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• Clonal haematopoiesis in patients with AAA (2019-04-03)

  Recent advances in genomics have demonstrated that clonal haemopoiesis driven by leukaemia associated somatic mutations is a relatively common phenomenon that increases in frequency with advancing age. Whilst individuals with clonal haemopoiesis have an increased risk of developing haematological malignancies, they also have an increased mortality from other causes. Additionally, certain mutations are almost exclusively seen in individuals aged 70 years or older, whilst others are seen in individuals with non-haematological cancers including breast and ovarian. Recently, clonal haemopoiesis was found to be associated with a significantly increased risk of atherosclerotic cardiovascular disease. This association is thought to be causative with clonally-derived macrophages showing elevated expression of several chemokine and cytokine genes that contribute to atherosclerosis. Another vascular pathology, abdominal aortic aneurysm (AAA), increases with age and shares risk factors with atherosclerosis (including smoking, male sex, high cholesterol). However, the impact of these risk factors and the overlap between AAA and atherosclerosis is poorly understood. To investigate a possible link between clonal haemopoiesis and AAA, we will study DNA samples from 300 patients with AAA and up to 200 controls for evidence of clonal haemopoiesis. This will be done using target DNA enrichment with biotinylated RNA baits followed by high throughput sequencing. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004895

• Profiling of 27 type 2 diabetes GWAS loci using next-generation (NG) capture C in a human beta-cell model

  Most type 2 diabetes association signals are due to variants that impact gene regulation but identifying the genes they impact is challenging. We generated interaction profiles at 27 T2D GWAS loci using next-generation (NG) capture C (6 replicates) in the human beta-cell model, EndoC-betaH1, along with chromatin accessibility profiling using ATAC-Seq (9 replicates).

  Dataset EGAD00001008655

• Mapping_regulatory_variation_in_sensory_neurons_using_IPS_lines_from_the_HIPSCI_project

  Sensory neurons are nerve cells that are activated by sensory input such as heat, light and convey information to the brain. Although a key cell type in complex organisms, human sensory neurons are challenging to study because they are impossible to obtain from living donors. We have collaborated with the Neucentis Pharmaceutical Research Unit to differentiate sensory neuron like cells from human induced pluripotent stem cells derived as part of the Human Induced Pluripotent Stem Cells Initiative. We will sequence RNA from 100 IPS lines derived from healthy individuals and perform RNA-seq on the differentiated cells to identify noncoding variants that alter gene expression in human sensory neurons.

  Study EGAS00001001149

• Stanford Acute Myeloid Leukemia Single-Cell DNA Sequencing

  These are single-cell DNA sequencing data from acute myeloid leukemia samples from Stanford University. They were used in multiple publications to understand clonal evolution in AML.

  Study phs003853

• The Genetic Basis of Aggressive Prostate Cancer, The Role of Rare Variation

  In this study, we will apply a multi-staged approach to reveal genes harboring rare variants that are associated with aggressive PCa. Whole-exome sequencing (Aim 1a) of 2,774 aggressive cases and 2,776 non-aggressive cases of European ancestry will be conducted followed by rare variant analysis of single sites and gene burden testing to identify novel susceptibility loci/genes for aggressive disease. We will validate the most significantly associated genes (~500) through targeted sequencing in an additional 6,415 aggressive and 5,586 non-aggressive cases and 1,890 controls (Aim 1b). Next, we will investigate the clinical predictive utility of the genes/variants identified in 2,291 cases in the STHM3 trial who are undergoing biopsy based on PSA and genetic risk score stratification (Aim 2). Through this tiered approach we expect to significantly advance knowledge of aggressive PCa etiology and health disparities as well as guide the development of early detection and prognostic strategies for the subset of men who are most susceptible to this fatal form of disease. In this case-case study of aggressive vs non aggressive prostate cancer, aggressive cases are defined as prostate cancer as cause of death, (T4 disease or T3 disease) and Gleason 8+. Non-aggressive cases are men with T1/2 disease and Gleason ACKNOWLEDGMENTS and CONTRIBUTING SITES CAPS, PROCAP, STHM1, STHM2: Swedish Cancer Society (CAN 2016/818), Swedish Research Council (2014/2269).STHM3: Stockholm County Council (Stockholms Läns Landsting).MEC: Funding provided by the National Cancer Institute: Understanding Ethnic Differences in Cancer, 2U01CA164973 and The Genetic Basis of Aggressive Prostate Cancer, The Role of Rare Variation, 5R01CA196931-02.ATBC: The ATBC Study is supported by the Intramural Research Program of the U.S. National Cancer Institute, National Institutes of Health, and by U.S. Public Health Service contract HHSN261201500005C from the National Cancer Institute, Department of Health and Human Services.COSM: The Swedish Research Council/National Research Infrastructure Grant (VR 2014/6397; VR 2015/5997) The Swedish Cancer Foundation (CAN 2013/456; CAN 2016/727)CPSII: The authors express sincere appreciation to all Cancer Prevention Study II participants and to each member of the study and biospecimen management group. The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II cohort.MCCS/APCS/PCFS: The Melbourne Collaborative Cohort Study (MCCS) recruitment was funded by VicHealth and Cancer Council Victoria and further supported by Australian National Health and Medical Research Council (NHMRC) grants 209057 and 396414. The Aggressive Prostate Cancer Case-Control Study (APCS) was funded by NHMRC grant 623204. The Prostate Cancer Family Study (PCFS) was fully funded by Cancer Council Victoria. Cancer Council Victoria funds the continuing maintenance and updating of the MCCS, APCS and PCFS. Cases and their vital status are ascertained and followed up through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database.PLCO: The Prostate Lung Colorectal Ovarian Cancer Screening Trial (PLCO) was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and by contracts from the Division of Cancer Prevention, National Cancer Institute, US National Institutes of Health, Department of Health and Human Services. EPIC: The coordination of EPIC is financially supported by International Agency for Research on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam- Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology - ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A19170 and C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford). (United Kingdom). DFCI: Linda and Arthur Gelb and Rebecca and Nathan Milikowsky. HPFS and PHS: The Health Professionals Follow-up Study was supported by U01 167552 and P01 CA228696 from the National Cancer Institute, and with support from the Prostate Cancer Foundation. The Physicians' Health Study was supported by grants CA34944, CA40360, CA097193, HL26490 and HL34595.Northwestern: P50CA180995 (Catalona) 08/01/15 – 07/31/20 NIH/NCI SPORE in Prostate Cancer; The Urological Research FoundationPROMPT: MRC UK - Project reference G0500966, Cambridge BRC infrastructure funding, Cambridge Biomedical Research Campus (BRC-1215-20014), CRUK Cambridge Cancer Centre infrastructure funding (they are requesting this statement is written in blue for publications).ICR: This work was supported by the NIH R01 grant 5R01CA196931-02. The samples from the UK were from UKGPCS and PrompT. The UKGPCS study was supported by Cancer Research UK (grant numbers C5047/A7357, C1287/A10118, C1287/A5260, C5047/A3354, C5047/A10692, C16913/A6135 and C16913/A6835). We would like to acknowledge the NCRN nurses and Consultants for their work in the UKGPCS study. We thank all the patients who took part in this study. We also acknowledge The Institute of Cancer Research, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK for their ongoing support. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust.Funding:CIDR grant X01HG008336

  Study phs001524

• Kids First: Genomics of Orofacial Cleft Birth Defects in Latin American Families

  The Gabriella Miller Kids First Pediatric Research Program) (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). Both childhood cancers and structural birth defects are critical and costly conditions associated with substantial morbidity and mortality. Elucidating the underlying genetic etiology of these diseases has the potential to profoundly improve preventative measures, diagnostics, and therapeutic interventions. All of the WGS and phenotypic data from this study are accessible through dbGaP and https://kidsfirstdrc.org, where other Kids First datasets can also be accessed. The Kids First study of nonsyndromic orofacial cleft (OFC) birth defects in Latin American families is a whole genome sequencing study of 283 Latin-American parent-case trios drawn from ongoing collaborations led by Dr. Mary L. Marazita of the University of Pittsburgh Center for Craniofacial and Dental Genetics, and including a collaboration with Dr. Lina Moreno Uribe and Dr. Andrew Lidral of the University of Iowa. All families were ascertained through the Clinica Noel where patients with OFCs receive care from the Antioquia University School of Dentistry in Medellin, Colombia (key on-site colleagues included Dr. Luz Consuelo Valencia-Ramirez and Dr. Mauricio Arcos-Burgos). Genetic studies have shown that this population is comprised of an admixture of immigrant male Caucasians (mainly Spaniards and Basques) and native Amerindian females. Every subject has had a genetic evaluation, including a pedigree analysis for a family history of clefting and other birth defects, a pregnancy history for environmental exposures, and a complete physical exam to rule out suspected or known syndromes or environmental phenocopies. Sequencing was done by the Broad Institute sequencing center funded by the Kids First program (grant number U24-HD090743). The case in each of the Kids First OFC trios has cleft lip (CL, Figure A below), cleft palate (CP, Figure B), or both (CL+CP, Figure C): OFCs are genetically complex structural birth defects caused by genetic factors, environmental exposures, and their interactions. OFCs are the most common craniofacial anomalies in humans, affecting approximately 1 in 700 newborns, and are one of the most common structural birth defects worldwide. On average a child with an OFC initially faces feeding difficulties, undergoes 6 surgeries, spends 30 days in hospital, receives 5 years of orthodontic treatment, and participates in ongoing speech therapy, leading to an estimated total lifetime treatment cost of about $200,000. Further, individuals born with an OFC have higher infant mortality, higher mortality rates at all other stages of life, increased incidence of mental health problems, and higher risk for other disorders (notably including breast, brain, and colon cancers). Prior genome-wide linkage and association studies have now identified at least 18 genomic regions likely to contribute to the risk for nonsyndromic OFCs. Despite this substantial progress, the functional/pathogenic variants at OFC-associated regions are mostly still unknown. Because previous OFC genomic studies (genome-wide linkage, genome-wide association studies (GWAS), and targeted sequencing) are based on relatively sparse genotyping data, they cannot distinguish between causal variants and variants in linkage disequilibrium with unobserved causal variants. Moreover, it is unknown whether the association or linkage signals are due to single common variants, haplotypes of multiple common variants, clusters of multiple rare variants, or some combination. Finally, we cannot yet attribute specific genetic risk to individual cases and case families. Therefore, the goal of the current study is identify specific OFC risk variants in Latin American families by performing whole genome sequencing of parent-case trios.

  Study phs001420

• Research on the identification of cancer stem cells for peidatric and adult malignancies.

  In recent years, "cancer stem cells" have been reported to exist in many cancers. Many reports have shown that they are the main cause of cancer recurrence. The aim of this study is to test whether cancer stem cells can be identified using patient samples. We will perform omics analysis of rare pediatric cancers by integrating genomics, transcriptomics, proteomics and metabolomics, including somatic and germline mutations, to deepen our understanding of the biological characteristics of the target disease. Based on the knowledge gained and clinical information from patients, we will conduct preclinical pharmacological studies using mouse models and in vitro models to investigate the therapeutic effects of novel therapeutic agents and novel therapeutic strategies such as cellular therapies.

  Study JGAS000623

• UK10K NEURO ASD GALLAGHER

  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. This is an Irish sample set of individuals with ASD (approximately 50% with comorbid intellectual disability). Individuals have been diagnosed with ADI/ ADOS, measures of cognition/ adaptive function. They represent a more severe, narrowly defined cohort of ASD subjects. Family histories are available for some with measures of broader phenotype. For further information on this cohort please contact Nadia Bolshakova (bolshakn@tcd.ie).

  Study EGAS00001000112

• A spatiotemporal cancer cell trajectory underlies glioblastoma heterogeneity

  Abstract: Cancer cells display highly heterogeneous and plastic states in glioblastoma, an incurable brain tumour. However, how these malignant states arise and whether they follow a tractable cellular trajectory across tumours is poorly understood. Here, we generated a deep single cell and spatial multi-omic atlas of human glioblastoma that pairs transcriptomic, epigenomic and genomic profiling of 12 tumours across multiple regions. We identify that glioblastoma heterogeneity is driven by spatially-patterned transitions of cancer cells from developmental-like states towards those defined by a glial injury response and hypoxia. This cancer cell trajectory regionalizes tumours into distinct tissue niches and manifests in a conserved manner across tumours as well as genetically distinct tumour subclones. Moreover, using a new deep learning framework to jointly map cancer cell states and clones in situ, we show that tumour subclones are finely spatially intermixed through glioblastoma tissue niches. Finally, we show that this cancer cell trajectory is intimately linked to myeloid heterogeneity and unfolds across regionalised myeloid signalling environments. Our findings define a stereotyped trajectory of cancer cells in glioblastoma and unify glioblastoma tumour heterogeneity into a tractable cellular and tissue framework.

