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• Breast Cancer -Very young women with ER+ tumor

  Corresponding data set is composed of whole exome sequencing of Korean ER positive breast cancer under 35. This set provides 100 alignment files from normal-tumor paired whole exome sequencing of 50 patients. This is a part of total project data set.

  Dataset EGAD00001002256

• Sleep Abnormalities in Rare Genetic Disorders: Angelman Syndrome, Rett Syndrome, and Prader-Willi Syndrome

  This research study will investigate sleep behavior in the rare neurological disorders Angelman Syndrome (AS), Rett Syndrome (RTT), Prader-Willi Syndrome (PWS) and Early-onset Morbid Obesity (EMO). Sleep is very important to proper health and plays a critical role in learning, memory, brain development and brain function. Sleep disturbances can have negative effects on health and the quality of life of children and their families. Sleep disorders are common in individuals with AS, RTT, PWS and EMO. As a result, individuals with AS, RTT, PWS and EMO are at risk for sleep-related health problems that can worsen existing daytime behaviors and lead to additional problems with learning and memory. The principal objectives of the study are to: Characterize sleep behavior in individuals with Angelman Syndrome, Rett Syndrome, Prader-Willi Syndrome and Early-onset Morbid Obesity. Compare sleep behavior in these individuals with sleep behavior in normal controls Assess the natural history of sleep behavior in individuals with Angelman Syndrome, Rett Syndrome, Prader-Willi Syndrome and Early-onset Morbid Obesity About this Study This is a questionnaire study that will evaluate the sleep behavior of individuals with AS, RTT or PWS (participants), or unaffected siblings of individuals with AS, RTT or PWS (controls). Those participating in the study will already be registered with the Rare Disease Clinical Research Network (RDCRN) and have a confirmed diagnosis of AS, RTT or PWS or be an unaffected sibling. We will look at the medical records on file with the RDCRN to confirm diagnosis of AS, RTT or PWS. Study participants will participate in the study at their regularly scheduled RDCRN clinic visits. Participants will be asked to complete questionnaires at the study visits that will document their sleep behaviors. The questionnaires are not difficult and participants should be able to complete them in approximately 15-30 minutes. Parents and legal guardians are allowed to assist participants or answer on their behalf. Questionnaires will be administered at the initial study visit and at a 12 month follow-up visit to document any changes in sleep behavior. We hope to enroll approximately 1400 individuals with either AS, RTT or PWS or their unaffected siblings.

  Study phs001292

• 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

• DNA Methylation Analysis of Peripheral Blood Cells from Siblings Discordant for ASD

  Autism is a common disorder whose causes are poorly understood. Both genetic and environmental influences are thought to act together causing autism. Epigenetics is an area that bridges genetic and environmental influencing and commonly is studied by examining dynamic methylation changes to the DNA. We have screened a total of 78 autistic boys and their fathers by this method in 807 genes and find 116 methylation changes that together can correctly identify nearly 80% of the affected boys from their fathers. We propose to confirm these exciting data in a much larger set of genes in 1,200 autistic boys and their families, including unaffected brothers. These data not only could provide new insight into the causes of autism but could also result in a screening test for autism.

  Study phs000619

• Sampling of multi-centric lower grade glioma influences management and provides insight into gliomagenesis

  Rare multicentric lower-grade gliomas (LGG) represent a unique opportunity to study the heterogeneity between distinct tumor foci in a single patient, and to infer their origins and parallel patterns of evolution. In this study, we integrate clinical features, histology and immunohistochemistry for 4 patients with multicentric LGG, arising both synchronously and metachronously. For 3 patients we analyze the phylogeny of the lesions using exome sequencing, including one case with a total of 8 samples from the two lesions. We show that each tumor in multicentric LGG cases may arise independently or may diverge very early in their development, presenting as genetically and histologically distinct tumors suggesting that comprehensive sampling of these lesions can significantly alter diagnosis and management.

