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• Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Lipidomics Study

  The GOLDN study was initiated to assess how genetic factors interact with environmental (diet and drug) interventions to influence blood levels of triglycerides and other atherogenic lipid species and inflammation markers (registered at clinicaltrails.gov, number NCT00083369). The study recruited Caucasian participants primarily from three-generational pedigrees from two NHLBI Family Heart Study (FHS) field centers (Minneapolis, MN and Salt Lake City, UT). Only families with at least two siblings were recruited and only participants who did not take lipid-lowering agents (pharmaceuticals or nutraceuticals) for at least 4 weeks prior to the initial visit were included. A total of 1048 GOLDN participants were included in the diet intervention. The diet intervention followed the protocol of Patsch et al. (1992). The whipping cream (83% fat) meal had 700 Calories/m2 body surface area (2.93 MJ/m2 body surface area): 3% of calories were derived from protein (instant nonfat dry milk) and 14% from carbohydrate (sugar). The ratio of polyunsaturated to saturated fat was 0.06 and the cholesterol content of the average meal was 240 mg. The mixture was blended with ice and flavorings. Blood samples were drawn immediately before (fasting) and at 3.5 and 6 hours after consuming the high-fat meal. For the GOLDN lipidomics study, sterols and fatty acids were measured from stored plasma (-80 degrees Celsius) collected at fasting and 3.5 hours after the diet intervention using TrueMass Panels from Lipomics (West Sacramento, CA). A total of 11 sterols were quantified in nmols/gram of sample including total cholesterol, 7-dehydrocholesterol, desmosterol, lanosterol, lathasterol, cholestanol, coprostanol, beta-sitosterol, campesterol, stigmasterol, and 7alpha-hydroxycholesterol. A total of 35 fatty acids were quantified in nmols/gram of sample inlcuding myristic acid (14:0); pentadecanoic acid (15:0); palmitic acid (16:0); stearic acid (18:0); arachidic acid (20:0); behenic acid (22:0); lignoceric acid (24:0); myristoleic acid (14:1n5); palmitoleic acid (16:1n7); palmitelaidic acid (t16:1n7); oleic acid (18:1n9); elaidic acid (t18:1n9); vaccenic acid (18:1n7); linoleic acid (18:2n6); gamma-linolenic acid (18:3n6); alpha-linolenic acid (18:3n3); stearidonic acid (18:4n3); eicosenoic acid (20:1n9); eicosadienoic acid (20:2n6); mead acid (20:3n9); di-homo-gamma-linolenic acid (20:3n6); arachidonic acid (20:4n6); eicsoatetraenoic acid (20:4n3); eicosapentaenoic acid (20:5n3); erucic acid (22:1n9); docosadienoic acid (22:2n6); adrenic acid (22:4n6); docosapentaenoic acid (22:5n6); docosapentaenoic acid (22:5n3); docosahexaenoic acid (22:6n3); nervonic acid (24:1n9); and plasmalogen derivatives of 16:0, 18:0, 18:1n9, and 18:1n7.

  Study phs000741

• 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

• Exploratory novel biomarker and resistance mechanism of milademetan, an MDM2 inhibitor, in MDM2 amplified intimal sarcoma from an open-label phase 1b/2 trial ���NCCH1806/MK004���

  Amplified Murine double minute 2 (MDM2) is found in > 70% of intimal sarcoma, known as one of the ultra-rare sarcomas. Milademetan (DS3032, RAIN-32) is a novel, specific, small-molecule MDM2 inhibitor that disrupts MDM2 and the tumor suppressor protein p53 interactions in tumor cells. We conducted a phase 1b/2 trial (Trial registration No: JMA-IIA00402) in patients with amplified MDM2 wild-type TP53 intimal sarcoma as a sub-study under the nationwide large registry for rare cancers in Japan (MASTERKEY Project). Eleven patients were enrolled, and ten were included in the efficacy analysis. Two (20%) patients had durable responses for > 15 months. Milademetan provided clinical benefits in patients with amplified MDM2 intimal sarcoma. Predictive biomarkers other than amplified MDM2 and acquired resistance mechanisms for milademetan are unknown. Whole-exome and RNA sequencing analyses of pre-treatment tissue samples were conducted to identify determinants of response. Genomic alterations were analyzed for 10 patients, and gene expression was analyzed for 9 patients using their pre-treatment tissue samples. Targeted sequencing of cell-free DNA (cfDNA) samples (liquid biopsy) was also conducted sequentially at three points [before treatment with milademetan (baseline), at Cycle 2 Day1, and at the time of disease progression] to identify determinants of response and resistance. From whole-exome and RNA sequencing analyses of pre-treatment tissue samples, we could not find any molecular pathways associated with the anti-tumor activity of milademetan. Focusing on 8 genes (CDK4, CDKN2A, CDKN2B, EGFR, ERBB3, MDM2, PDGFRA, TP53) known to be frequently affected in intimal sarcoma and 10 genes (AKT1, ATM, BBC3, CDKN1A, CDKN1C, CHEK2, MDM4, PMAIP1, PPM1D, TWIST1) reported to be associated with MDM2 inhibitor responses; we found that anti-tumor activity correlated with amplified TWIST1 (p-value = 0.028) and negatively with CDKN2A loss (p-value = 0.071). Eight of the 10 patients had their cfDNA collected sequentially at baseline, at Cycle 2 Day1 and disease progression; however, one did not consent to the exploratory analysis study using cfDNA, and one had cfDNA collected at baseline but not at disease progression due to ongoing treatment. Of the eight patients, TP53 mutations in cfDNA were detected in one and five patients at baseline and disease progression, respectively. The cfDNA allele frequency of TP53 mutations increased with disease progression. CDKN2A loss and amplified TWIST1 could be associated with the anti-tumor activity of milademetan in patients with MDM2 amplified intimal sarcoma. Acquired TP53 mutations were detected in sequential liquid biopsies as loss-of-function mutations, and these TP53 mutations might compromise the anti-tumor activity.

