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• WES of head and neck cancer patients refractory to anti-PD-1 therapy

  Whole exome sequencing (WES) was performed on baseline (before low-dose 5-aza treatment) blood and tumor paired samples from 12 participants with head and neck cancer who were refractory to anti-PD-1 therapy. DNA extractions were performed by the Broad Institute of MIT and Harvard using Qiagen AllPrep DNA/RNA kits (Cat. 80204). In total, 24 150-bp pair-end WES data were generated by Illumina.

  Dataset EGAD50000001011

• APS-1 TCR Sequencing

  Single cell TCR sequencing of expanded regulatory T cells (Tregs) in 9 APS-1 patients and 9 ange and gender matched controls. T-cell receptor (TCR) libraries were generated using the Single Cell Human TCR Amplification Kit and the Library Construction Kit from 10x genomics.

  Dataset EGAD50000000261

• H3Africa AWI-Gen Phase 1 WGS data from 100 South Africans

  These samples were sequenced at the Broad Institute on an Illumina HiSeqX at 30x -- PCR Free. The CRAMS and VCF are as produced by Broad. The VCFs produced were generated by the Broad using GATK.

  Dataset EGAD00001006418

• MiRNome_pituitary_adenoma

  Small RNA (<100 bases in length) were purified from total RNA using miRNeasy kit (Qiagen), then sequenced. Libraries were prepared at the Genomics Platform of the Cochin Institute, following the TruSeq small RNA protocol (Illumina), starting from 1 µg of high quality total RNA. Single read (1 × 75 bp) sequencing was performed on a Nextseq 500 platform (Illumina). FASTQ sequences were aligned on miRBase v.2052, then counted with STAR (v.2.5.2a).

  Dataset EGAD00001004998

• ChIP-seq of blasts from leukemia patients and CD34+ cells from healthy donors

  CTCF ChIP-seq of 14 leukemia patients: 6 AML without 3q rearrangements, 1 AML with 3q26, 1 AML with t(3;8) and 6 T-ALL H3K27ac ChIP-seq of AML patients: 4 cases with t(3;8), another with inv(3) and another with normal karyotype. H3K27ac ChIP-seq of CD34+ cells from one healthy donor RUNX1 ChIP-seq of one t(3;8) AML patient

  Dataset EGAD00001006817

• Foregut Microbiome in Development of Esophageal Adenocarcinoma

  The distal esophagus is an important anatomical area where gastric acid reflux can cause reflux esophagitis (RE), Barrett's esophagus (BE) (intestinal metaplasia), and esophageal adenocarcinoma (EA). The incidence of EA has increased 6-fold in the U.S. since the 1970s, parallel to a significant increase in the prevalence of gastroesophageal reflux diseases (GERD). Although specific host factors might predispose one to disease risk, such a rapid increase in incidence must be predominantly environmental. The cause remains unknown. Our hypothesis is that changes in the foregut microbiome are associated with EA and its precursors, RE and BE in the GERD sequence. We will conduct a case control study to characterize the microbiome in every stage of the GERD sequence as well as analyze the trend in changes in the microbiome along disease progression toward EA. Specific Aim 1. To conduct a comprehensive population survey of the foregut microbiome and demonstrate its association with GERD sequence, by a 16S rRNA gene survey. We will analyze samples of the foregut microbiome at three anatomic loci: mouth, distal esophagus, and gastric corpus. Changes of the microbiota in the distal esophagus will be correlated with the phenotypes. Spatial relationship between the esophageal microbiota and upstream (mouth) and downstream (stomach) foregut microbiotas as well as temporal stability of the microbiome-disease association will also be examined. Specific Aim 2. To define distal esophageal metagenome and demonstrate its association with GERD sequence, by shotgun metagenomic analysis. We will first classify samples of the metagenome into metagenotypes by between-sample k-mer distance and correlate the metagenotypes with the four phenotypes. Subsequent detailed analyses will include pathway-disease and gene-disease associations. DNA viruses and fungi, if identified, also will be correlated with the phenotypes. A significant association between the foregut microbiome composition and GERD sequence, if demonstrated, will be the first step for eventually testing the causal hypothesis that an abnormal microbiome is required for the development of the sequence of phenotypic changes toward EA. If EA and its precursors represent a microbial ecological disease, treating the cause of GERD might become possible, for example, by normalizing the microbiome through use of antibiotics, probiotics, or prebiotics. Causative therapy for GERD could prevent its progression and reverse the current trend of increasing incidence of EA.

