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• Sequencing data for oesophageal and related samples - Abbas et al (RNA)

  Data supporting: "Mutational signature dynamics shaping the evolution of oesophageal adenocarcinoma" Abbas et al (RNA for 197 samples)

  Dataset EGAD00001011269

• RNA sequencing of control OM cells exposed to traffic-related air pollutants

  Total RNA sequencing of olfactory mucosa (OM) cells derived from cognitively healthy individuals exposed to traffic-related ultrafine particles (UFPs) for 24h and 72h in submerged cultures. The UFPs used for exposures were: A0, A20 and Euro6. Exposures were compared to the corresponding blank samples.

  Dataset EGAD00001011317

• DNA WGS BAM files for PMID: 41845530 titled: Multi-omics reveals key molecular and cellular features of advanced small cell lung cancers associated with distinct therapeutic opportunities

  DNA WGS BAM files for PMID: 41845530 titled: Multi-omics reveals key molecular and cellular features of advanced small cell lung cancers associated with distinct therapeutic opportunities

  Dataset EGAD00001016147

• Clinical Cancer Sequencing

  Translating whole exome sequencing (WES) for prospective clinical use may impact the care of cancer patients; however, multiple innovations are necessary for clinical implementation. These include: (1) rapid and robust WES from formalin-fixed paraffin embedded (FFPE) tumor tissue, (2) analytical output similar to data from frozen samples, and (3) clinical interpretation of WES data for prospective use. In this study, we describe a prospective clinical WES platform for archival FFPE tumor samples. The platform employs computational methods for effective clinical analysis and interpretation of WES data. When applied retrospectively to 511 exomes, the interpretative framework revealed a "long tail" of somatic alterations in clinically important genes. Prospective application of this approach identified clinically relevant alterations in 15/16 patients. Overall, this methodology may inform the widespread implementation of precision cancer medicine.

  Study phs000694

• single-cell RNA-seq Case-Control study of children progressing to Type1 diabetes

  Single-cell RNA sequencing was carried out on four selected PBMC samples of Finnish children at risk of developing Type 1 diabetes and their gender age and HLA matched control children using 10X genomic platform. All four Case children were positive for multiple islet specific autoantibodies and two of them also progressed to clinical disease during the follow up whereas the control children remain negative for all autoantibodies. Age at seroconversion for Case 2, 3, 5, and 9 children is 12, 18, 24, and 18 months, respectively. Age at sampling, in months, for the pairs was as follows (Case/Control): pair 2:24/24; pair 3: 12/18; pair 5: 12/12; pair 9:24/18.

  Study EGAS00001004070

• Exploring germline and somatic mutagenesis in the extended family with germline pathogenic variant in POLD1

  This study aimed to estimate the influence of POLD1 L474P on somatic and germline mutagenesis. We studied the mutation rate and spectra in germline and soma in an extended family harboring the POLD1 c.1421T>C p.(Leu474Pro) pathogenic variant. The study includes WGS data for DNA extracted from blood, fibroblasts or buccal swabs from the 16 family members, from DNA obtained single-cell derived colonies obtained from immortalized fibroblasts for 8 of 16 family members and from the same single-cell derived immortalized fibroblasts after approximately 40 passages for 6 out of 8 family members. Additionaly, study includes WXS sequencing for 1 tumor FFPE sample and adjacent normal tissue from the individual from one family member.

  Study EGAS00001006434

• SCLC study MGH - WES dataset

  For whole-exome sequencing 1 µg of DNA from fresh-frozen tumors was fragmented by sonication technology (for DNA from fresh-frozen tumors: Bioruptor, diagenode, Liѐge, Belgium; for DNA from FFPE material: Covaris). The fragments were end-repaired and adaptor-ligated, including incorporation of sample index barcodes. After size selection, libraries were subjected to an enrichment process with Sure select XT (Agilent). The final libraries were sequenced with a paired-end 2×75 bp protocol for an average coverage of 100-120x

  Dataset EGAD00001003970

• 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

• Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell RCC -- CA209-009

  Immune checkpoint inhibitors targeting the PD-1 pathway have transformed the management of many advanced malignancies, including clear cell renal cell carcinoma (ccRCC), but the drivers and resistors of PD-1 response remain incompletely elucidated. Here, we analyzed 592 tumors collected from advanced ccRCC patients enrolled in prospective clinical trials of treatment with PD-1 blockade (or mTOR inhibition as control arm) by whole-exome and RNA-sequencing, integrated with immunofluorescence analysis, to define the somatic alteration landscape of late-stage ccRCC and to uncover the immunogenomic determinants of therapeutic response. While conventional genomic markers (tumor mutation burden, neoantigen load) and degree of CD8+ T cell infiltration were not associated with clinical response, we discovered numerous chromosomal alterations in advanced ccRCC associated with response or resistance to PD-1 blockade. These advanced tumors were highly CD8+ T cell infiltrated, with only 22% and 5% with an immune desert and immune excluded phenotype, respectively. Our analysis revealed that CD8+ infiltrated tumors are depleted of favorable PBRM1 mutations and are enriched for unfavorable chromosomal losses of 9p21.3 when compared to non-infiltrated tumors. These data demonstrate how the interplay of somatic alterations and immunophenotypes impacts therapeutic efficacy.

