n = 5). Blood will be collected on all patients for genomic studies. For lung adenocarcinoma, we will collect resected FFPE lung tissues from 20 patients (10 from UCLA and 10 from Vanderbilt/Moffitt) with early stage lung adenocarcinoma that harbor atypical adenomatous (AAH) premalignant lesions in the resection margin. We will LCM multiple AAH regions (3-5 per patient) as well as adjacent regions of normal epithelium and invasive adenocarcinoma. In addition, blood will be collected on all patients for genomic studies. 3. IPMNs from pancreatic tissue: For pancreatic cancer PML, we will collect low and high grade lesions from 24 patients representing macroscopic Intraductal Papillary Mucinous Neoplasms (IPMN) (n=24) from surgically resected specimens along with blood samples. Archival FFPE specimens of microscopic PanIN lesions, occurring multi-focally adjacent to invasive PDAC, and archival IPMN lesions (with or without associated invasive cancer), along with the adjacent normal tissue, will undergo LCM and utilized for bulk DNA and RNA sequencing. If matched frozen tissues are available for a subset of these FFPE samples, we will bank for comparison of profiles. Because IPMNs are macroscopic lesions, they provide an opportunity for obtaining the samples fresh and therefore can be used for single cell sequencing (in contrast to PanINs). Therefore, 5 freshly obtained IPMNs will be used for the single cell RNA sequencing studies performed at both the Broad Institute and MDACC, and the matched FFPE and/or frozen sections from these lesions (obtained from the adjacent PML) will be sent to Broad Institute as a pilot to assess "single nuclei" RNA sequencing.4. PINs from prostate tissue: For prostate cancer PML, there will be 40 samples of Prostatic Intraepithelial Neoplasia (PIN) collected between the Stanford and JHU sites (20 cases per site). At the Stanford site, 20 prostate specimens detected by PSA screening who have/will undergo surgery (radical prostatectomy) for clinically localized disease will make up the final cohort. The age range of the participants would be 40-75, and we anticipate that 18 will be Caucasian, 1 Asian and 1 Latino or African American based on the practice demographics practice at Stanford. Clinical and MRI data will also be collected for these samples. We will collect low grade (e.g. Gleason score of 6/Grade group 1; n=10) and high grade (Gleason score 4+3=7 or higher/Grade group 3 or higher; n=10) PINs from FFPE samples that have prostate carcinoma. In addition to obtaining LCM archival samples of low and high grade PIN, we will also obtain normal prostatic epithelial from the peripheral, central and transition zones as well as multiple samples of prostate carcinoma in order to obtain the spectrum of Gleason grades in the carcinoma as needed. LCM samples will be used for bulk DNA and RNA sequencing. In addition, single cells will be dissected from FFPE samples to prepare single cell RNA seq libraries using techniques developed at Stanford, and FFPE tissue will be sent to the Broad for single nuclei sequencing. When available, flash frozen and fresh samples from these prostates will be archived and prepared for single nuclei and cell sequencing, respectively, at the Broad Institute and at Stanford (single cell only). JHU will also capture 10 cases (5 grade group 1 and 5 grade group 2) of high grade PIN, normal and invasive adenocarcinoma using frozen sections from fresh frozen tissues. When possible these will be from the same patients as the FFPE samples. Since frozen sections can be quite challenging to morphologically determine high grade PIN from normal epithelium, for these samples we will perform a number of additional tissue-based characterizations. These will include a multicolor combined basal cells (p63 and CK903) and PIN/carcinoma markers (AMACR) referred to in the cocktail as "PIN4", c-MYC (referred to as MYC) protein5, by IHC and mRNA by in situ hybridization (AM De Marzo, Q Zheng unpublished observations), telomere length by in situ hybridization6 and the 5'ETS/45S rRNA7. For these slides, the whole slides will be scanned with a Hammamatsu Nanozoomer with a 40x objective and regions of interest will be annotated as a guide for LCM.II. Laser-capture microdissection (LCM): FFPE tissue blocks will be sectioned at 7μm thickness and serial sections will be stained with H&E. LCM will be performed utilizing standard LCM systems, such as Leica LMD7000 and ArcturusXT at each site. Regions of premalignancy will be dissected and RNA/DNA will be extracted from microdissected cells using the Qiagen All Prep DNA/RNA FFPE Kit. Aim 2: Perform bulk RNA and DNA seq on premalignant FFPE samples and compare the genomic/transcriptomic alterations within and across organ sites. Rationale: There have been limited studies characterizing the genomic and transcriptomic landscape of premalignant lesions associated with breast, pancreatic, lung or prostate cancers. Characterizing the molecular determinants of premalignant disease that are unique and shared across multiple organs will enable new candidate biomarkers for early detection and novel therapeutic strategies for early intervention. MethodsBulk RNA-seq of LCM FFPE tissue: All participating sites will perform bulk RNA-seq in accordance with SOPs developed at BU. In brief, total RNA will be isolated from LCM'd lesion and associated microenvironment tissue using the Qiagen All Prep DNA/RNA FFPE Kit and quality will be assessed with the Agilent Bioanalyzer. Libraries will be generated with the Illumina TruSeq Access kit (for FFPE samples). They will be sequenced on the Illumina HiSeq2500 with 75base-pair paired-end reads. Quality of FASTQ files will be assessed with FastQC. Reads will be aligned to the human genome with STAR and gene-level and isoform-level expression will be quantified with RSEM. Splice junction saturation, transcript integrity, and biotype distributions will be calculated for each sample with RSeQC. DESeq2 and EdgeR will be used to identify associations between gene expression profiles and clinical variables while controlling for confounding covariates. BU will serve as an RNA-seq Core to assess reproducibility of FFPE RNA-seq methods across sites. We will perform RNA-seq according to the SOP listed above on a subset of samples for each organ type (total n ~ 20). Bulk seq of DNA from FFPE tissue: All participating sites will perform targeted or whole exome-seq (WES) in accordance with SOPs. In brief, DNA from laser captured material will be isolated using the Qiagen All Prep DNA/RNA FFPE Kit and undergo stringent quality control to ensure high quality input material for genomic profiling. Purified DNA (ideally 100-200 ng) will be used for library preparation and amplification, followed by next generation sequencing using standard protocols distributed by CDMG. Exome-seq methods are considered standardized, thus we will not need a DNA-seq Core to assess reproducibility across sites. We anticipate local centers will use Illumina paired end reads, following the following general approach. 1) DNA library preparation: Paired-end libraries will be prepared following the manufacturer's protocols (Illumina and Agilent), fragmented to 150-200 bp 2) Capture of targeted exome: Whole exome capture will be carried out using the protocol for Agilent's SureSelect Human All Exon kit. Purified capture products will be amplified using the SureSelect GA PCR primers (Agilent) for 12 cycles. 3) Sequencing will be carried out for the captured libraries using at least 100 bp paired-end reads. To achieve high level sensitivity and accuracy for detecting all the mutations in the whole exome, each sample will be sequenced at 200X mean depth. 4) Read mapping and alignment and variant analysis: Sequence short reads will be aligned to a reference genome (NCBI human genome assembly build 38) using BWA-MEM. Local realignment of aligned reads will be performed using Genome Analysis Toolkit (GATK).Data QC: To ensure scientific rigor and consistency among sites in RNA and DNA processing we will include a preliminary analysis of steps in processing and analysis. Protocols for extraction of high quality RNA and DNA from formalin fixed paraffin embedded (FFPE) tissues, which will be used extensively in these studies continue to improve and may have variable implementation among the sites participating in this study. To evaluate consistency of preliminary steps in processing and downstream analyses, we will initially distribute slides from one large FFPE fixed cancer of origin from prostate, breast, lung and pancreatic cancer. Analysis of these samples will allow us to review the DNA and RNA characteristics (yield, purity and strand length) among sites. Downstream analysis of these same samples will also allow us to compare among sites the consistency of variant calls among centers. We will be able to identify if there are some times of calls (such as small insertion deletions) that are more variable among centers versus other types of calls (such as relative gene expression or single base pair substitutions) that we expect to be less variable and to characterize the reliability of findings across sites. We are also including a 5% blind duplicate analysis of RNA sequencing. Samples will be analysed by the participating genomics cores without knowledge of the phenotype. RNA seq and CNA analyses are normalized for batch effects. We will also compare the observed sex to the self-reported sex as based on RNA profiles and exome sequencing of X chromosome genes as another check for processing accuracy and sample management. D. References 1. Wacholder, S. Precursors in Cancer Epidemiology: Aligning Definition and Function. Cancer Epidemiol. Prev. Biomark. 22, 521-527 (2013). PMID: 23549395.2. Berman, J. J. Precancer: The Beginning and the End of Cancer. (Jones & Bartlett Learning, 2011).3. Nasiell, K., Nasiell, M. & Vaćlavinková, V. Behavior of moderate cervical dysplasia during long-term follow-up. Obstet. Gynecol. 61, 609-614 (1983). PMID: 6835614.4. Merrick, D. T. et al. Persistence of Bronchial Dysplasia Is Associated with Development of Invasive Squamous Cell Carcinoma. Cancer Prev. Res. (Phila. Pa.) 9, 96-104 (2016). PMID: 26542061.5. Gurel, B. et al. Nuclear MYC protein overexpression is an early alteration in human prostate carcinogenesis. Mod. Pathol. Off. J. U. S. Can. Acad. Pathol. Inc 21, 1156-1167 (2008). PMID: 18567993.6. Meeker, A. K. et al. Telomere shortening is an early somatic DNA alteration in human prostate tumorigenesis. Cancer Res. 62, 6405-6409 (2002). PMID: 12438224.7. Guner, G. et al. Novel Assay to Detect RNA Polymerase I Activity In Vivo. Mol. Cancer Res. MCR 15, 577-584 (2017). PMID: 28119429.

