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• Coronary Artery Risk Development in Young Adults (CARDIA-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.Related Studies: Other CARDIA data available include:phs000236.v2.p2 PAGE: CALiCo: Coronary Artery Risk Development in Young Adults(CARDIA) phs000309.v3.p2 The CARDIA-GENEVA Study phs000399.v1.p2 NHLBI GO-ESP: Heart Cohorts Exome Sequencing Project (CARDIA) phs000613.v1.p2 NHLBI CARDIA Candidate Gene Association Resource (CARe) phs003675.v1.p1 CARDIA Multi-omics Obesity & CVD Substudy – Year 20 Untargeted Metabolomics Data phs001612.v3.p2 NHLBI TOPMed: CARDIA (Coronary Artery Risk Development in Young Adults) phs003045.v1.p1 Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Coronary Artery Risk Development in Young Adults Study (CARDIA) phs000285.v4.p3 CARDIA_Cohort Available Data: Available data for request include exam data through Year 30 and outcomes and death data through 2022. Additionally, data from 28 ancillary studies are available for request.Objectives: The original objectives of CARDIA were to document levels of risk factors for coronary artery disease and potential determinants of these risk factors in young adults; to study the interrelationships of risk factors and lifestyles and to document behavioral and environmental changes during the transition from young adulthood to middle age; to compare cross-sectional and longitudinal data on age-related trends in cardiovascular disease risk factors; and to compare levels and evolution of risk factors between men and women, blacks and whites, and in groups of differing socioeconomic status. Goals of the study have evolved to emphasize understanding determinants of left ventricular mass, emerging obesity and hypertension, and sequelae of hypertension in pregnancy.Background: CARDIA is designed to increase understanding of contributors to changes in cardiovascular disease (CVD) risk factors during the critical years of transition from young adulthood to middle age. CARDIA was funded initially in 1983 for a five-year cycle that included two rounds of examinations. Contract renewals have allowed for subsequent re-examinations.Participants: Black and white men and women; ages 18-30 years at entry with a range of attained education; original sample size: 5,115.Design: CARDIA is a population-based observational study of 5,115 participants aged 18-30 years recruited in 1985-1986. The sample was designed to achieve approximately balanced subgroups of race, gender, education (high school or less and more than high school) and age (18-24 and 25-30). Forty percent of the cohort had no more than a high school education. A total of nine examinations have been completed in the cohort with examination cycles at year 2 of the project and years 5, 7, 10 , 15, 20, 25, and 30.In addition to standard measurements of blood pressure, anthropometry, blood lipids, smoking behavior, physical activity, diet, pulmonary function, and many psychological factors, CARDIA has other included measurements (in subsets or in the full cohort) to obtain unique information on other aspects of risk factor development and early morbidity. These have included: graded exercise treadmill testing; echocardiography, particularly for measurement of left ventricular mass; cardiovascular reactivity; serum cotinine; Lp(a), apoE phenotype, apolipoprotein A1 and B; homocysteine; skin reflectance; body composition by dual X-ray absorptiometry; glucose tolerance testing; vascular resistance and compliance; and plasma renin activity and sympathetic nervous system activity.

