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• Dataset of Study assessing the efficacy and safety of durvalumab plus olaparib plus fulvestrant in selected metastatic or locally advanced ER-positive, HER2-negative breast cancer patients.

  This dataset is made of 166 samples extracted from 1) FFPE blocks of the most recent biopsy (metastasis when applicable or primary tumor) of the breast tumor 2) Plasma samples and blood samples at baseline, at Day 1 Cycle2, Day 1 Cycle 3 and at progression or treatment discontinuation. Target sequencing has been perfomed using GREAT_A_v3 panel

  Dataset EGAD50000001665

• Loss of RREB1 in pancreatic beta cells reduces cellular insulin content and affects endocrine cell gene expression

  Genome-wide studies have uncovered multiple independent signals at the RREB1 locus associated with altered type 2 diabetes risk and related glycaemic traits. However, little is known about the function of the zinc finger transcription factor Ras-responsive element binding protein 1 (RREB1) in glucose homeostasis or how changes in its expression and/or function influence diabetes risk.

  Dataset EGAD50000000515

• Congenital anosmia 1

  Congenital anosmias can be complete (the lack of a sense of smell) or specific (the inability to detect specific smells). To date, only a single recessive gene underlying complete anosmia has been identified. Here we sequenced the exomes of 10 individuals from a single family, including three with complete anosmia, across three generations to identify the genetic basis of congenital anosmia in this family. 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 (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002210

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  Study EGAS00001008448

• Analysis of mutational and proteomic heterogeneity of gastric cancer to monitor post-treatment tumor burden using circulating tumor DNA

  It remains unclear whether complete spectrum of tumor heterogeneity is reflected in circulating tumor DNA (ctDNA). Here, we performed multi-regional target sequencing of primary tumors from 10 patients who underwent surgical resection for gastric cancer. A total of 126 non-synonymous mutations were identified from 30 samples from 10 tumors. Of these, 16 (12.7%) were likely mutations that occurred early in tumor development (i.e., present in all three regions) and were designated founder mutations. Among those validated with highly sensitive digital PCR (dPCR), 93% of non-founder mutations were found in all three regions. Variant allele frequencies (VAFs) of founder mutations were significantly higher than those of non-founder mutations. Phylogenetic analysis demonstrated that five of eight tumors (62.5%, excluding two hypermutator) had at least one identifiable truncal/founder mutation. These findings led us to prioritize founder mutations for ctDNA detection with individually-designed primer/probe sets for dPCR. Marked proteomic diversity was present across the tumor regions both within and between patients independent of mutational status. In preoperative plasma, the average ctDNA VAF of founder mutations was significantly higher than that of non-founder mutations (p = 0.039). Our present data demonstrated that high VAF mutations in the tumor likely represent founder or truncal mutations. However, mutation patterns alone appear insufficient to predict protein heterogeneity. These results suggest that, in practice, mutations having high VAF identified without multi-regional sequencing may be useful for ctDNA monitoring but are inadequate to fully predict proteomic heterogeneity of the tumor.

  Study JGAS000231

• Prediction and quantification of splice events from RNA-seq data

  Analysis of splice variants from short read RNA-seq data remains a challenging problem. Here we present a novel method for the genome-guided prediction and quantification of splice events from RNA-seq data, which enables the analysis of unannotated and complex splice events. Splice junctions and exons are predicted from reads mapped to a reference genome and are assembled into a genome-wide splice graph. Splice events are identified recursively from the graph and are quantified locally based on reads extending across the start or end of each splice variant. We assess prediction accuracy based on simulated and real RNA-seq data, and illustrate how different read aligners (GSNAP, HISAT2, STAR, TopHat2) affect prediction results. We validate our approach for quantification based on simulated data, and compare local estimates of relative splice variant usage with those from other methods (MISO, Cufflinks) based on simulated and real RNA-seq data. In a proof-of-concept study of splice variants in 16 normal human tissues (Illumina Body Map 2.0) we identify 249 internal exons that belong to known genes but are not related to annotated exons. Using independent RNA samples from 14 matched normal human tissues, we validate 9/9 of these exons by RT-PCR and 216/249 by paired-end RNA-seq (2 x 250 bp). These results indicate that de novo prediction of splice variants remains beneficial even in well-studied systems. An implementation of our method is freely available as an R/Bioconductor package SGSeq.