  Study phs002225

• GEnomics and Transcriptomics of Human INsulinoma (GETHIN)

  The Genomics and Transcriptomics of Human Insulinoma (GETHIN) The common forms of diabetes - Types 1 and 2 - ultimately result from a deficiency of insulin-producing pancreatic beta cells. The Genomics and Transcriptomics of Human Insulinoma (GETHIN) study was performed in order to identify novel approaches to inducing human pancreatic beta cells to replicate and regenerate. As a corollary, developing drugs that are able to expand human beta cell mass in people with diabetes should reverse diabetes. Unfortunately, identifying druggable pathways that can enhance human beta cell replication has been a major challenge. In 2017, there is only one class of drugs - the harmine analogues - that can induce human beta cells to replicate, and in this case, higher replication rates are desirable. Thus, identifying additional drugs and druggable pathways is a priority in diabetes research. Insulinomas are rare, benign adenomas of the pancreatic beta cell that cause excess insulin production and hypoglycemia: exactly the opposite of Types 1 and 2 diabetes. Beta cell proliferation rates in insulinomas are abnormally high. Thus, the premise for The Genomics and Transcriptomics of Human Insulinoma study is that benign human insulinomas hold the genomic and transcriptomic "recipe", and the repertoire of druggable pathways, that can be exploited to induce regeneration or replication of human beta cells in diabetes. Because, insulinomas are so rare, are almost always benign (non-malignant), and are easily resected by laparoscopic surgery, little attention has been paid to understanding the genomics or transcriptomics of insulinoma. There are at present only three published studies employing next-gen sequencing in insulinoma (PMID:24326773; PMID:25787250; and PMID:25763608). These studies contained 10, 7 and 8 insulinomas, respectively, and highlighted likely mutations in YY1 and MEN1. Our goal was to markedly expand the database and to add RNAseq to these earlier studies. The GETHIN study reports next-gen sequencing on 38 insulinomas, by far the largest series of human insulinomas subjected to next-gen sequencing (see the Selected publications section for reference). This includes paired (genomic plus tumor) whole exome sequencing on 26 human insulinomas (22 sequenced at Mount Sinai, 4 downloaded from Cao et al, PMID:24326773), and 25 sets of RNAseq from insulinomas, some of which also had paired whole exome sequencing, and some of which did not. The insulinoma RNAseq was compared to RNAseq from 22 sets of FACS-sorted normal human beta cells. Since insulinomas are so rare, the 38 insulinomas were collected by several investigators at several institutions over several decades, but most (22 whole exome sets, and all RNAseq) were sequenced at the Icahn School of Medicine at Mount Sinai in New York. The current dataset contains whole exome sequencing and RNAseq on the 11 insulinomas harvested at Mount Sinai. The four from Cao et al can be retrieved from Cao et al PMID:24326773. Fastq files from the remaining 23 insulinomas will be added as the local IRBs and Institutional Certifications are acquired. Complete patient data are provided in our Nature Communications report. Going forward, our intention is to expand this series, with the goal of sequencing 100 human insulinomas. These will be added to dbGaP as they accrue. Paired-end whole exome sequencing (mean usable sequencing depth 79X and 105X for blood and insulinoma, respectively) was performed using an Illumina HiSeq 2500. Insulinoma and sorted normal beta cell RNAseq was performed on Ribozero and polyA paired end libraries using the Illumina HiSeq 2500. Complete sequencing and bioinformatic details are provided in our Nature Communications report. The principal findings from the study are that although each insulinoma has a different set of presumptive driver mutations, the majority converge on genes that are members of the Polycomb Complex, Trithorax Complex and other epigenetic modifying enzymes. In addition, 20% of insulinomas have copy number loss or loss of heterozygosity of all or most of chromosome 11, and the majority display abnormalities in CpG methylation and imprinting control on the imprinted Chr 11 p15.5-15.4 region that contains INS, IGF2, CDKN1C, KCNQ1, and other genes involved in beta cell specification and proliferation.

