Search Results - EGA European Genome-Phenome Archive

Dataset of PPB

Whole exome sequencing and miRNA-seq data of PPB.

Dataset EGAD00001000822

ChIPseq data

ChIPseq data for H3K4me1 and H3K9me3 in HT29; H3K4me1, H3K27me3, H3K9me3, H3K36me3 in LoVo.

Dataset EGAD00001003258

Genetics of Human Inherited Retinal Diseases (GHIRD)

The goal of this project is to identify novel mutations and genes involved in human retinal disorders, a stated priority of the National Eye Institute. To accomplish this, additional genes whose mutations cause inherited retinal diseases (IRD) will be identified by combining targeted gene panel sequencing, whole exome sequencing, and whole genome sequencing. We have collected about 1050 patient families around the world. In this proposal, we will identify the underlying mutations in these patients using a combination of targeted gene panel sequencing, whole exome sequencing, whole genome sequencing, bioinformatics, statistics, and functional studies. Genes targeted for gene panel sequencing are: ABCA4; ASIC2; JAG1; ABCC6; ARL3; ATP1B2; BBS1; BBS2; BBS4; OPN1SW; CAPN5; C21orf2; CA4; CACNA1F; CDH3; CHM; ERCC8; CLCN2; CLCN3; CLCN7; TPP1; CLN3; CLN5; CNGB1; CNGA1; CNGA3; COL2A1; COL9A1; COL11A1; CRX; VCAN; CTSD; TIMM8A; DMD; CLN8; ERCC6; EFEMP1; OPN1MW; GNAT1; GNAT2; GNGT1; GRM6; GUCA1A; GUCA1B; GUCY2F; GUCY2D; HARS; CFH; HK1; IDH3B; IMPDH1; IMPG1; KCNJ13; KIF11; LRP5; MAK; MITF; TRPM1; MTTP; MVK; MYO7A; NDP; NEK2; NEUROD1; NPHP1; NRL; OAT; OPA1; OTX2; PAX2; PAX6; PDCL; PCYT1A; PDE6A; PDE6C; PDE6G; PDE6H; PDE6B; PEX1; PEX6; PEX7; PEX10; PEX12; PEX13; PEX14; PFDN5; PGK1; PHYH; PIN1; PLA2G5; PPT1; PRKCZ; PEX19; PEX2; PEX5; RB1; RBP3; RBP4; OPN1LW; RDH5; PRPH2; RGR; RHO; GRK1; RLBP1; ROM1; RP9; RP1; RP2; RPGR; RPE65; RS1; SAG; ATXN7; CCL2; SLC6A6; ELOVL4; TEAD1; NR2F1; TIMP3; NR2E1; TTPA; TUB; TULP1; UCHL3; USH2A; CLRN1; VLDLR; BEST1; WFS1; ALMS1; MKKS; FZD4; OFD1; PEX3; CTSF; GBF1; ADAM9; RGS9; PEX11B; PROM1; UNC119; PRPF4; PRPF3; AIFM1; SLC24A1; LRAT; ASIC3; RAB28; PEX16; ITM2B; SLC4A7; IQCB1; CROCC; IFT140; DHX38; MFN2; NR2E3; USH1C; TOPORS; MERTK; FBLN5; CIB2; PRPF8; SDCCAG8; IFT27; TREX1; RRAS2; CEP164; ATF6; TRIM32; RIMS1; SNRNP200; CLUAP1; EMC1; ZNF423; TTLL5; AGTPBP1; RPGRIP1L; CRB1; TSPAN12; ARL2BP; AIPL1; PRPF6; FSCN2; ABHD12; PRPF31; TCTN3; IFT172; CNNM4; NPHP3; INVS; BBS9; FLVCR1; RDH8; IMPG2; WDPCP; RDH11; TMEM216; TMEM138; LZTFL1; CNGB3; AHI1; MKS1; CLN6; BBS7; POMGNT1; PEX26; SPATA7; KIZ; KLHL7; INPP5E; MDM1; CABP4; RPGRIP1; MCOLN1; CC2D2A; KIAA1549; SLC7A14; WDR19; NYX; CDH23; SEMA4A; MPP5; NMNAT1; TMEM237; PCDH15; C5orf42; GNPTAB; TMEM231; TCTN1; SRD5A3; BBS10; ZNF408; TTC21B; CSPP1; LPCAT1; DHDDS; PDZD7; PANK2; CEP290; OPA3; DNAJC5; PITPNM3; MFRP; HMCN1; ADGRV1; ARL6; FAM161A; TMEM126A; GNPTG; GJA10; RAX2; TUBGCP6; TMEM67; ACBD5; C12orf65; CDHR1; BBIP1; REEP6; CACNA2D4; RP1L1; CEP41; C1QTNF5; NXNL1; TTC8; USH1G; BBS5; ZNF513; RDH12; SLC38A8; C8orf37; VPS13B; NXNL2; BBS12; ADGRA3; LCA5; KCNV2; ADAMTS18; ARL13B; ATOH7; MFSD8; NPHP4; CCDC66; CYP4V2; CRB2; RD3; LRIT3; EYS; KIF7; CERKL; RGS9BP; C2orf71; GDF6; DTHD1; GPR179; CISD2; CCR2; PRCD; WHRN; ADGRA3. The current dbGaP study release makes available all sequencing-derived vcf data of study participants.