  Study EGAS00001001026

• PIAMA nasal RNAseq dataset

  This study included nasal gene expression data collected from nasal brushes of adolescents in PIAMA birth cohort, which is used in the project Nasal DNA methylation at three CpG sites predicts childhood allergic disease. 186 samples were included in this analysis, and phenotypes (age and sex) were provided together with a gene expression count table. Gene expression was measured by the Illumina HiSeq2500 sequencer.

  Dataset EGAD00001008767

• RNA-seq from time-course experiment treating cells for 72h

  RNA sequencing from MSTO-211H cell line cultures treated for 72h with vehicle solution, palbociclib 250nM, or abemaciclib 250nM (N = 3, each). RNA-seq prepared using TruSeq Stranded mRNA libraries and sequenced with Illumina HiSeq 4000. Data is in raw fastq format, paired end. Some samples have been split in two lanes, with a final count of 24 fastq files.

  Dataset EGAD00001007737

• CRISPR screening in human trophoblast stem cells reveals both shared and distinct aspects of human and mouse placental development

  The placenta serves as the interface between the mother and fetus, facilitating the exchange of gases and nutrients between their separate blood circulation systems. Trophoblasts in the placenta play a central role in this process. Our current understanding of mammalian trophoblast development relies largely on mouse models. However, given the diversification of mammalian placentas, findings from the mouse placenta cannot be readily extrapolated to other mammalian species, including humans. To fill this knowledge gap, we performed CRISPR knockout (KO) screening in human trophoblast stem cells (hTSCs). We targeted genes essential for mouse placental development and identified more than 100 genes as critical regulators in both human hTSCs and mouse placentas. Among them, we further characterized in detail two transcription factors, DLX3 and GCM1, and revealed their essential roles in hTSC differentiation. Moreover, a gene function-based comparison between human and mouse trophoblast subtypes suggests that their relationship may differ significantly from previous assumptions based on tissue localization or cellular function. Notably, our data reveal that hTSCs may not be analogous to mouse TSCs or the extraembryonic ectoderm (ExE) in which in vivo TSCs reside. Instead, hTSCs may be analogous to progenitor cells in the mouse ectoplacental cone and chorion. This finding is consistent with the absence of ExE-like structures during human placental development. Our data not only deepen our understanding of human trophoblast development but also facilitate cross-species comparison of mammalian placentas.