  Study phs001422

• Genetic Basis of Early Onset Bicuspid Aortic Valve Disease

  The study protocol was approved by the Committee for the Protection of Human Subjects at the University of Texas Health Science Center at Houston (HSC-MS-11-0185). Study recruitment began on July 1, 2017, and concluded on March 30, 2022. After written informed consent, we enrolled probands with early onset BAV disease (EBAV), which we defined as individuals with BAV who were under the age of 30 at the time of first clinical event. Clinical events were defined as aortic replacement, aortic valve surgery, aortic dissection, moderate or severe aortic stenosis or aortic regurgitation, large aneurysm (Z > 4.5), or intervention for BAV-related conditions. Those with hypoplastic left heart, known genetic mutations, genetic syndromes, or complex congenital heart disease were excluded. Samples were collected and genotyped as previously reported. For comparison, we analyzed a cohort of older individuals of European ancestry with sporadic BAV disease selected from the International BAV Consortium (BAVGWAS). Phenotypes were derived from record review with confirmation of image data whenever possible [25-26]. The computational pipeline for CNV analysis of Illumina single nucleotide polymorphism (SNP) array data included three independent CNV detection algorithms.GenomeStudio was used to exclude samples with indeterminate sex or more than 5% missing genotypes, and single nucleotide polymorphisms (SNPs) with GenTrain = 0. Principal component analysis was used to remove outliers that did not cluster with European ancestry. Prior to CNV analysis, each dataset was trimmed by selecting a common set of 650,000 SNPs that were genotyped on each of the microarrays used in this study.Three independent algorithms (PennCNV, cnvPartition, and QuantiSNP) were used to generate CNV calls and sample-level quality statistics from SNP intensity data. PennCNV and QuantiSNP were run on Unix clusters and cnvPartition data were exported from GenomeStudio. The analysis was run using default configurations. PennCNV was used to generate QC data and remove CNV calls that intersect with polymorphic genomic regions. Samples that met any of the following criteria were excluded, standard deviation of the LogR ratio (obtained from PennCNV) > 0.35 or number of CNVs > 2 standard deviations above the mean for each data set. CNV calls less than 20 Kilobases in length and/or spanned by fewer than 6 probes were excluded. The overlap function for rare CNVs in PLINK was used to construct CNV regions (CNVRs) after adjacent regions were merged using PennCNV. LogR ratio (LRR) and B allele frequency (BAF) data at CNVRs and calls of interest were visualized in GenomeStudio for validation. For segregation analysis, GenomeStudio was used to determine the presence of CNVs in relatives.A total of 22,014 unselected control Illumina Genotypes obtained from the Database of Genotypes and Phenotypes were analyzed using identical methods (S1 Table). The Wisconsin Longitudinal Study (WLS) includes data on a cohort of 10,300 individuals who graduated from Wisconsin high schools in 1957. The Health and Retirement Study (HRS) includes data on 37,000 individuals aged 50 above from 23,000 households across the United States. Principal component analysis was used to select European ancestry genotypes from these datasets for analysis. Datasets were paired for case-control analysis based on the concordance of log-transformed sample-level quality control statistics (number of CNV calls and standard deviation of logR ratios). Chi-squared or Fisher exact tests were used to compare CNV frequencies in cases and controls.Rare CNV functions in PLINK (v1.7) were used to perform permutation-based burden tests or gene set-based enrichment tests. Case control burden tests were restricted to CNVs that were longer than 110 Kb and less than 0.1% in frequency. CNV overlap functions in PLINK were used to identify rare CNVs that intersect between datasets or involve specific BAV or CHD genes . The list of candidate genes included 190 CHD genes that have strong cumulative evidence to cause BAV or related congenital malformations from human or animal model data. Genome Reference Consortium Human Build 37 was used for CNV annotation [34].

