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• Whole exome sequencing of 184 unrelated subjects with 22q11.2 deletion syndrome (DiGeorge syndrome/velo-cardio-facial syndrome)

  Our goal is to find genetic modifiers of major phenotypes in patients with 22q11.2 deletion syndrome, also known as DiGeorge syndrome or velo-cardio-facial syndrome. Whole exome sequencing was performed as part of a contract to the NHLBI, Resequencing and Genotyping Service. We have obtained cardiac phenotype information from the de-identified subjects enrolled in the study, either by echocardiography report or medical doctor report. All of the subjects have a 3 million base pair 22q11.2 deletion flanked by low copy repeats, LCR22, A-D.

  Study phs000987

• Drug screening of patient-derived organoids from colorectal peritoneal metastases

  Colorectal cancer (CRC) patients with peritoneal metastases (CRPM) have limited treatment options and the lowest CRC survival rates. We trialed the possibility of organoid directed precision treatment for CRPM patients. CRPM organoids (peritonoids) isolated from patients underwent next-generation sequencing and medium-throughput drug panel testing ex vivo to identify specific drug sensitivities for each patient. We measured the utility of such a service including: success of peritonoid generation, time to cultivate peritonoids, reproducibility of the medium-throughput drug testing, and documented changes to clinical therapy as a result of the testing. Peritonoids were successfully generated and validated from 68% (19/28) of patients undergoing standard care. Genomic and drug profiling was completed within 8 weeks and a formal report ranking drug sensitivities was provided to the medical oncology team upon failure of standard care treatment. This resulted in a treatment change for 2 patients, one of whom had a partial response despite previously progressing on multiple rounds of standard care chemotherapy. The barrier to implementing this technology in Australia is the need for drug access and funding for off-label indications. In conclusion, our approach is feasible, reproducible and can guide novel therapeutic choices in this poor prognosis cohort, where new treatment options are urgently needed. This platform is relevant to many solid organ malignancies.

  Study phs002023

• NHLBI TOPMed - NHGRI CCDG: The BioMe Biobank at Mount Sinai

  The IPM BioMe Biobank, founded in September 2007, is an ongoing, broadly-consented electronic health record (EHR)-linked clinical care biobank that enrolls participants non-selectively from the Mount Sinai Medical Center patient population. BioMe currently comprises >42,000 participants from diverse ancestries, characterized by a broad spectrum of longitudinal biomedical traits. Participants are enrolled through an opt-in process and consent to be followed throughout their clinical care (past, present, and future) in real-time, allowing us to integrate their genomic information with their EHRs for discovery research and clinical care implementation. BioMe participants consent for recall, based on their genotype and/or phenotype, permitting in-depth follow-up and functional studies for selected participants at any time. Phenotypic and genomic data are stored in a secure database and made available to investigators, contingent on approval by the BioMe Governing Board. BioMe uses a "data-broker" system to protect confidentiality. Ancestral diversity - BioMe participants represent a broad racial, ethnic and socioeconomic diversity with a distinct and population-specific disease burden. Specifically, BioMe participants are of African (AA), Hispanic/Latino (HL), European (EA) and other/mixed ancestry. BioMe participants are predominantly of African (AA, 24%), Hispanic/Latino (HL, 35%), European (EA, 32%), and other ancestry (OA, 10%). Participants who self-identify as Hispanic/Latino further report to be of Puerto Rican (39%), Dominican (23%), Central/South American (17%), Mexican (5%) or other Hispanic (16%) ancestry. More than 40% of European ancestry participants are genetically determined to be of Ashkenazi Jewish ancestry. With this broad ancestral diversity, BioMe is uniquely positioned to examine the impact of demographic and evolutionary forces that have shaped common disease risk. Phenotypes available in BioMe - BioMe has a high-quality and validated set of fully implemented clinical phenotype data that has been culled by a multi-disciplinary team of experienced investigators, clinicians, information technologists, data-managers, and programmers who apply advanced medical informatics and data mining tools to extract and harmonize EHRs. BioMe, as a cohort, offers a great versatility for designing nested case-control sample-sets, particularly for studying longitudinal traits and co-morbidity in disease burden. Biomedical and clinical outcomes: The BioMe Biobank is linked to Mount Sinai's system-wide Epic EHR, which captures a full spectrum of biomedical phenotypes, including clinical outcomes, covariate and exposure data from past, present and future health care encounters. As such, the BioMe Biobank has a longitudinal design as participants consent to make all of their EHR data from past (dating back as far as 2003), present and future inpatient or outpatient encounters available for research, without restriction. The median number of outpatient encounters is 21 per participant, reflecting predominant enrollment of participants with common chronic conditions from primary care facilities. Environmental data: The clinical and EHR information is complemented by detailed demographic and lifestyle information, including ancestry, residence history, country of origin, personal and familial medical history, education, socio-economic status, physical activity, smoking, dietary habits, alcohol intake, and body weight history, which is collected in a systematic manner by interview-based questionnaire at time of enrollment. The IPM BioMe Biobank contributed ~10,600 DNA samples for whole genome sequencing to the TOPMed program. Samples were selected for the Coronary Artery Disease (CAD) and the Chronic Obstructive Pulmonary Disease (COPD) working groups. Using a Case-Definition-Algorithm (CDA), we identified ~4,100 individuals with CAD (~50% women) and ~3,000 individuals as controls (65% women). In addition, we identified ~800 individuals with COPD (62% women) and 1800 individuals as controls (72% women). Another 600 BioMe participants with Atrial Fibrillation, all of African ancestry, were included.