  Study JGAS000619

• The Environmental Determinants of Diabetes in the Young Study (TEDDY)

  Study Overview The Environmental Determinants of Diabetes in the Young (TEDDY) Study is a longitudinal study that investigates genetic and genetic-environmental interactions, including gestational events, childhood infections, dietary exposures, and other environmental factors after birth, in relation to the development of islet autoimmunity and type 1 diabetes (T1D). A consortium of six clinical centers assembled to participate in the development and implementation of the study to identify environmental triggers for the development of islet autoimmunity and T1D in genetically susceptible individuals. Beginning in 2004, the TEDDY study screened over 400,000 newborns for high-risk HLA-DR, DQ genotypes from both the general population and families already affected by T1D. The TEDDY study enrolled around 8,676 participants across six clinical centers worldwide (Finland, Germany, Sweden and three in the United States) in the 15-year prospective follow-up. Participants are followed every three months for islet autoantibody (IA) measurements with blood sampling until four years of age and then at least every six months until the age of 15. After the age of four, autoantibody positive participants continue to be followed at three month intervals and autoantibody negative participants are followed at six-month intervals. In addition to the analysis of autoantibodies, additional data and sample collection are performed at each visit. Parents collect monthly stool samples in early childhood. The parents also fill out questionnaires at regular intervals in connection with study visits and record information about diet and health status in the child's TEDDY Book between visits. Continued long-term follow-up of the currently active TEDDY participants will provide important scientific information on early childhood diet, reported and measured infections, vaccinations, and psychosocial stressors that may contribute to the development of type 1 diabetes and islet autoimmunity. Additional information on the TEDDY study is available in the following articles: Rewers et al., 2008, PMID: 19120261 and Hagopian et al., 2006, PMID: 17130573. Details of the TEDDY protocol can be found in Hagopian et al., 2011, PMID: 21564455. TEDDY data currently available in dbGaP include: gene expression, SNPs, exome, microbiome (gut, nasal, and plasma), RNA sequencing, and whole genome sequencing. For more information on TEDDY Study version history please refer to TEDDY Study dbGaP README File. ImmunoChip SNP DNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for SNP genotyping. Genotyping was performed by the Center for Public Health Genomics at the University of Virginia using the Illumina ImmunoChip SNP array, which contains around 196,000 SNPs from 186 regions associated with 12 autoimmune diseases (Hadley et al., 2015, PMID: 26010309). Data cleaning and validation included the removal of subjects with a low call rate (< 5% SNPs missing) and differences in reported sex and prior genotyping at the TEDDY HLA laboratory. Additionally, SNPs with a low call rate or Hardy-Weinberg equilibrium P value < 10-6, except for chromosome 6 due to HLA eligibility requirements, were removed from the final dataset (Törn et al., 2015, PMID: 25422107).TEDDY-T1DExome ArrayDNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for genotyping. Genotyping was performed by the University of Virginia using the Illumina TEDDY-T1DExome array. The TEDDY-T1DExome array is a custom chip that contains 550,601 markers from the Infinium CoreExome-24 v1.1 BeadChip and an additional 90,214 tagSNPs specifically selected by the TEDDY investigators based on their associations with nutrients, vitamins, type 2 diabetes, autoimmune diseases, body-mass index, or other exposures and phenotypes measured by TEDDY study.The Illumina GenTrain2 algorithm was used for genotype calling. Sample quality control metrics included sample call rate, heterozygosity rate and concordance of gender between the information reported and genotyped. Gene Expression The TEDDY study collected peripheral blood for the extraction of total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA (200 ng) was further used for cRNA amplification and labeling with biotin using Target Amp cDNA synthesis kit (Epicenter catalog no. TAB1R6924). Labeled cRNA was hybridized to the Illumina HumanHT-12 Expression BeadChips based on the manufacturer's instructions. The HumanHT-12 Expression BeadChip provides coverage for more than 47,000 transcripts and known splice variants across the human transcriptome. Microbiome The TEDDY microbiome study aimed to characterize the longitudinal development of the microbiome, including bacteria, viruses and other microorganisms in the gut, plasma, and nasal cavity of prediabetic and diabetic subjects compared to autoantibody negative non-diabetic subjects. Stool samples used were collected monthly from 3 to 48 months, after which stool samples were collected every 3 months. Nasal swab samples were collected every 3 months starting at 9 months of age until 48 months, after which nasal swabs were collected every 6 months. Plasma samples were collected every 3 months starting at 3 months of age until 48 months, after which plasma samples were collected every 6 months. If the subject was autoantibody positive at 48 months then they remained on the 3 month collection interval for nasal swab and plasma samples. Samples underwent 16s rRNA gene sequencing, DNA and viral RNA metagenomics shotgun sequencing, and sequencing of the internal transcribed spacer (ITS) regions. Additional information on the TEDDY microbiome data is available in the following articles: Vatanen et al., 2018, PMID: 30356183, Stewart et al., 2018, PMID: 30356187, and Vehik et al., 2020, PMID: 31792456. RNA Sequencing The TEDDY study aimed to characterize the transcriptome in subjects with islet autoimmunity and type 1 diabetes compared to matched control subjects. Peripheral blood was collected to extract total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA was then sent to the Broad Institute for the generation of the TEDDY RNA sequencing (RNA-Seq) data. The RNA samples were prepped using Superscript III reverse transcriptase and Illumina's TruSeq Stranded mRNA Sample Prep Kit. The TruSeq libraries were run on the Illumina HiSeq2500 platform. Whole Genome Sequencing The TEDDY study aimed to conduct deep whole genome sequencing and examine the genomic variations in subjects with islet autoimmunity and type 1 diabetes compared to matched autoantibody negative and non-diabetic children. DNA from whole blood was obtained from TEDDY children for whole genome sequencing. The WGS data were generated on the Illumina HiSeq X Ten system.