  Study phs003705

• Prevention and Early Treatment of Acute Lung Injury (PETAL) - Low Tidal Volume Universal Support Feasibility of Recruitment for Interventional Trial (LOTUS FRUIT) (PETAL-LOTUS FRUIT-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives: The two main goals of PETAL-LOTUS FRUIT were to conduct a prospective, observational study within all PETAL Network sites to determine the frequency of and outcomes from acute respiratory failure and the current usual care for tidal volume ventilation in patients with and without acute respiratory distress syndrome (ARDS), and to simulate the design and power of the proposed LOTUS trial. Background: A previous study demonstrated improved survival among patients with ARDS receiving tidal volume (Vt) targeted to 6 ml/kg of predicted body weight (PBW). As a result, low–tidal volume ventilation (LTVV) is now recommended for all patients with ARDS, although penetration of this evidence-based practice has been limited, especially early in mechanical ventilation. LTVV may also benefit patients without ARDS. Consequently, there has been increasing call to apply LTVV for all patients with acute respiratory failure upon initiation of mechanical ventilation. The PETAL Network considered performing a pragmatic stepped-wedge, cluster-randomized, controlled, hybrid implementation trial to examine systematic implementation of a default 6 ml/kg PBW LTVV strategy in patients with acute respiratory failure requiring intubation to improve adherence to LTVV and decrease mortality in acute respiratory failure. Trial planning may be better estimated by simulation than routine, simplistic calculations, but such simulations require detailed data of initial parameters. Participants: 2848 patients from 49 hospitals were enrolled. Enrollment varied by hospital, ranging from 4 to 100 patients, the maximum enrollment allowed per site. Design: PETAL-LOTUS FRUIT was a multicenter, prospective, observational cohort study of patients with acute respiratory failure on mechanical ventilation in the PETAL Network hospitals. For all patients, baseline demographic data, the hospital location, indication for intubation (hypoxemic or hypercapnic respiratory failure or both, altered level of consciousness, or surgery), type of intensive care unit (ICU), and Sequential Organ Failure Assessment score in the first 24 hours after intubation were collected. Baseline ventilator data immediately after intubation, arterial blood gas results and oxygen saturation as measured by pulse oximetry, and presence of ARDS was also collected for all patients. ARDS was defined as a ratio of arterial oxygen tension to fraction of inspired oxygen (FiO2) less than or equal to 300 with a chest radiograph within 24 hours of the qualifying ratio of arterial oxygen tension to FiO2 that had bilateral infiltrates unexplained by mass, collapse, or effusion. For the first 50 patients enrolled at each hospital, daily data on ventilator mode, Vt, and presence of ARDS for the first 3 days after intubation was collected. Vt indexed to PBW was calculated from the set Vt for patients on ventilator mode with volume settings. For patients on pressure ventilation modes, Vt was calculated from the ratio of minute ventilation (in ml/min) to the respiratory rate. Enrolled patients were followed until hospital discharge or 28 days for clinical outcomes including mortality, ventilator-free days, and length of stay. To determine the possible improvement in mortality that could be observed with a reduction in Vt from current usual care in the PETAL-LOTUS FRUIT cohort to 6 ml/kg PBW, mortality was estimated as a function of initial Vt. Five models based on data from three distinct patient populations were used. 500 simulations of a stepped-wedge, cluster-randomized clinical trial using the model with greatest predicted benefit for lowering the Vt to 6 ml/kg PBW in PETAL-LOTUS FRUIT sites were performed. Conclusions: Use of initial tidal volumes less than 8 ml/kg predicted body weight was common at hospitals participating in the PETAL Network. After considering the size and budgetary requirement for a cluster-randomized trial of LTVV versus usual care in acute respiratory failure, the PETAL Network deemed the proposed trial infeasible (PMID: 30407869).

  Study phs003791

• Implementation, Adoption, and Utility of Family History in Diverse Care Settings

  The purpose of this study is to address the key question of whether and how family health history (FHH) is adopted as a tool to more efficiently manage patients at risk for breast, colon, ovarian, and hereditary cancer syndromes as well as thrombophilia and coronary heart disease (CHD) and to provide evidence supporting clinical utility -- improved health behaviors in patients and physician screening recommendations. Five health care delivery organizations will participate in this demonstration project: Duke University, the Medical College of Wisconsin, the Air Force, Essentia Health, University of North Texas. Duke will serve as a coordinating center for this project (Pro00043372) as well as a site. Healthcare Effectiveness Data and Information Set (HEDIS) measures as intermediate clinical effectiveness measures for Coronary Heart Disease (CHD) and selected cancers as well as survey/formative data and electronic medical record (EMR) data will be used as outcomes measures. The research model is purposely designed to mimic clinical delivery as an important step toward widespread implementation and sustainability. In addition, a cost-effectiveness analysis comparing usual care to the FHH guided preventive health model will be used. The completion of this project will result in an optimal strategy for integration of FHH data collection and clinical decision support (CDS) tools into an EMR and demonstrate the utility of the FHH intervention among diverse primary care patients, their settings, their providers, and the health systems that deliver their care. Specific Aim 1: To optimize the collection of patient entered FHH in diverse clinical environments for coronary heart disease, thrombosis, and selected cancers Specific Aim 2: To export FHH data to an open clinical decision support (open CDS) platform and return CDS results to providers and patients (and to EMRs where relevant). To explore the integration of genetic risk and FHH data at selected sites. Specific Aim 3: To assess the clinical and personal utility of FHH using a pragmatic observational study design to assess reach, adoption, integrity, exposure, and sustainability, and to capture, analyze, and report effectiveness outcomes at each stakeholder level: patient, provider, and clinic/system. Specific Aim 4: To take a leadership role in the dissemination of guidelines for a FHH intervention across in diverse practice settings. AAA Hypertension Lupus Multiple sclerosis Obesity Osteoporosis Thyroid disease Rheumatoid arthritis Blood clotting (6): Blood Clot (high risk features) Factor V PT mutation AT III deficiency Protein S deficiency Protein C deficiency Brain Disorders (5): Dementia Hemorrhagic stroke Ischemic stroke Macrocephaly Seizure Cancer/Adenomas (32): Bone cancer Brain cancer Breast cancer Colon cancer Adrenal cortex tumor Neuroendocrine tumor Paraganglioma Pheochromoctyoma Pituitary adenoma Medullary thyroid cancer Non-medullary (follicular or papillary) thyroid cancer Don't know type of thyroid cancer Thyroid nodule Other type of endocrine cancer Esophageal cancer Kidney cancer Leukemia Lipoma Liver cancer Muscle cancer Ovarian cancer Pancreatic cancer Prostate cancer Rectal cancer Retinoblastoma Melanoma Non-melanoma skin cancer Do not know type of skin cancer Small bowel cancer Stomach cancer Uterine cancer Other type of cancer Cardiovascular/heart/artery disease (5): Atrial fibrillation Carotid stenosis Heart attack/coronary artery disease Peripheral arterial disease Other heart disease Diabetes (3): Gestational diabetes Diabetes type I Diabetes type 2 Digestive Disorders (5): Colon polyp Crohn's disease Irritable bowel syndrome Ulcerative colitis Other digestive disorder Eye Disorder (3): Blindness Glaucoma Macular degeneration Hereditary Cancer Syndromes (22): Birt-Hogg-Dube syndrome Cowden syndrome Familial adenomatous polyposis Hereditary breast and ovarian cancer syndrome Hereditary diffuse gastric cancer Hereditary leiomyomatosis and renal cell carcinoma syndrome Hereditary melenoma Hereditary papillary renal cancer syndrome Hereditary paraganglioma-pheochromocytoma syndrome Hereditary retinoblastoma Juvenile polyposis Li-Fraumeni syndrome Lynch syndrome Mutyh-associated polyposis Malignant hyperthermia susceptibility Multiple endocrine neoplasia type 1 Multiple endocrine neoplasia type 2 Nevoid basal cell carcinoma syndrome Peutz-Jeghers syndrome Tuberous Sclerosis complex Von-Hippel Lindau syndrome Other hereditary cancer Hereditary Cardiovascular syndromes (10): Long qt Brugada Catecholaminergic polymorphic ventricular tachycardia Hypertrophic cardiomyopathy Dilated cardiomyopathy Left ventricular non-compaction syndrome Arrhythmogenic right ventricular dysplasia Ehlers-Danlos syndrome Marfan syndrome Other hereditary cardiovascular syndrome High cholesterol (2): Hyperlipidemia Familial hypercholesterolemia Kidney Disease (6): Cystic kidney disease Diabetic kidney disease Kidney nephrosis Nephritis Nephrotic syndrome Other kidney disease Liver Disease (6): Alpha 1 antitrypsinase deficiency Auto-immune hepatitis Hereditary hemochromatosis Primary biliary cirrhosis Sclerosing cholangitis Wilson's disease Lung Disease (3): Asthma COPD/chronic bronchitis/emphysema Other lung disease Psychological disorder (14): Addiction ADHD Autism Bipolar Depression Eating disorder Intellectual disability Obsessive compulsive disorder Panic disorder Personality disorder PTSD Schizophrenia Social phobia Sickle cell/thalassemia (3): Sickle cell disease Sickle cell trait Thalassemia

