11925 results for "coins fc Visit Buyfc26coins.com for latest FC 26 coins news..yU53"

in 21.82 milliseconds.

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library for library A95728A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 844 samples; filetype=bam

  Dataset EGAD00001004758

• Mapping of runs back to samples for snRNASeq data

  Mapping of runs back to samples for PDN, PDO and TIS snRNASeq data. 4 sets of matched PDN, PDO and TIS samples (12 unique samples total) underwent pooled snRNASeq (Parse Biosciences). 2 sublibraries were created from this and sequenced at separate times

  Dataset EGAD00001015609

• Combination Therapies for Personalised Cancer Medicine in drug resistant EGFR mutant lung cancer (2019-06-10)

  This study involves mutagenizing a range of different cell lines with ENU to identify those mutations which engender resistance to targeted treatment. . This dataset contains all the data available for this study on 2019-06-10.

  Dataset EGAD00001005080

• Single-Cell Data from "Spatiotemporal T-cell tracking for personalized T-cell receptor T-cell therapy designs in childhood cancer"

  This dataset contains the FASTQ files from the Single-Cell GEx+TCR 10X Genomics data from the study "Spatiotemporal T-cell tracking for personalized T-cell receptor T-cell therapy designs in childhood cancer" (EGAS00001008174)

  Dataset EGAD00001015632

• Genomic Origins and Admixture in Latinos (GOAL)

  The GOAL Study was designed to comprehensively evaluate the genomic architecture of diverse Hispanic/Latino individuals with origins from the Caribbean, Central and South America, and to understand the impact of this diversity on genetic disease studies. Population structure and admixture are key confounders in genome-wide association and medical resequencing studies. In particular, accounting for difference in ancestry among cases and controls, both in terms of genomic and geographic location, is critical for proper analysis and interpretation of studies with multi- and trans-ethnic samples. Genomic studies of Hispanics/Latinos, the largest and fastest growing minority group in the US, reveal that they are a highly genetically heterogeneous admixed group with immense variation among individuals and populations in the proportions of African, European, and Native American ancestry. Knowledge of the underlying complex genetic structure of US Hispanic/Latino and Caribbean populations is, therefore, essential to ensuring robustness of genotype-phenotype associations and understanding the medical relevance of associated variants across diverse populations in the US and throughout the Americas. Furthermore, since much is known about the African and European migrations into the Americas over the past 500 years, population genetic studies of Hispanics/Latinos serve as an excellent model for developing novel algorithms and approaches for characterizing fine-scale genetic structure of admixed populations, in general. This project extends current studies of population genetic structure in US Hispanics/Latinos by densely genotyping parent-offspring triads from individuals (sampled in the US) of Caribbean-descent from six Latin American countries: Puerto Rico, Cuba, Dominican Republic, Haiti, Honduras and Colombia. This study will provide immediate insights and new statistical methods to improve study design and genetic analysis for medical genomic studies in Hispanics/Latinos, other complex admixed groups, and multi- and trans-ethnic studies.

  Study phs000750

• PROGRESS/ELEMENT DNA Methylation Study

  An extension to the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) birth cohort of Mexico City, the Programming Research in Obesity, GRowth, Environment and Social Stress (PROGRESS) Cohort is an ongoing longitudinal pre-birth cohort, established in 2006 in Mexico City, partnering Icahn School of Medicine at Mount Sinai with Harvard University and the National Institute of Public Health in Mexico, which was designed to study the effects of prenatal exposure to toxic metals, air pollution, phthalates, and stress on childhood development. Pregnant women of 18 years of age and older, pregnant for less than 20 weeks of gestation, had no documentation of heart or kidney disease, no use of steroids or anti-epilepsy drugs, no daily alcohol consumption, had telephone access, and planned to live in Mexico city for the following 3 years, and receiving care through the Mexican Social Security System were initially enrolled (n=1,054). In addition to clinical, demographic and exposure data collected, cord blood was collected to interrogate DNA methylation across the genome for over 300 mother-child dyads. Clinical assessments and exposures were captured during several life stages, including prenatal, infant (0-1 year), youth (1-18 years), and adulthood (mother). The PROGRESS cohort added well-documented phenotyping of children for obesity, metabolic dysfunction, respiratory outcomes, and cardiovascular outcomes, as well as measures of air pollutant, personal care/consumer product, non-chemical stress, and metal mixture exposures. No clinical trials were conducted in this cohort. The data collected in this study should provide a unique resource to investigate DNA methylation as it relates to several environmental exposures and adverse cardiometabolic and neurocognitive health in mothers and children from a prospective birthing cohort. For access to demographic, clinical, and exposure data please directly contact study principal investigators.