  Study phs003739

• FEGA and European GDI: working together to improve human health

  The Federated EGA is a global resource for discovery of and access to sensitive human omics and associated data consented for secondary use, through a network of human data repositories to accelerate biomedical research and improve human health. The Federated EGA network was launched in September 2022 with five inaugural nodes, and since 2023 seven operational nodes can share data across national borders in adherence to European and national laws. A few weeks after FEGA's official launch, in November 2022, the European Genomic Data Infrastructure (GDI) project was kicked-off . This European Commission co-funded project, coordinated by ELIXIR, is aimed to deliver federated, sustainable and secure data infrastructure to access genomic and related phenotypic and clinical data across Europe. This project supports the aim of the 1+MG initiative (25 EU countries, Norway and UK) to enable personalised medicine and health through a shared framework and infrastructure for securely accessing and integrating high quality genomic data and other health data across borders.1+MG will be an integral component of the European Health Data Space (EHDS) for secondary use (Healthdata@EU) as an authorised participant. How are Federated EGA and GDI similar? Given their shared visions, the Federated EGA and European GDI networks have a lot in common. To begin, they share the same overall goal of establishing networks of “nodes” that host sensitive human data within a jurisdiction and connecting these nodes in a global network to support data discovery and promote human genome data access for research and healthcare. FEGA and GDI are both initially focused on enabling access to human genomic data, while FEGA ambition is to expand the scope to clinical research data and other omics data in the future. FEGA and GDI are both built on open and interoperable software solutions, a subset of which are based on the LocalEGA components. FEGA and GDI implementation solutions are based on international community standards, for example those developed by the Global Alliance for Genomics and Health, which contributes to making them interoperable. Both FEGA and GDI allow for the nodes to make use of any solution that is fully compatible. As illustrated in Figure 1, a final key point is the significant overlap of institutions involved in envisioning and operating FEGA and GDI nodes, with the clear wish to keep them interoperable in the future. What distinguishes Federated EGA from GDI? Despite being largely similar, there are some differences between the FEGA and GDI networks, which we aim to clarify in this post. The first difference is the governance model that the nodes will operate to comply with national laws and GDPR. In the FEGA network, nodes have taken inspiration from the EGA data access model, where the infrastructure is a data processor of the hosted data and data controllership remains with the originating Data Access Committees (i.e. Data controllers) for each dataset. On the other hand, in the GDI network the controllership of datasets will be transferred to a 1+MG European Digital Infrastructure Consortium (EDIC) legal entity created by the Member States (MS), who will make data access decisions, with data holders having veto powers for their datasets. Importantly, FEGA nodes have the flexibility to choose another model to fit with their data protection framework, including becoming data controllers for their datasets. The second difference is the inclusion criteria for data. While FEGA nodes are designed to accept almost any type of omics data in need of control access (e.g. genomics, transcriptomics, genotyping, single cells sequencing, patient-tracked metagenomics), GDI nodes are initially, but not only, focused on accepting whole genome and exome sequencing data and affiliated data from sources such as (i) the Genome of Europe use case of the 1+MG initiative which specifically aims to fulfil the mission of building “a European network of national genomic reference cohorts of at least 500,000 citizens”) (ii) data collection of other types of genomic data identified by countries through the 1+MG dashboard and (iii) genomic data coming from data holders which would need to fulfil EHDS requirements. The third difference is the maturity of the software stacks. The FEGA network provides a set of software for data and metadata submission, storage, permissions management, and file distribution (the LocalEGA package). GDI is building a complete set of open source reference for the five functionalities covering the full data life cycle (a few more compared to the LocalEGA) including federated processing (analytics, AI/ML), which is still under active development. Notably, one of the GDI functionalities - storage and interfaces - can be satisfied by using the LocalEGA storage solution, highlighting the ability of FEGA and GDI to be interoperable. In both FEGA and GDI, nodes are allowed to use alternative solutions as long as they are fully interoperable with the networks. Figure 1 provides a simple overview of these described commonalities and differences. Figure1: schematic overview of the main commonalities and difference among a FEGA and a GDI node Can the same institute run a FEGA and a GDI node at the same time? The answer is Yes! We believe this is entirely possible and encouraged. As long as 1+MG requirements are continuously fulfilled in the implementation. The same trained personnel could operate the infrastructure and leverage the same national funding to run a FEGA and a GDI node. Several European nodes, especially GDI vanguard nodes like Norway, Sweden, Finland and Spain are following this model. The idea is that a lot of the work can be reused, given the overlapping scope and the interoperable technology. Thus, the hosted datasets can be discovered via the two catalogues, perhaps accessible under the same or different governance models. From a FEGA node perspective, the GDI datasets could simply be a subset of the datasets hosted in the FEGA node, which have their specific governance as any other. What's next? So, the amazing teams of people building the Federated EGA network and the European GDI network are working together to create infrastructures able to provide secure access to human genomic and associated data around Europe. Because we all know how much human genomics can improve healthcare and precision medicine. And we all want to collaborate to make it happen. This article has been reviewed collectively by members of the EGA and the GDI coordination team.