  Study phs001641

• Serrated Colorectal Cancer: An Emerging Disease Subtype

  This dataset represents two combined study populations. Serrated Colorectal Cancer: An Emerging Disease Subtype (called the Advanced Colorectal Cancer of Serrated Subtype Study or ACCESS Study) was a grant awarded to investigate a newly-recognized, biologically-distinct subtype of colorectal cancer (CRC) called “serrated CRC.” The objective of this project was to characterize factors related to the genetic predisposition, clinical presentation, and prognosis of serrated CRC. The study recruited incident invasive CRC cases diagnosed between April 2016 and December 2018, aged 20-74 years at diagnosis. Cases were identified through the Surveillance, Epidemiology and End Results (SEER) cancer registry serving 13 counties in western Washington State. Eligibility for all individuals was limited to those who were English-speaking and could consent. Participation included completing a baseline epidemiologic questionnaire shortly after diagnosis, optional donation of a saliva sample for genetic analysis, and optional consent to release of medical records and tissue specimens related to their diagnosis. Tumor specimens were tested for serrated CRC-defining molecular characteristics. Further, we have vital status on all participants and cause of death on those that have died since enrollment. Hormones and Colon Cancer: Epigenetic Subtypes, Risks, and Survival (called the Post-Menopausal Hormones Study or PMH Study) was a grant awarded to investigate the impact of post-menopausal hormone use on colon cancer risk, tumor molecular characteristics, and outcomes. Eligible cases were females, newly diagnosed with invasive colorectal adenocarcinoma between October 1998 and February 2002, aged 50 to 74 years. Cases were residents of 10 out of the 13 counties in western Washington State served by the Surveillance, Epidemiology and End Results (SEER) cancer registry. Eligibility for all individuals was limited to those who were English-speaking with available telephone numbers, in which they could be contacted. Unrelated population-based controls were randomly selected according to age distribution (in 5-year age intervals) of the eligible cases by using lists of licensed drivers from the Washington State Department of Licensing (for individuals aged 50 to 64 years) and rosters from the Health Care Financing Administration (now the Centers for Medicare and Medicaid, for individuals older than 64 years). Participation included completing a baseline epidemiologic questionnaire, optional donation of a saliva sample for genetic analysis, and (for cases only) optional consent to release of medical records and tissue specimens related to their diagnosis. Tumor specimens were tested for epigenetic and other molecular characteristics. The ACCESS study was supported by funding from the National Cancer Institute of the National Institutes of Health (NCI/NIH) (R01CA196337, PI: Newcomb, PA), as was the PMH Study (R01CA076366, PI: Newcomb, PA). Additional support for the PMH Study came from the Seattle site of the Colon Cancer Family Registry (SCCFR) (U01CA167551, PI: Jenkins, M, and U01/U24CA074794, PI: Newcomb, PA). Additional support for case ascertainment was provided by the Cancer Surveillance System of the Fred Hutchinson Cancer Center, which is funded by Contract Number HHSN261201300012I; NCI Control Number: N01 PC-2013-00012; Contract Number HHSN261201800004I; and NCI Control Number: N01 PC-2018-00004 from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute with additional support from the Fred Hutchinson Cancer Center and the State of Washington. This research was also supported by the Genomics and Bioinformatics, Comparative Medicine, Specialized Pathology, Collaborative Data Services, and Experimental Histopathology Shared Resources of the Fred Hutch/University of Washington Cancer Consortium (P30 CA015704).Tumor marker testing was performed using formalin-fixed paraffin-embedded diagnostic tumor tissue specimens, and DNA extracted from those specimens. Testing for microsatellite instability (MSI) was based on either a 10-gene panel (BAT25, BAT26, BAT40, MYCL, D5S346, D17S250, ACTC, D18S55, D10S197, BAT34C4) or a 4-marker immunohistochemistry panel of DNA mismatch repair proteins (MLH1, MSH2, MSH6, PMS2). CpG island methylator phenotype (CIMP) testing was based on a validated quantitative DNA methylation assay using a five-gene panel (CACNA1G, IGF2, NEUROG1, RUNX3, SOCS1) or eight-gene panel (CACNA1G, IGF2, NEUROG1, RUNX3, SOCS1, MLH1, CRABP1, CDKN2A). Somatic p.V600E BRAF mutation status was tested for using a fluorescent allele-specific PCR assay. KRAS mutations in codons 12 and 13 were also assessed through forward and reverse sequencing of amplified tumor DNA. DNA was extracted from blood/saliva samples using conventional methods. The genotyping panel completed was the Build37 OncoArray500K-C, including 1%-6% blinded duplicates to monitor the quality of the genotyping. Quality control procedures were performed to 1) make sure that there were no patterns of missing data by batch, study, or plate, 2) check for gender discrepancies and kinship, 3) complete Principal Component Analysis, and 4) test for Hardy-Weinberg equilibrium (HWE). Samples were excluded based on call rate, heterozygosity, unexpected duplicates, gender discrepancy, and unexpectedly high identity-by-descent or unexpected genotypic concordance (>65%) with another individual. In addition, variants were excluded based on call rate (98%), lack of HWE in controls (P