  Study phs002754

• Center Common Disease Genomics [CCDG] - Cardiovascular: Partners Biobank

  This study is a part of NHGRI's Center for Common Disease Genomics, a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Current estimates anticipate that the CCDG program will perform whole genome sequencing of 140K individuals and whole exome sequencing of 225K individuals over the course of the project. The Cardiovascular Disease working group of the CCDG considered five diseases: early-onset coronary artery disease (EOCAD), stroke, atrial fibrillation, congestive heart failure and type 2 diabetes. CAD is the leading cause of death in the world and common variant association studies show that our understanding of the underlying molecular mechanisms is incomplete (e.g, >2/3 of the 63 common variants previously mapped for CAD do not directly relate to known risk factors). CAD is representative of a class of common diseases with onset at middle age. When CAD occurs early in life, there is a stronger inherited component. In addition, there is empirical evidence that a burden of rare coding alleles in individual genes contribute to risk for CAD. Source: https://ccdg.rutgers.edu/sites/default/files/CCDG_CVD_EOCAD_STROKE_FINAL.pdf For this study, The Broad Institute of MIT and Harvard generated genetic data for ~13,500 individuals from the Partners Biobank, including using whole exome sequencing and whole genome genotyping (Infinium Global Screening Array), with 550 samples from patients with myocardial infarction, a type of EOCAD. Partners Biobank, launched in 2010, is a biobank composed of samples from Partners hospitals, including Massachusetts General Hospital and Brigham and Women's Hospital. The Biobank has enrolled >100K individuals to study how genes, lifestyle, and other factors affect people's health and contribute to disease.

  Study phs002018

• Integration of Genomics and Transcriptomics in unselected Twins and in Major Depression

  Our goals are to develop a comprehensive understanding of the genomics of transcription in a population based unselected sample and to discover DNA and RNA biomarkers for major depressive disorder (MDD). This work is essential to developing a more complete understanding of the biological basis of MDD, a common complex trait associated with considerable morbidity, mortality, and personal/societal cost. All biological samples have been collected from well-defined populations, and are now available. First, we conduct a "genetical genomics" or eQTL study of ~800 MZ and ~800DZ twin pairs. Each subject has been assayed for genome-wide SNPs and CNVs and gene expression from peripheral blood sampled under standardized conditions. We determine the genetic architecture (genetic and non-genetic proportions of variance via twin analyses) for every transcript, and the genome-wide associations (i.e., SNP-transcript eQTL pairs). These analyses will be expanded to consider transcriptional modules. The key deliverable is a detailed catalogue of the general and specific architecture of transcription plus raw intensity files. Second, we seek to discover DNA and RNA biomarkers relevant to MDD, capitalizing on the results of a large MDD study with repeated clinical and biological assessments; we have previously shown that PB is a reasonable proxy for CNS expression and employ an advanced modelling framework: (a) Using baseline data, we identify biomarkers for MDD by comparing ~1000 controls with ~1400 MDD cases via comparisons of SNP, CNV, expression transcripts, and transcriptional modules. (b) Using longitudinal data, we contrast gene expression signatures assessed at baseline and two years later in ~200 controls and ~500 MDD cases.