  Study EGAS00001004290

• Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell RCC -- CA209-010

  Immune checkpoint inhibitors targeting the PD-1 pathway have transformed the management of many advanced malignancies, including clear cell renal cell carcinoma (ccRCC), but the drivers and resistors of PD-1 response remain incompletely elucidated. Here, we analyzed 592 tumors collected from advanced ccRCC patients enrolled in prospective clinical trials of treatment with PD-1 blockade (or mTOR inhibition as control arm) by whole-exome and RNA-sequencing, integrated with immunofluorescence analysis, to define the somatic alteration landscape of late-stage ccRCC and to uncover the immunogenomic determinants of therapeutic response. While conventional genomic markers (tumor mutation burden, neoantigen load) and degree of CD8+ T cell infiltration were not associated with clinical response, we discovered numerous chromosomal alterations in advanced ccRCC associated with response or resistance to PD-1 blockade. These advanced tumors were highly CD8+ T cell infiltrated, with only 22% and 5% with an immune desert and immune excluded phenotype, respectively. Our analysis revealed that CD8+ infiltrated tumors are depleted of favorable PBRM1 mutations and are enriched for unfavorable chromosomal losses of 9p21.3 when compared to non-infiltrated tumors. These data demonstrate how the interplay of somatic alterations and immunophenotypes impacts therapeutic efficacy.

  Study EGAS00001004291

• Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell RCC-- CA209-025

  Immune checkpoint inhibitors targeting the PD-1 pathway have transformed the management of many advanced malignancies, including clear cell renal cell carcinoma (ccRCC), but the drivers and resistors of PD-1 response remain incompletely elucidated. Here, we analyzed 592 tumors collected from advanced ccRCC patients enrolled in prospective clinical trials of treatment with PD-1 blockade (or mTOR inhibition as control arm) by whole-exome and RNA-sequencing, integrated with immunofluorescence analysis, to define the somatic alteration landscape of late-stage ccRCC and to uncover the immunogenomic determinants of therapeutic response. While conventional genomic markers (tumor mutation burden, neoantigen load) and degree of CD8+ T cell infiltration were not associated with clinical response, we discovered numerous chromosomal alterations in advanced ccRCC associated with response or resistance to PD-1 blockade. These advanced tumors were highly CD8+ T cell infiltrated, with only 22% and 5% with an immune desert and immune excluded phenotype, respectively. Our analysis revealed that CD8+ infiltrated tumors are depleted of favorable PBRM1 mutations and are enriched for unfavorable chromosomal losses of 9p21.3 when compared to non-infiltrated tumors. These data demonstrate how the interplay of somatic alterations and immunophenotypes impacts therapeutic efficacy.

  Study EGAS00001004292

• GSA reference

  reference whole exome sequence serving as a reference of individuals. Includes the raw GSa files and the called variants in vcf format merge for all individuals (S1-S5).

  Dataset EGAD50000000481

• Genome - MBD4-deficient AML

  Genome sequencing at diagnosis and post induction for WEHI-AML-1 and WEHI-AML-2. Whole genome sequencing was performed on an Illumina HiSeq X Ten.

  Dataset EGAD00001003568

• Colon cancer ctDNA

  Samples prepared using Safe-SeqS technology. All samples ran on an Illumina MiSeq instrument. Fastq files for read 1 and the index read present (R and I respectively).

  Dataset EGAD00001003579

• HIV exome pilot, exome data part 1 GRCh37_53

  exome sequence data for 49 HIV elite long term non-progressors and rapid progressors. Partial dataset (overlap with EGAD00001000087) of raw BAMs mapped to GRCh37_53.

  Dataset EGAD00001000047

• PacBio data of de novo assembly individual EGYPT

  This is the PacBio long read data used for performing de novo assembly of the EGYPT individual (mapped against GRCh38).

  Dataset EGAD00001006034

• Processed RNAseq data

  This dataset contains log2(TPM + 1) transformed counts for the 823 tumor samples profiled by RNAseq.

  Dataset EGAD00001006618

• Tumor from individual with germline POLD1 L474P

  This dataset includes WXS sequencing for 1 tumor FFPE sample and adjacent normal tissue from the individual from one family member.