  Study phs001442

• A Chromatin Accessibility Atlas of the Developing Human Telencephalon

  Gene expression differs between cell types and regions within complex tissues such as the developing brain. To discover regulatory elements underlying this specificity, we generated genome-wide maps of chromatin accessibility in nine anatomically-defined regions of the developing human telencephalon. Additionally, we defined the histone modification landscape of the prefrontal cortex and generated chromatin accessibility maps of its upper and deep layers. We predicted a subset of open chromatin regions (18%) that are most likely to be active enhancers, many of which are dynamic with 26% differing between early and late mid-gestation and 28% present in only one brain region. These predicted regulatory elements (pREs) are enriched proximal to genes with expression differences across developmental stages, regions, and cortical laminae; they harbor distinct sequence motifs that suggest potential upstream regulators. We leveraged this atlas to predict and validate novel regulatory elements of genes that control cortex laminar identity and genes associated with autism spectrum disorder (ASD). These include enhancers proximal to FEZF2 and BCL11A that were validated in mouse, and an enhancer of ASD gene SLC6A1 containing two functional de novo mutations in individuals with ASD whose enhancer function we validated via CRISPRa. We also include data for transcription factor binding in mid-fetal cortex tissues, using CUT&RUN methods.

  Study phs002033

• Exploring the role of mtDNA variation in Multiple Sclerosis in a large cohort of discordant monozygotic twins

  Several lines of evidence indicate that mitochondrial DNA (mtDNA) variants might predispose to multiple sclerosis (MS). We examined this in 49 monozygotic (MZ) twin pairs clinically discordant for MS at study entry. Since the nuclear DNA of MZ twins is identical, our study provides a unique setting to functionally associate unique mtDNA variants and skewed heteroplasmy with MS development. In order to identify mtDNA variants in the twin cohort, we deeply sequenced the mitochondrial genome in blood of all 49 MZ twin pairs using next generation sequencing (Illumina, HiSeq or MiSeq) with an average coverage depth of ~25,000 sequences per base. Heteroplasmic variants were validated using targeted deep sequencing (TDS) (Illumina, MiSeq) in DNA isolated from blood and buccal swabs. In addition, whole blood was also available of 5 additional MS-affected or healthy siblings and 1 dizygotic twin pair discordant for MS, comprising in total 6 non-identical sib pairs. Moreover, all cases were screened for pathogenic mtDNA polymerase gamma (POLG) mutations. The data provides valuable insights in the possible involvement of mtDNA variants in the pathogenesis of MS, and gives information on the segregation of heteroplasmic variants within MZ twins and non-identical siblings and across different tissues.

  Study EGAS00001001240

• A Protein Neddylation Inhibitor MLN4924 Suppresses Patient-Derived Glioblastoma Cells via Inhibition of ERK and AKT Signaling

  Glioblastoma is a highly aggressive and lethal brain tumor, with limited treatment options. Abnormal activation of the neddylation pathway is observed in glioblastoma, and the NEDD8-activating enzyme (NAE) inhibitor, MLN4924 was previously shown to be effective in glioblastoma cell line models. However, its effect has not been tested in patient-derived glioblastoma stem cells. Immunohistochemistry and public data were analyzed to determine whether NEDD8 pathway proteins are important in glioblastoma development and patient survival. The effects of MLN4924 were evaluated in 4 glioblastoma cell lines and 15 patient-derived glioblastoma stem cells using high content analysis. Immunoblot analysis assessed the NEDD8 pathway proteins and biomarkers. Whole transcriptome analysis identified pathways associated with MLN4924 sensitivity. NAE1 and UBA3 levels increased in glioblastoma patients; high NEDD8 levels were associated with poor clinical outcomes. Glioblastoma cell lines and patient-derived stem cells were highly susceptible to MLN4924, while normal human astrocytes were resistant. Genomic analyses indicated that MLN4924 sensitive cells exhibited enrichment of ERK and AKT signaling. We verified that MLN4924 inhibits ERK and AKT phosphorylation in MLN4924 sensitive cells. Our findings suggest that patient-derived glioblastoma stem cells in the context of ERK and AKT activation are sensitive and highly regulated by neddylation inhibition.

  Study EGAS00001004018

• PSCP_bisulphite analysis in hESCs (2018-08-13)

  Many studies over the past 10 years, culminating in the recent report of the International Stem Cell Initiative (ISCI, 2011) have shown that hPSC acquire genetic and epigenetic changes during their time in culture. Many of the genetic changes are non-random and recurrent, probably because they provide a selective growth advantage to the undifferentiated cells. Some are shared by embryonal carcinoma cells, the malignant counterparts of ES cells. The origins of these growth advantages are poorly understood, but may come from altered cell cycle dynamics, resistance to apoptosis or altered patterns of differentiation. Less is known about the nature and consequences of epigenetic changes, but it is likely that these similarly affect hPSC behaviour; e.g., enhanced expression of DLK1, an imprinted gene, is associated with altered hPSC growth (Enver et al 2005). Inevitably, these genetic and epigenetic changes will impact on our ability to use hPSC for regenerative medicine, either because malignant transformation of the undifferentiated cells or their differentiated derivatives to be used for transplantation compromises safety, or because they impede the function of those differentiated derivatives, or because they affect the efficiency with which the undifferentiated cells can be expanded and differentiated into desired cell types. Focusing initially upon the existing clinical grade hESC lines, later moving to iPSC, we will Consolidate and extend knowledge of the rate, type and functional impact of the genetic variations that occur during hPSC culture. We will use whole genome and exome sequencing as well as SNP arrays, together with clonal analysis and other cytogenetics techniques. Common changes will be compared with those found in the normal human population, at low frequency in the original cell population or observed during iPSC generation in the HIPSCI project currently based at the WTSI. These studies will provide a better understanding of the range of genetic changes that occur in hPSC beyond the CNVs already identified. In conjunction with cancer genome resources and expertise at WTSI, bioinformatic analyses of these hPSC data will allow us to assess potential impact on hPSC behaviour pertinent to applications in regenerative medicine, notably the likelihood that specific changes arising in undifferentiated PSC cultures may be associated with potential malignant transformation of differentiated progeny. 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/ . This dataset contains all the data available for this study on 2018-08-13.

