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• Gut metagenome associations with extensive digital health data in a volunteer-based EstMB cohort second timepoint data

  As part of the Estonian Biobank, we established the Estonian Microbiome Cohort which includes stool, oral and plasma samples from 2,509 participants and is supplemented with multi-omic measurements, questionnaires, and regular linkages to national electronic health records. Out of the 2,509 Estonian Microbiome Project participants, a sub-cohort of over 300 individuals provided an additional stool sample after a median follow-up period of 4.4 years. All participants of the EstMB cohort gave informed consent for the data and samples to be used for scientific purposes. In the second time point, the participants were instructed to send the samples by post immediately after taking the sample and time their sample collection to avoid shipping on the weekends. In the study, by utilising retrospective medication usage data from the electronic health records and the Estonian microbiome cohort shotgun metagenomics data set (n = 2,509), we systematically evaluate the long-term effects of antibiotics and human-targeted medications on the gut microbiome. We show that past usage of medications is associated with the gut microbiome. For example, the effects of antibiotics, psycholeptics, antidepressants, proton pump inhibitors, and beta-blockers are detectable several years after use. Furthermore, by analysing a subcohort (n >300 individuals) from the second time point, we show that similar changes in the gut microbiome occur after treatment initiation or discontinuation, possibly indicating causal effects.

  Study EGAS50000001181

• Identification of RNA biomarkers in Parkinson's disease patients

  Parkinson?s disease (PD) is an age-related, chronic and progressive neurodegenerative disorder characterized by a loss of dopaminergic neurons and subsequent motor symptoms. These overt motor symptoms are often preceded by prodromal non-motor symptoms. Though a number of genetic and environmental factors have been identified to play a role in PD, more exact methods for both diagnosing and assessing prognosis are yet to be discovered. By analyzing the transcriptome of early-stage PD patients, this study aims to identify genes (including important non-coding RNAs) and pathways altered at disease onset that may be used as novel biomarkers for PD diagnosis as well as potential new drug targets. The study consists of three main sources of data: first year blood transcriptomes, second year blood transcriptome and matched neural stem cell (NSC)/neuron transcriptomes. All transcriptome libraries were synthesized and sequenced using no-amplification non-tagging cap analysis of gene expression (nAnT-iCAGE) on the Illumina HiSeq 2500 platform. Resulting data was mapped to the human genome annotation (hg38), processed CAGE tags were clustered ready for differential expression analyses. First year blood sequencing consisted of 22 PD patients and 10 healthy controls across four lanes. In the second year, a further four lanes were sequenced containing 17 PD patients (12 patients from the first year and 5 new patients) along with 10 healthy controls and 5 first year blood samples matching the new second year patients.

  Study JGAS000119

• Genetic alterations that deregulate RB and PDGFRA signaling pathways drive tumor progression in IDH2-mutant astrocytoma

  In IDH-mutant astrocytomas, IDH2 mutation is quite rare and biological mechanisms underlying tumor progression in IDH2-mutant astrocytoma remain elusive. Here, we report a unique case of IDH2 mutant astrocytoma, CNS WHO grade 3 that developed tumor progression. We performed a comprehensive genomic and epigenomic analysis for primary and recurrent tumors and found that both tumors harbored recurrent IDH2R172K and TP53R248W mutation with CDKN2A/B hemizygous deletion. We also found amplifications of CDK4 and MDM2 with PDGFRA gain in the recurrent tumor and upregulated protein expressions of these genes. We further developed, for the first time, a xenograft mouse model of IDH2R172K mutant astrocytoma from the recurrent tumor, but not from the primary tumor. Consistent with parent recurrent tumor cells, amplifications of CDK4 and MDM2 with PDGFRA gain was found, while CDKN2A/B was identified as homozygous deletion in the xenografts, qualifying for integrated diagnosis of astrocytoma, IDH2-mutant, CNS WHO grade 4. Cell viability assay found that CDK4/6 inhibitor and PDGFR inhibitor potently decreased cell viability in recurrent tumor cells, as compared to primary tumor cells. These findings suggest that gene alterations that activate retinoblastoma (RB) signaling pathways and PDGFR may drive tumor progression and xenograft formation in IDH2-mutant astrocytoma, which is equivalent to progressive IDH1-mutant astrocytoma. Also, our findings suggest that these genomic alterations may represent therapeutic targets in IDH2-mutant astrocytoma.