  Dataset EGAD00001009281

• Annotated germline variant calls from the lungNENomics project

  Annotated germline variant calls derived from WGS data for 72 individuals from the lungNENomics cohort. Variant calling was performed with Strelka2 and annotation with InterVar.

  Dataset EGAD00001015672

• Genetic Analysis of Psoriasis and Psoriatic Arthritis: GWAS of Psoriatic Arthritis

  This study is a genome-wide study of genetic associations with psoriatic arthritis (PsA), an inflammatory musculoskeletal condition that develops in up to 30% of people who have chronic psoriasis skin lesions. 1,526 cases affected with psoriatic arthritis and 1,508 unaffected controls were recruited and typed on the Illumina HumanOmni1-Quad BeadChip array. After application of quality control measures to both samples and SNPs, genotypes for 791,217 autosomal SNPs were available for 1,430 PsA cases and 1,417 controls. Imputation using phased haplotypes for the EUR subpopulation of release 3, phase 1 of the 1000 Genomes Project as a reference, resulted in genotypes for 11,532,644 SNPs and 918,976 indels passing our imputation quality threshold (Mach r2 ≥ 0.3) that were available for downstream analysis. Association at a genome-wide threshold of significance was found for five regions (near genes TNIP1, IL12B, HLA-C, TRAF3IP2, and TYK2).

  Study phs000982

• Systems biology of Colorectal Cancer

  Colorectal cancer (CRC) is one of the most common cancers in both males and females, and it is perhaps the best understood of all epithelial tumors in terms of its molecular origin. Yet, despite large amount of work that has concentrated on understanding of colon tumorigenesis, we still do not know the full complement of molecular lesions that are individually necessary – and together sufficient – to cause colorectal cancer. Neither do we understand why some specific mutations that are relatively rare in other tumors (e.g. loss of the APC tumor suppressor) are extremely common in colorectal cancer. We propose here to use the tools of systems biology to develop a quantitative and comprehensive model of colorectal tumorigenesis. The model will include identification of cell-type specific and oncogenic pathways that contribute to colon tumorigenesis, and explain in molecular detail how a genotype of an individual CRC leads to activation of downstream genes that drive uncontrolled cell growth. This model will subsequently be used to find novel therapeutic targets, to guide genetic screening to identify individuals with elevated risk for developing CRC, and to classify patients into molecular subgroups to select the treatment combination that is optimal for each patient (personalized medicine). The specific objectives of the SYSCOL project are: 1. Identify genetic markers for individual risk using genotyping and sequencing of constitutional DNA from sporadic and familial CRC cases and controls 2. Identify genes and regulatory elements that contribute to colorectal cancer cell growth 3. Use data from Aims 1-2 to develop a quantitative model for colorectal tumorigenesis 4. Apply the model for identification of high-risk individuals, for molecular classification of the disease, and for identification of novel molecular treatment targets

  Study EGAS00001000854

• eIMPACT Trial: Modernized Collaborative Care to Reduce the Excess CVD Risk of Older Depressed Patients

  Cardiovascular disease (CVD) is the #1 killer of American men and women, and its economic burden is substantial and on the rise. Adults with depression are at elevated risk of CVD events and poor CVD prognosis. Unfortunately, past clinical trials of depression treatments have not observed the anticipated cardiovascular benefits. A novel explanation for these null results is that the depression interventions in these trials, which all involved patients with preexisting CVD, were delivered too late in the natural history of CVD. Accordingly, the objective of this single-center, parallel-group, assessor-blinded randomized controlled trial was to determine whether successful depression treatment before, versus after, the onset of clinical CVD reduces CVD risk in depression. Primary care patients with depression and elevated CVD risk from a safety net healthcare system (N = 216, Mage = 59 years, 78% female, 50% Black, 46% with income <$10,000/year) were randomized 1:1 to 12 months of the eIMPACT intervention (a modernized collaborative stepped care program involving internet cognitive-behavioral therapy [CBT], telephonic CBT, and/or select antidepressants) or usual primary care for depression (primary care providers supported by embedded behavioral health clinicians and psychiatrists). The primary outcome was endothelial dysfunction (brachial flow-mediated dilation) at 12 months, and the secondary outcomes were self-reported depressive symptoms (Hopkins Symptom Checklist-20), autonomic dysfunction (high-frequency heart rate variability), systemic inflammation (interleukin-6 and high-sensitivity C-reactive protein), and platelet activation (β-thromboglobulin and platelet factor 4) at 12 months. The central hypothesis was that the eIMPACT intervention would improve endothelial dysfunction in depressed adults by decreasing depressive symptoms, autonomic dysfunction, systemic inflammation, and platelet activation. Primary results are posted to ClinicalTrials.gov at NCT02458690. The de-identified limited dataset available on BDC consists of only the individual phenotypic data needed to confirm this trial’s primary results – i.e., randomization status, primary and secondary outcome variables, and participant characteristics utilized in the hypothesis-testing models (age, sex, race, education, income, and systolic blood pressure). We also provide the SAS code to replicate the results of the hypothesis-testing models reported in Table 4 and Supplemental Tables 1-3 in the main outcomes paper (#2 in Selected Publications). Of note, the user will need to specify the file path for the dataset to run the SAS code. Instructions for requesting individual-level data are available on BDC at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BDC. For questions about availability, you may contact the BDC team at https://biodatacatalyst.nhlbi.nih.gov/contact.