  Blog fega-and-gdi

• Age related Macular Degeneration (AMD) - Michigan, Mayo, AREDS, Pennsylvania (MMAP) Cohort Study: Association and Sequencing Studies

  Age-related Macular Degeneration (AMD) is a leading cause of incurable blindness in people over the age of 65. AMD is a late-onset multi-factorial neurodegenerative disease and its pathogenesis involves interaction of genetic and environmental factors. Several chromosomal regions have been associated with AMD susceptibility through linkage analysis (Swaroop et al., 2009). More recent studies provide strong evidence that variants within the CFH gene cluster on chromosome 1 and at/near LOC387715/ARMS2 on chromosome 10 are strongly associated with the disease. Variants at other genes including C2/BF, C3, CFI and APOE4, also contribute to AMD susceptibility. Our primary goals are to identify genetic variants and haplotypes that are associated with AMD. The underlying hypothesis is that DNA variation(s) in multiple genetic susceptibility loci will predispose individuals to AMD pathogenesis, and comparison of DNA of cases and controls should identify these susceptibility variants. Our studies are focused on the genetic analysis of advanced AMD and should provide novel insights into disease diagnosis, progression and pathology. We have assembled a collaborative group of researchers from the University of Michigan, Mayo Clinic, University of Pennsylvania, and the AREDS group including National Eye Institute intramural investigators, who collected clinical data and DNA from a large number of patients affected with AMD and from unaffected controls. The primary source of funding was National Eye Institute. Study 1: To identify genetic variants and haplotypes that are associated with AMD, we submitted and obtained usable genotyping data on 2185 patients and 1155 controls from the Center for Inherited Disease Research (CIDR). Study 2: To identify rare coding variants associated with a large increase in risk of AMD, 10 candidate loci spanning 57 genes were sequenced in 2,335 cases and 789 controls. Probes were designed to capture 96.5% of the coding sequence and 35% of total locus sequence, generating an average 123Mb of on-target sequence per individual at 127x average depth. Substudies: phs000182 AMD-MMAP Cohort Study: A Joint Genome-Wide Asscociation Study phs000246 Fuchs' Corneal Dystrophy GWAS phs000457 MMAP Methylation in AMD phs000685 Age-Related Macular Degeneration Targeted Sequencing Study

  Study phs000684

• Dac for "Combined single-cell transcriptomics and T-cell receptor sequencing reveal heterogeneity of mycosis fungoides between and within patients and identify a CD4+ cytotoxic subtype"

  Dac for "Combined single-cell transcriptomics and T-cell receptor sequencing reveal heterogeneity of mycosis fungoides between and within patients and identify a CD4+ cytotoxic subtype" with Name: Menghong Yin Email address: menghong.yin@dkfz-heidelberg.de Associated affiliation: Translational Skin Cancer Research Lab (DKFZ)

  Dac EGAC50000000168

• ATAC-seq, H3K27ac CUT&Tag, and UMI-4C in LCLs derived from constitutional MLH1 epimutation carriers and non-carrier relatives

  ATAC-seq, H3K27ac CUT&Tag, and UMI-4C in lymphoblastoid cell lines derived from constitutional MLH1 epimutation carriers and non-carrier relatives

  Study EGAS50000000499

• Single-cell RNA-sequencing for peripheral blood mononuclear cells and plasma proteomics data from COVID-19 patients and healthy controls of Japanese

  We performed single-cell RNA-sequencing of peripheral blood mononuclear cells and measured plasma protein expression from COVID-19 patients and healthy controls.

  Study JGAS000783

• H3Africa - Trauma And Neurobiology: Maternal stress and transgenerational impact. Search for Epigenetic Markers

  This project aims to characterize the transgenerational genomic impact of genocide exposure and post-traumatic stress disorder (PTSD) in women survivors of the Rwandan genocide and their offspring.