  Study JGAS000646

• Integration of genomics and histology reveals diagnosis and effective therapy of refractory cancer of unknown primary with PDL1 amplification (H021)

  Identification of the tissue of origin in cancer of unknown primary (CUP) poses a diagnostic challenge and is critical for directing site-specific therapy. Currently, clinical decision making in patients with CUP primarily relies on histopathology and clinical features. Comprehensive molecular profiling has the potential to contribute to diagnostic categorization and, most importantly, guide CUP therapy through identification of actionable lesions. We here report the case of an advanced-stage malignancy initially mimicking poorly differentiated soft-tissue sarcoma that did not respond to multi-agent chemotherapy. Molecular profiling within a clinical whole-exome and transcriptome sequencing program revealed a heterozygous, highly amplified KRAS G12S mutation, compound-heterozygous TP53 mutation/deletion, high mutational load, and focal amplification of chromosomes 9p (including PDL1 [CD274] and JAK2) and 10p (including GATA3). Integrated analysis of molecular data and conventional histopathology suggested a diagnosis of triple-negative breast cancer (TNBC) and provided a rationale for immune checkpoint inhibitor therapy with pembrolizumab, which resulted in rapid clinical improvement and a lasting partial remission. Analysis of 157 TNBC samples from The Cancer Genome Atlas revealed focal, high-level PDL1 amplification coinciding with excessive PDL1 mRNA expression in 24% of cases. Collectively, these results illustrate the diagnostic utility of multidimensional tumor profiling in cases with non-descript histology and immune phenotype, demonstrate the predictive power of genomic PDL1 amplification for immune checkpoint inhibition, and suggest a targeted therapeutic strategy in chromosome 9p24.1/PDL1-amplified cancers.H021

  Study EGAS00001001846

• UK10K COHORT TWINSUK

  The UK10K project proposes 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 directly analyse 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. The TwinsUK samples will be part of the cohort study and will undergo whole genome sequencing. For further information with regard to this cohort please contact Brent Richards (brent.richards@kcl.ac.uk) or Nicole Soranzo (ns6@sanger.ac.uk).

  Study EGAS00001000108

• Spatio-temporal Profiling of a Rhabdoid Tumor Case Study

  Background Immune checkpoint inhibition (ICI) has revolutionized oncology, offering extended survival and long-term remission in previously incurable cancers. While highly effective in tumors with high mutational burden, lowly mutated cancers, including pediatric malignancies, present low response rate and limited predictive biomarkers. Patients and methods We present a framework for the identification and validation of tumor-reactive T cells as a biomarker to quantify ICI efficacy and as candidates for a personalized TCR-T cell therapy. Therefore, we profiled a pediatric malignant rhabdoid tumor patient with complete remission after ICI therapy using deep single-cell T cell receptor (TCR) repertoire sequencing of the tumor microenvironment (TME) and the peripheral blood. Results Tracking T cell dynamics longitudinally from the tumor to cells in circulation over a time course of 12 months revealed a systemic response and durable clonal expansion of tumor-resident and ICI-induced TCR clonotypes. We functionally validated tumor reactivity of TCRs identified from the TME and the blood by co-culturing patient-derived tumor cells with TCR-engineered autologous T cells. Here, we observed unexpectedly high frequencies of tumor-reactive TCR clonotypes in the TME and confirmed T cell dynamics in the blood post-ICI to predict tumor-reactivity. Conclusion These findings strongly support spatio-temporal tracking of T cell activity in response to ICI to inform therapy efficacy and to serve as a source of tumor-reactive TCRs for personalized TCR-T designs.

