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• Asan Medical Center Data Access Committee

  Asan Medical Center Data Access Committee for BRISK study.

  Dac EGAC50000000733

• Hepatocellular Carcinoma Spatial transcriptomics Data Access Committee

  This is the DAC for study ST HCC

  Dac EGAC50000000635

• Pan Prostate Cancer Group Data Access Control Committee

  DACC for the PanProstate Cancer Group projects

  Dac EGAC50000000602

• DAC SysMed

  Data access control for datasets submitted by SysMed

  Dac EGAC50000000089

• MOSAIC Window DAC

  Data Access Committee for the MOSAIC Window initiative.

  Dac EGAC50000000398

• DAC for Genetic and Microenvironmental Analysis of PTCL at Department of Hematology, University of Tsukuba

  Dac EGAC50000000600

• DAC for "H3K27me3-mediated epigenetic repression regulates neuroblastoma development and contributes to biological heterogeneity"

  Dac EGAC00001003620

• CD3 bispecific antibody-induced cytokine release is dispensable for cytotoxic T cell activity

  Study EGAS00001003734

• Circulating tumor cells for comprehensive and multiregional non-invasive genetic characterization of multiple myeloma

  Study EGAS00001004288

• A method for multiplexed full-length single-molecule sequencing of the human mitochondrial genome

  Study EGAS00001006280

• Genome sequencing in monozygotic twins discordant for Mayer-Rokitansky-Küster-Hauser syndrome

  Study EGAS00001006371

• EGAD00010000052

  Monozygotic twins that are discordant for schizophrenia (Genotyping)

  Dataset EGAD00010000052

• EGAD00000000027

  eQTL data for European newborns

  Dataset EGAD00000000027

• Exome reads

  Exome sequencing for 2 infertile brothers

  Dataset EGAD00001003193

• Exome data for PDXs

  Exome data of PDX models.

  Dataset EGAD00001001863

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Multimodal Developmental Neurogenetics of Females

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002043

• Center for Common Disease Genomics [CCDG] - Neuropsychiatric: Genomics of Autism Spectrum Disorder (GASD)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of New York Genome Center (NYGC). WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal.An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002509

• Center for Common Disease Genomics [CCDG] - Neuropsychiatric: Simons Searchlight

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of New York Genome Center (NYGC). WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal.An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002512

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Autism Center of Excellence (ACE II)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002042

• Genome Wide Association for Asthma and Lung Function

  The SNP Typing for Association with Multiple Phenotypes from Existing Epidemiologic (STAMPEED) asthma project includes subjects with asthma and controls from the Chicago Asthma Genetics Study (CAG), NHLBI multicenter Severe Asthma Research Program (SARP) and NHLBI Collaborative Studies on the Genetics of Asthma CSGA (Wake Forest). All studies included European American and African American children and adults with asthma ranging from mild to severe and adult controls. CAG participants were collected at the University of Chicago. SARP participants were recruited at the NHLBI SARP sites with an emphasis on recruiting severe asthmatics (Moore et al, Am J Respir Crit Care Med, 2010, PMID: 19892860). CSGA cases and controls collected by the Wake Forest investigators were also included. Asthma status was based on both a physician's diagnosis and either bronchodilator reversibility or hyper-responsiveness to methacholine as well as less than 5 pack years of smoking. Genotyping was performed on the Illumina 1Mv1 platform, with individual genotypes called using clustering algorithms as implemented in the BeadStudio software by Illumina. The total number of markers following standard QC was 1,025,129. Imputation was performed using the HapMap phase 2, release 21 SNPs using MACH with the phased HapMap CEU and YRI haplotypes as a reference. Case/control association tests for asthma status were performed using logistic regression in R (http://CRAN.R-project.org/) on genotype dosages, and adjusting for the first principal component from EIGENSTRAT.

  Study phs000355

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Study of Autism Genetics Exploration (SAGE)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs001740

• Center for Common Disease Genomics (CCDG)-Neuropsychiatric: A Study of the Genetic Causes of Complex Pediatric Disorders (CAG)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002004

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Autism Genetic Resource Exchange

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs001766

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: The Autism Simplex Collection (TASC)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs001741

