15147 results for "cancer studies using rna-seq"

in 21.95 milliseconds.

• National Cancer Institute (NCI) Genome-Wide Association Study (GWAS) of Renal Cell Carcinoma in African Americans

  The NCI GWAS of renal cell carcinoma (RCC) in African Americans was undertaken to provide insight into genetic loci affecting susceptibility to this malignancy in a racial group known to be at elevated risk. We genotyped 1,136,723 single-nucleotide polymorphisms (SNPs) among 255 cases and 375 controls of African ancestry from the NCI Kidney Cancer Study, and further investigated 16 SNPs in a replication set consisting of 140 cases and 543 controls from a case-control study conducted at the University of Texas MD Anderson Cancer Center. The variant rs10771279 located on 12p11, 77kb from a European-ancestry RCC risk marker, was associated with RCC risk in the GWAS (P=1.2 x 10-7) but did not replicate (P=0.99). The variant 7105934 on 11q13.3, previously associated with RCC in a GWAS using European-ancestry samples, was associated with risk in both studies [meta-analysis odds ratio (OR)=0.76, 95% confidence interval (CI)=0.64-0.91; P=0.0022]. The frequency of this allele was higher than that observed in the European-ancestry GWAS (0.56 and 0.07 respectively among controls). The rs7105934 association was stronger for clear cell RCC (ccRCC: OR=0.56; P=7.4 x 10-7) and absent for cases of other or unknown histology (OR=1.02; P=0.86). In conclusion, this study provides evidence that rs7105934 is a susceptibility locus for RCC, and clear-cell RCC in particular, among African Americans.

  Study phs000863

• NICHE - RNA-seq of MMR proficient and MMR deficient early stage colon cancers

  The dataset includes 77 FASTQ files from single-end total RNA sequencing on Illumina HiSeq2500 for 39 patients.

  Dataset EGAD00001006042

• single-cell RNA sequencing of human lung epithelial cells

  Single-cell RNA seq data of epithelial cells ((EpCAM-enriched by FACS) isolated from cryopreserved human lung tissue (3 samples, 3 donors)

  Dataset EGAD00001010251

• RNA-seq of AD, MCI and control ONS cells

  Total RNA sequencing of cultured ONS cells derived from patients with Alzheimer's disease (AD), individuals with mild cognitive impairment (MCI) and cognitively healthy controls.

  Dataset EGAD00001009395

• Cystic fibrosis multi-omics study

  Cystic fibrosis (CF) is a genetic disorder affecting approximately 1 in 2,500 people in the UK. Here, we generated a number of high-throughput sequence data sets for monocytes and neutrophils from CF patients and matched healthy volunteers (HV); including ChIP-Seq (H3K27Ac), chromatin accessibility (ATAC-Seq), and transcript/gene expression (RNA-Seq) data. Analysis of gene expression and epigenetic profiles demonstrates a robust innate immune gene expression profile that distinguishes CF and HV during periods of disease exacerbation. Upregulation of pathways responsible for the production of proinflammatory cytokines is described.

  Study EGAS00001006421

• Sequencing of longitudinal glioma pairs

  Clonal evolution drives cancer progression and therapeutic resistance. Recent studies revealed divergent longitudinal trajectories in gliomas, but early molecular traits steering post-treatment cancer evolution remain unclear. We analyzed sequencing data of 544 initial-recurrent adult diffuse glioma pairs to identify genomic and transcriptomic early predictors of tumor evolution in each molecular subtype.