  Study EGAS00001008174

• Performance comparison of three DNA extraction kits on human whole-exome data from formalin-fixed paraffin-embedded normal and tumor samples

  We generated 42 human whole-exome sequencing data sets from fresh-frozen (FF) and FFPE samples. These samples include normal and tumor tissues from two different organs (liver and colon), that we extracted with three different FFPE extraction kits (QIAamp DNA FFPE Tissue kit and GeneRead DNA FFPE kit from Qiagen, Maxwell\textsuperscript{TM} RSC DNA FFPE Kit from Promega). Variant calling analysis shows a very high rate of concordance between matched FF / FFPE pairs and equivalent performance for the three kits we analyzed. We find a significant variation in the difference of total number of variants called between FF and FFPE samples for the three different FFPE DNA extraction kits. Coverage analysis shows that FFPE samples have less good indicators than FF samples, yet the coverage quality remains above accepted thresholds. We detect limited but significant variations in coverage indicator values between the three FFPE extraction kits. Globally, the GeneRead and QIAamp kits have better variant calling and coverage indicators than the Maxwell kit on the samples used in this study, although this kit performs better on some indicators and has advantages in terms of practical usage. Taken together, our results confirm the potential of FFPE samples analysis for clinical genomic studies, but also indicate that the choice of a FFPE DNA extraction kit should be done with careful testing and analysis beforehand in order to maximize the accuracy of the results.

  Study EGAS00001002631

• Long-read trio sequencing of unsolved patients with intellectual disability

  Long-read sequencing (LRS) has the potential to comprehensively identify all medically-relevant genome variation, including variation commonly missed by short read sequencing (SRS) approaches. To determine this potential, we performed LRS up to 40x coverage using the Pacific Biosciences Sequel instrument for 5 patient-parent trios with intellectual disability, whose aetiology remained unresolved after SRS exomes and genomes. A first systematic assessment of LRS coverage showed that ~35Mb of the genome was only accessible by LRS and not SRS. Structural variant (SV) calling yielded on average 28,292 SV calls per individual, totalling 12.9 MB of sequence. Trio-based analyses showed concordance for up to 95% of these SV calls across the genome, and 80% of the LRS SV calls were not identified by SRS. De novo mutation analysis did not identify any de novo SVs, confirming these are rare events. Because of high sequence coverage, we were also able to call single nucleotide substitutions. On average, we identified 3 million substitutions per genome, with a Mendelian inheritance concordance of up to 97%. Of these, approximately 100,000 were located in the ~35Mb of the genome that was only captured by LRS. Moreover, these variants affected the coding sequence of 64 genes, including 32 known to cause Mendelian disorders. Although no clinical diagnoses were made in these 5 patients, our data show the potential added value of LRS compared to SRS for identifying medically relevant genome variation.

  Study EGAS00001004319

• The Southern African Human Genome Programme

  The Southern African Human Genome Programme (SAHGP) is a national and regional initiative that aims to unlock the unique genetic character of southern African populations. Its vision is to improve quality of life through understanding human genetic diversity. It is timely to advance the SAHGP initiative since global genomic initiatives are increasingly pointing to populations in Africa, more specifically the San and Khoe from southern Africa, as harbouring the most ancient genetic signatures among the living populations of the world. Studies on African populations have already generated a wealth of information upon which the SAHGP can build. This information will be used to promote and support genomic research programmes both nationally and regionally to address critical questions that would benefit the people of our region. The SAHGP aims to make a significant contribution to the understanding of DNA variation among southern Africans and how this impacts on the health of the people of this region. Aims of the SAHGP: To develop capacity for genomic research in southern Africa; To establish sustainable resources for genomic research in the region; and To translate knowledge and information into improvements in human health in the region. The objective is to derive the maximum benefit from the SAHGP, and that the data should be widely shared with the scientific community for the benefit of the people of the sub-continent and beyond.