  Study JGAS000659

• Highlights from 2025: Our top 10 requested studies

  In 2025, as in previous years, EGA has facilitated the reuse of numerous datasets spanning a broad range of research and healthcare topics. To reflect on this activity, we would like to highlight the top 10 studies behind the most requested datasets of last year. Notably, the vast majority of these highly requested datasets are cancer-focused, particularly involving the study of immune checkpoint inhibitor therapies. Immune checkpoint inhibitors (ICIs) have become one of the most transformative advances in modern cancer therapy. These monoclonal antibodies enhance antitumor immune responses by targeting inhibitory immune checkpoint molecules. While ICIs have led to remarkably favorable and durable responses in some patients, treatment outcomes vary widely. This variability has fueled intense research efforts aimed at understanding resistance mechanisms, exploring combination therapies, and discovering predictive biomarkers to better stratify patients and guide treatment decisions. Study Title Disease 1 EGAS00001005503 Integrated genomic analyses reveal molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma Liver Cancer 2 EGAS00001005013 Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell lung cancer Lung Cancer 3 EGAS00001004809 BIOKEY: A single-cell catalogue of the dynamic changes underlying Checkpoint Immunotherapy response in Early Breast Cancer Breast Cancer 4 EGAS50000000138 BIOKEY: Immune heterogeneity in small cell lung cancer and vulnerability to immune checkpoint blockade Lung Cancer 5 EGAS50000000497 Bladder cancer subtyping study across 4 atezo clinical trials Bladder Cancer 6 EGAS50000000689 MOSAIC - Multi-Omics Spatial Atlas In Cancer Cancer Atlas 7 EGAS00000000083 METABRIC (Breast cancer - genome and transcriptome) Breast Cancer 8 EGAS00001005702 Human genomic and phenotypic synthetic data for the study of rare diseases Synthetic data 9 EGAS00001002556 TGF-β attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells Bladder Cancer 10 EGAS00001004353 Molecular subsets in renal cancer determine outcome to checkpoint and angiogenesis blockade Renal Cancer The most requested dataset of the year was EGAD00001008128. It belongs to a study that analyzed 358 liver cancer patients enrolled in clinical trials and treated with the combination of atezolizumab (anti–PD-L1) and bevacizumab (anti-VEGF), atezolizumab alone, or sorafenib. The study highlights how bevacizumab enhances anti-PD-L1 effect and identifies candidate biomarkers to predict treatment response. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the third leading cause of cancer-related death worldwide [1]. Because symptoms often appear late, many patients are diagnosed at advanced stages, when curative options are no longer viable. For many years, sorafenib—a multikinase inhibitor approved in 2007—was the only systemic treatment available. Although numerous clinical trials followed, it was not until a decade later that additional therapies showed meaningful clinical benefit. Among these advances, immunotherapy emerged as a promising first-line option [2]. In particular, the combination of atezolizumab and bevacizumab demonstrated improved survival in patients with advanced HCC, reshaping the treatment landscape. However, treatment efficacy remains limited by the complex and diverse hepatic microenvironments that influence immune responses, as well as by high intratumoral heterogeneity. Identifying robust predictors of treatment response is critical to enable effective patient stratification and maximize the benefits of these therapies [2]. The continued demand for these datasets highlights the interest and critical role of data reuse in deepening our understanding of cancer, accelerating the development of more effective therapies, and improving patient outcomes. By providing secure access to high-quality genomic and clinical data, EGA acts as a catalyst for biomedical progress. References Sung, Hyuna, et al. "Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries."CA: a cancer journal for clinicians 71.3 (2021): 209-249. Ladd, Alexandra D, et al.“Mechanisms of drug resistance in HCC.” Hepatology 79(4) (2024): 926-940

  Blog top-10-studies-2025

• Tumor Mutation Burden, Expressed Neoantigen and Immune Microenvironment in Diffuse Gliomas

  A consistent correlation between tumor mutation burden (TMB) and tumor immune microenvironment has not been observed in gliomas as in other cancers. Driver germline and somatic mutations, TMB, neoantigen, and immune cell signatures were analyzed using whole exome sequencing (WES) and transcriptome sequencing of tumor and WES of matched germline DNA in a cohort of 66 glioma samples (44 IDH-mutant and 22 IDH-wildtype). Fourteen samples revealed a hypermutator phenotype (HMP). Eight pathogenic (P) or likely pathogenic (LP) germline variants were detected in 9 (19%) patients. Six of these 8 genes were DNA damage repair genes. P/LP germline variants were found in 22% of IDH-mutant gliomas and 12.5%of IDH-wildtype gliomas (p= 0.7). TMB was correlated with expressed neoantigen but showed an inverse correlation with immune score (R =−0.46, p= 0.03) in IDH-wildtype tumors and no correlation in IDH-mutant tumors. Antigen Processing and Presentation (APP) score correlates with immune score. The APP is higher in non-hypermutator phenotype (NHMP) samples versus HMP samples in the IDH-wildtype gliomas, but higher in HMP samples versus NHMP samples in the IDH-mutant gliomas. Conclusion: TMB was inversely correlated with the immune score in the IDH-wildtype gliomas, and showed no correlation in the IDH-mutant tumors. The APP was correlated with the immune score, and may be further investigated as a biomarker for response to immunotherapy in gliomas. Studies of germline variants in a large glioma cohort are warranted.