  Study phs001644

• Tools

  EGA Tools This page provides details of the applications available at the EGA. Submission Tool Description EgaCryptor Encrypt and create md5sum values. Crypt4gh Encrypt, decrypt or re-encrypt files. INBOX Upload your files once encrypted. FTP Upload your files once encrypted. Submitter Portal Submit and manage your submission metadata online (SEQUENCING AND PHENOTYPE ONLY) Submitter Portal API Submit and manage your submission metadata programmatically. Webin Submit and manage your submission metadata online (array data) Webin API Submit metadata using XML files. Aimed to array-based submissions. Star2xml Format conversion tool to transform tabular formats (e.g. spreadsheets) into EGA's XMLs. Used for large programmatic submissions. Data Access Tool Description DAC Portal Manage and control data access request. Download Tool Description PyEGA3 Download and decrypt datasets requested through your EGA web account. Live Outbox Download and decrypt datasets requested through your EGA web account. Fuse Client A quick and convenient way to browse through EGA files without the need for complete downloads. EGA QuickView A quick and convenient way to browse through EGA files without the need for complete downloads.

  Documentation tools

• Genetic analysis of HLA and immune escape genes in Diffuse Large B-cell Lymphoma

  Fifty percent of diffuse large B-cell lymphoma (DLBCL) cases lack cell-surface expression of the class-I major histocompatibility complex (MHC-I), thereby escaping immune recognition by cytotoxic T cells. In order to comprehensively identify the mechanisms involved in MHC-I loss, we first performed immunophenotypic analysis of both MHC class-I and -II in 657 cases across the spectrum of B-NHL, revealing that loss of MHC-I, but not MHC-II, is preferentially restricted to DLBCL. We then used whole exome and targeted deep-sequencing to examine genes involved in MHC-I expression in 74 DLBCL samples representative of MHC-I positive and negative cases. We show here that somatic biallelic or monoallelic inactivation of B2M and/or HLA-I is present in 80.9% (34/42) of MHC-I negative tumors. Furthermore, 68.8% (22/32) of MHC-I positive DLBCLs also harbored monoallelic HLA-I genetic alterations (MHC-I positivemono) that lead to allelic imbalance, suggesting allele-specific inactivation. Both MHC-I negative and MHC-I positivemono cases showed a significantly higher mutational burden as well as a higher number of inferred neo-antigens, suggesting co-selection of HLA-I loss and sustained neo-antigen production. Interestingly, the analysis of > 500.000 individuals in two databases revealed homozygosity of germline HLA-I genes in 26-38% of DLBCL patients, a frequency significantly higher than that observed in any other cancer type. In mice, germinal-center B cells lacking HLA-I expression did not progress to lymphoma and were counterselected in the context of oncogene-driven lymphomagenesis, suggesting that additional events are needed to license immune evasion. These results suggest a multi-step process of HLA-I loss including both germ-line and somatic events in DLBCL development, and have direct implications for the pathogenesis and immunotherapeutic targeting of this disease.