  Study phs003283

• Analysis of Donor Pancreata Defines the Transcriptomic Signature and Microenvironment of Early Neoplastic Pancreatic Lesions

  The adult healthy human pancreas has been poorly studied given lack of indication to obtain tissue from the pancreas in the absence of disease and rapid postmortem degradation. We obtained pancreata from brain dead donors thus avoiding any warm ischemia time. We found that neoplastic lesions occur frequently in healthy organs, in donors as young as in their 3rd decade of life. Using the 10X Genomics Platform, we performed single cell RNA sequencing on 7 donor pancreata, 6 of which we separately sequenced tissue from the pancreas head and tail, for a total of 11 sequencing runs. Of note, because one of our runs (containing sequencing from pancreas head and tail) did not pass qc, our final analysis in our study utilized 6 donor pancreata, 5 of which contained pancreas head and tail sequenced separately. This is a robust single-cell dataset of healthy, nonpathologic pancreas tissue and includes acinar, ductal, and non-epithelial cells (myeloid cells, fibroblasts, endothelial cells, lymphocytes).Briefly, the donor pancreas was dissected by a clinically-trained pancreatobiliary surgeon. The organ was dissected into head, body, and tail, with portions of each placed into DMEM media with 1% BSA/10µM Y27632. Tissue was minced into 1mm3 pieces, then digested with 1 mg/mL collagenase P for 20-30 min at 37°C with gentle agitation. Digested tissue was rinsed three times with DMEM/1% BSA/10µM Y27632, then filtered through a 40µm mesh. Resulting cells were submitted to the University of Michigan Advanced Genomics Core for single cell sequencing using the 10x Genomics Platform. FASTQ files of all sequencing runs along with clinical annotation of donors is available through this dbGaP submission.

  Study phs003229

• M116 Proteome Extracellular Vesicle Profiling

  For extracellular vesicles (ECV) extraction from plasma, 1M ammonium acetate was added to precipitate ECVs on ice for 45 min. Then, 100 mM ammonium acetate was added to the mixture, and ECVs were precipitated by centrifugation at 20,000g for 30 min. ECVs were washed with 50 mM ammonium bicarbonate (Sigma-Aldrich, Cat. 1066-33-7). Then, 600 µl of 1% ammonium deoxycholate (Sigma Aldrich, Cat. K2755-1MG) were added. The concentration of protein in each sample was measured using a bicinchoninic acid assay (BCA assay). Ammonium bicarbonate was used to dilute 500 µg of protein into a final volume of 500 µl. Next, dithiothreitol (Sigma-Aldrich, Cat. D9779) was added to obtain a final concentration of 20 mM, followed by iodoacetamide (Sigma-Aldrich, Cat. I6125) to a final concentration of 40 mM. Next, trypsin (Roche, Cat. RTRYP-RO) was added to the sample in a 1:25 protein ratio and incubated at 37°C overnight. Next day, formic acid (ThermoFisher Scientific, Cat. 28905) in a final concentration of 0.1% was added and extraction of proteins was done using Empore™ Solid Phase Extraction Cartridges (3M), following manufacturer’s instructions. The eluted samples were then centrifuged for 90 min. using a speed vacuum centrifuge (Thermo, RC1010), followed by snap freezing in liquid nitrogen. Then, the samples were kept in a freeze dryer (LyoDry Compact Benchtop, MechaTech) overnight. Next, the samples were reconstituted in 30 µl of 0.1% formic acid (FA) and an o-Phthaladehyde (Oparil) assay was performed to determine the concentration of each sample. After that, the sample was prepared in a concentration of 0.5 µg/µl using 0.1% FA and alcohol dehydrogenase (ADH). The samples were prepared in glass mass spectrometry vials for proteomic analysis using a Waters Synapt G2Si High-Definition Mass Spectrometry (Waters Corporation) operated by the MassLynx 4.1., 110 min. running time with 2 µl of an injection containing 1 µg of peptide. Quality controls were also run along with samples to guarantee consistency. Pooled quality controls were made from all samples, in which the samples were run at the beginning, middle and end of the mass spectrometry run. Samples were randomized before running the experiment. The proteomic data was then imported into Progenesis software 4.2 (Nonlinear Dynamic, UK) to identify and quantify peptides and proteins.