  Study phs002653

• Single Cell Transcriptomic Data from CD4+ T Cells from Children with MIS-C

  Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infections in the pediatric population characterized by fever, multi-organ involvement and systemic inflammation. Single-cell RNA sequencing was used to evaluate the transcriptomic signature of CD4+ T cells obtained from the peripheral blood of patients with MIS-C before (n=3) and after (n=5) immunomodulatory treatment, along with pediatric controls (n=4). Compared to controls and treated children with MIS-C, T cells obtained from pre-treatment MIS-C patients upregulated genes associated with activation, such as NF-kB signaling. This single-cell transcriptomic data was used in the context of a larger study entitled, "Notch1-CD22-Dependent Immune Dysregulation in the SARSCoV2-Associated Multisystem Inflammatory Syndrome in Children."

  Study phs003086

• RNA-Seq data from 34 CAF-S3 subset in human breast and ovarian cancers

  This dataset is related to publications Costa et al. Cancer Cell 2018 and Givel et al. Nat. Commun. 2018 which describe the identification of 4 Cancer Associated Fibroblasts (CAF) in breast and ovarian cancer. This dataset contains transcriptomic profiles obtained by RNA-Seq of 34 CAF-S3 samples from breast and ovarian Tumors.

  Dataset EGAD00001004810

• TSCA-LI amplicon sequencing of called variants in WXS data of DSALL.

  Targeted resequencing of samples was done with TruSeq custom amplicon low input kit (TSCA-LI, Illumina). The oligo capture probes were designed to include a prefix of 8 random nucleotides at the 5 end of each probe. The assay is designed such that each targeted locus is annealed with two probes, resulting in amplicons tagged with unique molecular identifiers (UMI) (22) of 16 bases. Raw FASTQ sequencing files were processed as following: (a) The first 8 bases were trimmed from each read and recorded with the corresponding base quality scores (BQ) in the attribute field. (b) Reads were aligned with BWA. (c) First round of PCR duplicate cleaning was performed with picard tools markDuplicates using the parameters BARCODE_TAG=BC TAGGING_POLICY=All REMOVE_DUPLICATES=true (d) Since in the previous step only duplicate reads with identical UMIs were removed, a second pass of filtering was done. Reads with identical mapping were considered unique only if their corresponding UMIs were different in at least 3 positions (i.e., UMI edit distance > 2). (e) Paired-end read pairs overlapping genomic positions were clipped to avoid overestimation of the sequencing coverage using bamUtils clipOverlap.

  Dataset EGAD00001003275

• H3K27ac ChIP-seq in TMPRSS2:ERG positive and negative prostate cancer tissue samples

  The TMPRSS2:ERG (T2E) structural rearrangements typifies ~50% of prostate tumors and results in overexpression of the ERG transcription factor. Using chromatin data collected in T2E and non-T2E primary prostate tumors, we show a distinct cis-regulatory landscape between T2E and non-T2E, inclusive of Cluster Of Regulatory Elements (COREs). This is mediated by ERG co-option of HOXB13 and FOXA1 implementing a T2E-specific transcriptional profile. We also report a T2E-specific CORE on the structurally rearranged ERG locus arising from spreading of the TMPRSS2 locus pre-existing CORE, assisting in its overexpression. Finally, we show that the T2E-specific cis-regulatory landscape reveals a vulnerability against the NOTCH pathway. Taken together, our work shows that overexpressed ERG co-opts master transcription factors to deploy a unique cis-regulatory landscape inducing a druggable dependency on NOTCH signaling in T2E prostate tumors.

  Study EGAS00001002496

• Characterizing the Role of the Immune Microenvironment in Multiple Myeloma Progression at a Single Cell Level

  The plasma cell disease multiple myeloma (MM) is highly dependent on its bone marrow microenvironment (BMME). Early alterations within the bone marrow microenvironment that contribute to the progression of multiple myeloma (MM) from its precursor stages could hold the key to identifying novel therapeutic approaches, yet the intrinsic variability in cellular populations between patients together with differences in sample processing and analysis methods have made it difficult to identify consistent changes between datasets. Here, we used single-cell RNA sequencing of BM cells from precursor stages, monoclonal gammopathy of unknown significance (MGUS), smoldering MM (SMM) and newly diagnosed MM and analyzed our data in combination with a previously published dataset that used a similar patient population and sample processing. Despite vast interpatient heterogeneity, some alterations were seen consistently in both datasets. We identified changes in immune cell populations as disease progressed that were characterized by a substantial decrease in memory and naïve CD4 T cells and an increase in CD8+ effector T cells and T-regulatory cells. These alterations were further accompanied by an enrichment of non-clonal memory B cells and an increase of CD14 and CD16 monocytes in MM compared to its precursor stages. These results provide crucial information of the immune changes associated with the progression to clinical MM and can help to develop immune based strategies for patient stratification and early therapeutic intervention. 