Study phs001517

Diet, Genetic Factors, and the Gut Microbiome in Crohn's Disease

We investigate the hypothesis that consistent changes in the human gut microbiome are associated with Crohn's disease, a form of inflammatory bowel disease, and that altered microbiota contributes to pathogenesis. Analysis of this problem is greatly complicated by the fact that multiple factors influence the composition of the gut microbiota, including diet, host genotype, and disease state. For example, data from us and others document a drastic impact of diet on the composition of the gut microbiome. No amount of sequencing will yield a useful picture of the role of the microbiota in disease if samples are confounded with uncontrolled variables. We aim to characterize the composition of the gut microbiome while controlling for diet, host genotype, and disease state. Diet is controlled by analyzing children treated for Crohn's disease by placing them on a standardized elemental diet, and by testing effects of different diets on the gut microbiome composition in adult volunteers. Genotype is analyzed by large scale SNP genotyping, which is already underway and separately funded--team member Hakon Hakonarson is currently genotyping 50 children a week at ~half a million loci each and investigating connections with inflammatory bowel disease. Clinical status is ascertained in the very large IBD practice in the UPenn/CHOP hospital system. Effects of diet, host genotype, and disease state on the gut microbiome are summarized in a multivariate model, allowing connections between microbiome and disease to be assessed free of confounding factors. This project is divided into four sub-studies. In the Fecal Storage Methods (FSM) study, methods of stool storage and DNA extraction are compared to examine their impact on DNA sequence analysis results. The Controlled Feeding Experiment (CaFE) addresses the effects of controlled diets on the gut microbiome. In the Cross-sectional Study of Diet and Stool Microbiome Composition (COMBO), the effects of diet analyzed using surveys and deep sequencing of stool specimens. The fourth study, Pediatric Longitudinal Study of Elemental Diet and Stool Microbiome Composition (PLEASE), examines the effects of an elemental diet treatment on pediatric patients diagnosed with inflammatory bowel disease (IBD), particularly Crohn's disease. Fecal Storage Methods (FSM): Cross-sectional study Controlled Feeding Experiment (CaFE): Controlled trial Cross-sectional Study of Diet and Stool Microbiome Composition (COMBO): Cross-sectional study Pediatric Longitudinal Study of Elemental Diet and Stool Microbiome Composition (PLEASE): Longitudinal cohort study

Study phs000252

WTCCC case-control study for Multiple Sclerosis

WTCCC genome-wide case-control association study for Multiple Sclerosis (MS) using the 1958 British Birth Cohort collection as controls.

Study EGAS00000000022

LCM isolated buccal epithelial cell WGS of chimeric twins

Buccal epithelial cells of chimeric twins were isolated using laser-capture microdissection. Cells were pooled (13-60 cells) per batch to create a genomic DNA library using an NEB low-input kit.

Dataset EGAD50000000131

WTCCC case-control study for Ankylosing Spondylitis

WTCCC genome-wide case-control association study for Ankylosing Spondylitis (AS) using the 1958 British Birth Cohort collection as controls.

Study EGAS00000000018

Whole exome sequencing of peripheral T-cell lymphoma (PTCL)

Somatic mutations in 3 AITL and 3 PTCL, not otherwise specified (PTCL-NOS) specimens were explored using whole-exome sequencing.