  Study phs002171

• Distribution of data using Live Distribution

  Live Distribution Welcome to the documentation for using the Live Distribution feature for distributing data files securely through the EGA platform. This guide will walk you through the process of downloading encrypted files and decrypting them using Crypt4GH. Please follow the steps below to ensure a smooth experience. Before Downloading Create an EGA user. Make sure that you have the permissions to download the dataset of interest. In case you don’t have access, request access to the dataset. Add your Crypt4GH-compatible public key to your EGA account. Please allow a few hours for your public key to be synced with your profile. Afterwards, you will be able to connect to your EGA outbox using the SFTP protocol. Download Graphical User Interface (GUI) You can use any GUI that supports SFTP connections, such as FileZilla, an open-source FTP client. For Filezilla as your GUI, follow these steps to download files: Open FileZilla and access Site Manager (File > Site Manager). Create a new connection with the following settings (Figure 1): Protocol: SFTP - SSH File Transfer Protocol Host: __EGA_OUTBOX_DOMAIN__ Logon Type: Key file User: your EGA username Key file: path/to/your/private_key Figure 1: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the EGA outbox, ensuring seamless data transfers. Click Connect to access your Outbox. This folder serves as your storage space within the EGA cloud, containing files accessible for download in a secure way. Browse the remote directory on the right side of the FileZilla screen. Select the files you wish to download, right-click, and choose Download (Figure 2). Figure 2: Step-by-step process of manually downñoad files from __EGA_OUTBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can downñoad data from the EGA outbox to their local storage by following the depicted steps SFTP command line You can also use the SFTP command line to securely download files from the EGA Outbox. Using SFTP command line client in Linux/Unix Open a terminal window Enter the following command to connect: sftp username@hostname Enter your EGA password To see a list of available sftp commands type help sftp> put – Upload file sftp> get – Download file sftp> cd path – Change remote directory to ‘path’ sftp> pwd – Display remote working directory sftp> lcd path – Change the local directory to ‘path’ sftp> lpwd – Display local working directory sftp> ls – Display the contents of the remote working directory sftp> lls – Display the contents of the local working directory Type get command to download files. For example: get encrypted_file.c4gh Use the bye command to close the connection (SFTP session). Convenient SSH settings Include the following settings in your SSH config file, located in ~/.ssh/config Host __EGA_OUTBOX_DOMAIN__ EGA-outbox hostname __EGA_OUTBOX_DOMAIN__ User username IdentityFile path/to/the/private/key Replace username and path/to/the/private/key with the appropriate settings, and you will be able to connect to the __EGA_OUTBOX_DOMAIN__ simply using sftp EGA-outbox. How to decrypt Files archived at the EGA are encrypted based on Crypt4GH. Hence, to decrypt the files you need to install Crypt4GH. You can install a python implementation of it, with pip install crypt4gh or directly from the Github repository pip install git+https://github.com/EGA-archive/crypt4gh.git After installing Crypt4GH, decrypt files using the following command: crypt4gh decrypt --sk /path/private/key < encrypted_file.c4gh > decrypted_filename The command reads the encrypted file from stdin (with <) and output the decrypted version to stdout (with >). Replace encrypted_file.c4gh and decrypted_filename with the appropriate filenames but make sure to not use the same filename for both reading and writing because your SHELL would then truncate both files before you even read or write. Frequently Asked Questions What username should I use to log in to my outbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username, not your email address. Therefore, if you registered a username different from your email address when creating your EGA account, you must use that username to log in. If you have forgotten your registered username, please, contact our Helpdesk team for assistance. I see that some files in my dataset have 'unavailable' as extension. What should I do? Within your Outbox, you'll find a list of all the datasets available for download. Occasionally, certain files may be marked as "unavailable". These unavailable files can be identified by the "unavailable" extension added to their filenames (e.g. filename.fastq.gz.unavailable.c4gh). If you encounter an unavailable file that you need, please reach out to our Helpdesk. We'll promptly work on making the file accessible for download as soon as possible. Specific to using keys Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

  Documentation access/download/files/live-outbox

• A Genome-Wide Association Study in Patients Experiencing Musculoskeletal Adverse Events on NCIC CTG Trial MA.27 Evaluating Aromatase Inhibitors as Adjuvant Therapy in Early Breast Cancer. A Collaboration Between the NIH Pharmacogenetics Research Network and the RIKEN Yokohama Institute Center for Genomic Medicine.