  Dataset EGAD00001015516

• Characterization of Cell Free Plasma Methyl-DNA From Xenografted Tumors to Guide the Selection of Diagnostic Markers for Early-Stage Cancers

  Circulating cell-free methyl-DNA (mcfDNA) contains promising cancer markers but its low abundance and possibly diverse origin pose challenges toward the accurate diagnosis of early stage cancers. By whole-genome bisulfite sequencing (WGBS) of cell-free DNA (cfDNA) from about 0.5 mL plasma of mice xenografted with human tumors, we obtained and aligned the reads to the human genome, filtered out the mouse and carrier bacterial sequences, and confirmed the tumor origin of methyl-cfDNA (mctDNA) by methylation-sensitive restriction enzyme digestion prior to species-specific PCR. We estimated that human tumor-specific reads (ctDNA) or mctDNA comprised about 0.29 or 0.01%, respectively of the xenograft mouse cfDNA, and about 0.029 or 0.001% of the cfDNA of human early stage cancer patients. Similar WGBS of early stage (0-II, node- and metastasis-free) breast, lung or colorectal cancer samples identified hundreds of specific DMRs (differentially methylated regions) compared to healthy controls. Their association with tumourigenesis was supported by stage-dependent methylation, tumor suppressor or oncogene clusters, and genes also identified in the xenograft samples. Using 20 three-cancer-common and 17 colorectal cancer-specific DMRs in combination (top 0.0018% of the WGBS methylation clusters) was sufficient to distinguish the stage I colorectal cancers from breast and lung cancers and healthy controls. Our data thus confirmed the tumor origin of mctDNA by sequence specificity, and provide a selection threshold for authentic tumor mctDNA markers toward precise diagnosis of early stage cancers solely by top DMRs in combination. Biological Materials were provided by the Ontario Tumour Bank, which is funded by the Ontario Institute for Cancer Research Reference for citation: Ling Liu, Jinghua Feng, Julian Polimeni, Manli Zhang, Hai Nguyen, Urmi Das, Xu Zhang, Harminder Singh, Xiao-Jian Yao, Etienne Leygue, Sam K.P. Kung, and Xie J. Characterization of cell free plasma methyl-DNA from xenografted tumours to guide the selection of diagnostic markers for early-stage cancers. Frontiers in Oncology, 2021 Feb. 5. DOI: 10.3389/fonc.2021.615821.

  Study EGAS00001006175

• Longitudinal Study of the Porphyrias

  This is an observational, longitudinal study of approximately 800 individuals with various types of porphyria. Those participants will be evaluated annually for 5 years, or longer if they agree. Participation in this project will include: Participating in annual visits or contacts. Providing samples, including blood, buccal cells (cells from inside of the mouth), saliva, and urine. The type of samples and amounts may vary from person to person, depending on the type of porphyria. Giving permission for samples to be stored and used for porphyrias research. Granting permission to obtain your medical records. Providing information about your medical history and family history. Completing a questionnaire about your porphyria symptoms and quality of life.

  Study phs001278

• The clinical utility of genomics in childhood cancer extends beyond targetable mutations

  Survival of children with relapsed cancer remains unacceptably low. Cancer genomic profiling may identify new therapeutic options, enhance diagnostic accuracy, and improve understanding of tumors’ origins and continued evolution. We deeply sequenced 864 cancer-associated genes and the complete genomes and transcriptomes for 300 pediatric and adolescent/young adult (AYA) patients with poor prognosis or rare tumors. Using integrative somatic-germline analyses, we assessed the clinical utility of cancer genomics in this population. We analyzed tumor mutational signatures and burden to interrogate the relevance of germline variants in DNA repair genes and the impact of therapeutic exposures on tumor evolution. We also evaluated the frequency of changes to tumor drivers and therapeutic targets at relapse in those with serial samples. Integrative analysis yielded clinically actionable variants in a majority of patients. Improved diagnostic accuracy led to modified management in a subset. Therapeutically targetable variants were of unanticipated timing and type, with many of these derived from the germline. Enrichment in corroborating mutational signature 3 (‘BRCAness’) in patients with germline homologous recombination defects suggests they are drivers of pediatric cancers and potential targets for PARP inhibition. We show that mutation burden, which may in part be driven by prior therapeutic exposures, was significantly elevated in a minority of patients. Over one-third of patients with sequential samples analyzed showed a change in therapeutically-targetable drivers. Comprehensive somatic-germline cancer genomic profiling is useful at multiple points in the care trajectory for pediatric/AYA patients, supporting its integration into early clinical management. Re-biopsy at the time of relapse should be considered, and specific attention to mutational burden is indicated. Defective DNA repair genes in the germline may represent important targetable drivers.

  Study EGAS00001006034

• Finding the way towards the eradication of therapy-related myeloid neoplasms

  Therapy-related neoplasms are directly caused by the drugs used to eradicate a previous cancer. It is well known that a subset of drugs designed to kill cancer cells through DNA-damage-induced apoptosis can intrinsically, as secondary effect, cause DNA-damage in other cells, thus inducing a neoplasm. Those neoplasms have especially bad prognosis. Why are these drugs still in use? Because for many diseases we do not have anything better, at the moment. Approximately 0.5-1% of the kids treated for various types of cancers develop a therapy-related myeloid neoplasm (tMN) as a result of the treatment. Despite having the same origin, their prognostic and molecular features are different from the adult ones, and a complete understanding of the pathogenesis of tMN in children is still missing. A total of 84 paediatrics neoplasms have been profiled by either Whole Genome or Whole Exome sequencing by Jason R. Schwartz and his colleagues, in a collaborative effort guided at the St. Jude Children's hospital, in Memphis. They identified a mean of 28 somatic mutations per patient, a number which is significantly bigger than the number of mutations found in paediatrics primary neoplasms, as depicted in the original paper's figure 1b, shown here. Their sequencing data identified that Ras/MAPK pathway mutations, alterations in RUNX1 or TP53, and KMT2A rearrangements as the most frequent drivers of the disease. Clonal evolution analysis (the comparative study of the behaviour of cells harbouring different mutations) highlighted that, unlike in adults’ tMN, there were not pre-existing leukemic clones, and virtually all mutated cells were found arising as a direct consequence of cytotoxic therapy. This represents yet another important difference between adult and paediatric tMN. This important study have been published in Nature communications in February 2021. The sequencing data have been deposited at EGA under the study number EGAS00001004850. Thanks to this, other researchers around the world could re-use it to uncover more information, integrate and validate their studies. Revealing the molecular features underlying the development of cancers is the only way towards the development of new generation targeted therapies, which will allow us to finally drop the cytotoxic drugs eliciting such unwanted and catastrophic secondary effects as tMN. It is not an overstatement to say that, while improving the treatment of primary tumours, we could entirely eradicate therapy-related neoplasms right from their origin. Looking forward.

  Blog eradication-of-therapy-related-myeloid-neoplasms

• Breast cancer DNA repair

  Tumors of germline BRCA1/2 mutated carriers show homologous recombination (HR) deficiency (HRD), resulting in impaired DNA double strand break (DSB) repair and high sensitivity to Poly-(ADP-Ribose)-Polymerase (PARP) inhibitors. Although this therapy is expected to be effective beyond germline BRCA1/2 mutated carriers, a robust validated test to detect HRD tumors is lacking. In the present study we therefore evaluated a functional HR assay exploiting the formation of RAD51 foci in proliferating cells after ex vivo irradiation of fresh breast cancers (BrC) tissue: the RECAP test.Methods Fresh samples of 170 primary BrC were analyzed using the RECAP test. The molecular explanation for the HRD phenotype was investigated by exploring BRCA deficiencies, mutational signatures, tumor infiltrating lymphocytes (TILs) and microsatellite instability (MSI).Results RECAP was completed successfully in 148 out of 170 samples (87%). 24 tumors showed HRD (16%), while 6 tumors were HR intermediate (HRi) (4%). HRD was explained by BRCA deficiencies (mutations, promoter hypermethylation, deletions) in 16 cases. Several non-BRCA deficient HRD tumors showed BRCAness mutational signatures, suggesting that they are also bona fide HRD cases. HRD tumors showed an increased incidence of high TIL counts (p=0.023) compared to HR proficient (HRP) tumors and MSI was more frequently observed in the HRD group (2/20, 10%) than expected in BrC (1%) (p=0.017).Conclusion The RECAP test is a robust assay detecting both BRCA1/2 deficient and BRCA1/2 proficient HRD tumors, suggesting that this test identifies approximately 50% more patients that may benefit from PARPi treatment than BRCA gene testing only.

  Study EGAS00001002792

• Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

  Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

  Study EGAS00001004861

• CCDG CVD: VIRGO - Variation in Recover-Role of Gender on Outcomes of Young Acute Myocardial Infarction (AMI) Patients

  The VIRGO study is a four year research project funded by the NHLBI to study young women and men with heart attacks. A total of 2,000 women who are 18 - 55 years of age and a smaller comparison group of 1,000 similarly aged men will be enrolled in this multi-site observational study from approximately 120 different hospitals across the country. Young women have a much greater risk of dying after an acute myocardial infarction (AMI) than similarly aged men. Their mortality risk is markedly higher than that of young men, and limited data on young minority women suggest that they may have the highest risk of any young subgroup. Findings from the small number of published studies of young women with ischemic heart disease suggest that the biology, epidemiology, care, and outcomes of this group are distinct from those of men. To date, there have been no large studies of this population, even as the death toll is comparable to that from breast cancer in this country. This study will help researchers identify key factors that influence recovery from a heart attack and potentially improve the care and outcomes of young women and men with AMI. Specific Aims: To investigate the excess risk in young women with AMI and to identify key demographic, clinical, metabolic, psychosocial, health care delivery, and biological determinants of prognosis, we propose a national, prospective, observational study of 2,000 women who are 55 years or younger with AMI and a smaller comparison group of 1,000 men. Our prior work and preliminary studies indicate that the sex differences are most prominent in this age group and suggest that by 1 year after discharge, there are substantial sex differences in outcomes. We will also develop risk stratification tools that will help clinicians to identify young women with the highest and lowest risk. Specific Aim 1: Determine sex differences in outcomes following AMI. Specific Aim 2: Determine sex differences in the prevalence and prognostic importance of demographic, clinical, and psychosocial risk factors. Specific Aim 3:Determine sex differences in quality of care. Specific Aim 4: Determine sex differences in the prevalence and prognostic importance of selected biochemical biomarkers following AMI. These samples have been whole genome sequenced as part of the NHGRI's Center for Common Disease Genetics at the Broad Institute.

  Study phs001259

• eMERGE Network Imputed GWAS for 41 Phenotypes

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of ten participating sites (Cincinnati Children's Hospital Medical Center/Boston Children's Hospital, Children's Hospital of Philadelphia, Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University, Geisinger Clinic, Group Health Cooperative/University of Washington, Mayo Clinic, Icahn School of Medicine at Mount Sinai, Northwestern University, Vanderbilt University Medical Center) funded by the NHGRI to investigate the use of electronic medical record (EMR) systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 55,000 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 over forty phenotypes, including Abdominal aortic aneurysm; Ace-Inhibitor/Cough; Attention Deficit Hyperactivity Disorder; Age-related macular disease; Appendicitis; Asthma; Atopic Dermatitis; Autism; Benign Prostatic Hyperplasia; Carotid artery disease as a Quantitative Measure; caMRSA; Cataract; Clostridium difficile colitis; Extreme Obesity; Chronic Kidney Disease; Chronic Kidney Disease and Type 2 Diabetes; Chronic Kidney Disease, Type 2 Diabetes and Hypertension; Colon Polyps; Cardiorespiratory Fitness; Dementia; Diverticulosis; Diabetic retinopathy; Gastroesophageal Reflux Disease; Glaucoma; Height; Heart failure; Hypothyroidism; Lipids; Ocular hypertension; Peripheral Arterial Disease; QRS duration; Red blood cell indices; Remission of Diabetes after ROUX-EN-Y gastric bypass surgery; Resistant hypertension; MACE while on Statins; Type 2 Diabetes; Venous Thromboembolism; White blood cell indices; and Zoster virus infection, as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes. Sites and participants include: Children's Hospital of Pennsylvania (CHOP): The Center for Applied Genomics (CAG) at the Children's Hospital of Philadelphia (CHOP) is a high-throughput, highly automated genotyping and sequencing facility equipped with state-of-the-art genotyping and sequencing platforms. Children who are treated at the Children's Hospital Healthcare Network and their parents may be eligible to take part in a major initiative to collect more than 100,000 blood samples, covering a wide range of pediatric diseases. A large majority of participants consenting to prospective genomic analyses also consent to analysis of their de-identified electronic medical records (EMRs). EMRs are longitudinal, with a mean duration of 6.5 years. Cincinnati Children's Hospital Medical Center/Boston's Children's Hospital (CCHMC/BCH): Cincinnati Children's Hospital Medical Center (CCHMC) and Boston Children's Hospital (BCH) are pediatric institutions dedicated to improving health and welfare of children and to the shared purpose of discovery and practical application of new genomic information to the ordinary care of children. The CCHMC/BCH site has been built on a five-year history of collaboration, particularly in patient electronic record (ERM)-related informatics, the basis of much of eMERGE II. CCHMC and BCH together bring an extraordinary faculty to eMERGE II who are committed to diseases that afflict children, specifically phenotypes that focus upon diseases of children in ways that will leverage the available eMERGE adult GWAS and EMRs to discover meaningful use results. CCHMC/BCH plans to demonstrate real-time execution of phenotypic selection across their two distinct pediatric institutions as a model for ensuring phenotypic standardization and for national scalability. They will also look carefully at parents' responses to results and use of their children's research results and better understand the factors that influence their decisions about learning incidental findings. In addition to patient and parent perceptions CCHMC/BCH will also explore clinician perceptions of pharmacogenetic research results after EMR integration. Geisinger Health System: A research cohort of adult Geisinger Clinic patients was enrolled from community-based primary care clinics of the Geisinger Health System. Patients were eligible for enrollment if they were a primary care patient of a Geisinger Clinic physician and were scheduled for a non-emergent clinic visit. All participants provided written informed consent and HIPAA authorization. Consenting patients agreed to provide blood samples for broad biomedical research use, and permission to access data in their Geisinger electronic medical record for research. The enrollment rate was 90% of patients approached. The demographics of the cohort approximate those of the Geisinger Clinic outpatient population. Research blood samples were collected during an outpatient clinical phlebotomy encounter. Research blood samples are coded and stored in a central biorepository. Samples are linkable to clinical data in a de-identified manner for research via an IRB-approved data broker process. For genomic analysis, DNA is extracted from EDTA-anticoagulated whole blood. Group Health(GH)/University of Washington (UW): GH participants for the PGx project were enrolled in the eMERGE Network through the Northwest Institute of Genetic Medicine (NWIGM) biorepository, and provided the appropriate consent to receive clinically relevant genetic results (N~6300.) Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in GH's integrated group practice, and had completed an online Health Risk Appraisal. The selection algorithm was based on several data sources from the EHR at Group Health: 1. Demographics - participants with self-reported race as Asian or African ancestry were prioritized and selected to enrich for non-European ancestry; 2. Diagnosis and procedure codes - participants were selected if found to have a history of hypertension, atrial fibrillation (AF), or congestive heart failure (CHF). Participants with a history of arrhythmia were added if the entire selection algorithm did not generate 900 individuals. We also enriched for participants with EHR evidence of actionable indications related to PGRNSeq genes. Participants were selected if found to have an ICD9 code for malignant hyperthermia, hypertension, atrial fibrillation, congestive heart failure or long QT syndrome (LQTS); 3. Laboratory values - if participants had any laboratory event of creatine kinase (CK) >1000, and were dispensed statins within 6 months of the event, then they were selected; and 4. Medications - participants were excluded if ever on carbamazepine or had a current regimen of warfarin. Essentia Institute of Rural Health, Marshfield Clinic, Pennsylvania State University (Marshfield): 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. 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. Icahn School of Medicine at Mount Sinai School (Mt. Sinai): The Institute for Personalized Medicine (IPM) Biobank Project is a consented, EMR-linked medical care setting biorepository of the Mount Sinai Medical Center (MSMC) drawing from a population of over 70,000 inpatients and 800,000 outpatient visits annually. MSMC serves diverse local communities of upper Manhattan, including Central Harlem (86% African American), East Harlem (88% Hispanic Latino), and Upper East Side (88% Caucasian/white) with broad health disparities. IPM Biobank populations include 28% African American (AA), 38% Hispanic Latino (HL) predominantly of Caribbean origin, 23% Caucasian/White (CW). IPM Biobank disease burden is reflective of health disparities with broad public health impact: average body mass index of 28.9 and frequencies of hypertension (55%), hypercholesterolemia (32%), diabetes (30%), coronary artery disease (25%), chronic kidney disease (23%), among others. Biobank operations are fully integrated in clinical care processes, including direct recruitment from clinical sites, waiting areas and phlebotomy stations by dedicated Biobank recruiters independent of clinical care providers, prior to or following a clinician standard of care visit. Recruitment currently occurs at a broad spectrum of over 30 clinical care sites. 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. 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.