  Dataset EGAD00001004295

• RNA-seq analysis of transcriptome variation with human ESC subclones

  Many studies over the past 10 years, culminating in the recent report of the International Stem Cell Initiative (ISCI, 2011) have shown that hPSC acquire genetic and epigenetic changes during their time in culture. Many of the genetic changes are non-random and recurrent, probably because they provide a selective growth advantage to the undifferentiated cells. Some are shared by embryonal carcinoma cells, the malignant counterparts of ES cells. The origins of these growth advantages are poorly understood, but may come from altered cell cycle dynamics, resistance to apoptosis or altered patterns of differentiation. Less is known about the nature and consequences of epigenetic changes, but it is likely that these similarly affect hPSC behaviour; e.g., enhanced expression of DLK1, an imprinted gene, is associated with altered hPSC growth (Enver et al 2005). Inevitably, these genetic and epigenetic changes will impact on our ability to use hPSC for regenerative medicine, either because malignant transformation of the undifferentiated cells or their differentiated derivatives to be used for transplantation compromises safety, or because they impede the function of those differentiated derivatives, or because they affect the efficiency with which the undifferentiated cells can be expanded and differentiated into desired cell types. Focusing initially upon the existing clinical grade hESC lines, later moving to iPSC, we will Consolidate and extend knowledge of the rate, type and functional impact of the genetic variations that occur during hPSC culture. We will use whole genome and exome sequencing as well as SNP arrays, together with clonal analysis and other cytogenetics techniques. Common changes will be compared with those found in the normal human population, at low frequency in the original cell population or observed during iPSC generation in the HIPSCI project currently based at the WTSI. These studies will provide a better understanding of the range of genetic changes that occur in hPSC beyond the CNVs already identified. In conjunction with cancer genome resources and expertise at WTSI, bioinformatic analyses of these hPSC data will allow us to assess potential impact on hPSC behaviour pertinent to applications in regenerative medicine, notably the likelihood that specific changes arising in undifferentiated PSC cultures may be associated with potential malignant transformation of differentiated progeny 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 EGAD00001004266

• PSCP_mutation analysis in hESCs

  Many studies over the past 10 years, culminating in the recent report of the International Stem Cell Initiative (ISCI, 2011) have shown that hPSC acquire genetic and epigenetic changes during their time in culture. Many of the genetic changes are non-random and recurrent, probably because they provide a selective growth advantage to the undifferentiated cells. Some are shared by embryonal carcinoma cells, the malignant counterparts of ES cells. The origins of these growth advantages are poorly understood, but may come from altered cell cycle dynamics, resistance to apoptosis or altered patterns of differentiation. Less is known about the nature and consequences of epigenetic changes, but it is likely that these similarly affect hPSC behaviour; e.g., enhanced expression of DLK1, an imprinted gene, is associated with altered hPSC growth (Enver et al 2005). Inevitably, these genetic and epigenetic changes will impact on our ability to use hPSC for regenerative medicine, either because malignant transformation of the undifferentiated cells or their differentiated derivatives to be used for transplantation compromises safety, or because they impede the function of those differentiated derivatives, or because they affect the efficiency with which the undifferentiated cells can be expanded and differentiated into desired cell types. Focusing initially upon the existing clinical grade hESC lines, later moving to iPSC, we will Consolidate and extend knowledge of the rate, type and functional impact of the genetic variations that occur during hPSC culture. We will use whole genome and exome sequencing as well as SNP arrays, together with clonal analysis and other cytogenetics techniques. Common changes will be compared with those found in the normal human population, at low frequency in the original cell population or observed during iPSC generation in the HIPSCI project currently based at the WTSI. These studies will provide a better understanding of the range of genetic changes that occur in hPSC beyond the CNVs already identified. In conjunction with cancer genome resources and expertise at WTSI, bioinformatic analyses of these hPSC data will allow us to assess potential impact on hPSC behaviour pertinent to applications in regenerative medicine, notably the likelihood that specific changes arising in undifferentiated PSC cultures may be associated with potential malignant transformation of differentiated progeny. This data is part of a pre-publication release. For information on the proper use of pre-publication data shred by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002231

• PSCP_bisulphite analysis in hESCs

  Many studies over the past 10 years, culminating in the recent report of the International Stem Cell Initiative (ISCI, 2011) have shown that hPSC acquire genetic and epigenetic changes during their time in culture. Many of the genetic changes are non-random and recurrent, probably because they provide a selective growth advantage to the undifferentiated cells. Some are shared by embryonal carcinoma cells, the malignant counterparts of ES cells. The origins of these growth advantages are poorly understood, but may come from altered cell cycle dynamics, resistance to apoptosis or altered patterns of differentiation. Less is known about the nature and consequences of epigenetic changes, but it is likely that these similarly affect hPSC behaviour; e.g., enhanced expression of DLK1, an imprinted gene, is associated with altered hPSC growth (Enver et al 2005). Inevitably, these genetic and epigenetic changes will impact on our ability to use hPSC for regenerative medicine, either because malignant transformation of the undifferentiated cells or their differentiated derivatives to be used for transplantation compromises safety, or because they impede the function of those differentiated derivatives, or because they affect the efficiency with which the undifferentiated cells can be expanded and differentiated into desired cell types. Focusing initially upon the existing clinical grade hESC lines, later moving to iPSC, we will Consolidate and extend knowledge of the rate, type and functional impact of the genetic variations that occur during hPSC culture. We will use whole genome and exome sequencing as well as SNP arrays, together with clonal analysis and other cytogenetics techniques. Common changes will be compared with those found in the normal human population, at low frequency in the original cell population or observed during iPSC generation in the HIPSCI project currently based at the WTSI. These studies will provide a better understanding of the range of genetic changes that occur in hPSC beyond the CNVs already identified. In conjunction with cancer genome resources and expertise at WTSI, bioinformatic analyses of these hPSC data will allow us to assess potential impact on hPSC behaviour pertinent to applications in regenerative medicine, notably the likelihood that specific changes arising in undifferentiated PSC cultures may be associated with potential malignant transformation of differentiated progeny. 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 EGAD00001002235