  Study phs000260

• (Epi)genetic Risk Architectures of Opioid-Dependent Brain

  With nearly 20,000 overdose deaths in 2014 alone, opioid addiction (OA) has emerged as one of the most pressing public health crises in recent US history. One-fifth of individuals who try heroin develop an addiction to opioids. Genetics is a major contributor to OA with an estimated 60% heritability, only somewhat less than schizophrenia (80%) which has recently seen substantial gains in identified underlying genetics. Yet, studies to date have failed to uncover most of the genes that predispose individuals to OA, leaving the overwhelming fraction of OA heritability unexplained. The proposed study takes a novel, integrated "omics-based" strategy to investigate the molecular basis of OA and uncover both genetic and epigenetic factors associated with opioid addiction. The premise for our approach is founded on studies from our labs, and others, implicating regulatory variation in common traits and diseases, including those associated with complex brain phenotypes like addiction. We have collected the largest known cohort of postmortem brains from addicts who overdosed on opioids, along with matched control brains from non-users. From both cases and control specimens, we will isolate cells from 2 regions of the brain closely implicated in the addiction phenotype: the Prefrontal Cortex (PFC) and the Nucleus Accumbens (NAc). In Aim 1, we propose ChIP-seq studies to identify regulatory elements that distinguish cases from controls and define the opioid addiction phenotype. The regulatory differences will be connected to their gene targets through high resolution in situ Hi-C. In Aim 2, we propose QTL-based approaches to identify genetic variants that underlie the regulatory differences (hmQTLs). We also propose eQTL analyses to identify genetic variants that underlie differences in transcript levels between cases and controls. Aim 3 leverages the largest heroin addiction GWAS meta-analysis to date to test the hypothesis that SNPs in regions associated with chromatin and expression differences between cases and controls define novel loci for predisposition to OA. Each Aim has the potential for discovery independent of the others (differential HM, RNAexp, QTLs, and variant-phenotype associations) but their synergy is the most powerful component of the proposed study: identifying regulatory pathways that generate, not only phenotype associations, but hypothesized mechanisms for those associations which can be the focus of new opioid addiction prevention and treatment development.

  Study phs002724

• Genomic Characterization of Head and Neck Squamous Cell Carcinoma Cell Lines

  Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer by incidence worldwide(1). Various chemical carcinogens (tobacco, alcohol and betel nut), human papillomavirus (HPV) infection, and genetic predisposition contribute to the etiology of HNSCC, and to the complex genetic alterations in tumor subsets that differ in prognosis and response to therapies (2). Recently, a comprehensive landscape of genomic and transcriptomic alterations in HNSCC tumors has emerged from The Cancer Genome Atlas (TCGA) Network (3). TCGA revealed novel and previously recognized gene and chromosomal region copy number alterations (CNAs), mutations, and expression clusters, and defined their frequency, co-occurrence, and relationship to common and rare subtypes of HPV(-) and (+) tumors that vary in prognosis. To identify cell line models for determining the functional role and therapeutic importance of these alterations, we are performing whole exome and RNA sequencing and bioinformatic analysis of an expanded panel of 15 HPV(-) and 11 HPV(+) HNSCC cell lines and primary oral keratinocytes. We find that the recurrent genomic alterations in cell lines are remarkably consistent with those found in more aggressive tumors, from which cell lines have traditionally been most readily adapted to culture (4). Genome-wide correlation of CN (copy number) with expression identified a suite of potential drivers or modifier genes that differ by HPV status, and are of potential biologic and therapeutic relevance. Further, our findings elucidate and validate genomic alterations underpinning numerous discoveries made with these widely-used and recently derived HNSCC lines, and provide a roadmap for their potential use as models for future studies of tumor subtypes with worse prognosis. References Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87-108. Van Waes C, Musbahi O. Genomics and advances towards precision medicine for head and neck squamous cell carcinoma. Laryngoscope Investig Otolaryngol. 2017;2(5):310-9. Cancer Genome Atlas N. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature. 2015;517(7536):576-82. White JS, Weissfeld JL, Ragin CC, Rossie KM, Martin CL, Shuster M, et al. The influence of clinical and demographic risk factors on the establishment of head and neck squamous cell carcinoma cell lines. Oral Oncol. 2007;43(7):701-12.

  Study phs001581

• Rare Genetic Steroid Disease Consortium (GSD) Apparent Mineralocorticoid Excess (AME) Syndrome Natural History Clinical Protocol

  The objective of this project is to observe the natural history of apparent mineralocorticoid excess (AME). AME is a rare monogenic hypertensive disease first described by the Principal Investigator, Dr. Maria I. New, in 1977 [1], for which the first mutation in the causative HSD11B2 gene was reported by her group in 1995. [2] Little is known about the progression of this rare hypertensive disorder, which is defined by the presence of hypertension in conjunction with low renin, low to absent levels of the mineralocorticoid aldosterone, elevated urinary cortisol/cortisone metabolite ratio 5β-tetrahydrocortisol (THF) + allo-THF/ tetrahydrocortisone (THE) (i.e., THF+αTHF/THE) and the presence of mutations in the HSD11B2 gene on both alleles. The range of symptoms reflecting progressive end-organ damage is wide in the small number of affected individuals studied to date. It is important to develop accurate information on the longitudinal pattern of progression (natural history) of AME not only because it may improve the care of affected individuals, but also because it may serve as a platform for understanding the role of mineralocorticoid excess in other forms of low-renin hypertension, which constitute 40% of all forms of hypertension. In this study, we will recruit as widely as possible through the Rare Diseases Clinical Research Network (RDCRN) to identify individuals with the triad of low renin, low aldosterone, and elevated urinary THF+αTHF/THE and confirm their disorder as AME by molecular genetic testing of the HSD11B2 gene. We will gather clinical and biochemical data yearly for the duration of the grant. We will describe phenotype and genotype at diagnosis and follow the progression or regression of symptoms over time under standard treatment, with rigorous review of end organs potentially damaged by chronic hypertension. Finally, we will study the natural history of the carriers of HSD11B2 mutations, i.e., the heterozygote relatives of the probands, in order to ascertain evidence of existing or developing hypertension and resulting end-organ damage. This will be the first rigorous study of AME heterozygotes. The participant accrual for AME-affected patients and the carriers of HSD11B2 mutations will be at least two years, but as long as seven years.