  Study EGAS00001002639

• Disease and phenotype relevant genetic variants identified from histone acetylomes in human hearts

  Identifying genetic markers for heterogeneous complex diseases such as heart failure has been challenging, and may require prohibitively large cohort sizes in genome-wide association studies (GWAS) in order to demonstrate statistical significance1. On the other hand, chromatin quantitative trait loci (QTL), elucidated by direct epigenetic profiling of specific human tissues, may contribute towards prioritising variants for disease-association. Here, we captured non-coding genetic variants by performing enhancer H3K27ac ChIP-seq in 70 human control and end-stage failing hearts, mapping out a comprehensive catalogue of 47,321 putative human heart enhancers. 3,897 differential acetylation peaks (FDR < 0.05) pointed to recognizable pathways altered in heart failure (HF). To identify cardiac histone acetylation QTLs (haQTLs), we regressed out confounding factors including HF disease status, and employed the G-SCI test2 to call out 1,680 haQTLs (FDR < 0.1). A subset of these showed significant association to gene expression, either in cis (180), or through long range interactions (81), identified by Hi-C and Hi-ChIP performed on a subset of hearts. Furthermore, a concordant relationship was found between the gain or disruption of specific transcription factor (TF) binding motifs, inferred from alternative alleles at the haQTLs, associated with altered H3K27ac peak heights. Finally, colocalisation of our haQTLs with heart-related GWAS datasets allowed us to identify 62 unique loci. Disease-association for these new loci may indeed be mediated through modification of H3K27-acetylation enrichment and their corresponding gene expression differences.

  Study EGAS00001003586

• Genomic Diagnosis and Individualized Therapy of Highly Penetrant Genetic Diabetes

  The most common and well-known causes of diabetes are type 1 and type 2 diabetes; however, there are many other etiologies of diabetes. At least 1-2% of diabetes cases result from high penetrance single gene defects, most commonly in a gene encoding either a transcription factor involved in beta cell development and function or the enzyme glucokinase (GCK). Others have mutations in transcription factors necessary for beta cell development and function. This subset of monogenic diabetes cases is autosomal dominant and is known as "Maturity-Onset Diabetes of the Young" (MODY). Patients with type 1 diabetes are insulin dependent, meaning they lack insulin production and therefore require multiple daily injections of insulin, as well as multiple times a day blood glucose monitoring. This can be quite burdensome. Type 2 diabetes is predominantly an insulin resistance problem and can often be managed with pills initially, but there are many who also require insulin either at diagnosis or later on in their disease course. Usually, monogenic diabetes is misdiagnosed as type 1 or type 2 diabetes and thus treated suboptimally. Those with monogenic diabetes, due to a defect in a gene encoding a transcription factor, respond extremely well to a class of oral diabetes medications called sulfonylureas. Those with GCK-MODY often have a mild, stable, fasting, nonprogressive hyperglycemia for which pharmacological treatment is often not necessary. It is important to accurately diagnose our patients with the correct etiology to their diabetes for a multitude of reasons. Accurately diagnosing a patient with MODY, for example, can predict their clinical course and we can tailor treatment accordingly. If diagnosed with a transcription factor MODY, we can try switching the patient from insulin to an oral sulfonylurea, and if diagnosed with a GCK-MODY, we can consider discontinuing pharmacological treatment altogether. With specific tailoring of treatment, we would hope to improve quality of life by discontinuing perhaps multiple daily injections of insulin and glucose checking as these may not be necessary. In addition, we would hope to improve glycemic control with an eventual decreased cost to society by decreasing the complication rate of diabetes, resulting in less specialty referrals, lab tests, supplies and procedures. Further, given MODY is an autosomal dominant condition with a 50% chance of inheritance to first-degree family members, we could offer testing to asymptomatic family members which could lead to prevention and/or early diagnosis and treatment. Overall, testing for monogenic diabetes is underutilized clinically. Testing is being done in the U.K., but in the U.S. we are lagging behind. This is most predominantly due to lack of knowledge and concern over cost. In addition, it is difficult to differentiate the relatively small number of monogenic diabetes cases from the larger number with type 1 and type 2 diabetes as each of these etiologies have overlapping characteristics. In fact, many patients with monogenic diabetes are being misdiagnosed with type 1 and type 2 diabetes. There are recommendations set forth by different societies on who to consider screening for monogenic diabetes. However, these recommendations are difficult to employ clinically as they are general recommendations, there is no specific algorithm, and they are based on varying levels of evidence. The purpose of this study is to implement a personalized diabetes medicine program to enhance the identification of individuals and families affected by highly penetrant diabetes gene mutations through a combination of systematic screening and genetic testing at the University of Maryland Center for Diabetes and Endocrinology, the Baltimore VA Medical Center and additional partner centers at Geisinger Health System and Bay West Endocrinology Associates, and other UM outpatient clinics. Our hope is to develop an approach that can be eventually implemented across the United States so that we can more often uncover the correct etiology to diabetes, tailor treatment accordingly, and test asymptomatic family members.