• Data Use Ontology (DUO)

  Data Use Ontology Data Use Ontology at EGA The EGA is committed to its involvement in the work of GA4GH. In an effort to enhance data discoverability and streamline data access, EGA have implemented the use of the Data Use Ontology (DUO), based on consent codes as described in Dyke et al. 2017. The Data Use Ontology codes will be displayed on the live dataset page of your submission to advise any would be requestor on how the data can be used and also to enhance data discoverability as users will be able to search on these codes to find applicable datasets. DUO can be browsed online via the Ontology Lookup Service Learn more reading the Data Use Ontology publication and the GA4GH Machine-Readable Consent Guidance! Check our DAC Portal Take The Tour and learn how to add DUO codes to your policy! Term Shorthand Label Description DUO:0000004 NRES no restriction This data use permission indicates there is no restriction on use. DUO:0000042 GRU general research use This data use permission indicates that use is allowed for general research use for any research purpose. DUO:0000006 HMB health or medical or biomedical research This data use permission indicates that use is allowed for health/medical/biomedical purposes; does not include the study of population origins or ancestry. DUO:0000007 DS disease specific research This data use permission indicates that use is allowed provided it is related to the specified disease. DUO:0000011 POA population origins or ancestry research only This data use permission indicates that use of the data is limited to the study of population origins or ancestry. DUO:0000012 RS research specific restrictions This data use modifier indicates that use is limited to studies of a certain research type. DUO:0000015 NMDS no general methods research This data use modifier indicates that use does not allow methods development research (e.g., development of software or algorithms). DUO:0000016 GSO genetic studies only This data use modifier indicates that use is limited to genetic studies only (i.e., studies that include genotype research alone or both genotype and phenotype research, but not phenotype research exclusively) DUO:0000018 NPUNCU not for profit, non commercial use only This data use modifier indicates that use of the data is limited to not-for-profit organizations and not-for-profit use, non-commercial use. DUO:0000019 PUB publication required This data use modifier indicates that requestor agrees to make results of studies using the data available to the larger scientific community. DUO:0000020 COL collaboration required This data use modifier indicates that the requestor must agree to collaboration with the primary study investigator(s). DUO:0000021 IRB ethics approval required This data use modifier indicates that the requestor must provide documentation of local IRB/ERB approval. DUO:0000022 GS geographical restriction This data use modifier indicates that use is limited to within a specific geographic region. DUO:0000024 MOR publication moratorium This data use modifier indicates that requestor agrees not to publish results of studies until a specific date. DUO:0000025 TS time limit on use This data use modifier indicates that use is approved for a specific number of months. DUO:0000026 US user specific restriction This data use modifier indicates that use is limited to use by approved users. DUO:0000027 PS project specific restriction This data use modifier indicates that use is limited to use within an approved project. DUO:0000028 IS institution specific restriction This data use modifier indicates that use is limited to use within an approved institution. DUO:0000029 RTN return to database or resource This data use modifier indicates that the requestor must return derived/enriched data to the database/resource. DUO:0000043 CC clinical care use This data use modifier indicates that use is allowed for clinical use and care. DUO:0000044 NPOA population origins or ancestry research prohibited This data use modifier indicates use for purposes of population, origin, or ancestry research is prohibited. DUO:0000045 NPU not for profit organisation use only This data use modifier indicates that use of the data is limited to not-for-profit organisations. DUO:0000046 NCU non-commercial use only This data use modifier indicates that use of the data is limited to not-for-profit use. Point to Notice: For the consent code DUO:0000007 where data is restricted to use on a specific disease, please accompany it with an appropriate ontology from MONDO e.g., If the data is restricted to the use of research into juvenile idiopathic arthritis the code should be displayed as DUO:0000007; MONDO:0011429.

  Documentation access/data-access-committee/data-use-ontology

• University of Alabama at Birmingham (Xenotransplantation Project) - DAC

  This is a Data Access Committee (DAC) developed to govern access to sequence level data produced by the Porrett Lab, University of Alabama at Birmingham (Xenotransplantation Project), and headed by Dr. Jayme Locke, and Dr. Porrett M. Paige, Principal, and Co-Principal Investigators respectively. These data were largely used in the generation of the manuscript: “Spatiotemporal immune atlas of the first clinical-grade gene-edited pig-to-human kidney xenotransplant”. Access to these data will be granted to qualified investigators for appropriate use following data use approval from members of the DAC.

  Dac EGAC50000000188

• NHLBI TOPMed: Genetic Causes of Complex Pediatric Disorders - Asthma (GCPD-A)

  The Center for Applied Genomics (CAG) at the Children's Hospital of Philadelphia (CHOP) is focused on addressing the roles of both single nucleotide variants and structural copy number variants, and their functional impact, together with gene-environment interactions and their influence on asthma drug response.

  Study phs001661

• Data Access Committee for the DNA sequencing data included in the study "Multifocal cohort analysis unveils cell types associated with regional lymph node seeding in prostate cancer".

  A data transfer agreement (DTA), based on the example DTA provided by UZ Gent, is to be filled out by the requester and Multifocal RNA-seq and tNGS data from primary and metastatic tissues of locally advanced and de novo metastatic cohorts - Data Access Committee. https://www.uzgent.be/nl/over-uz-gent/organisatie/data-access-comite/wat-zijn-de-opdrachten-van-het-data-access-comite Coordinator: Muriel Fouquet E-mail: dataaccesscommittee@uzgent.be

  Dac EGAC50000000930

• A single-cell atlas of meningioma.

  In this study, we examined the breadth of cell types and states in meningioma using the largest single-cell cohort of meningioma by generating more than 7.5 million total cells from meningioma and surrounding dura, profiled using single-nuclear RNA sequencing and high-resolution spatial transcriptomics.