  Source of Patients The source of the patients for this genome-wide case control study was MA.27, which was conducted as a multi-cooperative group effort under the auspices of the NCI Breast Cancer Intergroup of North America. The NCIC Clinical Trials Group (CTG) serves as the coordinating group, with participation by the NCI-sponsored North Central Cancer Treatment Group, Eastern Cooperative Oncology Group (ECOG), Southwest Oncology Group, and Cancer and Leukemia Group B (CALGB). MA.27 involved postmenopausal women with histologically confirmed and completely resected invasive breast cancer with surgical margins clear of invasive carcinoma and DCIS in the following TMN categories (AJCC Version 6): pT1, pT2, pT3; pNx, pN0, pN1, pN2, pN3 (only when the sole basis is presence of 10 or more involved axillary nodes); MO. The primary tumor must have been ER and/or progesterone receptor positive. Patients were stratified by lymph node status at diagnosis, prior adjuvant chemotherapy, and trastuzumab use and were randomized to 5 years of adjuvant therapy with anastrozole or exemestane. The trial was activated on May 26, 2003, and reached its accrual objectives on July 31, 2008, after the randomization of 6827 North American patients, with the majority (79%) providing DNA and consent for genetic testing. Non-North American patients were also entered by the International Breast Cancer Study Group but they did not contribute DNA. From 2003 to December 21, 2004, patients also underwent a second randomization to celecoxib 400 mg twice daily or placebo but, after the entry of 1,622 patients, this treatment was discontinued because of reports of increased cardiovascular risk associated with celecoxib. Case Definition for Musculoskeletal Adverse Events Cases were defined to have had at least one of the following six MSAE): 1. pain - joint, 2. pain - muscle, 3. pain - bone, 4. arthritis, 5. joint - function, 6. musculoskeletal - other. Cases were required to have at least grade 3 toxicity, according to the NCI Common Terminology Criteria for Adverse Events v3.0, or go off treatment for any grade of the six MSAEs; and were required to have had an MSAE within the first two years (i.e., a MSAE occurring after 2 years was not be considered a case). Subjects were excluded from the case group who met the case definition while on celecoxib or in the three months after stopping celecoxib but subjects were allowed to be cases if they developed their first MSAE more than three months after stopping celecoxib. Study Design The design was a nested matched case-control study with two controls selected for each case. Random matching from the potential control group (i.e., risk-set sampling such that controls had at least the same length of followup as the timing of the case event) was done by matching the case on the following factors: 1. treatment arm (exemestane vs. anastrozole, blinded), 2. prior adjuvant chemotherapy (yes vs. no), 3. age at start of treatment (+/- 5 years), 4. Celecoxib allocation (yes vs. no). Attempts were made to exactly match each case with two controls according to the above criteria. If, however, controls could not be exactly matched, "close" matching was used to define a distance between each case and all potential controls in an optimal matching algorithm. Race is a potential confounding factor, and 94% of the MA.27 population is Caucasian, 3% are Black, 1% Asian, 1% American Indian, and 1% are of unknown race. Because there are so few non-Caucasian participants, was restricted to only Caucasians. Following identification of cases and controls, the list was sent to the CALGB Pathology Coordinating Office (PCO) at Ohio State University Medical Center for plating of the samples. Other patient factors that might influence the association of SNP genotypes with a MSAE, either as confounding factors or as effect modifiers, were included as investigational covariates. These include 1. body mass index (BMI), 2. bisphosphonate use, 3. fractures in past 10 years, 4. baseline ECOG performance status, 5. prior hormone replacement therapy, 6. prior adjuvant radiotherapy, and 7. prior taxane treatment. An Honest Broker Process was utilized to ensure patient confidentiality. Genotype Assays and Quality Control A GWAS was performed with the Illumina Human610-Quad at the RIKEN Center for Genomic Medicine. The DNA for all of the samples were adjusted to a concentration of 50 to 100 ng/&#181;L in a final volume of 50 &#181;L. The 96-well plates were designed to have a unique distribution pattern with x duplicates per plate to evaluate genotype concordance. Samples with &lt;98% call rate were removed from analyses. SNPs with ambiguous call will be excluded using the following criteria: 1. low signal intensity of each allele, 2. low cluster separation score, 3. SNP call rate &lt;0.98, 4. SNPs with Hardy Weinberg Equilibrium p-value &lt;0.001 were excluded. X-chromosome SNPs were used to validate female sex and quality control measures were examined across the different submitting centers. For all pairs of subjects, the percentage of matching genotypes were computed to screen for unexpected duplicate samples. All SNPs found to be associated with MSAE were subjected to visual inspection of the cluster plots to verify that the genotype clustering was adequate.

  Study phs000210

• Osteoporotic Fractures in Men (MrOS)

  The Osteoporotic Fractures in Men Study (MrOS) is a multi-center prospective, longitudinal, observational study of risk factors for vertebral and all non-vertebral fractures in older men, and of the sequelae of fractures in men. The original specific aims of the study include: (1) to define the skeletal determinants of fracture risk in older men, (2) to define lifestyle and medical factors related to fracture risk, (3) to establish the contribution of fall frequency to fracture risk in older men, (4) to determine to what extent androgen and estrogen concentrations influence fracture risk, (5) to examine the effects of fractures on quality of life, (6) to identify sex differences in the predictors and outcomes of fracture, (7) to collect and store serum, urine and DNA for future analyses as directed by emerging evidence in the fields of aging and skeletal health, and (8) define the extent to which bone mass/fracture risk and prostate diseases are linked. The MrOS study population consists of 5,994 community dwelling, ambulatory men aged 65 years or older from six communities in the United States (Birmingham, AL; Minneapolis, MN; Palo Alto, CA; Monongahela Valley near Pittsburgh, PA; Portland, OR; and San Diego, CA). Inclusion criteria were designed to provide a study cohort that is representative of the broad population of older men. The inclusion criteria were: (1) ability to walk without the assistance of another, (2) absence of bilateral hip replacements, (3) ability to provide self-reported data, (4) residence near a clinical site for the duration of the study, (5) absence of a medical condition that (in the judgment of the investigator) would result in imminent death, and (6) ability to understand and sign an informed consent. To qualify as an enrollee, the participant had to provide written informed consent, complete the self-administered questionnaire (SAQ), attend the clinic visit, and complete at least the anthropometric, DEXA, and vertebral X-ray procedures. There were no other exclusion criteria. The baseline examination was completed over a 25-month period from March 2000 to April 2002. Participants completed questionnaires regarding medical history, medications, physical activity, diet, alcohol intake, and cigarette smoking. Objective measures of anthropometric, neuromuscular, vision, strength, and cognitive variables were obtained at a clinic visit. Skeletal assessments included DEXA, calcaneal ultrasound, and vertebral radiographs. Vertebral and proximal femoral QCT was performed on a subset (65%) of participants. Serum, urine, and whole blood specimens were collected. During the study period, participants complete a tri-annual questionnaire every four months that obtains information concerning the occurrence of incident falls and fractures. Follow-up is over 99% complete. All reported fractures are confirmed using physician review of radiology reports or study radiologist review of x-rays. All deaths are also centrally adjudicated using death certificates and recent hospitalization records. In additional to the baseline study visit and tri-annual questionnaires, all surviving active participants were invited to follow-up visits and questionnaires. A second comprehensive clinic visit was completed between March 2005 and May 2006 or about 4.5 years after baseline. Most baseline measures, including vertebral radiographs, were repeated at the second visit. A third comprehensive clinic visit was completed between March 2007 and March 2009 or about 7.0 years after baseline. Many baseline measures were repeated at the third visit. A measure of energy expenditure was also obtained utilizing an accelerometer based armband device. Surviving participants have also completed two extensive mailed questionnaires designed to updated information obtained at clinic visits. The first questionnaire was mailed between baseline and visit 2 between July 2002 and March 2004. The second questionnaire was mailed after the third questionnaire between March 2009 and February 2011. The MrOS Sleep Study, an ancillary study of the parent MrOS cohort, was conducted between December 2003 and March 2005 and recruited 3,135 MrOS participants for a comprehensive sleep assessment. Men were screened for nightly use of mechanical devices during sleep including pressure mask for sleep apnea (CPAP or BiPAP), mouthpiece for snoring or sleep apnea, or oxygen therapy and were generally excluded. The sleep visit included objective measures of sleep using actigraphy and polysomnography. Other measures at the sleep visit include neuropsychiatric measures, performance measures, health related questionnaires, routine exam measures previously completed at MrOS clinic visits and a urine and serum specimen collection. During the sleep study period, participants complete a tri-annual questionnaire every four months that contains information about incident cardiovascular events. All reported events are centrally adjudicated utilizing hospital records, study ECGs and other supporting documentation. The Hip Osteoarthritis study, another ancillary study of the parent MrOS cohort, was completed at the second clinic visit between March 2005 and May 2006. Participants were asked for consent for a hip x-ray and osteoarthritis measurements were obtained from these x-rays. DNA was extracted from baseline whole blood samples for use in genetic research. Consent for use of DNA was obtained through written consent. Raw GWAS data is housed at the study&#39;s coordinating center and not released as part of the dbGAP release. The raw data can be requested from the study&#39;s coordinating center. Only QC cleaned Plink files are available on dbGAP.