  Study phs000486

• Binding of Epstein Barr Virus EBNA2 Unifies Multiple Sclerosis Genetic Mechanisms

  This study is focused on multiple sclerosis (MS). MS is likely caused by a combination of genetic and environmental factors; however, the mechanisms contributing to these factors remain poorly understood. Epstein-Barr virus (EBV) in particular is a well-established environmental risk factor for MS. We have created a computational algorithm that systematically searches for common molecular mechanisms that might be impacted at multiple MS-associated loci. Using this algorithm, we have discovered that over 40% of MS-associated loci contain MS genetic variants that fall within regions of the human genome occupied by the EBV-encoded EBNA2 protein. The same MS-associated variants also impact gene expression levels of MS-associated genes in EBV-infected B cell lines. Our hypothesis is that allele-dependent binding of EBNA2 and its co-factors explains the allele-dependent risk at many MS genetic loci. Importantly, this hypothesis links the genetic associations of MS to the known molecular roles played by EBV and Notch signaling in disease processes.Subjects with and without MS were recruited into an Institutional Review Board (IRB)-approved protocol through the Cincinnati Autoimmune Registry and Repository. From each subject, DNA from peripheral blood was isolated for whole genome sequencing (WGS). These data will be used to identify genotype-dependent transcriptional regulation in cells assessed from patients and controls. This WGS data was deposited before the full integration of sequence data with other functional genomic data types.From each subject, 90 mL of blood is drawn for isolation of peripheral blood mononuclear cells from which primary B and T cells will be isolated. For each B cell isolation, an EBV-transformed B cell line will be derived.

  Study phs003240

• Enhancing Open Data Sharing for Functional Genomics Experiments: Measures to Quantify Genomic Information Leakage and File Formats for Privacy Preservation

  This study was designed to develop methods that address the privacy risk associated with genetic variants obtained from genomic sequencing experiments. The preponderance of readily available genetic sequencing experiments has enabled the generation of datasets whose target purposes range from the genotyping of individuals for medical and forensic analyses, to the acquisition of molecular information for biological model development. However, by virtue of the experimental procedure, the raw sequences are tagged with small bits of patients' private variant information. In addition, depending on the protocol, these experiments may identify sequences from exogenous species. Raw human and microbial sequences can be analyzed as a whole to learn both general facts about biology, but can also be used to determine sensitive information about an individual. This presents a privacy conundrum for data sharing.To address privacy concerns, we have developed several mathematical and computational approaches to quantify the risk of disclosure of sensitive information from genetic sequence data, and to sanitize the associated reads in a manner that optimally maintains the utility of the data. In addition, we have generated environmental samples containing genetic information from consented individuals to evaluate the risk posed by such data. These samples were then used to assess the efficacy of the computational methods. Human-derived and -associated samples that are damaged or obscured by environmental factors represent the scope of samples used for experimentation in this study. Samples include, but were not limited to, swabs of contacted objects and glass slides coated in saliva from consented individuals. Blood samples were also acquired from the same consented individuals to create gold-standard comparison datasets.

  Study phs003166

• How to use the EGA search box

  Tips on how to search Tip 1: Use simple and clear keywords No matter what you are searching: a study, a dataset or a committee; the search will look recursively in all the fields of all the entities in the database. Thus, there is no need to specify which type of data you are looking for. Try to use single and specific words and avoid long and complex expressions. Try it out: cancer rna-seq instead of cancer studies using RNA-seq. Tip 2: Narrow your results using search operators Search operators give you more control over the results. They manage the way keywords are used to restrict the search. By default, the search engine applies an OR operator when a space is inserted between two words. Include AND (capitalised) between the keywords in your search in order to show only pages containing both terms. Try it out: Search all the studies based on transcriptomic data, including those ones that use RNA sequencing to obtain this data. Tip 3: Do not worry about details The search engine automatically checks for the most common spelling mistakes of given words and suggests the use of the correct version. It also ignores capital letters and most punctuation so none of these mistakes will hamper the accuracy of your search. Besides, when using a combination of words, the search engine will suggest similar combinations with a higher number of results. Tip 4: Use words that are likely to appear within the text Unfortunately, our search engine is not able to interpret words yet. Therefore, words which do not appear in the body text will not return any result despite them denoting a group of present terms. For instance, if we are looking for studies focused on neurological diseases, using neurological as a keyword would not be the best option. Instead, you should use the name of each neurological disease such as bipolar disorder, schizophrenia or depression. In the same fashion, incomplete words do not return any result. Please include an asterisk (*) before and/or after the incomplete word in order to allow for partial matching. Try it out: canc* instead of canc(without wildcard). Also you can try out *oma, or even *geneti* Tip 5: Narrow down your search by filtering the results You can filter the results by the following options: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