Study EGAS00001000557

Profiling heterogeneity in Human derived IPSC-neurons (2020-05-18)

The impact of genetic variants on molecular pathways that give rise to neurodegenerative diseases such as Alzheimer's and Parkinson's is best elucidated in the appropriate cell types and molecular contexts. Existing studies have focused on bulk profiling of mixed cell types, but have ignored assaying genetic effects across development and cell differentiation. At the core of this proposal is the idea to use single-cell assays to study genetic effects during differentiation of dopaminergic and cortical neurons to identify the sequence of molecular events from variants to healthy and diseased cell states in a cell-specific manner. 1) 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/ . This dataset contains all the data available for this study on 2020-05-18.

Dataset EGAD00001006157

Low coverage whole-genome sequencing of samples from the Cretan Greek isolate collection HELIC-MANOLIS

Whole-genome sequencing at 1x of samples from the Cretan Greek isolate collection HELIC-MANOLIS. Genome-wide association studies of complex traits have been successful in identifying common variant associations, but a substantial heritability gap remains. The field of complex trait genetics is shifting towards the study of low frequency and rare variants, which are hypothesised to have larger effects. The study of these variants can be empowered by focusing on isolated populations, in which rare variants may have increased in frequency and linkage disequilibrium tends to be extended. This work focuses on an isolated population from Crete, Greece. Sequencing is very efficient in isolated populations, because variants found in a few samples will be shared by others in extended haplotype contexts, supporting accurate imputation.

Dataset EGAD00001001637

Genome Diversity in Africa Project - ancient samples - standard libraries

Given the central importance of Africa to studies of human origins, genetic diversity and disease susceptibility, large-scale and representative characterisation of genetic diversity in Africa is needed. Analyses of ancient DNA from Africa would complement sequencing of modern African populations and provide unique opportunities to transform our understanding of the pre-history of the region. This approach would greatly refine our understanding of population structure and gene flow in Africa and globally, including genetic signatures of ancient admixture. This low coverage sequencing experiment will allow us to test and refine our pipeline for ancient DNA sequencing. 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 EGAD00001002211

SNV and indel calls from 8086 individuals in the British Autozygosity Populations BioResource dataset

This includes variant calls (single nucleotide variants and small insertions/deletions) from 8086 (mostly British Pakistani/British Bangladeshi) individuals from the following studies: 1. 3781 British Pakistani/British Bangladeshi adults from East London Genes and Health 2. 2791 British South Asian mothers from Born in Bradford 3. 1428 British South Asian adults from Birmingham 4. 86 individuals (mixed ancestries) from families with rare diseases, from Queen Mary University London All of the Birmingham and most of the Born in Bradford samples were previously sequenced as part of PMID: 26940866. Mapping was done with bwa-mem and variant calling was carried out with GATK HaplotypeCaller. We removed variant sites for which the following was true: SNPs: "QD < 2.0 || FS > 30 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0" Indels: "QD < 2.0 || FS > 30 || ReadPosRankSum < -20.0"