  Study EGAS00001006645

• Foregut Microbiome and Risk of Gastric Intestinal Metaplasia, and Gastric Cancer Risk

  The study is composed of two sub-studies. The first study, conducted by NYU Grossman School of Medicine, is a case-control study of 89 cases with gastric intestinal metaplasia (IM) and 89 matched controls who underwent upper gastrointestinal endoscopy at three sites affiliated with NYU Langone Health. Shotgun metagenomic sequencing using oral wash samples from the 89 case-control pairs and antral mucosal brushing samples from 55 case-control pairs were conducted. The other study, conducted by Vanderbilt University Medical Center, is a prospective case-control study on the oral microbiome and gastric cancer risk, utilizing shotgun metagenomic sequencing data of pre-diagnostic buccal samples from 319 Asians, 118 African Americans and 51 European Americans. This study includes data from three population-based studies: the Southern Community Cohort Study (SCCS), the Shanghai Women's Health Study (SWHS), and the Shanghai Men's Health Study (SMHS). The SWHS and SMHS include 74,941 women aged 40-70 years and 61,480 men aged 40-74 years, respectively, who were permanent residents of Shanghai and recruited to the cohorts between 1997 and 2006. The SCCS is an ongoing prospective cohort study, including ~86,000 participants recruited during 2002 and 2009 from 12 Southeastern states. Nearly 67% of study participants are African Americans (AAs) and the remaining are European American (EA) subjects.

  Study phs002566

• Children's Hospital of Philadelphia (CHOP) Control Copy Number Variation (CNV) Study

  We present a database of copy number variations (CNVs) detected in 2,026 disease-free individuals, using high-density, SNP-based oligonucleotide microarrays. This large cohort analyzed for CNVs in a single study using a uniform array platform and computational tools, comprises mainly of Caucasians (65.2%) and African-Americans (34.2%), We have catalogued and characterized 54,462 individual CNVs, 77.8% of which were identified in multiple unrelated individuals. These non-unique CNVs mapped to 3,272 distinct regions of genomic variation spanning 5.9% of the genome; 51.5% of these were previously unreported, and >85% are rare. Our annotation and analysis confirmed and extended previously reported correlations between CNVs and several genomic features such as repetitive DNA elements, segmental duplications and genes. We demonstrate the utility of this data set in distinguishing CNVs with pathologic significance from normal variants. Together, this analysis and annotation provides a useful resource to assist with the assessment of CNVs in the contexts of human variation, disease susceptibility, and clinical molecular diagnostics. The CNV resource is available at: http://cnv.chop.edu. Reprinted from Shaikh T., et al., High-Resolution Mapping and Analysis of Copy Number Variations in the Human Genome: A Data Resource for Clinical and Research Applications Genome Research. 2009, with permission from Genome Research. CHOP CNVs from 2,026 disease-free individuals are available through dbVar at http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd21.

  Study phs000199

• Identification and characterisation of pathogenic and non-pathogenic FGF14 repeat expansions

  Repeat expansions in FGF14 cause autosomal dominant late-onset cerebellar ataxia (SCA27B) with estimated pathogenic thresholds of 250 (incomplete penetrance) and 300 AAG repeats (full penetrance), but the sequence of pathogenic and non-pathogenic expansions remains unexplored. Here, we demonstrate that STRling and ExpansionHunter accurately detect FGF14 expansions from short-read genomic data using outlier approaches. By combining long-range PCR and nanopore sequencing in 169 patients with cerebellar ataxia and 802 controls, we compare FGF14 expansion alleles, including interruptions and flanking regions. Uninterrupted AAG expansions are significantly enriched in patients with ataxia from a lower threshold (180-200 repeats) than previously reported based on expansion size alone. Conversely, AAGGAG hexameric expansions are equally frequent in patients and controls. Distinct 5' flanking regions, interruptions and pre-repeat sequences correlate with repeat size. Furthermore, pure AAG (pathogenic) and AAGGAG (non-pathogenic) repeats form different secondary structures. Regardless of expansion size, SCA27B is a recognizable clinical entity characterized by frequent episodic ataxia and downbeat nystagmus, similar to the presentation observed in a family with a previously unreported nonsense variant (SCA27A). Overall, this study suggests that SCA27B is a major overlooked cause of adult-onset ataxia, accounting for 23-31% of unsolved patients. We strongly recommend re-evaluating pathogenic thresholds and integrating expansion sequencing into the molecular diagnostic process.

  Study EGAS50000000481