  Study phs001771

• Sequencing data for oesophageal and related samples - BOs release 2 (RNA)

  Dataset EGAD00001003840

• DCC_R26.2.PRAD-CNSM-Array

  Raw Array data from the PRAD-CA for ICGC DCC Release27

  Dataset EGAD00010001519

• gz_bjREP

  DNA for 2979 individuals from Guangzhou was extract from peripheral blood and genotyped by Sequenom, with digit-number working memory, visuospatial working memory, recent long-term memory measured.

  Dataset EGAD00010001528

• Sequencing data for oesophageal and related samples - BOs release 4 (RNA)

  Dataset EGAD00001004023

• Genentech_cell_line_SNP

  SNP array data for 668 cancer cell lines

  Dataset EGAD00010000951

• Sequencing data for oesophageal and related samples - BOs release 5 (RNA)

  Dataset EGAD00001005385

• ChIP-Seq files for RMS.

  ChIP-Seq files accompanying the paper titled "Identification of Therapeutic Targets in Rhabdomyosarcoma Through Integrated Genomic, Epigenomic, and Proteomic Analyses".

  Dataset EGAD00001006398

• Sequencing data for oesophageal and related samples - BOs release 3 (RNA)

  Dataset EGAD00001003901

• Effects of interferon-gamma treatment on human small intestinal organoids.

  Intestinal organoids treated with interferon-gamma 1 ng/mL either for 6h or 18h.

  Dataset EGAD50000000115

• Small RNA sequencing dataset from human prefrontal cortex tissue - ALS vs CTR

  For mature miRNAs and hairpins, BAM and FASTq files

  Dataset EGAD50000000468

• TOAPR-B patient cell-free DNA WGS and WES

  Sequencing files for TOPARP-B patients as described in manuscript

  Dataset EGAD50000000407

• RNAseq RPKM

  RNA-seq data from ILC samples. This dataset includes counts normalized for sequencing depth and gene length.

  Dataset EGAD50000000828

• Centre Leon Berard - DAC - seq data Neuroendocrine tumors - B Gibert

  Review committee for the neuroendocrine tumor genetic data set uploaded by Benjamin GIBERT (CRCL), Anthony FERRARI (CLB) and Thomas WALTER (HCL)

  Dac EGAC50000000218

• DAC for "Capturing disease severity in LIS1-lissencephaly reveals proteostasis dysregulation in patient-derived forebrain organoids" with Julia Ladewig(Julia.ladewig@zi-mannheim.de), Moritz Mall(m.mall@dkfz-heidelberg.de), and Matteo Gasparotto(matteo.gasparotto@zi-mannheim.de)

  Dac EGAC00001003588

• Walter and Eliza Hall - Aix-Marseille Université

  Data Access Committee for the Liao et al. 2025 study, D4Z4End2End: complete genetic and epigenetic architecture of D4Z4 macrosatellites in FSHD, BAMS and reference cohorts.