  Study phs000888

• Transcriptome sequencing of human colon organoid after co-cultivation with Bifidobacterium longum

  The large intestine has a dense milieu of indigenous bacteria, generating a complex ecosystem with crosstalk between individual bacteria and host cells. In vitro host cell modeling and bacterial interactions at the anaerobic interphase have elucidated the crosstalk molecular basis. Although classical cell lines derived from patients with colorectal cancer including Caco-2 cells are used, whether they adequately mimic normal colonic epithelial physiology is unclear. To address this, we performed transcriptome profiling of Caco-2 and Monolayer cells derived from healthy Human Colonic Organoid (MHCO) cultured hemi-anaerobically. Coculture with the anaerobic gut bacteria, Bifidobacterium longum subsp. longum differentiated the probiotic effects of test cells from those of physiologically normal intestinal and colorectal cancer cells. We cataloged non- or overlapping gene signatures where gene profiles of Caco-2 cells represented absorptive cells in the small intestinal epithelium, and MHCO cells showed complete colonic epithelium signature, including stem/progenitor, goblet, and enteroendocrine cells colonocytes. Characteristic gene expression changes related to lipid metabolism, inflammation, and cell-cell adhesion were observed in cocultured live Bifidobacterium longum and Caco-2 or MHCO cells. B. longum-stimulated MHCO cells exhibited barrier-enhancing characteristics, as demonstrated in clinical trials. Our data represent a valuable resource for understanding gut microbe and host cell communication.

  Study JGAS000740

• Comparison of 3 protocols for deriving pancreatic progenitors from hPSC with RNA-seq and ATAC-seq

  Several distinct differentiation protocols for deriving pancreatic progenitors (PP) from human pluripotent stem cells have been described but it remains to be shown how similar the PP are across protocols, and how well they resemble their in vivo counterparts. Here we evaluated three differentiation protocols; performed RNA and ATAC-seq on isolated PP derived with these, and compared them to fetal human pancreas populations. This enabled us to define a shared transcriptional and epigenomic signature of the PP, including several genes not previously implicated in pancreas development. Furthermore, we identified a significant and previously unappreciated cross-protocol variation of the PP through multi-omics analysis and demonstrate how such information can be applied to refine differentiation protocols for derivation of insulin-producing beta-like cells. Together, our study highlights the importance of a detailed characterization of defined cell populations derived from distinct differentiation protocols, and provides a valuable resource for exploring human pancreatic development.

  Study EGAS00001003513

• Field Studies of Human Immunity to Amebiasis in Bangladesh (NIH Birth Cohort) and Exploration of the Biologic Basis for Underperformance of Oral Polio and Rotavirus Vaccines in Bangladesh (PROVIDE)

  NIH birth cohort study Amebiasis is a common cause of diarrhea and is associated with malnutrition in grade-school aged children in an urban slum of Dhaka, Bangladesh. Field Studies of Human Immunity to Amebiasis in Bangladesh was designed to determine the contribution of amebiasis to illness in the first 2 years of life when most deaths due to diarrhea occur, and understand the immunologic and genetic factors that protect children from amebiasis. The hypothesis underlying the study is that susceptibility to amebiasis is determined by host innate and acquired immune responses that vary between individuals in part due to: human genetic polymorphisms; environmental influences including malnutrition and concurrent geohelminth infection; and virulence differences among Entamoeba histolytica genotypes. Specific aims proposed in the design of the study were to: a) Measure the incidence of amebiasis and correlate it with human and parasite genetic polymorphisms, immune responses, and environmental factors such as geohelminth infection and malnutrition; b) Test the hypothesis that protective immunity is mediated both by innate immune responses initiated via TLR stimulation as well as by mucosal IgA against the Gal/GalNAc lectin and systemic IFN-γ; c) Test for the association of common genetic polymorphisms in host innate and acquired immune genes with incidence of amebiasis. 629 newborn babies were enrolled and followed regularly through bi-weekly surveillance for diarrheal episodes, anthropometry at 3-month interval until 60 months of age. The infants that were consented for GWAS analysis were genotyped in 3 separate batches at different times, on 3 different arrays. Quality control was performed on the 3 separate data sets and then jointly after merging. Genetic data available on 447 infants together with their phenotype data is made available in this submission PROVIDE (Performance of Rotavirus and Oral Polio Vaccines in Developing Countries) Study Oral polio and rotavirus vaccines are significantly less effective in children living in the developing world. Environmental enteropathy, which is associated with intestinal inflammation, decreased absorption and increased permeability, may contribute substantially to oral vaccine failure in developing country settings. The primary objective of the study was to determine the association of environmental enteropathy with the efficacy of oral polio and rotavirus vaccines in children in Bangladesh. Secondary and exploratory objectives were designed to address other possible causes of oral vaccine underperformance including malnutrition, interference with maternal or breastmilk antibodies, changes in gut microbiota, and genetic susceptibility. The PROVIDE study was structured as a 2x2 clinical trial with a prospective birth cohort enrollment of 700 infants and their mothers in the urban slum of Mirpur, Dhaka, Bangladesh. All children received the full series of Bangladesh's Expanded Program on Immunization vaccines, including oral polio vaccine (OPV) and followed for their first 2 years of life. Half the children were randomized to receive an inactivated polio vaccine (IPV) for their 4th dose at 39 weeks instead of the standard OPV dose. In the other trial, half the infants received the 2-dose rotavirus vaccine (Rotarix) with delayed dosing at 10 and 17 weeks of age. A detailed description of the study design and procedures can be obtained from PROVIDE methods publication: Kirkpatrick B.D., Colgate R.E., Mychaleckyj J.C., Haque R., Dorothy D.M., Carmolli M.P., et al. (2015) Am J Trop Med Hyg 92, 744-751 The Poliovirus trial and results are described in Mychaleckyj et al, Vaccine 2016 The Rotavirus trial and results are described in Colgate et al, Clin Infect Dis 2016 The PROVIDE infants were genotyped on a custom Affymetrix Axiom array (30K SNPs) at the University of Virginia Center for Public Health Genomics; and on a pre-production version of the Illumina MEGA v2 chip at the Wellcome Trust Sanger Institute, UK. The latter genotyping was performed under the auspices of the VaccGene Consortium. Analyses are ongoing to identify variants associated with the included phenotypes. A manuscript describing the genotyping methods and analysis is in preparation for the initial phenotypes. The Infection Malnutrition Bangladesh Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs001475 Infection Malnutrition Bangladesh Cohort. phs001476 Amebiasis Immunity phs001478 Polio Rotavirus Vaccines phs001665 Cryptosporidiosis Enteropathogens

  Study phs001475

• Shedding light over COVID-19 susceptibility and severity

  “One of the most remarkable features of SARS-CoV-2 infection is the variation in consequence ranging from asymptomatic to life-threatening” state the authors of the majestic Nature paper released in accelerated pre-view on July 2021. This variation in consequences represent not only an issue of burning medical relevance, but also a scientific and intellectual challenge for scientists around the globe. Researchers are known to be easily triggered by a challenge, and they recently demonstrated to be even better at double their efforts to focus on an urgent societal need. In this work led and coordinated by the COVID-19 Host Genetics Initiative (COVID-19 HGI), researchers gathered data from 49,562 cases and two million controls from 46 studies performed worldwide. The majority of the data has been deposited at the EGA, where 29 datasets have been collected under the study number EGAS00001005304. Data gathered from diverse sources conferred a huge robustness to the results. Genetic data from different ancestry origin were included in the analyses, even though data from patients with European origin was highly predominant (approx. 75%). Separated analysis of the different ancestries gave rise to relevant differences in the results, highlighting how important it is to take this aspect in consideration for future studies. Data from the individual studies was compiled into three categories according to COVID-19 severity for a meta-analysis: (a) critically ill cases (b) moderate or severe cases (c) all cases. They were paired with non-COVID-19-reported controls. All three analysis resulted in a total of 13 genetic loci associated with COVID-19 severity. Out of those, 9 loci associated specifically with critical illness, while 6 (a few overlapped) associated with moderated illness. Some genomic position matched previous findings. Interestingly, working with such an enormous amount of data allowed the depth to discriminate association with disease susceptibility vs severity, an issue of large medical relevance. Several loci mapped into biological relevant genes, like TYK2, PPP1R15A and FOXP4, previously implicated in immune response and/or lung functions. Researchers participating in the COVID-19 Host Genetics Initiative have worked in an unprecedented time-tight collaboration, prioritizing data sharing and collective results to tackle the pandemic affecting the world. The scientific community won’t miss any of the lessons to learn from this, and the EGA is ready to keep working to enable safe data sharing and fruitful collaborations.

  Blog covid-19-susceptibility-and-severity

• GoNL aligned sequence data in BAM format.

  We mapped the data to the UCSC human reference genome build 37 using BWA 0.5.9-r16. We first mapped each read pair separately using bwa aln. Then we used bwa sampe to map the paired reads together to a BAM9 file. The BAM file was then sorted by genomic position and indexed using PicardTools-1.32 SortSam. To prevent PCR artifacts from influencing the downstream analysis of our data, we used Picard to mark the duplicate reads, which were ignored in downstream analysis. We used GATK IndelRealigner on our data around known indels (from 1KG Pilot). The IndelRealigner creates all possible read alignments using the source and computes the likelihood of the data containing the indel based on the read pileup. Whenever the maximum likelihood contains an indel, the reads are realigned accordingly. Each base is associated with a phred-scaled base quality score. Calibration of Phred scores is crucial as they are used in some of the downstream analysis models. We used GATK to recalibrate the base qualities with respect to (i) the base cycle, (ii) original quality score, and (iii) dinucleotide context. To minimize issues stemming from mapping problems around indels, we decided to undergo a second round of indel realignment using the GATK IndelRealigner by family rather than by individual. For this second round, we considered two sources of possible indels: 1KG Phase 1 indels and indels aligned by BWA in the GoNL data.