  Documentation discovery/metadata/search-box

• Resolving the Full Spectrum of Human Genome Variation using Linked-Reads

  Large-scale population based analyses coupled with advances in technology have demonstrated that the human genome is more diverse than originally thought. Standard short-read approaches, used primarily due to accuracy, throughput and costs, fail to give a complete picture of a genome. They struggle to identify large, balanced structural events, cannot access repetitive regions of the genome and fail to resolve the human genome into its two haplotypes. Here we describe an approach that retains long range information while harnessing the power of short reads. Starting from only 1ng of DNA, we produce barcoded short read libraries. The use of novel informatic approaches allows for the barcoded short reads to be associated with the long molecules of origin producing a novel datatype known as 'Linked-Reads'. This approach allows for simultaneous detection of small and large variants from a single Linked-Read library. We have previously demonstrated the utility of whole genome Linked-Reads (lrWGS) for performing diploid, de novo assembly of individual genomes. In this manuscript, we show the utility of reference based analysis using a single Linked-Read library for full spectrum genome analysis. We demonstrate the ability of Linked-Reads to reconstruct megabase scale haplotypes and to recover parts of the genome that are typically inaccessible to short reads, including phenotypically important genes such as STRC, SMN1 and SMN2. We demonstrate the ability of both lrWGS and Linked-Read Whole Exome Sequencing (lrWES) to identify complex structural variations, including balanced events, single exon deletions, and single exon duplications. The data presented here show that Linked-Reads provide a scalable approach for comprehensive genome analysis that is not possible using short reads alone.

  Study EGAS00001003121

• UK10K NEURO FSZ

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. These Finnish schizophrenia samples have been collected from a population cohort using national registers. The entire sample collection consists of 2756 individuals from 458 families of whom 931 are diagnosed with schizophrenia spectrum disorder, each family having at least two affected siblings. 170 families originate from an internal isolate (Kuusamo) with a three-fold lifetime risk for the trait. The genealogy of the internal isolate is well documented and the individuals form a “megapedigree” reaching to the 17th Century. All diagnoses are based on DSM-IV and for a large fraction of cases there is cognitive data. For further details/descriptions with regard to this data set please contact Tiina Paunio (tiina.paunio@thl.fi)

  Study EGAS00001000118

• 'Targeted High Throughput Sequencing in Clinical Cancer Settings: Formaldehyde fixed-paraffin embedded (FFPE) tumor tissues, input amount and tumor heterogeneity'

  "Background: Massively parallel sequencing technologies have brought an enormous increase in sequencing throughput. However, these technologies need to be further improved with regard to reproducibility and applicability to clinical samples and settings. Methods: Using identification of genetic variations in prostate cancer as an example we address three crucial challenges in the field of targeted re-sequencing: Small nucleotide variation (SNV) detection in samples of formalin-fixed paraffin embedded (FFPE) tissue material, minimal amount of input sample and sampling in view of tissue heterogeneity.Results: We show that FFPE tissue material can supplement for fresh frozen tissues for the detection of SNVs and that solution-based enrichment experiments can be accomplished with small amounts of DNA with only minimal effects on enrichment uniformity and data variance. Finally, we address the question whether the heterogeneity of a tumor is reflected by different genetic alterations, e.g. different foci of a tumor display different genomic patterns. We show that the tumor heterogeneity plays an important role for the detection of copy number variations, but is of minor importance for the detection of somatic variations.Conclusions: The application of high throughput sequencing technologies in cancer genomics opens up a new dimension for the identification of disease mechanisms. In particular the ability to use small amounts of FFPE samples available from surgical tumor resections and histopathological examinations facilitates the collection of precious tissue materials. However, care needs to be taken in regard to the locations of the biopsies taken, which can have an influence on the prediction of copy number variations. Bearing these technological challenges in mind will significantly improve many large-scale sequencing studies and will - on a long-range - result in a more reliable prediction of individual cancer therapies."