  Study phs002756

• Epigenetic dysregulation in autism spectrum disorder

  Autism spectrum disorder (ASD) is a collection of neuro-developmental disorders characterized by deficits in social interaction and social communication, along with restricted and repetitive behaviour patterns. we globally interrogated the histone acetylomes of enhancers in a large cohort of ASD and control samples by analyzing tissue from three brain regions postmortem: prefrontal cortex (PFC), temporal cortex (TC) and cerebellum (CB). H3K27ac was selected as the representative acetylation mark and 288 ChIP-seq were performed on these postmortem samples.

  Dataset EGAD00001002725

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Oesophageal adenocarcinoma

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute (40X and 20X depth respectively). Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. This dataset contains all the data available for this study on 2021-09-27.

  Dataset EGAD00001006083

• Elevated somatic mutation burdens in normal human cells due to defective DNA polymerases

  Mutation accumulation over time in normal somatic cells contributes to cancer development and is proposed as a cause of ageing. DNA polymerases POLE and POLD1 replicate DNA with high fidelity during normal cell divisions. However, in some cancers defective proofreading due to acquired mutations in the exonuclease domains of POLE or POLD1 causes markedly elevated somatic mutation burdens with distinctive mutational signatures. POLE and POLD1 exonuclease domain mutations also cause familial cancer predisposition when inherited through the germline. Here, we sequenced normal tissue DNA from individuals with germline POLE or POLD1 exonuclease domain mutations. Increased mutation burdens with characteristic mutational signatures were found to varying extents in all normal adult somatic cell types examined, during early embryogenesis and in sperm. Mutation burdens were further markedly elevated in neoplasms from these individuals. Thus human physiology is able to tolerate ubiquitously elevated mutation burdens. Indeed, with the exception of early onset cancer, individuals with germline POLE and POLD1 exonuclease domain mutations are not reported to show abnormal phenotypic features, including those of premature ageing. The results, therefore, do not support a simple model in which all features of ageing are attributable to widespread cell malfunction directly resulting from somatic mutation burdens accrued during life.

  Dataset EGAD00001006212

• 10X snRNA-seq data from human FCDII postoperative brain tissues with mTOR pathway mutations

  Focal cortical dysplasia type II (FCDII) is linked to mTOR pathway mutations and drug-resistant epilepsy. Using 10X snRNA-seq on postoperative FCDII brain tissues, we characterized transcriptional alterations across cell types, revealing mTOR-related dysregulation. These findings provide insights into FCDII pathology and potential therapeutic target

  Study EGAS50000000964

• RaScALL: Rapid screening of RNA-seq in acute lymphoblastic leukaemia

  RNA-sequencing (RNA-seq) efforts in acute lymphoblastic leukaemia (ALL) have identified numerous prognostically significant genomic alterations which can guide diagnostic risk stratification and treatment choices when detected early. However, a full RNA-seq Bioinformatics workflow is time-consuming and costly in a clinical setting where rapid detection and accurate reporting of clinically relevant alterations are essential. To accelerate the identification of ALL-specific alterations (including gene fusions, single nucleotide variants and focal gene deletions), we developed the rapid screening tool RaScALL, capable of identifying more than 100 prognostically significant lesions directly from raw sequencing reads. RaScALL uses the k-mer based targeted detection tool km and known ALL variant information to achieve a high degree of accuracy for reporting subtype defining genomic alterations compared to standard alignment-based pipelines. Gene fusions, including difficult to detect fusions involving EPOR and DUX4, were accurately identified in 98% (164 samples) of reported cases in a 180-patient Australian study cohort and 95% (n=63) of samples in a North American validation cohort. Pathogenic sequence variants were correctly identified in 75% of tested samples, including all cases involving subtype defining variants PAX5 p.P80R (n=12) and IKZF1 p.N159Y (n=4). Accurate detection of intragenic IKZF1 deletions resulting in aberrant transcript isoforms was also detectable with 98% accuracy. Importantly, the median analysis time for detection of all targeted alterations averaged 22 minutes per sample, significantly shorter than standard alignment-based approaches, ensuring accerelated risk-stratification and therapeutic triage.