  Dataset EGAD00001001038

• Risk Assessment of Cerebrovascular Events (RACE) Study

  This study includes 1,220 cases with young onset stroke (stroke before age 60 years) who are participants of the larger RACE study. Risk Assessment of Cerebrovascular Events (RACE) is an on-going existing case-control study of stroke now involving over 5000 imaging confirmed cases of stroke and 5000 controls, recruited from seven centers in Pakistan. The study is aimed to investigate the genetic, biomarker and lifestyle determinants of stroke and its subtypes. Cases are eligible for inclusion in the study if they: (i) are aged at least 18 years; (ii) present with a sudden onset of neurological deficit respecting a vascular territory with sustained deficit at 24 hours verified by medical attention within 72 hours after onset (onset is defined by when the patient was last seen normal and not when found with deficit); and (iii) the diagnosis is supported by CT/MRI; and (iv) present with a Modified Rankin Score < 2 prior to the stroke. Findings from patient's history, 12-lead ECG and CT or MRI of the brain. The mandatory procedures for inclusion in this investigation are: (i) clinical verification of cerebrovascular event within 72 hours of onset; (ii) neuroimaging CT (non-contrast) or MRI (MRI is not a mandatory investigation but recorded whenever ordered by the attending physician); and (iii) 12-lead ECG. All other ancillary investigations ordered by the attending physician are recorded as well. The TOAST classification method is used to classify ischemic stroke based on aetiology whereas the Oxfordshire classification is used to classify stroke neuro-anatomically. Control participants for this subset of young onset stroke were individuals enrolled in the Pakistan Risk of Myocardial Infarction Study (PROMIS), a case-control study of acute MI based in Pakistan. RACE capitalizes on the genetic data (including information on GWAS) that has already been collected from the healthy participants enrolled in PROMIS. RACE and PROMIS share similar methodology of recruitment. Participants from both these investigations are derived from similar catchment areas, hence providing an attractive opportunity for RACE to utilize PROMIS controls as common controls for genetic investigations. Controls in PROMIS were recruited following procedures and inclusion criteria as adopted for RACE cases. In order to minimize any potential selection biases, PROMIS controls selected for this stroke substudy were frequency matched to RACE cases based on age and gender and were recruited in the following order of priority: (1) non-blood related or blood related visitors of patients of the out-patient department; (2) non-blood related visitors of stroke patients; (3) patients of the out-patient department presenting with minor complaints (e.g. back pain, minor gastric complaints). Control subjects from the PROMIS study were genotyped at the Wellcome Trust Sanger Institute on the Illumina 660W Quad array. The Center for Non-Communicable Diseases, Pakistan, serves as the coordinating center for both RACE and PROMIS. More information on these research investigations can be found at www.cncdpk.com. This young onset stroke component to the RACE study was funded through the Gene Environment Association Studies initiative (GENEVA, www.genevastudy.org as one of three studies designed to assess the genetics of young onset stroke and modification of genetic effects by smoking. GENEVA is part of the trans-NIH Genes, Environment, and Health Initiative (GEI). Genotyping of 1,220 young onset stroke cases was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to stroke through large-scale genome-wide association studies of cases and controls recruited within Pakistan. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000456

• Kids First: Genomic Studies of Orofacial Cleft Birth Defects

  The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). Both childhood cancers and structural birth defects are critical and costly conditions associated with substantial morbidity and mortality. Elucidating the underlying genetic etiology of these diseases has the potential to profoundly improve preventative measures, diagnostics, and therapeutic interventions. WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. The Kids First study of nonsyndromic orofacial cleft birth defects (OFCs) is a whole genome sequencing study of 415 White parent-case trios drawn from ongoing collaborations led by Dr. Mary L. Marazita of the University of Pittsburgh Center for Craniofacial and Dental Genetics, including collaborations with Dr. George Wehby of the University of Iowa, Dr. Jacqueline Hecht of the University of Texas, and Dr. Terri Beaty of Johns Hopkins University. Sequencing was done by the Washington University McDonell Genome Institute. The case in each of the Kids First trios has cleft lip (CL, Figure A), cleft palate (CP, Figure B), or both (CL+CP, Figure C): OFCs are genetically complex structural birth defects caused by genetic factors, environmental exposures, and their interactions. OFCs are the most common craniofacial anomalies in humans, affecting approximately 1 in 700 newborns, and are one of the most common structural birth defects worldwide. On average a child with an OFC initially faces feeding difficulties, undergoes 6 surgeries, spends 30 days in hospital, receives 5 years of orthodontic treatment, and participates in ongoing speech therapy, leading to an estimated total lifetime treatment cost of about $200,000. Further, individuals born with an OFC have higher infant mortality, higher mortality rates at all other stages of life, increased incidence of mental health problems, and higher risk for other disorders (notably including breast, brain, and colon cancers). Prior genome-wide linkage and association studies have now identified at least 18 genomic regions likely to contribute to the risk for nonsyndromic OFCs. Despite this substantial progress, the functional/pathogenic variants at OFC-associated regions are mostly still unknown. Because previous OFC genomic studies (genome-wide linkage, genome-wide association studies (GWAS), and targeted sequencing) are based on relatively sparse genotyping data, they cannot distinguish between causal variants and variants in linkage disequilibrium with unobserved causal variants. Moreover, it is unknown whether the association or linkage signals are due to single common variants, haplotypes of multiple common variants, clusters of multiple rare variants, or some combination. Finally, we cannot yet attribute specific genetic risk to individual cases and case families. Therefore, the goal of the current study is to identify specific OFC risk variants in Whites by performing whole genome sequencing of parent-case trios.

  Study phs001168

• From research to data sharing: exploring EGA user's experiences

  The Cell Plasticity and Regeneration Group at the Bellvitge Biomedical Research Institute-IDIBELL focuses on the process of recruitment of macrophages that takes place in the small intestine during injury and healing. They recently published a paper titled “Mucosal Macrophages Govern Intestinal Regeneration in Response to Injury" in Gastroenterology Journal. As part of the research, some experiments were conducted using human intestinal organoid lines. These cells were processed for RNA sequencing, and the sequencing data were deposited at the EGA to be made available to the scientific community. When dealing with human genomic information, repositories must ensure the availability of the datasets while ensuring data protection. In this context, the European Genome-phenome Archive stands as a service for secure archiving and sharing of genetic, phenotypic and clinical data resulting from biomedical research. Following the recent publication of their paper, we took the opportunity to talk to Ilias Moraitis, first author, and Jordi Guiu, group leader, to find out about their experience with data sharing. Could you explain the focus of your research? In the lab, we study intestinal regeneration and how immune cells participate in this process. We use several techniques: engineered mouse models, image tracing, as well as mouse and human cells intestinal organoids. What challenges do you face regarding data management? We didn’t have a lot of problems. Always the informatic part, the data analysis, can give problems. But everything was smooth and working. We think it’s very important to deposit the data in repositories. For the mouse data it is very straightforward, but for us it was the first time depositing human data, which is sensitive because it comes from patients, and this is legally regulated. That’s why we thought about the EGA, and it was our first time. Why do you think it is important to submit data to repositories such as EGA? Because we think data is important for the science. Nowadays, we are sequencing a lot everywhere worldwide and having these resources shared with the scientific community it’s not only good for science, but it is also saving money and reducing costs. And there is so much data in there that can be used for other projects and for other questions. And it saves time! How was the process of submitting the data to the EGA? The communication worked very well but it was slower than we would like. But I think this is something we learn on the way. People usually wait until the last minute to do this before publication but if you know it in advance, you can start the process earlier. If you had to repeat the process, would you do anything differently? It’s like everything, isn’t it? For the first time you don’t know, you aren’t sure, but for the second time you know the steps, you know what to do and it’s faster. Of course, we would to do it earlier. Also, there were some things related to the control access that we didn’t know how to manage, but it was something more internal to us. Who is going to receive the communications and when, is our ethical committee going to evaluate this? All these technicalities that we didn’t know before starting this process, so we had to think about all these things. Next time it will be faster on our side. Do you have any suggestions for us to improve the submission process? Maybe we’ll need to ask our bioinformatician who did it! He was in charge of this part. Besides that, we think if the process were faster, it would be better for everyone. Also, in science a lot of times you have to do a lot of things at the very last minute, because of the nature of the experiments, or the need to accomplish for submitting a paper. Being faster in the process would be a plus. On your side, we went through the process and were able to find some information, but there were aspects we weren't aware of and weren't sure how to handle. I'm not sure if other institutes have different protocols for managing data requests or how they handle these internally. We believe the main issue lies in how the forms are filled out. Do you have any recommendations for other submitters? The main advice is to do this with enough time. Submit the data when you have the sequences, and don’t wait until the last minute. And then it can be under embargo, so you don’t need to make it public at that moment. As soon as you sequence, upload it and then it will be there for whenever you need to publish. How do you think we could encourage other researchers to submit their data? There are different layers here. One is that it is mandatory. You have to do this. The other is that we belong to a research community, to the same community and sharing this is making our research community stronger and more efficient. And then, also because this gives you visibility. The data is there, other people can analyse your data, and this also will bring citations to your papers. It has many advantages.

  Blog from-research-to-data-sharing

• Tracing the origin of disseminated tumor cells in breast cancer using single-cell sequencing

  Background Single-cell micro-metastases of solid tumors often occur in the bone marrow. These disseminated tumor cells (DTCs) may resist therapy and lay dormant or progress to cause overt bone and visceral metastases. The molecular nature of DTCs remains elusive, as well as when and from where in the tumor they originate. Here, we apply single-cell sequencing to identify and trace the origin of DTCs in breast cancer. Results We sequence the genomes of 63 single cells isolated from six non-metastatic breast cancer patients. By comparing the cells DNA copy number aberration (CNA) landscapes with those of the primary tumors and lymph node metastasis, we establish that 53% of the single cells morphologically classified as tumor cells are DTCs disseminating from the observed tumor. The remaining cells represent either non-aberrant ‘normal’ cells or aberrant cells of unknown origin that have CNA landscapes discordant from the tumor. Further analyses suggest that the prevalence of aberrant cells of unknown origin is age-dependent, and that at least a subset is hematopoietic in origin. Evolutionary reconstruction analysis of bulk tumor and DTC genomes enables ordering of CNA events in molecular pseudo-time and traced the origin of the DTCs to either the main tumor clone, primary tumor subclones, or subclones in an axillary lymph node metastasis. Conclusions Single-cell sequencing of bone marrow epithelial-like cells, in parallel with intra-tumor genetic heterogeneity profiling from bulk DNA, is a powerful approach to identify and study DTCs, yielding insight into metastatic processes. A heterogeneous population of CNA-positive cells is present in the bone marrow of non-metastatic breast cancer patients, only part of which are derived from the observed tumor lineages.

  Study EGAS00001002102

• HELIUS cohort

  The gut microbiota composition is unique to every individual but is shaped by common factors including diet, lifestyle, medication use, early-life determinants, living environment or genetics. Most of these factors may be influenced by ethnicity. This study explored variations in fecal microbiota composition in 6048 individuals with different ethnic backgrounds living in the same geographical area (Amsterdam, the Netherlands). The HELIUS data are owned by the Amsterdam University Medical Centers, location AMC in Amsterdam, The Netherlands. To allow sharing of microbiome data collected in HELIUS with (inter)national researchers, 16s rRNA sequence analysis has been stored at the European genome-phenome archive (EGA; accession code EGAD00001004106). This requires that access needs to be granted, also because the HELIUS data are stored with relevant phenotypical variables. Access is granted to all researchers affiliated with an internationally recognized research institution who request to use the HELIUS data within the EGA context, after having signed the data transfer agreement. Any researcher can request the data by submitting a proposal to the HELIUS Executive Board as outlined at http://www.heliusstudy.nl/en/researchers/collaboration, by email: heliuscoordinator at amsterdamumc dot nl. The HELIUS Executive Board will check proposals if they do not conflict with ethical approvals and informed consent forms of the HELIUS study.

  Study EGAS00001002969

• Genetic determinants of monocyte splicing are enriched for disease susceptibility loci including for COVID-19

  These data correspond to samples used in the following papers: 1. GWAS and meta-analysis identifies 49 genetic variants underlying critical Covid-19 2. Genetic determinants of monocyte splicing are enriched for disease susceptibility loci including for COVID-19. 3. Identification of Genetic Determinants of Transcriptional Response to Metformin in Primary Human Monocytes. They are the raw RNA sequencing files as used in all downstream analysis.

  Dataset EGAD00001010176

• ALCHEMIST Study

  The ALCHEMIST study will accrue patients that are potentially eligible for the Intergroup adjuvant studies and perform central EGFR and ALK genotyping using a central reference laboratory certified by the Clinical Laboratory Improvement Amendments of 1988 (CLIA). Patients may either present prior to surgery with resectable NSCLC, may present following complete resection (before or after adjuvant chemotherapy). Eligibility is limited to those with NSCLC of a non-squamous histological subtype and those with adequate performance status and organ function for future trial eligibility. All subjects must submit tissue for central EGFR and ALK genotyping, as well as additional tissue for advanced genomics at the CCG. Subjects may have had local genotyping done prior to registration, however if these results shows no targetable EGFR or ALK alterations (or if it shows a KRAS mutation) then the patient will not be eligible for this screening protocol given the primary aim is to facilitate accrual to the adjuvant studies. All subjects will provide peripheral blood for matched normal DNA. All subjects (including those known to have EGFR or ALK alterations with local genotyping) will have formalin-fixed tissue collected for central genotyping. The testing will be performed at Response Genetics (Los Angeles, CA), a commercial CLIA-certified laboratory. ALK FISH will be performed using the Vysis break-apart probe and EGFR sequencing will be performed of exons 18-21. It is preferred that a full tumor block be submitted, but if unavailable, 15 unstained slides can be submitted. Genotyping results are expected to be provided to the treating clinician within 14 business days of submission so they can be used to determine eligibility for the randomized adjuvant studies, or to confirm the local results. Results will also be reported at intervals to the study team for update into the Alliance database. In addition to the commercial genotyping at Response Genetics, tissue will be collected for research genomics by CCG. For those patients with a block available, this will be forwarded to the CCG after clinical testing at Response Genetics. For patients without a block available, 15 unstained slides should be submitted to the CCG BCR for exploratory analysis. A peripheral blood specimen will also be collected and sent to the CCG BCR to use as matched normal. Specimens will be coded. Over the course of the study, the CCG will perform advanced genomic analysis of the resected lung cancer specimens in a research, non-CLIA environment. Following completion of the genomic analysis, the results can be matched with the clinical follow-up results using a link between the samples codes and the patient identifiers for correlative analyses. Subjects participating in the follow-up portion of the ALCHEMIST study, as well as those participating in the adjuvant therapeutic studies, are expected to undergo a standard-of-care diagnostic biopsy to confirm recurrence. At least two core biopsies, minimum, should be obtained as part of this recurrence biopsy. Paraffin embedded tissue from this biopsy will be used for confirmation of recurrence, for CLIA genomics to plan subsequent clinical trials, and for additional research genomics by the CCG. Plasma will additionally be collected at recurrence and sent to CCG for study of circulating free DNA (cfDNA). Data for the ALCHEMIST study is not yet available to be accessed by the public at this time; when ready it will be made available at the GDC Data Portal.