  Study EGAS00001000136

• Enhanced detection of circulating tumor DNA by fragment size analysis

  Existing methods to improve detection of circulating tumor DNA (ctDNA) have focused on sensitivity for detecting genomic alterations but have rarely considered the biological properties of plasma cell-free DNA (cfDNA). We hypothesized that differences in fragment lengths of circulating DNA could be exploited to enhance sensitivity for detecting the presence of ctDNA and for non-invasive genomic analysis of cancer. We surveyed ctDNA fragment sizes in 344 plasma samples from 200 cancer patients using low-pass whole-genome sequencing (0.4×). To establish the size distribution of mutant ctDNA, tumor-guided personalized deep sequencing was performed in 19 patients. We detected enrichment of ctDNA in fragment sizes between 90–150 bp, and developed methods for in vitro and in silico size selection of these fragments. Selecting fragments between 90–150 bp improved detection of tumor DNA, with more than 2-fold median enrichment in >95% of cases, and more than 4-fold enrichment in > 10% of cases. Analysis of size-selected cfDNA identified clinically actionable mutations and copy number alterations that were otherwise not detected. Identification of plasma samples from patients with advanced cancer was improved by predictive models integrating fragment length and copy number analysis of cfDNA, with AUC> 0.99 compared to AUC < 0.80 without fragmentation features. Increased identification of cfDNA from patients with glioma, renal, and pancreatic cancer was achieved with AUC > 0.91, compared to AUC < 0.5 without fragmentation features. Fragment size analysis and selective sequencing of specific fragment sizes can boost ctDNA detection. This could reduce the required depth of cfDNA sequencing for clinical applications, earlier diagnosis and study of tumor biology.

  Study EGAS00001003258

• Spiradenocarcinoma

  • Spiradenocarcinoma is a rare cutaneous sweat gland adnexal cancer with potential for aggressive behaviour. They are classified histologically into low- and high-grade tumours, with morphologically low-grade tumours thought to behave more favourably. However, limited information is available, with only 18 published cases. • We have collected morphologically low-grade spiroadenocarcinomas (one with a lung metastasis) and high-grade spiroadenocarcinomas, as well as some spiradenomas (benign lesions), cylindromas (another type of malignant cutaneous sweat gland adnexal tumour) and hybrid spiradenoma-cylindromas. • H&E-stained sections were reviewed, follow-up was obtained, and immunohistochemistry for Ki-67, p53 and, MYB has been performed. The tumours were solitary, measuring 0.8-7?cm (median: 2.7?cm), with a predilection for the head and neck of elderly patients (median age: 72 years; range 53-92) without gender bias. Histologically, the tumours were multinodular and located in deep dermis and subcutis. A pre-existing spiradenoma was present in all cases. The malignant component was characterized by expansile growth with loss of the dual cell population, up to moderate cytological atypia and increased mitotic activity (median: 10/10 HPF; range 1-28). Additional findings included squamoid differentiation (n=9), necrosis (n=7), and ulceration (n=5). P53 expression was variable and no significant differences were noted in the benign compared with the malignant parts of the tumours. In contrast, in the malignant components the Ki-67 proliferative index was slightly increased, and MYB expression was lost. Follow-up (median: 67 months; range: 13-132) available for 16 patients (84%) revealed a local recurrence rate of 19% but no metastases or disease-related mortality. • Here we wish to exome sequence these cases to define the first genomic landscape for this malignancy.