• Center for Sub-Cellular Genomics

  A cell is a highly complex system with distributed molecular physiologies in structured sub-cellular compartments whose interplay with the nuclear genome determine the functional characteristics of the cell. A classic example of distributed genomic processes is found in neurons. Learning and memory require modulation of individual synapses through RNA localization, localized translation, and localized metabolites, such as those from dendritic mitochondria. Dendrites of neurons integrate distributed synaptic signals into both electrical and nuclear transcriptional response. Dysfunction of these distributed genomic functions in neurons can result in a broad spectrum of neuropsychiatric diseases such as bipolar and depressive disorders, autism, among others. The Center for Sub-Cellular Genomics' (CSG) mission is to develop new technologies to enable the dissection of complex genomic interactions within a single cell. New technologies include methods to target DNA and RNA sampling at sub-cellular resolution in live cells; high-throughput isolation and microfluidics chemistry for sub-cellular fractions; sub-cellular resolution mass-spectrometry; and computational tools for analysis of sub-cellular genomics data. The new technologies developed by CSG will enable the broader genomics community to associate sub-cellular processes to cell function and dysfunction. These technologies will be utilized in the center to generate a comprehensive atlas of genomic signatures of sub-cellular compartments in the mammalian neuron system. The technologies and neuron-focused data obtained here will be relevant to neurodegenerative and neuropsychiatric diseases including schizophrenia, depression, autism spectrum disorder, traumatic brain injury, and Alzheimer's.

  Study phs002120

• NHLBI TOPMed: Pulmonary Fibrosis Whole Genome Sequencing

  This is a set of cases diagnosed with idiopathic pulmonary fibrosis, a fatal interstitial lung disease. These cases were included in the TOPMed phase three studies. The planned study will compare these cases to within-TOPMed controls for genome-wide association studies.

  Study phs001607

• Bulk RNA Sequencing of 86 Human Donor Lungs

  The goal of this study was to characterize the effect of age on gene expression in normal human lung. We collected distal lung samples from 86 human donors distributed in age between 16 and 76 years and extracted RNA for Illumina-based sequencing.

  Study phs002484

• Whole-Genome and Exome Sequencing in clear-cell Renal Cell Carcinoma (ccRCC)

  Study to evaluate the molecular genetics of ccRCC through integrative analyses of tumors and tumorgrafts using multiple platforms in clinically annotated samples. Genome and exome data for patients giving consent to have the information available on a public, secure, database is being submitted.

  Study phs000491

• Phase 2 Study of Pembrolizumab in Combination with Gemcitabine and Cisplatin as Neoadjuvant Therapy

  The purpose of the clinical trial (NCT02690558) is to evaluate whether adding pembrolizumab (Keytruda) to the combination of gemcitabine and cisplatin will improve shrinkage of the tumor before having a cystectomy, for people with muscle-invasive bladder cancer (MIBC).

  Study phs003452

• Transplant Outcomes in Aplastic Anemia (TOAA): GWAS and Whole Exome Sequence Data

  The TOAA study is a research collaboration between the National Cancer Institute and the Center for International Blood and Marrow Transplant Research. The study aims to identify molecular predictors of outcomes after hematopoietic cell transplant in patients with severe aplastic anemia.

  Study phs001710

• COVID_Methyl_scRNA

  DAC for the study of whole-genome enzymatic DNA methylation sequencing and single-cell RNA sequencing of nasal cells from COVID-19 patients. Nasal swabs were collected from COVID-19 patients during the acute phase of infection as well as 3 and 12-months post-infection. Cells isolated from nasal swabs were subjected to whole-genome enzymatic DNA methylation sequencing (n=33) and scRNA-seq (n=24). For n=12 former COVID-19 pateints, follow-up samples were collected at 3 and 12 months post-infection and subjected to scRNA-seq.

  Dac EGAC50000000197

• Comprehensive molecular profiling with whole-exome sequencing (WES) of PDX tumors

  To identify biomarkers for the antitumor effects of molecularly targeted therapy, we performed whole-exome sequencing on 795 patient-derived xenograft (PDX) samples established from 269 patients with solid tumors and 125 frozen tissue samples obtained at the time of surgery.

  Study JGAS000853

• Target sequencing of 8 hereditary prostate cancer genes in Japanese

  We performed a case control association study for variants in coding regions of 8 hereditary prostate cancer genes in 7,636 male unselected prostate cancer patients, 12,366 male controls of Japanese ancestry. The 8 genes were ATM, BRCA1, BRCA2, BRIP1, CHEK2, HOXB13, NBN, and PALB2.

  Study JGAS000203

• Phase II clinical trial of adult Philadelphia chromosome-negative precursor B-cell acute lymphocytic leukemia with combination chemotherapy

  Adults with B-ALL showed extremely inferior outcomes when compared to children. Genetic basis undelying adult B-ALL is largely unknown. We performed integrated analysis of RNA-seq and target DNA-seq for the patients enrolled in BALL213 study.

  Study JGAS000278

• Single cell RNA sequencing of human umbilical cord blood lymphoid progenitors

  We performed single cell RNA sequencing of human umbilical cord blood. To explore the differentiation pathway for human lymphoid lineage deeply, our target cells contain CD34+CD38+CD45RA+CD19-CD7-CD10+ cells (CD10SP). All these populations are evaluated by single cell RNA sequencing.