  Study phs001140

• DAC Portal documentation

  DAC Portal Welcome to the DAC Portal documentation! If you are involved in governance or legal aspects, technical or operational aspects, or serving as a data steward, this page will be helpful for you. By exploring these materials, you can define your own Data Access Committee (DAC) and policies, understand the minimal requirements for a DAC and a policy object, and comprehend how the EGA data access requests are managed. DAC Portal Index Setting up your account DACs and Policies Pending Request table Manage Data Requests History page Audit your DACs, policies, and datasets Deprecation User Preferences DAC API - A programmatic approach Setting up your account Register yourself as an EGA user. The Helpdesk team will validate your account (this could take up to 48 hours). After validation, you will receive an email with a link to verify your email - Make sure you click on the link to verify your account! If you don’t receive the email, please check your spam folder. Once your account is active you can login to the DAC Portal. We recommend you to check out the Take The Tour! DACs and Policies Create a DAC Click on Create a DAC Add the title and description to your DAC. Once ready, click on the ‘Create’ button. You will have to wait until the Helpdesk Team validates the creation of your DAC. Once approved by Helpdesk, your DAC will be assigned with a persistent identifier (EGAC). Edit a DAC Once your DAC is registered and approved, you can edit it and add contacts. Remember, you can add as many contacts as needed. And, you can also remove/add contacts anytime. To add a contact, you must write the username or email of a registered EGA user in the Members field. Once you have typed the whole username or email, a drop-down menu will appear where you can select the contact. Select a contact from the drop-down menu. Make sure you write the full username or email of the contact so it appears as an option. Make sure that you select one main contact. Set up the role you want to grant to the new contact of the DAC. We recommend you to check out the FAQ to learn more about the different roles! After adding all the necessary contacts and assigning roles, click on Update. This will send an email notification to the new DAC member, letting them know about the invitation to managing the DAC. Once they log into the DAC Portal, they will be able to either accept or turn down the invitation. Create a policy First, please note that all information registered in a policy metadata object will be publicly available on the EGA website. For each released dataset, all users will be able to read the policy under the “Data Access” tab. Select Create a Policy from the Policies tab. Select the DAC that you want your policy to depend on. Add the title and terms and conditions for accessing and using your data. If you already have an external website for data access requests, you can add the URL directly here (see example here). You can find a template of a Data Access Agreement (DAA) document in our Policy documentation page. The DAA template is provided for guidance only and should be adapted to suit your own purpose depending on the security policies, terms for publication or embargoes, and restrictions on data use or sharing. You can also add Data Use Ontologies (DUO) to your policy. These DUO codes are used to semantically tag the terms and conditions of using the data linked to the policy (example). For more information, you can refer to the Data Uso Ontology documentation. Once completed, make sure to click the Create button in order to register your policy. A persistent identifier (EGAP) will be assigned automatically. Edit a policy Please, note that you will not be able to edit the policy if it shows in orange. Meaning that you are a member of the DAC linked to that policy. Members don’t have edit rights. Only admins can edit objects. You can check your registered policies on My policies, available at the menu in the top-right corner. Select your policy. It will then display the information of that policy, allowing you to edit the information. Once you are done with the modifications, click Update. Pending Requests Table Table Settings Upon selecting a specific DAC, you will be presented with a customisable table showcasing all pending data access requests. This table allows you to tailor the displayed information according to your preferences by selecting desired columns. Available Columns (Displayed with the Eye Icon): Date: The date when the data access request was submitted. Full Name: Name and surname of the user submitting the request. Email: Email address associated with the user's request. Username: The username linked to the EGA account of the user submitting the request. Organisation: Affiliated organisation of the user submitting the request. Dataset: EGA accession ID for the requested EGA dataset. Dataset Title: Title of the requested dataset. DAC Comment: Space provided for internal comments related to the request. Expiration: Option to specify an expiration date for granted permissions. Check more information on the pending request table here! Table Filters By default, all data access requests are displayed. However, you have the option to apply filters to refine your view. Follow these steps to apply filters: Click on the More button. This action will reveal the different columns available for applying filters. As you select columns, the checked fields will be displayed at the top of the request table. Choose the specific values within the selected columns that you want to filter for. Click the Search button to apply the selected filters. Do you find yourself frequently applying the same filters? You can save multiple combinations of filters for easy access at any time! Here's how: Click the Save button. Provide a descriptive name for the filter combination. Click Save to confirm. To load saved filters, simply select the saved filter from the dropdown menu to apply it automatically. Check more information on filters here! Advanced Search For users requiring more granular control over their data access requests, the DAC Portal offers an Advanced Search feature using JIRA Query Language (JQL). With JQL, you can craft precise and complex queries to filter and retrieve specific data access requests based on various criteria such as date, user details, dataset attributes, and more. Using JQL's flexibility, you can create custom queries to meet your specific needs, allowing for advanced filtering options beyond the standard filters provided. You can find the Advanced Search feature following these steps: Click the … button Select Advanced To view the allowed fields for filtering and explore all available options, simply click on the info icon. Once you have written the filtering values, click the Search button to apply. Check more information on filters here! Manage Data Requests Upon logging into the DAC Portal, you'll notice a sand clock icon next to the DAC ID, indicating the number of pending requests. Click on the DAC to review these requests. Accept requests Click on the right side of the toggle button to grant access to the user. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with granting permissions. Deny requests Click on the left side of the toggle button to deny access to the user. Provide a reason for the denial. Note that the user will receive an email with this reason. Click on the Done button. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with denying permissions. Tips! Apply a filter to view all requests to be managed at once. Use the toggle in the row with column names to grant or deny permissions for multiple requests simultaneously. You can grant and deny permissions in the same action, simplifying the process. The confirmation box will provide a summary of all actions, including grants, denials, DAC comments, and expiration dates. History Page The History page serves as a dedicated space to view information regarding all requests managed by all DAC members. Here, you can review active permissions and revoke them as needed. Go to the History page by clicking on the "HISTORY" button from a DAC page in the DAC Portal. Here are the different row types you may find: Current permissions: row in green with a toggle button to revoke permissions on the right. Approved requests: row in green, with no toggle button. Request denied: row in red with “request denied” on the right. Permission revoked: row in red with “permission revoked” on the right. Distinctions to Note! Between Request Denied and Permission Revoked: Request Denied: Refers to requests that were rejected from the outset, indicating that access to the dataset was never granted. Permission Revoked: Indicates that permissions were previously granted but have since been revoked. Users with permissions revoked have previously accessed the dataset. Between Approved Request and Current Permissions: Approved Request: Represents an entry when a data access request has been approved in the past. Current Permissions: Denotes ongoing permissions where a user has present access to the dataset. An approved request may now appear as Permission Revoked in the present. By observing these different rows, users can gain insight into the complete history of a user's interactions and permissions regarding a dataset over time. To revoke access to a specific user for a dataset, follow these steps: Go to History page Look for the row with the specific permissions. You can use the filters! Click on the toggle button to revoke access. Add a denial reason. Bear in mind that the requester will receive the denial reason! Click on the Apply button. Check more information on the Hisotry page here! Audit your metadata objects In the DAC Portal, you can efficiently manage and audit various metadata objects pertinent to your role as a Data Controller. Upon accessing the DAC Portal, you will encounter three primary tabs on the homepage: DAC: Contains information about Data Access Committees (DACs). Policies: Provides insights into linked policies. Datasets: Displays datasets and relevant details. Within each tab, you'll find a comprehensive list of the objects you manage, these being grouped by type (DAC, Policies, Datasets), as well as by your role (member/admin). Whilst the lists give you a quick look, to check how things are connected, we've added a table at the bottom DACs and policies. DACs Let’s check which policies are linked to a specific DAC: Go to "My DACs." Select a DAC. Click on "EDIT" to see more details. You are now on this page: Scroll down to see the linked policies. Click on “List of linked policies of this DAC” and you will see a list of all policies linked to your selected DAC. Policies Do you want to view a list of datasets connected to a particular policy? Follow the same steps mentioned earlier, but head to the policy tab this time. In the policy tab, you'll find a list of all your policies. Here, you may notice two different icons next to the policy ID (EGAP): DAC Icon (): This represents the DAC. Hover your mouse over the icon to see the DAC ID. Dataset icon (): This indicates datasets falling under that policy. The number next to the icon tells you how many datasets are linked to the policy. For example, if you see "2" next to the icon, it means there are two datasets linked to that policy. If you want to check the linked datasets and their relevant information, simply click on a specific policy. You'll then find the "List of linked datasets of this policy" at the bottom of the page. Datasets Finally, in the dataset tab, by default you will see a list of all the datasets you can manage with all your DACs and policies. Yet, we've included two handy ways to organise them: DAC vs. policy: You can group by datasets, either by DAC or policy. Released vs. unreleased: You can sort out datasets based on their release status. Feel free to experiment with both options! For instance, if you want to see which DACs have unreleased datasets, simply select DAC and unreleased, and you'll get the details you need! Deprecation Do you have a bunch of metadata objects like DACs and policies that you don’t need anymore? This section shows you how to get rid of them! But what does "deprecation" mean for EGA? It's basically changing the status of a metadata object to "deprecated," which means we won't be using it in the future. In simple terms, it's like saying these objects are no longer useful. However, because we believe in making metadata FAIR, once an object has a persistent identifier, we can't just delete it. So, instead of deleting, we deprecate it. Here's a helpful tip! If you want to make a metadata object disappear from the DAC Portal, deprecate it. You won’t see it in the portal anymore! Let’s say you want to deprecate a DAC. Let’s do it! Go to "My DACs." Select a DAC. Click "EDIT" to see more details. You are now in this page: Click on the Deprecate button It will then appear a message. There are two options here: Your DAC is not linked to any policy, hence it’s ready to be deprecated. Click on “Yes. Confirm” to deprecate your DAC object! Your DAC is linked to at least one policy, and you need to either: Deprecate the linked policy first, or Link the policy to a different DAC Now, let’s say you don’t want to get rid of the policy altogether, but you want to change the DAC it’s linked to. Here's how: Go to the policy tab. Find the policy you want to change (for example, EGAP50000000019). Choose a new DAC to link it to. Click on Update. See the Edit Policy section for more details. After ensuring that the DAC we want to deprecate isn’t linked to any policy, return to the "My DACs" section and follow the steps outlined previously. This will lead you to a confirmation message. You can only deprecate DACs and Policies. To deprecate a dataset, please contact our Helpdesk team. Check more information on how deprecation works here! User Preferences We have implemented email notifications in the DAC Portal. Here’s the complete list: DACs: Pending requests Approved by Helpdesk Rejected by Helpdesk DAC Invitation Requesters: Data access request approved Data access request denied Permissions revoked Upcoming expiration date As a DAC member, you will be able to decide whether you want to receive the DAC notifications or not. For that, go to the top-right corner menu, select User Preference. You will be able to decide whether you want to receive notifications for: Approved by Helpdesk Rejected by Helpdesk DAC Invitation For pending requests, you will be able to select how often do you want to receive the notification: Daily Weekly Fortnightly As a DAC member, you are responsible for managing data access requests. Consequently, you will receive notifications for pending requests. If you prefer not to manage these requests, please arrange to be removed as a contact for your DAC. Here's a tip! Do you have a pending request that you don’t want to receive a notification? Add a DAC comment! The EGA understands that a request resolution can take some time, for this reason, if you add a comment (make sure you save it by clicking the APPLY button!) we will filter those requests at the time of sending the notification! DAC API - A programmatic approach In addition to the new DAC Portal, we are excited to announce the release of the DAC API. This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can click the button below. Check out the DAC API specification!

  Documentation access/data-access-committee/dac-portal

• Mutational Patterns in Metastatic Cutaneous Squamous Cell Carcinoma

  Cutaneous squamous cell carcinoma (cSCC) from the head and neck typically metastasizes to the lymph nodes of the neck and parotid glands. When a primary is not identified, they are difficult to distinguish from metastases of mucosal origin and primary salivary gland SCC. Ultraviolet radiation causes a mutation pattern that predominantly features cytosine to thymine transitions at dipyrimidine sites and has been associated with cSCC. In this study, we used whole genome sequencing data from 15 cSCC metastases and show that a UV signature mutation is pervasive across the cohort and distinct from mucosal SCC. The mutational burden was exceptionally high and concentrated in some regions of the genome, especially insulator elements (mean 162 mutations / Mb). We therefore evaluated the likely impact of UV-induced mutations on the dipyrimidine rich binding site of the main human insulator protein, CCCTC-binding factor (CTCF), and the possible implications on CTCF function and the spatial organization of the genome. Our findings suggest that mutation signature analysis may be useful in determining the origin of metastases in the neck and the parotid gland. Furthermore, UV-induced DNA damage to insulator binding sites may play a role in the carcinogenesis and progression of cSCC.