Dataset EGAD00001004581

ProHealth: Kaiser Permanente Genome-wide Association Study of Prostate Cancer

A genome-wide association study (GWAS) of prostate cancer (PCa) was conducted in Kaiser Permanente (KP) Northern California health plan members (7,783 cases, 38,595 controls; 80.3% non-Hispanic white, 4.9% African-American, 7.0% East Asian, and 7.8% Latino) [PMID: 26034056]. The data for these members were drawn from three KP cohort studies: Research Program in Genes, Environment and Health (RPGEH) ProHealth, and California Men's Health Study (CMHS) (described further under Study History). Four custom arrays were designed for genotyping, one for each of the four major race-ethnicity groups in the RPGEH cohort: African Americans, East Asians, Latinos, and Non-Hispanic Whites. The number of SNPs and SNP content varied by array, with SNP content designed to maximize the genome-wide coverage of low frequency and more common variants specific to the different race-ethnicity groups, including newly identified SNPs from sequencing projects, and SNPs with established associations with disease phenotypes and risk factors [PMIDs: 21565264, 21903159]. Within the total study cohort, n=34,736 completed a consent which permitted deposition of data to NIH. Genotyping followed the same general procedure described in [PMIDs: 26092718, plus additional quality control (QC) steps for the additional men, in order to control for potential batch and kit effects, described in [PMID: 26034056. Briefly, we first repeated the filters described in [PMID: 26092718] for all four arrays (EUR, LAT, EAS, AFR). Then, on an array-wise basis, we removed SNPs with MAF<0.01, with a call rate<95%, or with Hardy-Weinberg Equilibrium (HWE) p-value in homogeneous groups<1x10ˆ-5. Furthermore, on the EUR array, to adjust for potential kit effect, we conducted a GWAS of kit, and removed those kit associated SNPs with p<1x10ˆ-6; we also re-genotyped each of the new samples (those not genotyped with the original GERA data) with some of the original GERA data, and removed SNPs with >13/1,268 (1%) mismatches. For the AFR array, to adjust potential plate batch issues, we conducted a GWAS of whether an individual was in the original GERA data vs. in the newly genotyped data and removed those batch-associated SNPs with p<0.05 (we used a stronger threshold than that used for the EUR array because there were fewer individuals on the AFR array); we also re-genotyped each of the new samples with the original GERA data and removed SNPs with >2/78 (2.6%). After the QC described above, imputation was performed as described in [PMID: 26034056]. Imputation was performed on an array-wise basis, pre-phasing with SHAPE-IT v2.5 [PMID: 22138821], and imputing from the 1000 Genomes Project October 2014 release as a cosmopolitan reference panel with IMPUTE2 [PMID: 22384356]. In addition to the GWAS described above, a nested exome-wide association study (EWAS) of PCa was also conducted (7,489 cases, 7,323 controls; 78% non-Hispanic white, 9% African-American, 3% East Asian, 6% Latino, 4% Other). A custom EWAS array primarily focused on rare variants was designed for genotyping that complemented the GWAS arrays [PMID: 26034056]. The EWAS array content included missense and loss-of-function mutations, and rare exonic mutations from The Cancer Genome Atlas (TCGA) and dbGaP prostate cancer tumor exomes [PMID: 26544944; PMID: 26544944]. Much of the EWAS array design content overlapped with the probesets on the UK Biobank Affymetrix Axiom array [PMID: 30305743]. Genotyping and QC steps taken to filter out samples exhibiting low quality and variants with low call rates are described in Emami et al., 2020 [biorXiv]. The resulting EWAS array genotypes are provided here.

Study phs001221

Nala TAS-LRS PGx Study

Sequencing was performed on the PromethION Flow Cell R10 (M version) using the P2 Solo Sequencer (MinION release 24.02.16). The library was divided into three portions and loaded in a tapered manner across three time points. At the first time point, the initial portion of the library was loaded, and sequencing began in whole-genome mode (without adaptive sampling), to monitor QC (N50 within the expected range). After one hour, adaptive sampling was activated. A custom BED file was used to define the target regions (N=326). Each region was extended by 20 kb upstream and downstream, and overlapping regions were merged into non-redundant intervals using bedtools43, covering a total of 1.3% of the human genome. At the second time point (20–24 hours after sequencing began, or when fewer than 2000 pores remained active), the flow cell was washed following ONT’s Flow Cell Wash Kit protocol, and the second portion of the library was loaded. At the third time point (40–48 hours), the second portion of the library was retrieved, and merged with the third portion. The flow cell was washed and reloaded with the mixed library. POD5 files were basecalled using Dorado v0.8.1 in super-accuracy (SUP) mode with the dna_r10.4.1_e8.2_400bps_SUP@v5.0.0. Reads from the initial 1 h Whole Genome Sequencing (WGS) and subsequent 72 h adaptive sampling were filtered for Q-scores >10. Passing reads were demultiplexed using Dorado’s demux function, then combined per sample and mapped to the GRCh38 reference genome using Minimap2 v2.22. Files were converted to CRAM using samtools.

Dataset EGAD50000001609

Integrative Profiling of T790M Negative EGFR Mutated NSCLC Reveals Pervasive Lineage Transition and Therapeutic Opportunities

We performed whole exome and transcriptome analysis of 59 first- and second-generation EGFR TKI resistant metastatic EGFR mutated patients to characterize and compare molecular alterations mediating resistance in T790M positive and negative disease. Transcriptomic analysis revealed ubiquitous loss of adenocarcinoma lineage gene expression in T790M negative tumors, orthogonally validated using multiplex immunohistochemistry. There was enrichment of genomic features such as TP53 alterations, 3q chromosomal amplifications, whole genome doubling and non-aging mutational signatures in T790M negative tumors. Almost half of resistant tumors were further classified as Immunehot, with clinical outcomes conditional on immune cell infiltration state and T790M status. Finally, using a Bayesian statistical approach, we explored how T790M negative and positive disease might be predicted using comprehensive genomic and transcriptomic profiles of treatment-naïve patients.

Study EGAS00001005389

DAC: Whole transcriptome RNA sequencing as comprehensive diagnostic tool for acute myeloid leukemia.

Dac EGAC00001000956

15028 results for "cancer studies using rna-seq"

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