  Dac EGAC50000000655

• Division of Cancer Evolution National Cancer Center Research Institute Data Access Committee

  Data Access Committee for data from Division of Cancer Evolution, National Cancer Center Research Institute

  Dac EGAC50000000737

• MD Anderson OPMD DAC

  Datasets and agreement used for the OPMD dataset access of the publication Reconstructing oral cavity tumor evolution through brush biopsy, Springer Nature, DOI: 10.1038/s41598-024-72946-3

  Dac EGAC50000000384

• Reference epigenome OB56_N_PreA_smRNA-Seq data generated from KEP study

  A OB56_N_PreA_smRNA-Seq single end data for Preadipocytes(fat)

  Dataset EGAD00001003495

• Uncovering tumor intrinsic and extrinsic factors that regulate hepatocellular carcinoma growth using patient derived xenograft assays

  Exome Sequencing and RNA Sequencing Data for PDX Samples

  Dataset EGAD00001005734

• scWGS PDX

  Single cell WGS (low pass) for PT1-derived PDX samples

  Dataset EGAD00001006137

• Reference epigenome IPS03_N_ENeuron_WGBS data generated from KEP study

  A IPS03_N_ENeuron_WGBS paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003475

• Reference epigenome IPS02_N_NPC_WGBS data generated from KEP study

  A IPS02_N_NPC_WGBS paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003474

• Reference epigenome OB57_D_PreA_mRNA-Seq data generated from KEP study

  A OB57_D_PreA_mRNA-Seq paired end data for Preadipocyte(fat)

  Dataset EGAD00001003487

• Reference epigenome CKD25_C_Podo_smRNA-Seq data generated from KEP study

  A CKD25_C_Podo_smRNA-Seq single end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003499

• Illumina HiSeq 2000

  Three files of patients 20, 23 and 25 with WGS done on Illumina HiSeq 2000. For research purpose and authorised user only.

  Dataset EGAD00001003438

• Reference epigenome CKD25_C_Podo_mRNA-Seq data generated from KEP study

  A CKD25_C_Podo_mRNA-Seq paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003483

• RNA sequencing data to study "A biobank of patient-derived pediatric braintumor models"

  RNA sequencing data for the PDOX model EPD-613FH

  Dataset EGAD00001003573

• scRNA primaries

  Single cell RNA-seq for primary samples

  Dataset EGAD00001006135

• Reference epigenome CKD24_C_Podo_mRNA-Seq data generated from KEP study

  A CKD24_C_Podo_mRNA-Seq paired end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003482

• Reference epigenome OB57_D_PreA_smRNA-Seq data generated from KEP study

  A OB57_D_PreA_smRNA-Seq single end data for Preadipocyte(fat)

  Dataset EGAD00001003496

• RNA-Seq Pair End

  RNA analysis of two patients 11 and 15 with WGS done on Illumina HiSeq2000. For research purpose and authorised user only.

  Dataset EGAD00001003429

• Reference epigenome CKD24_C_Podo_smRNA-Seq data generated from KEP study

  A CKD24_C_Podo_smRNA-Seq single end data for Podocytes(CD90(-) Podocalyxin(+), kidney)

  Dataset EGAD00001003498

• V2 Colorectal panel test

  Targeted gene screen of cell line tumour samples for testing the new V2 Colorectal gene panel.

  Dataset EGAD00001003253

• ATAC-seq for placental and buffycoat cells.

  We performed ATAC-seq experiments using 2 placental samples and 2 buffycoat samples.

  Dataset EGAD00001004291

• ChIP-Seq files for RMS.

  ChIP-Seq files accompanying the paper titled "Identification of Therapeutic Targets in Rhabdomyosarcoma Through Integrated Genomic, Epigenomic, and Proteomic Analyses".

  Dataset EGAD00001004312

• Fecal 16S HV metadata

  Metadata for fecal 16S-V4 rRNA gene sequence data from NCT02749630 healthy volunteers.

  Dataset EGAD00001008821

• Treatment arm information and sample sheet

  Table of treatment arm information for the 418 RNAseq evaluable population.

  Dataset EGAD00001009502

• H3Africa SIREN Phenotype

  Phenotype data for 3421 Samples from Nigeria and Ghana, sequenced with the Illumina NestSeq 500.

  Dataset EGAD00001011075

• Res1_PC9_exp2_MC_01.04.22

  8 cell pellet samples for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kosuke primers.

  Dataset EGAD00001012233