  Study EGAS00001001799

• Analysis of error profiles in deep next-generation sequencing data

  BackgroundSequencing errors are key confounding factors for detecting low-frequency genetic variants that are important for cancer molecular diagnosis, treatment, and surveillance using deep next-generation sequencing (NGS). However, there is a lack of comprehensive understanding of errors introduced at various steps of a conventional NGS workflow, such as sample handling, library preparation, PCR enrichment, and sequencing. In this study, we use current NGS technology to systematically investigate these questions.ResultsBy evaluating read-specific error distributions, we discover that the substitution error rate can be computationally suppressed to 10-5 to 10-4, which is 10- to 100-fold lower than generally considered achievable (10-3) in the current literature. We then quantify substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing by using multiple deep sequencing datasets. We find that error rates differ by nucleotide substitution types, ranging from 10-5 for A>C/T>G, C>A/G>T, and C>G/G>C changes to 10-4 for A>G/T>C changes. Furthermore, C>T/G>A errors exhibit strong sequence context dependency, sample-specific effects dominate elevated C>A/G>T errors, and target enrichment PCR led to aprox 6-fold increase of overall error rate. We also find that more than 70% of hotspot variants can be detected at 0.1% - 0.01% frequency with the current NGS technology by applying in-silico error suppression.ConclusionsWe present the first comprehensive analysis of sequencing error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS analysis accuracy both experimentally and computationally, ultimately enhancing the precision of deep sequencing.

  Study EGAS00001003444

• eMERGE Network Genome-Wide Association Study of Red Cell Indices, White Blood Count (WBC) Differential, Diabetic Retinopathy, Height, Serum Lipid Levels, Specifically Total Cholesterol, HDL (High Density Lipoprotein), LDL (Low Density Lipoprotein), and Triglycerides, and Autoimmune Hypothyroidism.

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of five participating sites (Group Health Seattle, Marshfield Clinic, Mayo Clinic, Northwestern University, and Vanderbilt University) funded by the NHGRI to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 19,000 individuals using EMR-derived phenotypes and DNA from linked Biorepositories. Using electronic phenotyping methods, the consortium used DNA samples from all participating sites to explore the genetic determinants of red cell indices, white blood count (WBC) differential, diabetic retinopathy, height, serum lipid levels, specifically total cholesterol, HDL (high density lipoprotein), LDL (low density lipoprotein), and triglycerides, and autoimmune hypothyroidism as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes. eMERGE led studies for which original genotyping was performed and are included in this merged set: Genome-Wide Association Study on Cataract and HDL in the Personalized Medicine Research Project Cohort, phs000170 Development and Use of Network Infrastructure for High-Throughput GWA Studies, phs000234 Vanderbilt Genome-Electronic Records (VGER) Project: QRS Duration, phs000188 Northwestern NUgene Project: Type 2 Diabetes, phs000237 A Genome-Wide Association Study of Peripheral Arterial Disease, phs000203 Sites and participants include: Vanderbilt University: BioVU, Vanderbilt's DNA databank, is an enabling resource for exploration of the relationships among genetic variation, disease susceptibility, and variable drug responses, and represents a key first step in moving the emerging sciences of genomics and pharmacogenomics from research tools to clinical practice. BioVU acquires DNA from discarded blood samples collected from routine patient care. The biobank is linked to de-identified clinical data extracted from Vanderbilt's EMR, which forms the basis for phenotype definitions used in genotype-phenotype correlations. Marshfield