  Study EGAS00001002495

• Identification of Modifiers of 22q11.2 Deletion Syndrome in Whole Genome Sequence - CIDR

  This project aims to identify genetic modifiers of congenital heart disease (CHD), which occurs among 22q11.2 deletion syndrome (22q11.2DS; also known as DiGeorge syndrome/velo-cardio-facial syndrome) patients. In contrast to the prevalence of ~0.8% in the general population, about 60%-70% of 22q11.2DS patients have CHD, of which most are conotruncal heart defects, making 22q11.2DS a very ideal model to study the genetic underpins of CHD. The chromosome 22q11.2 region is susceptible to meiotic chromosome rearrangements leading to hemizygous deletions, because there are large blocks of low copy repeats, termed LCR22s, that are dispersed in the region. Non-allelic homologous recombination during meiosis can lead to specific-sized deletions. Approximately 90% have a typical hemizygous 3 million base pair (Mb) deletion between LCR22-A and -D, while some have nested deletions between LCR22-A and B or LCR22-A and C. Our goal is to perform a case-control association study, in which cases have CHD, while controls have normal heart structures, all with 22q11.2DS. All individuals are unrelated and have been collected from collaborators in the US, Canada and Europe. All subjects received a clinical diagnosis of 22q11.2DS and signed informed consent to allow a blood sample to be collected for research. The DNA samples were de-identified in 2 steps to protect their privacy. We then determined deletion type using multiplex ligation-dependent probe amplification (MLPA). This information is provided for the great majority of subjects. We also obtained cardiac phenotype information from echocardiogram reports as well as reported race and ethnicity (Hispanic vs. non-Hispanic information), which is required for the analysis. We will obtain 30X whole-genome sequence data from NIH CIDR. We will first annotate the sequence variants identified to uncover rare ( For genotype data, we will provide the raw fastq data from whole-genome sequencing as well as the called variants file in a VCF file. For phenotype data, we will provide the sample relationship files (ped files), CHD affection status, deletion type, and race.

  Study phs002514

• WTCCC2 case-control study for Ulcerative Colitis

  WTCCC2 project genome-wide case-control association study for Ulcerative Colitis (UC) using the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000084

• Validation_of_a_Haloplex_platform_for_targeted_re_sequencing_of_the_exons_of_25_genes

  We recently used the Agilent SureSelect platform to re-sequence a set of genes known to bemutated in human AML. The results from 10 AML DNA samples were very satisfactory, butthe effort required was significant.Thus, we decided to re-sequence the same genes using the Haloplax system for targetenrichment in 48 AML samples. We planned to do this using MiSeq and have data from apilot of 3 samples. The data is promising but coverage appears pathcy so far.However, in order to get a better understanding of the data we will need deeper sequencing. Wewill need two lanes of HiSeq to get the same degree coverage as Sureselect.his data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001000285

• Germline MBD4 Mutations and Predisposition to Uveal Melanoma

  Uveal melanoma (UM) arises from malignant transformation of melanocytes from the uveal tract. This rare tumor has a poor outcome with frequent liver metastases and primary resistance to treatment1. The only strong predisposing condition for UM is the BAP1 tumor predisposition syndrome due to germline BAP1 deleterious mutations. UMs are generally characterized by a very low tumor mutation burden (TMB). However, some UMs display a high level of CpG to TpG mutations associated with MBD4 inactivation. Here, we explored the incidence of germline MBD4 variants in a consecutive series of 1, 099 primary UMs, and identified 8 deleterious MBD4 mutations, an aprox 10-fold increased incidence compared with the general population. These mutations were associated with a high TMB in tested UMs. We thus demonstrated that MBD4 is the second major predisposing gene for UM.

  Study EGAS00001003941

• Genomewide Association Study of Inflammatory Bowel Disease

  WTCCC genome-wide case-control association study for Inflammatory Bowel Disease (IBD) using the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000006