  Study EGAS50000001589

• A clinically and genomically annotated Early onset colorectal cancer and late onset colorectal cancer

  This study aimed to identify genomic characteristics of early-onset colorectal cancer (EOCRC) compared to late-onset cases. Whole-genome sequencing (WGS) was performed on EOCRC and late-onset colorectal cancer (LOCRC) tissues and blood samples to analyze their genomic profiles.

  Study EGAS50000000544

• A first-in-human clinical study of an allogenic iPSC-derived corneal endothelial cell substitute transplantation for bullous keratopathy

  iPSC-derived corneal endothelial cell substitutes recover pathological corneal edema. No adverse reactions are observed after one year follow-up. Gene mutations may appear despite whole genome sequencing of the master cell bank. This study contains the RNA-seq data.

  Study EGAS50000000673

• Genetic dysregulation of gene expression and splicing during a ten-year period of human aging in the PIVUS study

  We performed RNA sequencing in whole-blood from the same individuals from the PIVUS study at ages 70 and 80 to quantify how gene expression, alternative splicing, and their genetic regulation are altered during this 10-year period of advanced aging.

  Study EGAS00001003583

• Multiplexed quantification of four neuroblastoma DNA targets in a single droplet digital PCR reaction

  Evaluation of quadruplex ddPCR-based copy number assessment in blood plasma samples paired with genomic DNA from the corresponding primary neuroblastoma from three patients and comparison with copy numbers determined by duplex and triplex ddPCR and re-analyzed from whole-exome sequencing (WES) data.

  Study EGAS00001004275

• Raw FASTQ files from TSO500 hybrid capture sequencing of prostate cancer tissue and plasma.

  This dataset contains raw genomic sequencing data in FASTQ format for a total of 49 libraries. It includes 22 samples derived from formalin-fixed paraffin-embedded (FFPE) tissue and 27 samples from plasma cell-free DNA (cfDNA). All samples were processed using the Illumina TruSight Oncology 500 (TSO500) hybrid capture panel to identify relevant genomic alterations. These files represent the primary, non-aligned data used for the comparative performance analysis described in the associated study.

  Dataset EGAD50000002463

• Error-corrected targeted sequencing of rectal mucus from patients suspected to have colorectal cancer

  This dataset contains error-corrected next-generation sequencing (ecNGS) data generated from 161 rectal mucus samples. These samples belong to colorectal cancer, adenoma, and control participants. Genomic DNA was extracted and processed using a hybrid-capture panel targeting 50 colorectal cancer–associated genes, incorporating unique molecular identifiers (UMIs) for duplex consensus error correction. Libraries were sequenced on an Illumina NovaSeq 6000 (paired-end 150 bp) producing paired-end FASTQ files. These data support analyses of DNA mutations in CRC detection and progression.

  Dataset EGAD50000002032

• Single nucleus mRNA sequencing of immune cells from diverse CNS regions in human health and diseases

  CNS nuclei were isolated from frozen specimen, stained with anti-NeuN and anti-Olig2, FACS purified (DAPI+NeuN-Olig2-), and analyzed with single nucleus RNA-seq. One batch of samples was also labelled with CMO and allows distinguishing indivudal samples within the analysis. A combined seurat object was generated to include all the analyses and samples were integrated using Harmony integration.

  Dataset EGAD50000001835

• Long-read single-cell RNA-seq in COVID-19

  We report the first application of long-read single-cell RNA sequencing on nasopharyngeal swabs from COVID-19 patients and healthy controls to resolve transcript-level changes across cell types. Samples were collected from three patients with moderate COVID-19, six patients with critical COVID-19, and three healthy controls. Single-cell libraries were generated using the 10x Genomics Chromium 3' protocol, producing unfragmented cDNA. Libraries were sequenced on the Oxford Nanopore Technologies PromethION platform. Basecalling was performed with Guppy (v1.6), generating a single FASTQ file per sample.

  Dataset EGAD50000001836

• Whole-genome and single-cell sequencing of lethal metastatic castration resistant prostate cancer

  Contains a total of 9 patients with whole-genome sequencing of 30 tumours or germline samples, 10X single-cell multiome RNA-seq + ATAC-seq of 24 tumours, 10X 5' single-cell RNA-seq of 10 tumours, and snPATHO-seq of 2 primary tumours. All single-cell experiments have matched whole-genome sequencing. When matched tumour (and germline) whole-genome sequencing is missing, it can be found under EGAS00001006598.

  Dataset EGAD50000001869

• Single-cell RNA sequencing of metastatic colorectal cancer organoids treated with cetuximab

  Organoids were seeded in 12-well plates at a density of 5 × 10⁴ cells per well, embedded in 200 µL of Matrigel diluted 1:1 with culture medium, and maintained under EGF-deprived conditions. After four days of growth, organoids were either left untreated or treated with cetuximab (20 µg/mL). Following 72 hours of treatment, organoids were harvested, dissociated, and live single cells were sorted for single-cell RNA sequencing using the 10x Genomics platform.

  Dataset EGAD50000002104

• NAR-GAB 2025 deposit data

  Paired-end whole-exome sequencing data derived from Illumina hybrid-capture experiments (Agilent SureSelect Human All Exon kits). Six human probands with rare diseases; one sample per subject. Sequencing reads from each individual were processed into analysis-ready gVCF files following standard best-practice pipelines, including alignment, duplicate marking, base quality calibration, and variant calling. All gVCF files are encrypted using Crypt4GH and are available under controlled access through our DAC in accordance with a strict DUO-based policy.

  Dataset EGAD50000002100

• RNASeq Whole Transcriptome Expression Profiles of NFE2 and PF4 as Translational Biomarkers for BET Inhibition-Induced Thrombocytopenia in Preclinical and Clinical Studies

  Longitudinal blood samples for exploratory biomarker analysis were collected from 51 out of 83 patients enrolled in CA011-001 (NCT02419417) study of BMS-986152, totaling 425 samples collected on three different dosing schedules (A, B, and C). Whole transcriptome expression profiling was performed on the collected blood samples using RNASeq, with Illumina TruSeq library preparation and paired sequencing. Gene expression data was quantified as transcripts per million (TPM).