Clinic: The Marshfield Clinic Personalized Medicine Research Project is a population-based biobank in central Wisconsin with more than 20,000 adult subjects who provided written, informed consent to access their medical records and provided a blood sample from which DNA was extracted and plasma and serum stored. In addition to an average of 30 years of medical history data, a questionnaire about environmental exposures, including a detailed food frequency questionnaire, is available to facilitate gene/environment studies. Northwestern University: The NUgene Project is a repository with longitudinal medical information from participating patients at affiliated hospitals and outpatient clinics from the Northwestern University Medical Center. Participants' DNA samples are coupled with data from a self-reported questionnaire and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 2 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators. Group Health(GH)/University of Washington (UW): Aging and Dementia eMERGE study biorepository leverages rich population-based longitudinal data from both electronic medical records and in-depth research data to explore genome wide associations. Participants include Seattle-area members of GH (a large integrated health care system in Washington State) consented and enrolled in 1) the UW Alzheimer's Disease Patient Registry (ADPR) and 2) the Adult Changes in Thought (ACT) study. The ADPR (PI: Eric B. Larson; NIH/NIA U01 AG 006781) is a population-based registry of incident dementia cases designed to identify all new Alzheimer's Disease cases within GH from 1987 to 1996. Medical history, physical, laboratory testing, and neuropsychological testing were performed on all consenting potential cases for determination of dementia status by a consensus conference. The study base of the ADPR population was stable with an attrition rate of less than 1%/year. The ACT study (PI: Eric B. Larson; NIH/NIA U01 AG 006781) is an ongoing community-based cohort study of aging and dementia. The original cohort of 2,581 randomly selected dementia-free members age 65 and older was enrolled in 1994-1996 and expanded by 811 in 2000-2002. Continuous enrollment to maintain a cohort of 2,000 dementia free persons began in 2005. Participants receive biennial assessment including cognitive status determination. The ACT sub-sample is stable; for the original cohort, median enrollment in GH was 19 years prior to joining the ACT study, and 85% of the cohort has ≥10 years of GH enrollment. DNA for the ADPR participants were obtained through a companion study, Genetic Differences in Cases and Controls (PI: Walter Kukull; NIH/NIA R01 AG007584). DNA obtained through both studies were extracted from blood using Gentra Systems Puregene methods. DNA concentration is determined by UV optical density. All samples are checked for quality by 260/280 ratio. For long-term storage, samples are aliquoted and stored at -70°C. Mayo Clinic: The Mayo biobank is a disease-specific biobank for vascular diseases including peripheral arterial disease (PAD). PAD patients were identified from individuals referred to the non-invasive vascular laboratory for lower extremity arterial evaluation. Since 1997, laboratory findings have been recorded into an electronic database employing an in-house software package for data archiving and retrieval; this data becomes part of the Mayo EMR. Patients referred to the center with suspected PAD undergo a comprehensive non-invasive evaluation including the ankle-brachial index (ABI) - the ratio of blood pressure measured in the upper arms divided by blood pressure measured at the ankles. Controls subjects are identified from patients referred to the Cardiovascular Health Clinic for stress ECG. The prevalence of PAD in patients with normal exercise capacity who do not have inducible ischemia on the stress ECG, was <1%. Data regarding risk factors for atherosclerosis such as diabetes, dyslipidemia, hypertension, and smoking are ascertained from the EMR.