  Dataset EGAD50000001679

• Immune Checkpoint Blockade for Relapsed Hematologic Malignancy Post-HSCT

  Relapse after allogeneic hematopoietic stem cell transplantation (HSCT) is characterized by poor outcomes and novel therapeutic options are urgently needed. Immune checkpoint blockade using CTLA-4 and PD-1 blocking antibodies are a potential concept for reinstating a dormant graft-versus-leukemia (Gvl) effect. This study investigated transcriptomic changes in biopsies obtained from patients who received immune checkpoint blockade for relapsed hematologic malignancy post-HSCT and elucidated transcriptional programs associated with successful reinvigoration of GvL. Differential gene expression analysis showed evidence of increased T cell infiltration in biopsies of responders to ipilimumab treatment and demonstrated shared gene programs between GvL and GvHD.

  Study phs003291

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Genetic Epidemiology of COPD Study (COPDGene)

  Cohort Description The Genetic Epidemiology of COPD (COPDGene) study is a non-interventional, multicenter, longitudinal, case-control study at 21 US sites of smokers with a ≥10 pack-year history of smoking, with and without COPD, and healthy never smokers. The initial goal was to characterize disease-related phenotypes and explore associations with susceptibility genes. Data Being Submitted Wave 1 questionnaire data includes 397 variables for up to 3683 COPDGene participants in C4R. Wave 2 questionnaire data includes 447 variables for up to 2191 COPDGene participants in C4R. Dried Blood Spot/Serosurvey data includes 7 variables for up to 1692 COPDGene participants in C4R. Derived data includes 43 variables for up to 4082 COPDGene participants in C4R. Phenotype data includes 113 variables for up to 4082 COPDGene participants in C4R.

  Study phs002910

• Centers for Common Disease Genomics (CCDG) - Whole Genome Sequencing in Type 1 Diabetes (T1DGC)

  The Type 1 Diabetes Genetics Consortium (T1DGC) was established to collect resources (biological samples and data) and conduct research to better understand the genetic basis of type 1 diabetes (T1D). Collection was initiated by ascertaining affected sib-pair families (both parents, two affected siblings and, when available, an unaffected sibling), collected from five geographic regions through four recruitment networks (Asia-Pacific, Europe, North America, United Kingdom). In addition, the T1DGC collected trio families (both parents and affected child) and cases and controls from low-prevalence populations (African-American, with four grandparents self-reporting as African ancestry; Mexican-American, with four grandparents self-reporting as ancestry from Mexico). The T1DGC also served as a repository for contributed collections from other studies, all meeting the broad data-sharing policy of the T1DGC, for inclusion in the genetic studies. These collections include T1D case samples ascertained from the UK Genetic Resource Investigating Diabetes (UK GRID) cohort, SEARCH for Diabetes in Youth (SEARCH), The Genetics of Kidneys in Diabetes (GoKinD), and control samples obtained from the British 1958 Birth Cohort, the UK National Blood Services collection, CLEAR (Consortium for the Longitudinal Evaluation of African-Americans with Early Rheumatoid Arthritis), the New York Cancer Project (NYCP), and other cohorts. For the NHGRI-funded Centers for Common Disease Genomics (CCDG) project, participants with T1D and ancestry-matched controls were identified through the T1DGC, either through direct ascertainment or by contribution from other sources to the T1DGC. As the CCDG has focused initially on non-Caucasian populations for whole genome sequencing, T1DGC participants of African, Mexican and Asian ancestry (targeting ~1200 cases and ~1200 controls in each ancestral group) and a small group of participants of Northern European ancestry (~100 cases, ~100 controls) were to be contributed to the study. Whole genome sequencing of T1DGC samples would be conducted at Washington University McDonnell Genome Institute and based upon matching case-control status within an ancestry group and prioritization by the CCDG.

  Study phs001222

• bulk RNAseq analysis of tumor from 3 non-muscle-invasive bladder cancer patients

  We performed screenings of patient-specific neo-epitopes by tumor-infiltrating CD8+ T lymphocytes of 4 non-muscle invasive bladder cancer patients. After the tumor resection, these patients went on to receive BCG treatment as they had high-risk disease. In order to select the neo-epitopes, we performed bulk RNAseq of the bladder tumor to select the mutated genes that were expressed in the tumor (TPM >1). For patient UC2, 3 different tumors were resected at the same time and RNA from these 3 tumors were sent separately for RNAseq. For patient UC4, no RNA was available, so this patient is not included in this study.

  Study EGAS50000001383

• Isotype-resolved sequencing of B-cell receptor in sorted memory populations (2017-09-13)

  Optimisation of ex vivo Memory B cell Expansion/Differentiation for Interrogation of Rare Peripheral Memory B Cell Subset Responses 1) This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/ This dataset contains all the data available for this study on 2017-09-13.