  Dataset EGAD50000001659

• RNAseq data from 112 samples of benign or malignant ovarian tumours

  This dataset contains ~1.2 TB RNA sequencing data in fastq format from 112 samples of fresh-frozen ovarian tumours from a total of 111 women, two samples being replicates. The samples were collected from the U-CAN collection at Uppsala Biobank and include both benign (n = 18) and malignant (n = 94) tumours. The RNA sequencing samples were sequenced using paired-end sequencing (2x150 bp) on an Illumina NovaSeq 6000 instrument at the SciLifeLab National Genomics Infrastructure (NGI) in Uppsala.

  Dataset EGAD50000001521

• Metagenomic sequencing of fecal samples from celiac disease patients and controls

  150 nt paired-end metagenomic shotgun sequencing (Illumina HiSeq 2000) of fecal samples from 130 (mostly treated, i.e. adhering to gluten-free diet) celiac disease patients and 106 controls from the CeDNN cohort from the University Medical Center Groningen, The Netherlands. For six celiac disease patients, data from two timepoints (before and during gluten-free diet) are included, resulting in a total of 242 samples. The dataset includes 2 encrypted fastq files per sample (.fq.gz.c4gh) en sample phenotypes (.txt.c4gh).

  Dataset EGAD50000001397

• RNA-seq from Glioblastoma treated with Indisulam

  The dataset includes RNA-sequencing results from four patient-derived glioblastoma cell lines treated with DMSO (vehicle control) or the splicing modulating-drug indisulam (at 6 and 16 hours), resulting in 12 RNA-seq samples. Paired-end sequencing produced 24 FASTQ.gz files. The RNA libraries were prepared using PolyA selection and ERCC spike-ins and sequenced on a NovaSeq platform. This dataset supports the analysis of drug-induced transcriptomic changes and the prediction of subsequent non-canonical tumor-specific antigens (ncTSAs) using the NovumRNA pipeline.

  Dataset EGAD50000000957

• cfRRBS methylation profiling of cfDNA from cerebrospinal fluid from pediatric CNS tumor patients

  In this study, we explore the potential of classifying pediatric brain tumors based on methylation profiling of the cell-free DNA in cerebrospinal fluid (CSF). For this proof-of-concept study, we collected 20 cerebrospinal fluid samples of pediatric brain cancer patients via a ventricular drain placed for reasons of increased intracranial pressure. For 11 patients in this study we collected matched tumor DNA. This cohort contains fastQ files of cfRRBS data of these samples.

  Dataset EGAD50000000554

• Whole exome sequencing of Ewing sarcoma and CIC-DUX4 sarcoma

  Whole exome sequencing of a collection of 6 Ewing sarcoma and 3 CIC-DUX4 sarcoma tumoroids plus 4 matched patient tumors and normal tissue. Tumoroids were established from digested patient tumor material as 3D culture in Matrigel. After 2-3 month of growth, Genomic DNA was isolated from individual tumoroid models using the DNeasy Blood & Tissue Kit (Qiagen) and used for whole exome sequencing. Genomic DNA from corresponding patient tumors and matched normal tissue was tested in parallel, if available.

  Dataset EGAD50000000556

• Dataset belonging to the article "Genotyping from targeted NGS data based on a small set of SNPs correctly matches patient samples"

  This dataset consists of raw sequencing data from targeted next generation sequencing of samples from patients with chronic lymphocytic leukaemia. These samples were used to validate an approach for detection of sample mislabelling and swapping in clinical cohorts using single nucleotide polymorphisms (SNPs), as described in the associated article. The data are divided into two parts - data from the initial validation experiment (discovery cohort) and data from an additional independent cohort.

  Dataset EGAD50000000757

• snRNA-seq data of 11 regionally sampled GBM tissue for 4 patients

  This dataset contains snRNA-seq data of 11 regionally sampled GBM tissue (peritumoral region, tumor edge, and tumor core). Regionally sampled GBM patient tissue was dissociated and nuclei were processed in an unbiased manner without any sorting procedure. Nuclei were dissociated from frozen tissue using Chromium Nuclei Isolation Kit. Nuclei barcoding, cDNA preparation, and library construction were performed following the Evercode WT or WT mini User Manual, by combinatorial barcoding to assign a unique barcode to each cell.

  Dataset EGAD50000000778

• WGS & RNAseq of paired tumor-normal patients presenting a BRCA2 alteration

  Raw data FASTQ files of RNAseq (Stranded mRNA prep Ligation-Illumina on NovaSeq 6000, 100bp paired-end) & WGS (PCR Free Roche KAPA Hyper Prep Library on Illumina Novaseq 6000, 150bp paired-end) for 40 samples corresponding to the matched tumor/normal of 10 patients. Raw data FASTQ files of WGS (PCR Free Roche KAPA Hyper Prep Library on Illumina Novaseq 6000, 150bp paired-end) of the tumor of two additional patients.

  Dataset EGAD50000000871

• WES data from study: Molecular underpinnings of dedifferentiation and aggressiveness in chromophobe renal cell carcinoma

  The ChRCC study WES dataset contains raw whole exome sequencing data of 17 tumor and 7 adjacent normal samples from 7 UTSW patients, who have consented to depositing their genomic data to public repository. WES was performed using 75bp paired-end fragments at an average read depth > 100x on a HiSeq2500 platform (Illumina, San Diego, CA, USA). The raw data is in fastq format.

  Dataset EGAD50000000415

• Nurminen et al ("GP2Men") Study Primary and Metastatic Prostate Cancer Whole Genome Sequence Data

  We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones.