  Study phs000360

• Epidemiological study comparing rates and risk factors for dementia in African Americans in Indianapolis and Yoruba living in Ibadan, Nigeria

  In 1991 collaboration between researchers at Indiana University School of Medicine and the University of Ibadan, Ibadan, Nigeria established the Indianapolis-Ibadan Dementia Project. It is a longitudinal, prospective population-based comparative epidemiological study of the prevalence and incidence rates and risk factors of Alzheimer's disease and other age associated dementias. The project compares samples of community-dwelling elderly (age > 70 years) African Americans living in Indianapolis to Yoruba living in Ibadan, Nigeria, employing the same research design, methods, and investigators. It initially reported significantly lower prevalence rates of disorders in the Yoruba compared to the African Americans. In subsequent waves of the study (1994-1995, 1997-1998) incidence rates, rates of newly diagnosed cases, were also found to be significantly lower in the Yoruba. In genetic studies, the frequency of the APOE 4 allele was about the same in the two groups. APOE 4 was a significant risk factor for Alzheimer's disease and dementia in the Americans, while no association was found for the Yoruba. The APOE 2 allele appears to be protective in the Americans, but not the Yoruba. A constellation of factors often associated with vascular risk including a history of hypertension, diabetes, and high cholesterol levels is less common in the Yoruba than in the American group. An interaction was observed between cholesterol, APOE genotype and Alzheimer's disease in both study groups. In 2001-2002 survivors of the original cohort were once again evaluated (N~800 in each site) and 2,000 additional individuals age 70 years and older were enrolled in each site. Blood samples were collected from approximately 1,500 study participants in each site for genetic studies and analysis of biochemical risk factors for vascular disease. Subsequent waves of field work were conducted in 2004, 2007, 2009, and 2011. This fieldwork followed the classic two-stage study design. The study design involves an in-home screening interview with the study participant, which includes a cognitive assessment, medical history and current medications, brief neurological examination, height and weight, blood pressure measurement and assessment of social involvement. There is also a screening interview with a close relative of the study participant to assess activities of daily living, personality change, and medical history of the study participant. On the basis of the screening interview a sample of study participants (N~500 in each site) is selected for a full clinical diagnostic dementia work up which includes a neurological test battery, extensive interview with a family member and examination by a clinician. Diagnoses are made in a consensus diagnosis conference using the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition (DSM-III R) and International Classification of Diseases 10th Revision (ICD-10) for dementia. The criteria of the National Institute for Neurological and Communicative Disease and Stroke-Alzheimer's Disease and Related Disorders Association were used to diagnose possible and probable Alzheimer's disease. The focus of the study is risk factors for dementia and Alzheimer's disease, but also of particular importance is the question of mild cognitive impairment. This refers to the condition of having some decline in cognition but the decline is not sufficient to meet the criteria for dementia. We have studied this over the course of this project. In follow up studies of individuals who have this diagnosis about one third of them are better at follow up, about a third are about the same, and about a third decline more to meet the criteria for dementia. This is a very important issue for researchers because the ultimate goal of the research is to figure out how to identify the individuals who will definitely progress to dementia. If there are clear identifiers, it would be possible to make interventions, while individuals still function well, and possibly prevent the development of dementia altogether or delay the onset significantly. This is crucial because at the moment individuals usually do not enter into the medical care system until the dementia symptoms are quite severe, and the pathological damage to the brain cannot be undone.

  Study phs000378

• Causative Mutations for TAR DAC

  Dac EGAC00001000008

• Data Access Committee for TraIT

  Dac EGAC00001000212

• Data Committee for EMSEQ project

  Dac EGAC00001000265

• DAC for HTS MRD study

  Dac EGAC00001000359

• DAC for Korean BC data

  Dac EGAC00001000740

• DAC for A Karadimitris Lab

  Dac EGAC00001001003

• Data Access Commitee for EGAS00001003258

  Dac EGAC00001001038

• Data access comittee for Institut Curie

  Dac EGAC00001001042

• DAC for the study EGAS00001003572

  Dac EGAC00001001174

• DAC for hepatitis B analysis

  Dac EGAC00001001228

• DAC for Wigler Group

  Dac EGAC00001001256

• DAC for panc_met data set

  Dac EGAC00001001480

• Center for Medical Genetics Ghent

  Dac EGAC00001001507

• DAC for mUC sequencing data

  Dac EGAC00001001710

• Data Access Commitee for ECA

  Dac EGAC00001001856

• DAC for Hypermutated Gliosarcoma project

  Dac EGAC00001001867

• Data Access Committee for EGAS00001005426

  Dac EGAC00001002194

• Data Access Committee for cfMeDIP

  Dac EGAC00001002250

• Center for Medical Genetics Ghent

  Dac EGAC00001002384

• DAC for intraphepatic cholangiocarcinoma

  Dac EGAC00001002521

• DAC for cfMethyl-Seq data

  Dac EGAC00001002534

• Data access committee for FL

  Dac EGAC00001002552

• Center for Medical Genetics Ghent

  Dac EGAC00001002909

• Center for Medical Genetics Ghent

  Dac EGAC00001003264

• EGAD00010000694

  HCC array for cnv

  Dataset EGAD00010000694

• Data Access Committee for EGAS00001007298

  Dac EGAC00001003422

• Data Access Committee for EGAS00001007299

  Dac EGAC00001003268

• DAC for noninvasive lung cancer subtyping

  Dac EGAC00001003474

• DAC for Molecular Oncology lab

  Dac EGAC50000000268

• DAC for EGAS00001007531

  Dac EGAC00001003437

• UCL Centre for Longitudinal Studies

  Dac EGAC00001003495