  Study EGAS00001003370

• IMCISION RNAseq

  RNA sequencing of 32 primary head and neck squamous cell carcinoma (HNSCC) samples prior to treatment with neoadjuvant anti-PD-1 (n=6) or anti-PD-1 + anti-CTLA-4 (n=26) immunotherapy, and 30 paired on-treatment HNSCC samples (i.e. after neoadjuvant immunotherapy). RNA quantity used: 10ng. Library Preparation Kit: SMART Stranded Total RNA Seq Kit (Takara). Sequencing parameters: NovaSeq 6000, 2x 100 bp. File type: fastQ

  Dataset EGAD00001008127

• National Human Genome Research Institute (NHGRI) GENEVA Genome-Wide Association Study of Venous Thrombosis (GWAS of VTE)

  Overview: Our overall long-term goal is to determine risk factors for the complex (multifactorial) disease, venous thromboembolism (VTE), that will allow physicians to stratify individual patient risk and target VTE prophylaxis to those who would benefit most. In this genome-wide association case-control study (1300 cases and 1300 controls) we hope to identify susceptibility variants for VTE. Mutations within genes encoding for important components of the anticoagulant, procoagulant, fibrinolytic, and innate immunity pathways are risk factors for VTE. We hypothesize that other genes within these four pathways or within other pathways also are VTE disease-susceptibility genes. Therefore, we performed a genome wide association (GWA) screen and analysis using the Illumina 660W platform to identify SNPs within 1,300 clinic-based, non-cancer VTE cases primarily from Minnesota and the upper Midwest USA, and 1300 clinic-based, unrelated controls frequency-matched on patient age, gender, myocardial infarction/stroke status and state of residence. This is a subset of a slightly larger candidate gene study using 1500 case-control pairs to identify haplotype-tagging SNPs (ht-SNPs) in a large set of candidate genes (n~750) within the anticoagulant, procoagulant, fibrinolytic, and innate immunity pathways. Study Populations. Cases. VTE cases were consecutive Mayo Clinic outpatients with objectively-diagnosed deep vein thrombosis (DVT) and/or pulmonary embolism (PE) residing in the upper Midwest and referred by Mayo Clinic physician to the Mayo Clinic Special Coagulation Laboratory for clinical diagnostic testing to evaluate for an acquired or inherited thrombophilia, or to the Mayo Clinic Thrombophilia Center. Any person contacted to be a control but discovered to have had a VTE was evaluated for inclusion as a case. Cases were primarily residents from Minnesota, Wisconsin, Iowa, Michigan, Illinois, North or South Dakota, Nebraska, Kansas, Missouri and Indiana. A DVT or PE was categorized as objectively diagnosed when (a) confirmed by venography or pulmonary angiography, or pathology examination of thrombus removed at surgery, or (b) if at least one non-invasive test (compression duplex ultrasonography, lung scan, computed tomography scan, magnetic resonance imaging) was positive. A VTE was defined as: Proximal leg deep vein thrombosis (DVT), which includes the common iliac, internal iliac, external iliac, common femoral, superficial [now termed "femoral"] femoral, deep femoral [sometimes referred to as "profunda" femoral] and/or popliteal veins. (Note: greater and lesser saphenous veins, or other superficial or perforator veins, were not included as proximal or distal leg DVT). Distal leg DVT (or "isolated calf DVT"), which includes the anterior tibial, posterior tibial and/or peroneal veins. (Note: gastrocnemius, soleal and/or sural [e.g., "deep muscular veins" of the calf] vein thrombosis was not included as distal leg DVT). Arm DVT, which includes the axillary, subclavian and/or innominate (brachiocephalic) veins. (Note: jugular [internal or external], cephalic and brachial vein thrombosis was not included in "arm DVT"). Hepatic, portal, splenic, superior or inferior mesenteric, and/or renal vein thrombosis. (Note: ovarian, testicular, peri-prostatic and/or pelvic vein thrombosis was not included). Cerebral vein thrombosis (includes cerebral or dural sinus or vein, saggital sinus or vein, and/or transverse sinus or vein thrombosis). Inferior vena cava (IVC) thrombosis Superior vena cava (SVC) thrombosis Pulmonary embolism Patients with VTE related to active cancer, antiphospholipid syndrome, inflammatory bowel disease, vasculitis, a rheumatoid or other autoimmune disorder, a vascular anomaly (e.g., Klippel-Trénaunay syndrome, etc.), heparin-induced thrombocytopenia, or a mechanical cause for DVT (e.g., arm DVT or SVC thrombosis related to a central venous catheter or transvenous pacemaker, portal and/or splenic vein thrombosis related to liver cirrhosis, IVC thrombosis related to retroperitoneal fibrosis, etc.), with hemodialysis arteriovenous fistula thrombosis, or with prior liver or bone marrow transplantation were excluded. Controls. A Mayo Clinic outpatient control group was prospectively recruited for this study. Controls were frequency-matched on the age group (18-29, 30-39, 40-49, 50-59, 60-69, 70-79, and 80+ years), sex, myocardial infarction/stroke status, and state of residence distribution of the cases. We selected clinic-based controls using a controls' database of persons undergoing general medical examinations in the Mayo Clinic Departments of General Internal Medicine or Primary Care Internal Medicine. Additionally persons undergoing evaluation at the Mayo Clinic Sports Medicine Center, and the Department of Family Medicine were screened for inclusion as controls. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to venous thrombosis through large-scale genome-wide association studies of 1,300 clinic-based, VTE cases and 1300 clinic-based, unrelated controls. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000289

• Transdisciplinary Research Into Cancer of the Lung (TRICL) - Exome Plus Targeted Sequencing

  The study was conducted under the auspices of the Transdisciplinary Research In Cancer of the Lung (TRICL) Research Team, which is a part of the Genetic Associations and MEchanisms in ONcology (GAME-ON) consortium, and associated with the International Lung Cancer Consortium (ILCCO). Ethics All participants provided written informed consent. All studies were reviewed and approved by institutional ethics review committees at the involved institutions. Sequencing data are derived from four sub-studies. The sub-studies that contributed include Harvard, Liverpool, Toronto, and IARC. The IARC and Toronto studies are described above. A description of the Harvard and Liverpool studies is provided below. Liverpool Lung Project: The Liverpool Lung Project (LLP)1 is a case control and cohort study, which has over 11,500 individuals, with detailed epidemiological, clinical and outcome data with associated specimens (i.e. tumour tissue, blood, plasma, sputum, bronchial lavage, EBUS and oral brushings). The participants have completed a detailed lifestyle questionnaire and updated data on clinical outcome and hospital events are collected through the Office of National Statistics, Cancer Registry and from Health Episode Statistics. The project is registered on the UK National Institute for Health Research (NIHR) lung cancer portfolio and has all the required ethical approvals and sponsorship arrangements in place. The LLP has detailed standard operating procedures (SOP) for all aspects of the recruitment, data, specimen collection as well as the data storage. The LLP Cohort study has 8,224 participants with blood and 7,761 with plasma samples. The LLP case-control samples have been incorporated into in a large number of international GWAS and molecular studies 2,3, methylation 4-7, microRNA 8and next generation studies 9-11, resulting in high ranking publications, as well as forming the basis for the LLP risk prediction model 12-14 which has been utilised in the UK lung cancer screening trial (UKLS) 15-17 Patient and control DNAs were derived from EDTA-venous blood samples. Harvard Samples. David Christiani at the Harvard University School of Public Health has been directing research studies to investigate etiological factors influencing lung cancer development since 1983 and has amassed a collection of 2000 controls and 5055 lung cancer cases. He has been actively collecting and storing snap frozen tumor samples since 1992. Around 1500 tumor samples have been collected and the average wet tumor yield is about 30 grams of tumor, of which 631 cases have completely annotated clinical and survival information. Pathology confirmation is provided by two pathologists. At the time of surgery, a minimum of 30 grams of wet lung tumor tissue and 30 grams of non-involved tissue from the same lobe is sectioned, flash frozen and sent to Dr. Christiani's lab for logging and storage. A blood sample for DNA and serum is collected. A structured interview by trained research staff is conducted on each case, and clinical outcomes and treatments is extracted and entered into the molecular epidemiology data base at Harvard. Fresh frozen samples have been collected from 1451 lung cancer and are available for study. Samples from this collaborative study have played key roles in major studies, including the initial finding describing EGFR mutations in lung cancer 22. Participants in this study are patients, > 18 years of age, with newly diagnosed histologically confirmed lung cancer. Samples that are included in the analysis have the following histologies: Adenocarcinoma: 8140/3, 8250/3, 8260/3, 8310/3, 8480/3 8560/3; LCC: 8012/3, 8031/3; squamous carcinoma: 8070/3, 8071/3, 8072/3, 8074/3; and other NSCLC: 8010/3, 8020/3, 8021/3, 8032/3, 8230/3. The Toronto Study: The Toronto study was conducted in the Great Toronto Area between 1997 and 2014. Cases were recruited at the hospitals in the network of University of Toronto and Lunenfeld- Tanenbaum Research Institute. At the time of recruitment in the clinical setting, provisional diagnoses of lung carcinoma were first assigned based on clinical criteria. Diagnoses for all cases included were histologically confirmed by the reference pathologist who is a specialist in pulmonary pathology, based on review of pathology reports from surgery, biopsy or cytology samples in 100% of cases. Diagnostic classification was done initially according to ICD-9, ICD-10, and ICD for oncology-2, and subsequently converted to ICD-O-3. Tumors were grouped into the major categories included in this analysis according to primary cancer type based on the ICD-3 definitions. Controls were randomly selected from individual visiting family medicine clinics and Ministry of Finance Municipal Tax Tapes. All subjects were interviewed using a standard questionnaire and information on lifestyle risk factors, occupational history, medical and family history was collected. Blood samples were collected from more than 85% of the subjects. IARC: The IARC data are derived from case-control studies conducted in Russia and include samples that have available tissue samples. Patient and control DNAs were derived from EDTA-venous blood samples. The lung cancer patients were classified according to ICD-O-3; SQ: 8070/3, 8071/3, 8072/3, 8074/3; AD: 8140/3, 8250/3, 8260/3, 8310/3, 8480/3, 8560/3, 8251/3, 8490/3, 8570/3, 8574/3; with tumous with overlapping histologies classified as mixed. The Lung Cancer Transdisciplinary Research Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" section of this top-level study page phs000876 Lung Cancer Transdisciplinary Research Cohort. phs000877 Meta Analysis phs000878 CIDR Lung Cancer phs001681 Affy Axiom Array

  Study phs000876

• 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

• Screening_for_human_epigenetic_variation_at_CpG_islands

  Many human characteristics, including susceptibility to disease, are determined genetically. An unexplored alternative to such genetic determination concerns epigenetic mechanisms such as DNA methylation. CpG islands (CGIs) are generally constitutively hypomethylated, however there are circumstances in which they become heavily methylated and, when coincident with a gene promoter, this invariably causes transcriptional silencing. CGI methylation occurs in normal tissues during processes such as X-inactivation, but abnormal patterns of methylation have also been implicated in disease. The vast majority of evidence relates to cancer, where silencing of multiple genes in this way appears to be a causal contributor to the cancer state. To address the role and extent of CGI methylation in ‘normal’ and diseased cells we applied MBD-affinity purification in conjunction with next generation sequencing in a panel of human brain autopsy samples. These samples represent a panel of individuals as well as specific brain regions and neurological pathologies.

  Study EGAS00001000074

• Evaluating genetic ancestry and self-reported ethnicity in the context of carrier screening

  Current professional society guidelines recommend genetic carrier screening be offered on the basis of ethnicity, or when using expanded carrier screening panels, they recommend to compute residual risk based on ethnicity. We investigated the reliability of self-reported ethnicity in 9125 subjects referred to carrier screening. We identified several discrepancies between the two sources of self-reported ethnicity and genetic ancestry. As a result, carrier rates and residual risks provided for patient decision-making are impacted if using self-reported ethnicity. We recommend the routine use of pan-ethnic carrier screening panels in reproductive medicine. Furthermore, the use of an ancestry model would allow better estimation of carrier rates and residual risks.

  Study phs001482

• Novel mutational mechanisms and drivers in Pancreatic Neuroendocrine Tumours

  Pancreatic neuroendocrine tumours (PanNETs) are increasing in prevalence due to earlier detection, consequently creating challenges in clinical care, as current classification permits unsatisfactory therapeutic stratification. We performed whole genome sequencing of 102 primary PanNETs and defined the genomic events underlying their pathogenesis. We describe the mutational signatures they harbour, including a novel G:C>T:A Base-Excision-Repair-deficiency signature due to MUTYH inactivation. We uncover a larger than expected proportion of germline mutations in clinically sporadic PanNETs. These include previously unreported mutations in DNA repair genes MUTYH, CHEK2, BRCA2, and PALB2, which together with MEN1 and VHL occur in 16% of patients. Somatic mutations (point mutations and structural rearrangements) commonly occurred in genes involved in 4 main pathways: chromatin remodelling, DNA damage repair, activation of mTOR signalling (including novel EWSR1 gene fusions) and telomere maintenance. Aberrations in these mechanisms are associated with subtypes of PanNET with potential clinical application for prognostic and therapeutic stratification.