  Dataset EGAD00001003747

• Pathogenic Neurofibromatosis type 1 (NF1) RNA splicing resolved by targeted RNAseq

  Neurofibromatosis type 1 (NF1) is caused by loss-of-function variants in the NF1 gene. Approximately 10% of these variants affect RNA splicing and are either missed by conventional DNA diagnostics or are misinterpreted by in silico splicing predictions. Therefore, a targeted RNAseq-based approach was designed to detect pathogenic RNA splicing and associated pathogenic DNA variants. an in-house developed tool (QURNAS) was used to calculate the enrichment score (ERS) for each splicing event. RNA enrichment of NF1 and SPRED1 was done using SPET (NUGEN - NF1 only) and using SureSelect (Agilent - NF1 and SPRED1).

  Study EGAS00001005530

• Type 1 Diabetes Genetics Consortium (T1DGC): Copy Number Variant (CNV) Study

  Type 1 Diabetes Genetics Consortium (T1DGC) was formed to address issues of limited sample size and consistency of phenotyping that had limited genetic investigations on risk of type 1 diabetes (T1D). The T1DGC first collected affected sib pair (ASP) families from four geographic networks (Asia-Pacific, Europe, North America, United Kingdom). In addition, T1D parent-offspring trios as well as cases and controls were ascertained from existing and de novo collections. For T1D, the genome-wide association study (GWAS) design has been successful at detecting ~50 loci that contribute disease risk. However, in the case of T1D as well as almost all other traits, the sum of these loci does not fully explain the heritability estimated from familial studies. One possibility for the undiscovered contribution to familial aggregation is that additional variants exist but have not yet been found because they have not effectively been targeted by the GWAS design. In this study, we focus on a specific class of large deletions/duplications -- copy number variants (CNVs) - and particularly to the subset of these loci that mutate rapidly, are not tagged by SNPs, and are highly polymorphic. The T1DGC assembled 2,601 T1D ASP families and 69 Parent-T1D offspring trios, that were eligible for this study. All samples included in this series have reported or self-declared European ancestry. All DNA samples were collected after approval from relevant institutional research ethics committees. Importantly, the source of DNA for CNV evaluation was uniform within a family (either all from PBMC or from EBV-transformed cell lines). We use a family based design that was optimized to capture these previously untested variants. We then perform a genome-wide scan to assess their contribution to T1D.

  Study phs000910

• RNASeq from PPGL-derived PC12 cell lines cultivated in normoxia and hypoxia conditions.

  PC12 cells derived from rat PPGL were then either continued cultivated in the same normoxia condition (37˚C, 5% CO2 and 21% O2 balanced with N2) or were placed in a hypoxic incubator, that maintained a constant hypoxic environment (37˚C, 5% CO2 and 1% O2 balanced with N2), for different short (12h, 24h, and 48h) or prolonged time (36 days, approximately 860h).

  Study EGAS50000000814

• RNA_expression_profiling_of_melanoma_patient_derived_xenograft

  Patient-derived xenografts (n=96) were derived from metastatic melanoma patients. RNA expression profiling will be preformed to study 1. HLA-typing and 2. the effect of the tumour microenvironment on tumour growthThis data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001001537

• The_impact_of_the_human_leukaemia_virus_HTLV_1_on_host_gene_expression

  PBMCs were purified from blood samples of 8 HTLV-1 infected individuals, and cryo-preserved in fetal calf serum containing 10% DMSO. DNA from each samples was extracted using Qiagen Blood & Tissue kit according to the manufacturer's protocol. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002259

• Batch1_Genotypes_Raw

  Genotypes generated for Puno cohort Batch 1. Case (PRE) and control (PUN) families were recruited in hospital, and additional unrelated controls were recruited in university (UNA). Raw genotypes no QC.

  Dataset EGAD00010002126

• Liver Tumours WGS (2020-02-20)

  The aim of this study is to define the mutational landscape of human liver tumours. . This dataset contains all the data available for this study on 2020-02-20.

  Dataset EGAD00001005993

• G3BP2-KIT drives leukemia amenable to kinase inhibition in Ph-like ALL

  G3BP2-KIT drives leukemia amenable to kinase inhibition in Ph-like ALL: RNAseq for human sample

  Dataset EGAD00001007564

• RNA-sequencing of cohesin-mutated and wildtype adult AMLs

  Transcriptomic data generated by RNA-sequencing for adult human AMLs with STAG2 or RAD21 mutations or no cohesin mutations (CTRL-AMLs).

  Dataset EGAD00001011197

• Exome sequencing of osteosarcoma, blood and saliva

  We profiled 31 osteosarcoma tumor patient samples, 18 blood and 1 saliva control samples by exome sequencing, for a total of 50 samples. The raw fastqs are provided.