  Study EGAS00001006460

• RNA and ChIP Sequencing datasets from the study Kalirin-RAC controls nucleokinetic migration in ADRN-type neuroblastoma

  This dataset contains the RNA and ChIP Sequencing data from the study Kalirin-RAC controls nucleokinetic migration in ADRN-type neuroblastoma. The data is organized in 7 experiments which are divided by both sequencing technology or the application of siRNA or drug interventions (or lack thereof) on neuroblastoma cell lines. The experiment names and the file names have been chosen in each respective experiment to guide future users of the data to replicate the analyses in the manuscript.

  Dataset EGAD00001006964

• Action to Control Cardiovascular Risk in Diabetes (ACCORD) Clinical Trial

  The Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial was a randomized, multicenter, double 2 x 2 factorial design study involving 10,251 middle-aged and older participants with type-2 diabetes who are at high risk for CVD events because of existing CVD or additional risk factors. The purpose of ACCORD was to determine if intensive glycemic control, intensive lipid management and intensive blood pressure control could prevent major cardiovascular events (myocardial infarction, stroke or cardiovascular death) in adults with type 2 diabetes mellitus. Secondary hypotheses included treatment differences in other cardiovascular outcomes, total mortality, microvascular outcomes, health-related quality of life and cost-effectiveness. The ACCORD trial failed to show a beneficial effect of intensive blood pressure or lipid therapy on the primary outcome, and intensive glycemia management actually increased mortality. The hypothesis underlying this ancillary study is that the failure of ACCORD to achieve its goal of reducing cardiovascular risk in diabetic patients through intensive management of hyperglycemia, dyslipidemia, and hypertension may be the result of variation in drug response due to genetic variation between individual participants. Benefits of intensive therapy may accrue to subsets of subjects with specific genetic variants predisposing to efficacious responses to particular therapeutic regimens, and harms may accrue to those with other variants predisposing to poor efficacy or adverse events. Identifying these variants could lead to a precision medicine approach to treating diabetes where each patient's genetic profile could identify the most efficacious treatment regimen with the lowest likelihood of adverse events. To test this hypothesis, a genome-wide genetic analysis was undertaken, incorporating both common variants distributed across the genome and rare variants targeted to exonic regions. Associations of genetic variants with short term responses to individual medicines as well as long term outcomes were investigated. The dataset is composed of genetic data from the ~6100 participants who agreed to participate in the ACCORD optional genetic studies and who allowed broad investigator access to their samples and the data derived from those samples, and from whom a DNA sample of sufficient quality was obtained. While a total of 8514 participants consented to the optional genetics studies, not all consented to broad investigator access, and those who did not are not included in this dataset, although they were also genotyped. Access to these additional genotypes can only be obtained by direct collaboration with the investigators of this study. Phenotype data used in the association analyses are derived from the ACCORD public release clinical data set, which has been made available through BioLINCC.

  Study phs001411

• ERDERA WES reanalysis - DPF2 Batch 7

  This dataset comprises processed genomic data from 1000 samples, corresponding to 1000 whole-exome sequencing experiments from ERDERA Data Processing Freeze 2. Data files include CRAM, gVCF (containing short variants, copy number variants, and structural variants), RoH (BED), B-allele frequencies (BW), and variant IGV visualizations (XML). Associated phenotypic and pedigree information for all samples is also included.

  Dataset EGAD50000002503

• ERDERA WES reanalysis - DPF1 Batch 4

  This dataset comprises processed genomic data from 1063 samples, corresponding to 1063 whole-exome sequencing experiments. Data files include CRAM, gVCF (containing short variants, copy number variants, and structural variants), RoH (BED), B-allele frequencies (BW), and variant IGV visualizations (XML). Associated phenotypic and pedigree information for all samples is also included.

  Dataset EGAD50000002389