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• DAC for EGAS00001007531

  Dac EGAC00001003437

• UCL Centre for Longitudinal Studies

  Dac EGAC00001003495

• DAC for v9 panel assay

  Dac EGAC00001003580

• Kenya_GWAS-2.5M_b37

  Genome-wide study of resistance to severe malaria in eleven worldwide populations:Kenya

  Dataset EGAD00010000904

• Malawi_GWAS-2.5M_b37

  Genome-wide study of resistance to severe malaria in eleven worldwide populations:Malawi

  Dataset EGAD00010000903

• Gambia_GWAS-2.5M_b37

  Genome-wide study of resistance to severe malaria in eleven worldwide populations:Gambia

  Dataset EGAD00010000902

• The Jerusalem Perinatal Study

  The Jerusalem Perinatal Study (JPS) aimed to examine the developmental origins of cardiometabolic risk.

  Dataset EGAD00010001816

• EXPR_COUNT_VALUES

  Gene expression gene-level count values from Stringtie processing including alignment to GRCh37 or GRCh38 genome version. Software: HISAT2 v2.1.0;StringTie v1.3.4d.

  Dataset EGAD00010001924

• FunGeST - Functional Genomics from cancer research to personalized medicine

  Dac EGAC00001002924

• UCL Translation Uro-Oncology Data Access Committee will govern access to data generated from the Translation Uro-Oncology groups of the UCL Cancer Institute and UCL Division of Surgery & Interventional Science

  Dac EGAC00001000471

• WES

  Whole Exome Sequencing data from Lauss et al Nat Comm 2024. Molecular patterns of resistance to immune checkpoint blockade in melanoma.

  Dataset EGAD50000000380

• Whole-genome sequencing of Tibetans from China

  Twenty-seven Tibetan samples from China were whole-genome sequenced to investigate high-altitude adaptation, population genetics and demographic history.

  Dataset EGAD00001004797

• Finnish - THL Health2000 Dataset

  The dataset encompasses 208 Runs from the WGSPD Project 3 - Genomic Strategies to Identify High-impact Psychiatric Risk Variants Project

  Dataset EGAD00001008684

• Targeted deep sequencing of somatic mutations

  We used targeted deep sequencing to accurately establish the allele frequencies of the mutations identified by exome sequencing

  Dataset EGAD00001000763

• Osteosarcoma RNAseq

  We propose to definitively characterise the somatic genetics of Osteosarcoma cancer through generation of comprehensive catalogues of somatic mutations by high coverage genome and transcriptome sequencing.

  Dataset EGAD00001000826

• Mapping the Human Connectome - Structure, Function, and Heritability: Healthy Young Adults (Age 22-35 Years) Including Twins and their Non-Twin Siblings

  The Human Connectome Project (HCP) has acquired vast amounts of data about the pattern of long-distance connections (wiring) in the brains of large numbers of healthy participants aged 22-35 using cutting-edge MRI and MEG neuroimaging, and extensive behavioral testing. Types of MR data collected included diffusion MRI (dMRI), resting-state functional MRI (r-fMRI), and structural MRI at both 3T and 7T, and task-evoked functional MRI (t-fMRI) at 3T. Following an extensive period of development and optimization of data acquisition and analysis methods, at Washington University in St. Louis we studied 1,206 participants comprising twins and their non-twin siblings. A subset of 184 twins was scanned again at the University of Minnesota using a 7T scanner. A different subset of 95 twins was studied at St. Louis University using combined resting state and/or task-activated MEG. We were able to collect genetic data on 1142 of our 1206 participants, including 149 pairs of genetically-confirmed monozygotic twins (298 participants) and 94 pairs of genetically-confirmed dizygotic twins (188 participants). Overall, there are 457 different families in the study, as determined by genetic analysis. Our rich set of imaging, behavioral and genetic data - available through the Human Connectome database - will enable many types of analysis of brain circuitry, its relationship to behavior, and the contributions of genetic and environmental factors to brain circuits.

  Study phs001364

• DAC for the study EGAS00001001738

  Dac EGAC00001000450

• DAC for sputum fungal microbiome data

  Dac EGAC00001003006

• DAC for Oncogenomics QIMR Berghofer

  Dac EGAC00001001088

• Data Access Committee for WES RCC

  Dac EGAC00001003161

• DAC for study Vel Exome Sequencing

  Dac EGAC00001000012

• DAC for Sardinia FACS pilot project

  Dac EGAC00001000132

• DAC for study POT1 splice

  Dac EGAC00001000184

• DAC for SNP phenotyping Kiel

  Dac EGAC00001000316

• DAC for Mesothelioma Genomics Study

  Dac EGAC00001000615