  Study EGAS00001001732

• Spatiotemporal evolution and inter-patient heterogeneity in primary and recurrent/metastatic head and neck squamous cell carcinoma

  Head and neck cancer squamous cell carcinomas (HNSCCs) are heterogeneous in terms of origin and aetiology. In addition, there is uncertainty about the spatiotemporal genetic evolution from initial diagnosis to recurrence/metastasis (R/M) after primary treatments and further disease progression following systemic treatment. Changes in the genetic profile have implications on the selection of appropriate treatments for patients, especially in the era of targeted therapies and immunotherapies. Here, we analysed a cohort of 11 HNSCC patients with metachronous R/M, among whom nine had paired primary and R/M samples suitable for next-generation sequencing (multiple R/M samples collected at different time points were available for some of the patients). At the genomic level, the R/M samples shared a fraction of the somatic single nucleotide variants (SNVs) with the index primary tumours, but they also acquired many additional mutations, while losing only a few others. A similar behaviour was also observed when examining the changes of mutational signatures between primary and R/M samples. Overall, R/M appeared thus more genetically diverse than the respective primary tumours. The transcriptomic analysis showed that R/M samples had lower immune cell infiltration and several genes related to immune response were significantly downregulated compared to the primary samples. Our results underline the importance of analysing multiple samples per patient to obtain a more complete picture of the patient’s tumour and advocates a re-biopsy in the event of R/M and treatment failure, in order to select the most appropriate therapeutic strategy.

  Study EGAS00001007464

• Confronting historical legacies of biological anthropology in South Africa—Restitution, redress and community-centered science: The Sutherland Nine

  We describe a process of restitution of nine unethically acquired human skeletons to their families, together with attempts at redress. Between 1925–1927 C.E., the skeletonised remains of nine San or Khoekhoe people, eight of them known-in-life, were removed from their graves on the farm Kruisrivier, near Sutherland in the Northern Cape Province of South Africa. They were donated to the Anatomy Department at the University of Cape Town. This was done without the knowledge or permission of their families. The donor was a medical student who removed the remains from the labourers’ cemetery on his family farm. Nearly 100 years later, the remains are being returned to their community, accompanied by a range of community-driven interdisciplinary historical, archaeological and analytical (osteobiographic, craniofacial, ancient DNA, stable isotope) studies to document, as far as possible, their lives and deaths. The restitution process began by contacting families living in the same area with the same surnames as the deceased. The restitution and redress process prioritises the descendant families’ memories, wishes and desire to understand the situation, and learn more about their ancestors. The descendant families have described the process as helping them to reconnect with their ancestors. A richer appreciation of their ancestors’ lives, gained in part from scientific analyses, culminating with reburial, is hoped to aid the descendant families and wider community in [re-]connecting with their heritage and culture, and contribute to restorative justice, reconciliation and healing while confronting a traumatic historical moment. While these nine individuals were exhumed as specimens, they will be reburied as people.

  Study EGAS50000000971

• Ovarian Cancer Organoid Biobank - RNASeq data

  Ovarian cancer (OC) is a heterogeneous disease usually diagnosed at a late stage. Experimental in vitro models that faithfully capture the hallmarks and tumor heterogeneity of OC are limited and hard to establish. We present a novel protocol that enables efficient derivation and long-term expansion of OC organoids. Utilizing this protocol, we have established 56 organoid lines from 32 patients, representing the spectrum of ovarian neoplasms, including non-malignant borderline tumors, as well as mucinous, clear-cell, endometrioid, low- and high-grade serous carcinomas. OC organoids recapitulate histological and genomic features of the pertinent lesion from which they were derived, illustrating intra- and inter-patient heterogeneity, and can be genetically modified. We show that OC organoids can be used for drug screening assays and capture different tumor subtype responses to the gold standard platinum-based chemotherapy, including acquisition of chemoresistance in recurrent disease. Finally, OC organoids can be xenografted, enabling in vivo drug sensitivity assays. Taken together, this demonstrates their potential application for research and personalized medicine.

  Dataset EGAD00001004509

• HELIUS DAC The HELIUS data are owned by the Amsterdam University Medical Centers, location AMC in Amsterdam, The Netherlands. To allow sharing of microbiome data collected in HELIUS with (inter)national researchers, 16s rRNA sequence analysis has been stored at the European genome-phenome archive (EGA; accession code EGAD00001004106). This requires that access needs to be granted, also because the HELIUS data are stored with relevant phenotypical variables. Access is granted to all researchers affiliated with an internationally recognized research institution who request to use the HELIUS data within the EGA context, after having signed the data transfer agreement. Any researcher can request the data by submitting a proposal to the HELIUS Executive Board as outlined at http://www.heliusstudy.nl/en/researchers/collaboration, by email: heliuscoordinator at amsterdamumc dot nl. The HELIUS Executive Board will check proposals if they do not conflict with ethical approvals and informed consent forms of the HELIUS study.

  Dac EGAC00001000895

• CRISPR-Mediated ASD Gene Knockout Reduces Neuronal Activity

  Autism Spectrum Disorder is phenotypically and genetically heterogeneous. We present a CRISPR gene editing strategy to insert a protein tag and premature termination sites creating an induced pluripotent stem cell (iPSC) knockout resource for functional studies of 10 ASD-relevant genes (AFF2/FMR2, ANOS1, ASTN2, ATRX, CACNA1C, CHD8, DLGAP2, KCNQ2, SCN2A, TENM1). Neurogenin 2 (NGN2)-directed induction of iPSCs allowed production of excitatory neurons, and mutant proteins were not detectable. RNAseq revealed convergence of several neuronal networks. Using both patch-clamp and multi-electrode array approaches, the electrophysiological deficits measured were distinct for different mutations. However, they culminated in a consistent reduction in synaptic activity, including reduced spontaneous excitatory post-synaptic current frequencies in AFF2/FMR2-, ASTN2-, ATRX-, KCNQ2- and SCN2A-null neurons. Despite ASD susceptibility genes belonging to different gene ontologies, isogenic stem cell resources can reveal common functional phenotypes, such as reduced functional connectivity.

  Study phs001816

• Privacy Notice for Helpdesk service

  Privacy Notice for EGA Helpdesk service This Privacy Notice explains what personal data is collected by the specific service you are requesting, for what purposes, how it is processed, and how we keep it secure. Note that this service collects personal data directly provided by the user, and also collects personal data from users that is provided by other organisations. 1. Who controls your personal data and how to contact us? European Genome- Phenome Archive - EGA offers a service for permanent archiving and sharing of all types of personally identifiable genetic and phenotypic data resulting from biomedical research projects, jointly managed by European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI) and Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG). EMBL-EBI and CRG represent joint Data Controllers’ of processing of your personal data. They and their Data protection officers may be contacted for data protection queries and for exercising your rights under Section 8. You may contact EMBL-EBI, represented by dr. Thomas Keane, by: email at: tk2@ebi.ac.uk or post at EMBL-EBI, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridgeshire, UK. EMBL’s Data Protection Officer may be contacted by: telephone at +49 6221 387-8590, email at dpo@embl.org , or post at EMBL Heidelberg, Data protection officer, Meyerhofstraße 1, 69117 Heidelberg, Germany. You may contact CRG, whose EGA team is represented by dr. Jordi Rambla de Argila, by: email at jordi.rambla@crg.eu, or post at Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG), Dr.Aiguader 88, PRBB Building, 08003 Barcelona, Spain. CRG Data protection officer may be contacted by: email at dpo@crg.eu post at Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG), C/ Dr. Aiguader, 88, PRBB Building, 08003 Barcelona, Spain. 2. Which is the lawful basis for processing personal data? We process your personal data on the grounds of important public interest. For monitoring your activities on the website, we process your personal data on the grounds of important public interest. Such legal basis is found in Article 5(1)(a) of EMBL Internal Policy No 68 on General Data Protection (hereinafter IP 68), which is equivalent to Article 6 (1)(e) of the EU General Data Protection Regulation (hereinafter GDPR) and upon which personal data are processed for the achievement of the aims laid down in 1973 agreement establishing EMBL, such as the promotion of the cooperation in the fundamental research, in the development of advanced instrumentation and in advanced teaching in molecular biology and dissemination of information. 3. What personal data is collected from users of the service? How do we use this personal data? We collect the following personal data from users of the service: Name Email address Job title Date and time when the support request was sent We will use your personal data for the following purposes: To provide uninterrupted user access to the service To better understand the needs of the users and guide future improvements of the service To create anonymous usage statistics 4. Who will have access to your personal data? The personal data will be disclosed to: Authorised staff in the data controller’s institutions acting on data controller`s behalf and instructions. 5. Will your personal data be transferred to third countries (i.e. countries not part of EU/EEA) and/or international organisations? There are no personal data transfers to third countries or international organizations. 6. How long do we keep your personal data? Any personal data directly obtained from you will be retained as long as the service is live. Such duration serves the purpose of enabling scientific research and ensures legal compliance and facilitates internal and external audits if they arise. By contrast, the log files for the data categories related to anonymous usage statistics (raw web service logs) are processed only for 30 days and thereafter erased. 7. The joint Data Controllers provide these rights regarding your personal data You have the right to: Not be subject to decisions based solely on an automated processing of data (i.e. without human intervention) without you having your views taken into consideration. Request at reasonable intervals and without excessive delay or expense, information about the personal data processed about you. Under your request we will inform you in writing about, for example, the origin of the personal data or the preservation period. Request information to understand data processing activities when the results of these activities are applied to you. It must be clarified that rights under points 4 and 5 are only available whenever you need support whilst using our website. For other processing based on the grounds of important public interest you cannot exercise your rights to object, rectify or erase your personal data according to the Article 13(2)(a)(b) of IP 68 (equivalent to Article 17(3)(b)(d) and Article 21(6) of the GDPR). 8. Supervisory authority If you wish to complain against the processing of your personal data, you may do so by post at: EMBL Heidelberg, Data Protection Committee, Meyerhofstraße 1, 69117 Heidelberg, Germany, or Autoritat Catalana de Protecció de Dades (Catalan Data Protection Authority), C/Rosselló 214, Esc A, 1r 1a, Barcelona 08008, Spain. Published at: February 6, 2019

  Documentation data-protection/privacy-notice/ega-helpdesk

• GA4GH

  Standards The EGA is a long-standing supporter of the Global Alliance for Genomics & Health (GA4GH) to enhance responsible sharing of human genetic data through the development of interoperable global standards for human data access. The EGA is one of the founding GA4GH Driver Projects and has contributed to the development and implementation of several GA4GH standards and APIs. Below is a list of the GA4GH standards and APIs that are currently available or planned for implementation at EGA. TechnicalStandards Purpose SpecificationVersion SupportedVersion Implementation Large Scale Genomics htsget A protocol for secure, efficient, and reliable access to sequencing read and variation data. V1.3.0 V1.0.0 Specification Documentation Endpoint Read File Formats (SAM/BAM/CRAM) Specifications for storing next-generation sequencing read data. V3.0.0 V3.0.0 Implementation Example of Usage Variation File Formats (VCF/BCF) The specifications for Variant Call Format Files (VCF) and its binary counterpart BCF. V4.0.0 V2.0.0 V4.0.0 V2.0.0 Implementation Example of Usage Crypt4GH v1.0 Enables direct byte-level compatible random access to encrypted genetic data stored in community standards (e.g. CRAM, VCF) V1.0 V1.0 Specification Documentation Endpoint refget API Enables access to reference sequences using an identifier derived from the sequence itself. V1.2.6 NA Specification RNAget API v1 Provides a means of retrieving data from several types of RNA experiments including (i) feature-level expression data from RNA-seq type measurements and (ii) coordinate-based signal/intensity data similar to a bigwig representation via a client/server model. V1.0.0 NA Documentation Discovery Beacon v2 Supports discovery of genomic variants, phenotypes, and individuals V1.0.1 V0.3 Web UI API Source Code Service Info API v1 The Service Info API is an endpoint for describing GA4GH service metadata, designed for extension and inclusion in other APIs. Service info is used to describe a single service, while Service Registry is used to describe multiple services. V1.0.0 NA Documentation Service Registry API v1 provides information about other GA4GH services, primarily for the purpose of organizing services into networks or groups and service discovery across organizational boundaries. V1.0.0 NA Documentation Data Use Researcher Identities Data Use Ontology (DUO) Allow users to semantically tag genomic datasets with usage restrictions, allowing them to become automatically discoverable based on a health, clinical, or biomedical researcher’s authorisation level or intended use. 2021-02-23 2021-02-23 Specification Documentation Endpoint Authentication & Authorization Infrastructure (AAI) The GA4GH AAI specification leverages OpenID Connect (OIDC) Servers for use in authenticating the identity of researchers desiring to access clinical and genomic resources from data holders adhering to GA4GH standards, and to enable data holders to obtain security-related attributes of those researchers. V1.2.0 V1.2.0 API URI: ega.ebi.ac.uk:8443 Documentation Repository Researcher IDs (passport, visa) Specify the collection of researchers that may access a dataset at any given time, and the credentials they must supply. V1.0.1 V1.0.1 Specification Documentation Endpoint Cloud Tool Registry Service API TRS is a standard API for exchanging tools and workflows to analyze, read, and manipulate genomic data. V2.0.1 NA Documentation Repostiory Data Repository Service API ​DRS API is a standard for building data repositories and adapting access tools to work with those repositories, works with other approved APIs from the GA4GH Cloud Work Stream to allow researchers to discover algorithms across different cloud environments and send them to datasets they wish to analyse. V1.0.3 NA Documentation Repostiory Workflow Execution Service API This API lets users run a single workflow (defined using CWL or WDL) on multiple different platforms, clouds, and environments, and be confident that it will work the same way. The API provides methods to request that a workflow be run, pass parameters to that workflow, get information about running workflows, and cancel a running workflow. V1.0.1 NA Documentation Repostiory Genomic Knowledge Standards Variation Representation v1 Provides a flexible framework of computational models, schemas, and algorithms to precisely and consistently exchange genetic variation data across communities. V1.3.0 EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Clin/ Pheno Data Capture Phenopackets Provides information models with different levels of complexity to enable high level clinical phenotype information as well as deep clinical phenotype information to be exchanged. V2.0.0 Included in Ongoing Submissions EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Driver Project The EGA, jointly coordinated by the EBI and the CRG, was announced, in 2017, to be one of the 15 Driver Projects for GA4GH. Driver Projects are international genomic data initiatives, focussed on real projects and challenges that will guide the development efforts in order to accelerate and enable completely responsible and standarised data sharing by 2022. All chosen Driver Projects make a cross-sectional effort by playing an important role across the different workstreams. Thomas Keane, Jordi Rambla, Mallory Freeberg, and Aina Jené have been named Driver Project Champions for the EGA. All Driver Project Champions will be leading this ambitious initiative for the following years.