  Dataset EGAD00001011370

• Engineered cartilage: deriving design principles from human developmental pathways (2025-10-02)

  Engineered cartilage: deriving design principles from human developmental pathways . This dataset contains all the data available for this study on 2025-10-02.

  Dataset EGAD00001015724

• Engineered cartilage: deriving design principles from human developmental: RNA (2025-07-30)

  Engineered cartilage: deriving design principles from human developmental pathways . This dataset contains all the data available for this study on 2025-07-30.

  Dataset EGAD00001015664

• Genomics and Epigenomics of the Elderly Response to Pneumococcal Vaccines

  Pneumococcal Vaccine Study: This is a study designed to develop a detailed transcriptional and cellular profile of the immune response to pneumococcal vaccination with conjugated and non-conjugated polysaccharide vaccines in the senescent immune system of older adults. In this study, 39 healthy adults ages 60 and older that have never received pneumococcal vaccination, have been randomized to receive Prevnar 13 (Pfizer), a conjugated 13-valent vaccine or Pneumovax 23 (Merck), a non-conjugated 23-valent vaccine. Four (4) study visits occurred over 70 days at Day -7 (baseline), day 1, day 10 and day 60 (± 5d) post vaccination.Bulk RNA-sequencing data are available on the 4 time points for 31 of the participants and 1 replicate. ATAC-Doublet Detector Study: We generated single nucleus ATAC-seq (snATAC-seq) data from PBMCs of 2 baseline blood samples from the pneumococcal study. Generated data was then used to benchmark a novel computational method for detecting multiplets in snATAC-seq data, namely, AMULET (https://github.com/UcarLab/AMULET). Multiplets confound downstream analyses by introducing combined epigenomic profiles from 2 or more cells/nuclei. AMULET detects multiplets by using unique snATAC-seq data features that allow it to reliably use read counts to detect multiplets for their removal from data analyses.

  Study phs002361

• Single-cell level characterization of B cell depletion and repopulation following rituximab in systemic lupus erythematosus

  Rituximab, a CD20+ B cell depletion therapy, is frequently used in the treatment of systemic lupus erythematosus (SLE). However, variability in patient response highlights the need for a deeper understanding of the underlying immune cell dynamics of B cell depletion and repopulation. In this study, we conducted longitudinal single-cell profiling of nine SLE patients treated with rituximab from pretreatment to up to 15 months post-treatment. These were compared to eight healthy controls. We profiled PBMCs via 10X Genomics single-cell RNA, surface protein (CITE-seq), B cell receptor (BCR), and T cell receptor (TCR) sequencing and sequenced bulk BCR repertoires in parallel. For single cell sequencing, 10 pools were created with an equal number of cells from 4 samples each. Samples collected at different timepoints from the same patient were distributed across different pools to allow demultiplexing of individuals using genotypes captured from the scRNA-seq data. Libraries were pooled using a ratio of GEX:CITE:TCR:BCR=9:2:1:1 and were sequenced across two lanes of the NovaSeq 6000. For bulk BCR sequencing, individual samples were demultiplexed using primer barcodes and paired-end BCR amplicon reads were merged prior to submission; data are provided as unmapped single-end reads.

  Study EGAS00001006798

• Chromatin Accessibility Landscape of Human Pancreatic Ductal Adenocarcinoma (PDAC)

  Therapeutic options in pancreatic ductal adenocarcinoma (PDAC) are largely limited to cytotoxic chemotherapy without the benefit of molecular markers predicting response. By ATAC-seq analyses of EpCAM+ PDAC malignant epithelial cells sorted from 54 freshly resected human tumors, we discovered a signature of 1092 chromatin loci displaying differential accessibility between patients with disease-free survival (DFS) < 1 year and patients with DFS > 1 year. Using this signature, we further develop a chromatin accessibility microarray methodology termed “ATAC-Array”, an easy-to-use platform obviating the time and cost of next-generation sequencing. PDAC prognosis can be predicted by ATAC-array, which represents a low-cost, clinically feasible technology for assessing chromatin accessibility profiles.

  Study phs002394

• TenK10K Phase 1: scRNA-seq fastq format sequencing data

  Single cell RNA-sequencing data for PBMCs from the Tenk10k Phase 1 cohort (1925 individuals post-QC). Libraries were prepared using the 10x Genomics 3’ Chromium Next GEM Single Cell HT V3.1 kit and sequenced on the NovaSeq 6000 platform. This analysis consists of 4,768 fastq format files in 1,192 runs, and an analysis which provides the relationship of 298 library pool samples to 1,925 individual samples.