  Dataset EGAD50000000005

• Genotypic data of the individuals in HPP project

  Dataset contains imputed SNP genetics data for human phenotype project https://humanphenotypeproject.org/. The samples are genotypes of 8958 individuals that underwent genetic sequencing based on buccal swab.Sequencing is performed at Neogen GeneSeek Laboratories using Illumina Novaseq at low-pass, targeting coverage levels of 0.5× and 1×. Imputation was done using loimpute-v0.1.0 over the aligned reads. Imputation is done for the autosomal chromosomes (1-22) using The 1000 Genomes phase 3 haplotypes (1KGP3) as a reference panel. The samples are provided in merged PLINK 1 binaries (.bed/.bim/.fam)

  Dataset EGAD00010002714

• MassArray1-80

  234 samples genotyped at 40 loci using the MassArrayiPLEX genotyping assay using the iPLEX Gold genotyping kit (Agena Biosciences, cat. 10148-2) Gabriel et al. (2009) [Gabriel, S. , Ziaugra, L. and Tabbaa, D. (2009), SNP Genotyping Using the Sequenom MassARRAY iPLEX Platform. Current Protocols in Human Genetics, 60: 2.12.1-2.12.18. doi:10.1002/0471142905.hg0212s60]). Products were detected on a MassArray mass spectrophotometer and data were acquired in real time with MassArray RT software 4.0.0.2 (Agena Biosciences). SNP clustering and validation was carried out with Typer 4.0.26.75 software (Agena Biosciences).

  Dataset EGAD00010001906

• lebanon

  Genotype data from Chad, Lebanon, and Yemen

  Dataset EGAD00010001102

• Raw sequencing data - Intra-tumor heterogeneity and clonal evolution patterns towards platinum-resistant high-grade serous ovarian cancer

  This dataset includes 69 sampels of whole-exome sequencing data of high-grade serous ovarian carcinoma (HGSOC). We included patients with advanced (International Federation of Gynecology and bstetrics [FIGO] stage IIIeIV) HGSOC for which biopsies were obtained during debulking surgery, the first at initial diagnosis and the second at disease relapse. Where possible, matched normal DNA from each participating patient was obtained from a whole-blood sample. Written informed consent was obtained from all patients and approved by the local ethics committee.

  Dataset EGAD00001001381

• RNA sequencing data of pediatric ETV6::RUNX1 acute lymphoblastic leukemia (set 2)

  The dataset includes RNA sequencing FASTQ files from diagnostic tumor samples of four ETV6::RUNX1 patients (ALLT-388, ALLT-392, ALLT-394, ALLK-114) and from a second relapse sample of one ETV6::RUNX1 patient (ALLK-104). TruSeq Stranded Total RNA Library Prep (Illumina) kit was used in library preparation, and Novogene's internal method was utilized in rRNA removal. The diagnostic and relapse tumor samples were sequenced using Illumina Novaseq 6000, and the target sequencing depth was 21 Gb per sample.

  Dataset EGAD50000002589

• Evolutionary Genome Analysis of Transformation into Small Cell Carcinomas from Lung Adenocarcinomas

  This dataset includes the high-throughput sequencing data from a study entitled "Clonal History and Genetic Predictors of Transformation into Small Cell Carcinomas from Lung Adenocarcinomas". Whole-genome sequencing libraries were generated by PCR-free methods, and sequencing run was made in HiSeq X or HiSeq 2500 machines. PCR duplicates-marked, indel-realigned, and base-recalibrarted BAM files are provided in our dataset.

  Dataset EGAD00001003315

• 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/ .

  Dataset EGAD00001004169

• Congenital mesoblastic nephroma and infantile fibrosarcoma

  The genetic basis of many rare childhood cancers remains unknown. These include a spectrum of infant soft tissue tumors without canonical gene fusions, encompassing congenital mesoblastic nephroma (CMN) of the kidney and infantile fibrosarcoma (IFS). Here, we integrated whole genome and transcriptome sequencing and identified diagnostic markers and novel therapeutic strategies.

  Dataset EGAD00001003885

• Identification of low frequency variants associated with ulcerative colitis using whole-genome sequencing

  2000 ulcerative colitis cases drawn from the UKIBD Genetics Consortium cohort and whole-genome sequenced at 2X depth. A case control association study using control samples whole-genome sequenced by UK10K will be undertaken to identify common, low-frequency and rare variants associated with ulcerative colitis. Data will be combined with similar data across 3000 Crohn's disease cases from the same cohort to identify inflammatory bowel disease (IBD) loci and better understand the genetic differences and similarities of the two common forms of IBD.

  Dataset EGAD00001000409

• Sequencing probands and families with severe insulin resistance syndromes

  This is an ongoing project and continuation to all the sequencing we have been doing over the last few years. We have some additional families and probands with syndromes of insulin resistance not previously sequenced within uk10k or other core funded projects. We would like to complete the sequencing in all of the good quality families and probands we have, this would require another ~50 samples to be WES sequenced. This cohort has already proven to be a rich source of interesting findings with papers in Science and Nature genetics.

  Dataset EGAD00001000694

• Targeted resequencing of Acute Myeloid Leukemia patients with an acquired inv(3)(q21q26) or t(3;3)(q21;q26).

  Targeted resequencing on the specific regions chr3:126036241-130672290 and chr3:157712147-175694147 in hg19 centered on the chromosomal regions 3q21 and 3q26 respectively. The focus lies on the detection of the exact breakpoints in Acute Myeloid Leukemia (AML) patients having acquired a inv(3)(q21q26) or t(3;3)(q21;q26). This dataset contains all information to detect all structural variants contained within these regions, including the 3q-aberrations inducing the overexpression of the proto-oncogene EVI1.

  Dataset EGAD00001000727

• Lung Multi-site Targeted Sequence Capture

  DNA extracted from multiple biopsies taken from different areas of primary lung tumours will be subjected to targeted re-sequencing and analysed in order to assess intra-tumour heterogeneity with respect to mutations in a selection of cancer related genes.

  Dataset EGAD00001001017