  Study JGAS000551

• DAC for STimage project

  The STimage Data Access Committee (DAC) oversees the access and distribution of datasets associated with the STimage project. This project focuses on the development and benchmarking of deep learning frameworks for spatial transcriptomics and digital pathology. The datasets managed by this DAC typically comprise paired histology images (H&E) and spatial gene expression data (e.g., 10x Genomics Visium) derived from human tissue samples. Our primary objective is to facilitate bona fide biomedical research while strictly protecting participant privacy and ensuring data is used in accordance with the original consent and ethical approvals.

  Dac EGAC50000000867

• single cell RNA sequencing and ATAC sequencing, and Whole Genome sequencing of ALS patients

  To investigate ALS pathology at the cellular level, we conducted a multiome (single-nucleus RNA sequencing and assay for transposase-accessible chromatin using sequencing) analysis using the motor cortex and spinal cord of patients with ALS and control individuals.

  Study JGAS000852

• SNU_PROSPECTIVE

  This study includes 5 paired samples of Korean AML paitients. 5 AML tumor samples are paired with each normal(Saliva or BM derived) sequencing data.The results of this WES study were analyzed by Mutect and GATK for ranking germline variants and cancer variants to make mutational profile matrix.

  Study EGAS00001002154

• WGS data for ctDNA monitoring for NSCLC in TRACERx

  Ultrasensitive circulating tumour DNA (ctDNA) detection can stratify clinical risk in early-stage non-small cell lung cancer (NSCLC). Here, we applied a whole-genome, tumour-informed approach to study ctDNA in more than 400 NSCLC of the TRACERx project

  Study EGAS50000001187

• Clonal expansion and epigenetic reprogramming following deletion or amplification of mutant IDH

  Initially IDH1 mutant gliomas are shown to have IDH1 is deletions or amplifications at recurrence yielding a higher-grade tumor with a reprogrammed epigenome. We also report systematic selection for cells with IDH1 CNA in vitro and in vivo.

  Study EGAS00001002618

• A Novel APP p.V742L variant in a patient with ischemic small vessel disease enhances FE65 signalling

  We identified a novel variant in the APP gene associated with cerebral ischemic strokes, which is not seen before with APP variants. This results in increased affinity of APP for FE65, APP’s most important intracellular interactor that regulates gene transcription.

  Study EGAS50000001283

• Yemen_and_Chad_Genotyping

  HumanOmni2.5-8 data from Chad and Yemen.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/

  Study EGAS00001001231

• Comprehensive genomic characterization of early stage bladder cancer - nanopore sequencing

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed long read sequencing data from 4 patients using the Nanopore platform. The dataset is composed of 8 BAM files: 4 tumor samples and 4 paired blood samples.

  Study EGAS50000000510

• Comprehensive genomic characterization of early stage bladder cancer - shallow whole genome sequencing data

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed shallow WGS data from 362 patients. The dataset is composed of 392 BAM files: 362 tumor samples, 15 female normal samples and 15 male normal samples.

  Study EGAS50000000513

• SNU_WGS_AML

  This study includes 10 paired samples of Korean AML paitients.10 AML tumor samples are paired with each normal(Saliva or BM derived) sequencing data.The results of this WGS study were analyzed by Mutect and GATK for ranking germline variants and cancer variants to make mutational profile matrix.

  Study EGAS00001001906

• WTCCC2 Reading and Mathematics (RM) samples

  A WTCCC2 project genome-wide association study for reading and mathematics ability in 3665 12-year-old individuals from the UK, genotyped on the Affymetrix 6.0 array. Details of the WTCCC2 analysis can be found in Davis et al. [Nat. Commun. 2014 July;5:4204]

  Study EGAS00001000886

• Molecular profiling of HGBCL-DH-BCL2 patients treated in the HOVON-152 trial

  Using targeted sequencing and shallow whole genome sequencing for simultaneous screening of mutations, copy number alterations, and translocations, this study aims to identify genomic alterations associated with outcome in HGBCL-DH-BCL2 patients of the HOVON-152 trial.

  Study EGAS50000001453

• Pre_clinical_evolution_of_haematological_malignancies

  Deep targeted sequencing of 291 peripheral blood samples taken 1-20 years prior diagnosis of a haematological malignancy alongside age- and sex-matched controls. Key objective is to distinguish indolent age-related clonal haematopoiesis from pre-cancer and to derive predictive models for early risk-stratification.

  Study EGAS00001002964

• Oxel Pilot Study

  This pilot study will provide preliminary data regarding the role of a peripheral immunoscore (PIS) in predicting the benefit of immune checkpoint inhibition with or without chemotherapy for high risk patients with triple-negative breast cancer (TNBC) and residual disease after effective neoadjuvant chemotherapy.

  Study EGAS50000000222

• Gut 16S rRNA/FINRISK 2002

  The main goal of the project is the study the associations between the gut 16S rRNA and human health. The dataset contains data for FINRISK 2002 participants (a random sample of individuals living in six geographic areas in Finland) who underwent fecal sampling.

  Study EGAS50000000198