  Dataset EGAD50000002517

• TenK10K Phase 1: Whole Genome Sequencing tandem repeats multi-sample VCFs

  Tandem repeat variants were called for 1,925 samples from phase 1 of the TenK10K project. Variant calling from WGS alignments to the GRCh38 reference assembly was completed with ExpansionHunter v5. This dataset is comprised of autosomal tandem repeat variants provided as multisample compressed VCF format files. SNV-derived principal component scores used in the manuscript (Tanudisastro et al.) are also provided in this dataset.

  Dataset EGAD50000002378

• RNAseq of pre- and post-5AZA-treated head and neck cancer patients refractory to anti-PD-1 therapy

  RNA sequencing (RNA-seq) was performed on baseline (before low-dose 5-aza treatment) and on-treatment (low-dose 5-aza treatment for 5 or 10 days) tumor samples from eight participants with head and neck cancer who were refractory to anti-PD-1 therapy. RNA library preparations, sequencing reactions, and initial bioinformatics analyses were conducted at GENEWIZ, LLC. In total, 16 150-bp pair-end RNAseq data were generated.

  Dataset EGAD50000001010

• Multi-region sequencing of 10 neuroblastoma cases

  To investigate intratumour heterogeneity and to better understand tumour evolution in neuroblastoma, we have performed a multi-region whole-exome sequencing on a total of 51 spatially separated tumor samples from 9 primary neuroblastomas (2 low-risk, 1 intermediate-risk and 6 high-risk) and 1 relapsed neuroblastoma. We also assessed the impact of chemotherapy on the clonal expansion by sequencing tumour regions from one medium risk and one high-risk tumour for which we had matched samples obtained at diagnosis and after chemotherapy.

  Dataset EGAD00001008020

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW4_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW4_M

  Dataset EGAD00001001834

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW49_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW49_M

  Dataset EGAD00001001831

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW49_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW49_C

  Dataset EGAD00001001829

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW47_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW47_M

  Dataset EGAD00001001828

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW47_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW47_F

  Dataset EGAD00001001827

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW47_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW47_C

  Dataset EGAD00001001826

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW46_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW46_M

  Dataset EGAD00001001825

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW46_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW46_F

  Dataset EGAD00001001824

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW46_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW46_C

  Dataset EGAD00001001823

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW3_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW3_M

  Dataset EGAD00001001822

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW3_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW3_F

  Dataset EGAD00001001821

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW3_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW3_C

  Dataset EGAD00001001820

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW38_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW38_M

  Dataset EGAD00001001819

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW38_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW38_F

  Dataset EGAD00001001818

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW38_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW38_C

  Dataset EGAD00001001817

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW32_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW32_M

  Dataset EGAD00001001816

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB33_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB33_F

  Dataset EGAD00001001731

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB31_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB31_F

  Dataset EGAD00001001728

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB28_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB28_C

  Dataset EGAD00001001721

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB24_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB24_F

  Dataset EGAD00001001713

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB23_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB23_F

  Dataset EGAD00001001710

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB22_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB22_M

  Dataset EGAD00001001708

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB23_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB23_C

  Dataset EGAD00001001709

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB51_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB51_F

  Dataset EGAD00001001761

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB52_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB52_C

  Dataset EGAD00001001763

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB52_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB52_M

  Dataset EGAD00001001765

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB55_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB55_F

  Dataset EGAD00001001767

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB57_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB57_C

  Dataset EGAD00001001769

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB57_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB57_F

  Dataset EGAD00001001770

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB57_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB57_M

  Dataset EGAD00001001771

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB58_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB58_C

  Dataset EGAD00001001772

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB60_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB60_C

  Dataset EGAD00001001775

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB60_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB60_F

  Dataset EGAD00001001776

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB60_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB60_M

  Dataset EGAD00001001777

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB62_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB62_C

  Dataset EGAD00001001778

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB62_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB62_F

  Dataset EGAD00001001779

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB62_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB62_M

  Dataset EGAD00001001780

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW12_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW12_C

  Dataset EGAD00001001784

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW12_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW12_M

  Dataset EGAD00001001786

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW14_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW14_C

  Dataset EGAD00001001787

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW14_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW14_M

  Dataset EGAD00001001789

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW15_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW15_C

  Dataset EGAD00001001790

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW15_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW15_M

  Dataset EGAD00001001792

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW18_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW18_C

  Dataset EGAD00001001793

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW18_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW18_F

  Dataset EGAD00001001794

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW18_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW18_M

  Dataset EGAD00001001795

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW20_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW20_F

  Dataset EGAD00001001797

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW20_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW20_M

  Dataset EGAD00001001798

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW22_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW22_F

  Dataset EGAD00001001800

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW24_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW24_C

  Dataset EGAD00001001802

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW24_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW24_F

  Dataset EGAD00001001803

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW24_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW24_M

  Dataset EGAD00001001804