  Study EGAS00001005054

• Everything you need to know about Federated EGA Affiliates: Q&A

  Addressing common questions about Federated EGA (FEGA) Affiliates and providing answers. FEGA Affiliates have recently been defined as the third tier within the newly approved “Federated EGA Structure and Organisation” document, alongside Central EGA (CEGA) and the FEGA Nodes. In this blog post, we aim to answer the questions that may arise on this topic. For more general information about FEGA Affiliates, you can refer to this blog post. Let's now start with the questions and answers! What is the difference between a FEGA Affiliate and a FEGA Node? FEGA Nodes A FEGA Node is a national-level service that accepts submissions and provides archiving and access services within a single country's jurisdiction. Nodes provide end-to-end services for submitters in that jurisdiction, including submission support, processing, permissions management, and distribution. FEGA Affiliates A FEGA Affiliate is not a national submission service. Instead, it archives data from its own legal entity and/or selected partner institutions (for example, consortium partners). Affiliates are designed for organisations and consortia that need to keep data stewardship and access decisions within their own governance model, while still enabling discovery through the broader FEGA ecosystem. What do Affiliates commit to? Here is a practical checklist of the core expectations for a FEGA Affiliate. FEGA Affiliate checklist Discovery and metadata Submit non-personal metadata to CEGA to obtain EGA accessions and enable discovery (with metadata validation handled by the CEGA Helpdesk). Operations and resilience Maintain a data storage system and a defined backup/recovery approach. Ensure operational capacity (personnel and infrastructure) to deliver Affiliate services. Maintain systems and practices that support local data security and align with EGA best-practices. Access and user support Provide data distribution or access (e.g in a trusted research environment) to authorised users to datasets hosted at the Affiliate. Provide permissions management for datasets archived by the Affiliate (the CEGA DAC (Data Access Committee) Portal is always at your disposal to use). Provide a minimal helpdesk with sufficient capacity to support relevant DACs and approved users. Membership term Commit to a minimum membership term of four years. Governance and participation (lightweight by design) Affiliates integrate into the FEGA network operationally, but without the committee obligations associated with Nodes. When is becoming an Affiliate the right fit? Consider the Affiliate tier if: You host (or plan to host) controlled-access datasets that must be managed under your organisation’s or consortium’s governance, you want studies to be discoverable through EGA, but need access control and/or distribution to remain local, and you are not aiming to operate a national submission service tier.. What if a FEGA Node already exists in my jurisdiction? We encourage any potential Affiliate to reach out to their national FEGA Node to explore collaboration at the national level. Why? Federated models within a country already exist in FEGA, where a national Node operates like a Central EGA for their country. FEGA Nodes can also represent the interests of any Affiliates within their jurisdiction at the FEGA Committees. FEGA Nodes have experience and can support you on your FEGA journey. Ready to join as a FEGA Affiliate? We’re excited to open this formally defined pathway for organisations and consortia to participate in the federation. If you think the FEGA Affiliate tier fits your needs, start by assessing: Your scope (organisation/consortium-held data rather than public external submissions), Your ability to provide the core services (permissions, distribution/access, helpdesk, monitoring), and Your readiness to submit non-personal metadata to CEGA for accessioning and discovery. To express interest, please contact us here, and we'll help you understand the onboarding steps and how Affiliates integrate into the FEGA network.

  Blog fega-affiliates-q&a

• WES of melanoma tumours prior to combined immune checkpoint blockade treatment.

  Paired melanoma tumor and normal (PBMC) WES data from a cohort of 26 patients subsequently treated with combined immune checkpoint blockade.

  Dataset EGAD00001005941

• The Sea Islands Genetic Network (SIGNET): Identifying genetic contributors to diabetes and dyslipidemia in African Americans

  Recent genome-wide association studies (GWAS) have successfully identified genetic variants that influence diabetes risk in European populations, however most do not have a major impact on diabetes risk in populations of African descent. The African American (AA) population from the Sea Islands of coastal South Carolina and Georgia has high rates of type 2 diabetes, low levels of admixture, and in general, consume a diet rich in saturated fats. We postulate that this unique combination of ancestral and environmental factors results in a more consistent penetrance of diabetes risk alleles, as well as enrichment of risk alleles of African origin. The existing DNA samples and rich phenotypic data from the Sea Island Families Project comprise a unique resource for genetic studies of type 2 diabetes and related metabolic traits such as dyslipidemia. Our central hypothesis is that the increased risk for T2DM in AA compared with European American (EA) is due, in part, to susceptibility alleles of African origin, and that these alleles can be identified using a GWAS. The Specific Aims are to: 1) Identify genetic risk factors for type 2 diabetes utilizing DNA samples and data from the Sea Island Families Project, Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study recruited from SC, GA, NC, and AL; and a GWAS approach; 2) Identify genetic contributors to lipoprotein subclasses in African Americans using the lipoprotein subclass profile (particle size and concentration for multiple subclasses of VLDL, LDL, and HDL) assessed by NMR at LipoScience, Inc., and the GWAS data from Aim 1. The rationale for this project is that identification and validation of novel pathophysiological pathways and informed selection of candidate genes for diabetes risk will inform development of new, targeted prevention and treatment strategies in this underserved, high risk population.

  Study phs000433

• PYDP Papuan Y chromosome Diversity Panel

  The PYDP dataset includes 26 bam files of Y chromosome sequences for Papua New Guinean individuals from different locations, extracted from whole genome sequences. DNA was extrated from saliva samples (Oragen kit). Sequencing libraries were prepared using the TruSeq DNA PCR-Free HT kit. 150 bp paired-end sequencing was performed on the Illumina HiSeq X5 sequencer.

  Dataset EGAD00001008572

• Verification of BCR reconstruction from single-cell RNA-seq using BraCeR

  This dataset was made to verify the computational reconstruction of B cell reseptors from single-cell RNA-seq using BraCeR. The dataset contains BCR-derived reads from single-cell RNA-seq from 13 cells using the Smart-seq2 protocol, as well as targeted BCR-sequencing data from the same cells.

  Dataset EGAD00001004199