  Dataset EGAD00001009845

• The Haemgen RBC study

  Anaemia is a major determinant of global ill-health. To refine our understanding of the genetic factors influencing red blood cell formation and function, we carried out a meta-analysis of genome-wide association studies (GWAS) for six red blood cell traits: haemoglobin (HB), mean cell haemoglobin (MCH), mean cell haemoglobin concentration (MCHC), mean cell volume (MCV), packed cell volume (PCV) and red blood cell count (RBC). We provide genome-wide association results for 62,553 people of European ancestry using up to 2,644,161 autosomal SNPs. Participants with extreme measurements (>+/-3SD from mean) were excluded on a per phenotype basis. Imputation was done using haplotypes from HapMap Phase 2. SNP associations with each phenotype were tested by linear regression using an additive genetic model. Associations were tested separately in each cohort, with principal components and other study specific factors as covariates to account of population substructure. We then carried out meta-analysis of results from the individual cohorts using z-scores weighted by square root of sample size. SNPs with MAF<1% (weighted average across cohorts) were removed, as were SNPs with weight <50% of phenotype sample size. Anaemia is a major determinant of global ill-health. To refine our understanding of the genetic factors influencing red blood cell formation and function, we carried out a meta-analysis of genome-wide association studies (GWAS) for six red blood cell traits: haemoglobin (HB), mean cell haemoglobin (MCH), mean cell haemoglobin concentration (MCHC), mean cell volume (MCV), packed cell volume (PCV) and red blood cell count (RBC). We provide genome-wide association results for 62,553 people of European ancestry using up to 2,644,161 autosomal SNPs. Participants with extreme measurements (>+/-3SD from mean) were excluded on a per phenotype basis. Imputation was done using haplotypes from HapMap Phase 2. SNP associations with each phenotype were tested by linear regression using an additive genetic model. Associations were tested separately in each cohort, with principal components and other study specific factors as covariates to account of population substructure. We then carried out meta-analysis of results from the individual cohorts using z-scores weighted by square root of sample size. SNPs with MAF<1% (weighted average across cohorts) were removed, as were SNPs with weight <50% of phenotype sample size.

  Study EGAS00000000132

• Functional Significance of Prostate Cancer Risk-SNPs

  Prostate Cancer (PrCa), the most frequently diagnosed solid tumor in men in the U.S., results in ~192,000 new cases and ~27,000 deaths per year. Although the variation of PrCa incidence is likely to be the result of several factors, there is a large body of literature that strongly implicates a genetic etiology. Genome-wide association studies (GWAS) have emerged as the most widely used contemporary approach to identify genetic variants (in particular SNPs) that are associated with increased risk of human disease. For PrCa, at least five GWAS have now been performed yielding a substantial number of well-validated SNPs that are associated with an increased risk of PrCa, and that number continues to grow. A significant problem for many of the PrCa risk-SNPs identified so far, however, is that they do not lie within or near a known gene and they have no obvious functional properties. These findings suggest that many (if not most) of these risk-SNPs will be located in regulatory regions that control gene expression rather than in coding regions that may directly affect protein function. Therefore, in order to define the functional role of currently known risk-SNPs, the target genes must first be identified. A promising strategy to address this problem involves the use of expression quantitative trait loci (eQTL) analysis. Unfortunately, most of the publically available SNP-Transcript eQTL datasets utilize lymphoblastoid cells with only a handful using tissue from target organs. Although useful, these datasets alone are unlikely to be sufficient. Recent studies have demonstrated that gene expression and gene regulation occur in both a tissue-specific and tissue independent fashion and suggest that a complete repertoire of regulatory SNPs can only be uncovered in the context of cell type specificity. To date, such a tissue-specific dataset for normal prostate tissue does not exist. In this study, we have constructed a normal prostate tissue specific eQTL data set.

  Study phs000985

• Dataset for synovial_sarcoma-WHOLE_GENOME

  Rare cancer sequencing data of 6 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008899

• ETMR ATACSeq

  ATACSeq library amplification was performed on 5 ETMR tumour samples using the NEBnext High Fidelity 2xPCR Master Mix (New England Biolabs, Cat#M0541S) according to the manufacturer’s protocol. ATAC-seq libraries were sequenced using single-end 50 bp reads on the Illumina HiSeq 2000 platform. ATAC-seq peaks analysed was conducted as published previously(Torchia et al. 2016).

  Dataset EGAD00001004805