  Documentation about/projects-and-funders/ga4gh

• Mechanism and Response of Thymoglobulin in Patients with Myelodysplastic Syndrome (MDS)

  In people with MDS, the bone marrow stops making healthy blood cells and instead produces poorly functioning, malformed, and immature blood cells. This can lead to anemia resulting from too few healthy red blood cells, infection resulting from too few healthy white blood cells, and bleeding resulting from too few healthy platelets. The exact cause of MDS remains unknown, but it may be caused by abnormal autoimmune activity in which activated T cells, a type of white blood cell, prevent normal bone marrow production. ATG, a medication that inhibits immune function, can restore normal blood production in some people with MDS, but it is not known how this happens and why it does not happen in all MDS patients. The purpose of this study is to examine the effects of ATG in adults with MDS and to determine which individuals with MDS are most likely to benefit from treatment with ATG. Based on disease severity and likely disease progression, participants will be separated into either a high-risk group or a low-risk group. Participants will be hospitalized for a 4-day period during which they will receive daily infusions of ATG. Oral prednisone will be given 2 days before hospitalization, throughout hospitalization, and then for 14 days after hospitalization to limit the side effects of ATG. Antihistamines and acetaminophen will also be given during hospitalization to reduce the chances of an allergic reaction to ATG. After discharge, all participants will attend monthly study visits that will include blood collection, review of disease symptoms, and evaluation of medication response. At Week 16, participants in the high-risk group will undergo additional blood collection, a bone marrow biopsy, and a thorough evaluation of disease progression and the effects of MDS on daily living abilities. Participants in the low-risk group will undergo these same procedures at Week 24. Follow-up for all participants may last up to 2 years.

  Study phs000697

• Virtual Growing Child 5-Dimensional Functional Models for Treating Respiratory Anomalies (dMRI-VGC)

  Objectives We are sharing a database of dynamic magnetic resonance imaging (dMRI) scans of normal children, which can serve as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The database can also be useful to advance future AI-based research on image-based object segmentation and analysis. Background In pediatric patients with respiratory abnormalities, it is important to understand the alterations in regional dynamics of the lungs and other thoracoabdominal components, which in turn requires a quantitative understanding of what is considered as normal in healthy children. Currently, such a normative database of regional respiratory structure and function in healthy children does not exist. Participants 200 normal children (ages 6-18 years) participated in our research study related to this dataset. DesignThe shared open-source normative database is from our ongoing virtual growing child (VGC) project, which includes 4D dMRI images representing one breathing cycle for each normal child and also segmentations of 10 objects at end expiration (EE) and end inspiration (EI) phases of the respiratory cycle in the 4D image. The lung volumes at EE and EI as well as the excursion volumes of chest wall and diaphragm from EE to EI, left and right sides separately, are also reported. The database has thus 4,000 3D segmentations from 200 normal children in total. The database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. All dMRI scans are acquired from normal children during free-breathing. The dMRI acquisition protocol was as follows: 3T MRI scanner (Verio, Siemens, Erlangen, Germany), true-FISP bright-blood sequence, TR=3.82 ms, TE=1.91 ms, voxel size ~1×1×6 mm3, 320×320 matrix, bandwidth 258 Hz, and flip angle 76o. With recent advances, for each sagittal location across the thorax and abdomen, we acquired 40 2D slices over several tidal breathing cycles at ~480 ms/slice. On average, 35 sagittal locations are imaged, yielding a total of ~1400 2D MRI slices, with a resulting total scan time of 11-13 minutes for any particular study participant.The collected dMRI scan data then went through the procedure of 4D image construction, image processing, object segmentation, and volumetric measurements from segmentations. 4D image construction: For the acquired dMRI scans, we utilized an automated 4D image construction approach to form one 4D image over one breathing cycle (consisting of typically 5-8 respiratory phases) from each acquired dMRI scan to represent the whole dynamic thoraco-abdominal body region. The algorithm selects 175-280 slices (35 sagittal locations × 5-8 respiratory phases) from the 1400 acquired slices in an optimal manner using an optical flux method. Image processing: Intensity standardization is performed on every time point/3D volume of the 4D image so that image values have the same tissue-specific meaning across all subjects. Object segmentation: For each subject, there are 10 objects segmented at both EE and EI time points in this database. They include the thoracoabdominal skin outer boundary, left and right lungs, liver, spleen, left and right kidneys, diaphragm, and left and right hemi-diaphragms. All dMRI scans utilize large field of view images, which include the full thorax and abdomen to the inferior aspect of the kidneys in the sagittal plane. We used a pretrained U-Net based deep learning network to first segment all objects, and then all auto-segmentation results were visually checked and manually refined as needed, under the supervision of a radiologist with over 25 years of expertise in MRI and thoracoabdominal radiology. Manual segmentations have been performed for all objects in all datasets. Volumetric measurements based on object segmentations for lung volumes (left and right separately) at EE and EI, as well as for chest wall and diaphragm excursion volumes (left and right separately) are reported. ConclusionsThe provided database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. The database has 4,000 3D segmentations from 200 normal children, which to our knowledge is the largest and only such dMRI dataset to date. All images and object segmentations are saved in DICOM. All DICOM files (176,574 in total) have been anonymized, and PHI has been removed. The database can be used as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The large amount of object segmentations can potentially benefit AI-based research on image-based object segmentation and analysis.

  Study phs004002

• Center for Craniofacial and Dental Genetics: Study of Dental Caries and Cleft Lip/Palate in Guatemala

  This study provides an opportunity to investigate the genetics of both dental caries and orofacial clefts (OFCs) in one set of families ascertained in Guatemala. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org), which was developed through the trans-NIH Genes, Environment, and Health Initiative (GEI). Furthermore this study brings together multiple research priorities of the University of Pittsburgh Center for Craniofacial and Dental Genetics (www.ccdg.pitt.edu). A genome-wide panel of 610,000 SNPs was genotyped at the Broad Institute to be comparable to our other pertinent GENEVA studies that are also part of dbGaP (dbGaP accession number phs000095, "Dental Caries: Whole Genome Association and Gene x Environment Studies" and dbGaP accession number phs000094, "International Consortium to Identify Genes and Interactions Controlling Oral Clefts"). The goal of this study is to investigate genetic determinants to dental caries and to OFCs in a novel study population. To date, most genetic studies of dental caries have been conducted in Caucasians, and of OFCs in Caucasians and Asians. The Guatemalan population under study is rural and ethnically mixed with a high proportion of Native-South-Americans. Thirty-six Guatemalans from this dataset were also part of the recent GWAS studies of cleft lip and cleft palate (Beaty et al., 2010 and 2011, dbGaP Study Accession: phs000094, "International Consortium to Identify Genes and Interactions Controlling Oral Clefts"). In addition to the families ascertained in Guatemala, some subjects were genotyped with this cohort to augment the data in the parent GENEVA study (Dental Caries: Whole Genome Association and Gene x Environment Studies, dbGaP accession number phs000095), in particular individuals from the IOWA and PITT GENEVA study sites. Their data are available with the parent study. Dental caries (also known as tooth decay) remains the most common chronic disease of childhood, five times more common than asthma and seven times more common than environmental allergies, with more than 40% of children exhibiting caries when they enter kindergarten. In 2005, it was estimated that dental health care costs were approximately $84 billion, of which 60% or about $50 billion were related to treatment of dental caries. The etiology of dental caries has been studied for many years. Multiple factors contribute to a person's risk for caries, including: 1) environmental factors such as diet, oral hygiene, fluoride exposure and the level of colonization of cariogenic bacteria and 2) host factors such as salivary flow, salivary buffering capacity, position of teeth relative to each other, surface characteristics of tooth enamel and depth of occlusal fissures on posterior teeth. In spite of all that is known about this disease, there are still individuals who appear to be more susceptible to caries and those who are extremely resistant, regardless of the environmental risk factors to which they are exposed, implying that genetic factors also play an important role in caries etiology. This conclusion is supported by studies in both humans and animals, with the most compelling evidence coming from studies of twins reared apart in which investigators found significant resemblance within monozygotic (MZ) but not dizygotic (DZ) twin pairs for percentage of teeth and surfaces restored or carious and estimated the genetic contribution to caries as 40%. Other recent studies of twins reared together estimated the heritability for caries, adjusted for age and gender, as ranging from 45-64%. In our study populations of families, we also estimated caries heritability as approximately 54%-70% of variation in primary dentition caries scores and 35%-55% in the permanent dentition (Wang et al., 2010). Orofacial clefts (OFCs), particularly cleft lip with or without cleft palate (CL/P) and isolated cleft palate (CP) are a major public health problem, affecting one in every 500-1000 births worldwide thus representing the most common facial birth defect and one of the most common of all congenital anomalies. CL/P is a major structural birth defect that is notable for significant lifelong morbidity and complex etiology. The extensive psychological, surgical, speech and dental involvement emphasize the importance of understanding the underlying causes of CL/P. Therefore, many research groups have attempted to elucidate the etiology of CL/P, with some recent success by our research group and others (see Beaty et al., 2010, 2011; Dixon et al., 2011). It is clear that CL/P can occur as part of Mendelian syndromes, that certain chromosomal abnormalities include CL/P in the phenotype, and that certain teratogens can increase the risk of having an offspring with CL/P. However, phenotypes of known etiology comprise only a small portion of all individuals with a CL/P or CP, and the major focus of research into OFCs is to develop an understanding of the etiology of nonsyndromic (NS) forms of clefting. A major focus of the University of Pittsburgh CCDG has been to study additional phenotypes within nonsyndromic OFC families in order to identify sub-clinical expressions of OFC risk genes or risk variants, e.g. SNPs (see Weinberg et al., 2006). A detailed oral exam is conducted as part of these extended phenotypic studies, including a dental caries exam. Note that although there are some reports in the literature of higher caries experience in individuals with clefts, the most recent meta-analysis of those literature reports concluded that individuals born with these defects do not have a higher frequency of caries (Hasslöf and Twetman, 2007). Notably, we investigated the association of CL/P and caries in three of our study populations (including part of the Guatemalan population in this GWAS study) and also found no increase in caries rates of CL/P cases versus controls (Jindal et al., 2011). This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org), which was developed through the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to dental caries and oral clefts through large-scale genome-wide association studies of well-characterized Guatemalan families and individuals. Genotyping was performed at the Broad Institute of MIT and Harvard. The study was supported by the National Institute of Dental and Craniofacial Research (NIDCR, U01-DE018903). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000440

• Understanding Self- Organising Capacity of Stem Cells during Implantation and Early Post-implantation Development in vitro and in vivo: Implications for Human Development (2020-04-20)

  The aim of this project is to differentiate human embryonic stem cells to an extra-embryonic fate, specifically the hypoblast. This is of uttermost importance given the current lack of human hypoblast stem cells. We hypothesized that the pluripotent characteristics of the starting human embryonic stem cell population may dictate the competency for extra-embryonic cell fate specification. Based on this hypothesis and using human embryonic stem cells maintained in different naïve-like culture regimes, we have now developed conditions that allow the differentiation of human embryonic stem cells to a stable GATA6+ SOX2- population. This suggests that these cells may be putative human hypoblast stem cells. To validate this finding here we propose to perform RNA sequencing experiments of the differentiated human embryonic stem cells. By comparing their RNA expression profile to the single cell sequencing data of the human embryo that we are currently generating, we will be able to determine the identity of our GATA6+ SOX2- cells, and establish whether they represent the in vivo human hypoblast. This dataset contains all the data available for this study on 2020-04-20.

  Dataset EGAD00001006056

• Combination Immune Checkpoint Inhibition in Australian Rare Cancers_WES

  Combination immune checkpoint inhibition (ICI) with anti-CTLA-4 and anti-PD-1 blockade has demonstrated significant clinical activity across tumour types. Rare cancer patients have limited treatment options due to the scarcity of studies of novel treatment options, so they are often offered chemotherapies untested in their disease. ONJ2016-001/CA209-538 is a prospective, multicentre clinical trial of combination ICI (ipilimumab and nivolumab) in patients with advanced rare cancers, including biliary tract, adrenocortical, rare gynaecological and neuroendocrine cancers (https://clinicaltrials.gov/ct2/show/NCT02923934). The primary endpoint is clinical benefit rate (complete response + partial response + stable disease >3 months) by RECIST 1.1. This study involves generation and analysis of pre-treatment formalin fixed paraffin embedded (FFPE) tumour DNA sequencing data, paired with germline DNA sequencing data, to potentially identify genomic biomarkers that may associate with clinical benefit or other clinical efficacy / toxicity metrics.

  Dataset EGAD00001015465