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• NHLBI TOPMed: Characterizing the Response to a Leukotriene Receptor Antagonist and an Inhaled Corticosteroid (CLIC)

  Within-subject clinical responses to either inhaled corticosteroids or Montelukast were compared in 126 children with mild to moderate asthma.

  Study phs001729

• MATCH-molecular driver

  Patients were biopsied at progression on molecular targeted agents and WES and/or WTS were performed to identify resistance mechanisms. 271 underwent one or sequential tissue biopsies.

  Dataset EGAD50000000697

• Transcriptome - Uveal Melanomas MPI

  Total RNA sequencing on 4 uveal melanoma samples. Libraries were prepared using the TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Gold. Paired-end libraries (2 x 75 bp) were sequenced on HiSeq 4000 instrument (Illumina).

  Dataset EGAD50000001138

• Genotype data of Japanese

  Genotype data of the Japanese population (N=142) were collected at Osaka University. Genotyping was performed using Infinium Asian Screening Array (Illumina, USA).

  Study EGAS00001006950

• Microdissection_sequencing_of_normal_human_prostate

  Histology sections from pathologically benign human prostates were prepared and microdissections retrieved from Laser-Capture Microdissection are submitted to low-input whole-genome sequencing.

  Study EGAS00001003049

• Epigenomic data of Human muscle stem cell

  To profile the epigenomic landscape of Human muscle stem cell, ChIP-seq of histone marks , WGBS and RNA-seq data were generated.

  Study EGAS00001006159

• Transcriptome analysis of CD8 T-cells from blood, fat, liver and skin

  CD8 Tcells were FACS sorted and processed with 10x Genomics Chromium Next GEM SingleCell V(D)J Reagents Kits v1.1 sequencing. In total 6 samples were processed. Fastq files are supplied.

  Dataset EGAD00001010007

• Transcriptome analysis of Tcells from blood, fat and skin

  Tcells were isolated fom Tissues and FACS sorted. In total 50 samples were processed in 5 replicates with the Takara SmartSeq Stranded kit. Single end fastq-files are supplied.

  Dataset EGAD00001007663

• RNAseq data

  Whole genome transcriptome poly-A selected strand specific 100bp paired-end RNA sequencing of post-mortem brain tissue from prefrontal cortex and orbitofrontal cortex were performed. Brain tissue samples were collected from four different biobanks in England and USA.

  Dataset EGAD00001005948

• Search for new loci and low-frequency variants influencing glioma risk by exome-array analysis

  To identify protein altering variants (PAVs) for glioma we analysed Illumina HumanExome BeadChip exome array data on 1,882 glioma cases and 8,079 controls from three independent European populations. In addition to single variant tests we incorporated information on the predicted functional consequences of PAVs and analysed sets of genes with a higher likelihood of having a role in glioma on the basis of the profile of somatic mutations documented by large-scale sequencing initiatives. Globally tThere was a strong relationship between effect size and SNPs predicted to be damaging (P=2.29x10-49); however, these variants which are most likely to impact on risk, are rare (MAF<5%). While no single variant showed an association which was statistically significant at the genomewide threshold a number of represented promising Notable associations - were seen with the BRCA2:c.9976A>T, p.(Lys3326Ter)BRCA2 p.Lys332X variant, which has been shown documented to influence breast and lung cancer risks (OR=2.3, P=4.00x10-4 for glioblastoma [GBM]) and IDH2:c.782G>A, p.(Arg261His)IDH2 p.Arg261His (OR=3.21, P=7.67x10-3, for non-GBM). Additionally, gene burden tests revealed a statistically significant association for HARS2 and risk of GBM (P=2.20x10-6). Genome scans of low frequency PAVs represent a complementary strategy to identify disease-causing variants compared with scans based on tagSNPs. Strategies to lessen the multiple testing burden by restricting analysis to PAVs with higher priors affords an opportunity to maximise study power.

  Study EGAS00001001258

• Widespread hypertranscription in aggressive human cancers

  Cancers are often defined by the dysregulation of specific transcriptional programs; however, the importance of global transcriptional changes is less understood. Hypertranscription is the genome-wide increase in RNA output. Hypertranscription’s prevalence, underlying drivers and prognostic significance are undefined in primary human cancer. This is due in part to limitations of expression profiling methods, which assume equal RNA output between samples. Here, we developed a computational method to directly measure hypertranscription in 7,494 human tumors, spanning 31 cancer types. Hypertranscription is ubiquitous across cancer, especially in aggressive disease. It defines patient subgroups with worse survival, even within well-established subtypes. Our data suggest that loss of transcriptional suppression underpins the hypertranscriptional phenotype. Single-cell analysis reveals hypertranscriptional clones, which dominate transcript production regardless of their size. Finally, patients with hypertranscribed mutations have improved response to immune checkpoint therapy. Our results provide fundamental insights into gene dysregulation across human cancers and may prove useful in identifying patients that would benefit from novel therapies.

  Study EGAS00001006365

• The Genomic Map of Poland in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000092

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000095

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000093

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000118

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000117

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000116

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000115

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000121

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000120

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000119

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000113

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000112

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000111

• The Genomic Map of Polad in Open Access

  The main goal is to prepare and share WGS/WES of Polish population

  Study EGAS50000000110