  Study phs000373

• NHLBI TOPMed: SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS)

  Subpopulations and Intermediate Outcome Measures in COPD Study Description Subpopulations and intermediate outcome measures in COPD study (SPIROMICS) supports the prospective collection and analysis of phenotypic, biomarker, genetic, genomic, and clinical data from subjects with COPD for the purpose of identifying subpopulations and intermediate outcome measures. It is funded by the National Heart, Lung, and Blood Institute and is coordinated by the University of North Carolina at Chapel Hill. Molecular fingerprinting and extensive subject phenotyping will be performed to identify disease subpopulations and to identify and validate surrogate markers of disease severity, which will be useful as intermediate outcome measures for future clinical trials. Secondary aims are to clarify the natural history of COPD, to develop bioinformatic resources that will enable the utilization and sharing of data in studies of COPD and related diseases, and to create a collection of clinical, biomarker, radiographic, and genetic data that can be used by external investigators for other studies of COPD. Participating Institutions: Research participants for SPIROMICS will be enrolled, phenotyped, and followed at twelve SPIROMICS Clinical Centers: Columbia University, Temple University, Johns Hopkins University, Wake Forest University, University of Michigan, University of Illinois at Chicago, University of Iowa, University of Utah, National Jewish Health, University of California at San Francisco, and University of California at Los Angeles. The University of North Carolina at Chapel Hill serves as the Genomics and Informatics Center. The Radiology Reading Center is based at the University of Iowa. The PFT Reading Center is based at the University of California at Los Angeles. Study Design: SPIROMICS is a prospective cohort study that enrolled approximately 2,981 participants at twelve clinical centers over five years. Participants are distributed across four enrollment strata (i.e., Never-smokers, Smokers without COPD, Mild/Moderate COPD, and Severe COPD). Study Visits: Participants have up to four in-person visits (Baseline and Annual Clinic Visits at years 1, 2, 3 after Baseline). Study questionnaires and spirometry are completed at all main study visits. Blood and urine samples are collected at visits 1, 2, and 4. Sputum samples are collected at Visit 1. The CT scans are completed and Baseline and Visit 2. Participants also receive quarterly follow-up calls to assess health status and determine if an exacerbation has occurred. Substudies Bronchoscopy and Immunophenotyping Substudy The Bronchoscopy Substudy will enroll 50 subjects per site, for a total of 300 participants. These participants will be recruited across all four study strata. This substudy includes two study visits. During the first visit, sputum samples are collected for Immunophenotyping analysis at the Immunophenotyping Core Lab based at the University of Michigan. Participants then return for a second visit during which samples are collected via bronchoscopy, including bronchoalveolar lavage, epithelial brushings and bronchial biopsies. Immunophenotyping analysis is also conducted on BAL and blood collected during the bronchoscopy study visit. Repeatability Substudy The entire baseline clinic visit was repeated on 98 volunteers to determine reliability of measurement procedures. All baseline study-related procedures and questionnaires, including the CT scan, were re-administered and new samples of blood, urine, saliva, and sputum were collected. Field center staff processed these biospecimen samples according to the same protocol. Exacerbation Substudy The Exacerbation Substudy is a prospective, longitudinal observational study of up to 400 participants, which will allow the assessment of participant-driven health care utilization (HCU) events and symptom-defined exacerbation events over time. HCU-driven events are defined by change in medical treatment in response to a perceived COPD Exacerbation. Symptom-based events will be defined by using EXACT-PRO (EXacerbations of Chronic Pulmonary Disease Tool - Patient Reported Outcome), a daily symptom diary developed to measure the frequency, severity, and duration of exacerbations in clinical trials. All participants are provided with a PDA on which to complete the diary. Participants reporting a possible COPD exacerbation will be brought into the study clinic for a study visit to collect biological specimens and questionnaire data. The overall objectives of the Exacerbation Substudy are to: Obtain clinical data and specimens on SPIROMICS participants before and during an acute COPD exacerbation defined by Health care utilization triggered by the subject, or Symptomatic change (triggered by an EXACT defined threshold) Describe symptomatic changes occurring around HCU and symptom-defined (EXACT) events and their relationship to clinical and specimen data, Characterize the non-exacerbation "stable" state in COPD using the EXACT, including: Symptom variability (EXACT), Clinical data and specimen parameters during a stable state (baseline), The relationship between clinical and specimen data and symptom severity and variability. Explore the characteristics of stable patients, defined as those who do not experience HCU or symptom-defined (EXACT) events during the sub-study period, using baseline clinical data and specimens, and compare these characteristics with patients who experience HCU and/or symptom-defined events. Examine the relationship between HCU and symptom-defined exacerbation frequency during the first one-year period of follow-up for exacerbations and health outcomes, including 12-month change in lung function and COPD health status, and longer-term morbidity and mortality, with the latter derived from long-term data from the larger SPIROMICS study.

  Study phs001927