  Study phs000603

• Genome_Diversity_in_Africa_Project__Benin

  Globally, human populations show structured genetic diversity as a result of geographical dispersion, selection and drift. Understanding this genetic variation can provide insights into the evolutionary processes that shape both human adaptation and variation in disease. Populations from SSA have the highest levels of genetic diversity. This characteristic, in addition to historical genetic admixture, can lead to complexities in the design of studies assessing the genetic determinants of disease and human variation. However, such studies of African populations are also likely to provide new opportunities to discover novel disease susceptibility loci and variants and refine gene–disease association signals. A systematic assessment of genetic diversity within SSA would facilitate genomic epidemiological studies in the region.The Genome Diversity in Africa Project (GDAP) aims to produce a comprehensive catalogue of human genetic variation in SSA, including single nucleotide polymorphisms (SNPs), structural variants, and haplotypes. This resource will make a substantial contribution to understanding patterns of genetic diversity within and among populations in SSA, as well as providing a global resource to help design, implement and interpret genomic studies in SSA populations and studies comprising globally diverse populations, complementing existing genomic resources. Specifically, we plan to carry out high depth whole genome sequencing of up to 2000 individuals across Africa (25 individuals from each ethnolinguistic group).Our scientific objectives are to: 1) develop a resource that provides a comprehensive catalogue of genetic variation in populations from SSA accessible to the global scientific community; 2) characterise population genetic diversity, structure, gene flow and admixture across SSA; 3) develop a cost-efficient, next-generation genotype array for diverse populations across SSA; and 4) facilitate whole genome-sequencing association studies of complex traits and diseases by developing a reference panel for imputation and resource for enhancing fine-mapping disease susceptibility loci. These scientific objectives will be supported by cross-cutting operational activities, including network and management of the consortium, research ethics, and research capacity building in statistical genetics and bioinformaticsThis 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 EGAS00001001688

• RNA_Seq_in_Patients_with_Primordial_Dwarfism

  Transcriptome studies in patients with rare genetic diseases can potentially aid in theinterpretation of likely causal genetic variation through identification of altered transcriptabundance and/or structure. RNA-Seq is the most sensitive assay for both investigatingtranscript structure and abundanceThe primary aim of this pilot project is to investigate to what degree integrating exome-Seqand RNA-Seq data on the same individual can accelerate the identification of causal allelesfor rare genetic diseases. There are two main strands to this: (i) identifying which variantsdiscovered in exome-seq appear to be having a functional impact on transcripts, and (ii)identifying transcript outliers, especially among known causal genes, that may not necessarilyhave a causal variant identified from exome sequencing. The latter may identify the presenceof causal variants that lie far from coding regions (e.g. the formation of cryptic splice sitesdeep within introns, or loss of long range regulatory elements), which can be confirmed withfurther targeted genetic assays. Just over 50% of all disease-causing variants recorded in theHuman Gene Mutation Database (HGMD) affect transcript structure and abundance (e.g.nonsense SNVs, essential splice site SNVs, frameshifting indels, CNVs).This pilot project will study RNA from lymphoblastoid cell-lines from 12 patients withprimordial dwarfism syndromes, for 10 of these samples we have previously generate exomedata as part of our collaboration with the group of Prof Andrew Jackson. The two remainingsamples are positive controls where the causal mutation is known, and is known to affecttranscript structure and/or abundance.Primordial dwarfism is a prime candidate for these RNA-seq studies because all knowncausal mutations to date have key roles in DNA replication and thus, unsurprisingly, theproducts of the causal genes are typically ubiquitously expressed.Each RNA will be sequenced, with two technical replicates (independent RT-PCR and libraries) persample, and each replicate run in 1/2 of a HiSeq lane using 100bp paired reads. Samples preparation was as follows :The cells were grown to confluency, then pellets frozen at -80. RNA samples were prepared using the Qiagen RNeasy kit, then nanodropped and analyzed using the bioanalyzer to determine concentration and purity.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 EGAS00001000283

• Type 1 Diabetes Genetics Consortium (T1DGC): Genome-Wide Association Study in Type 1 Diabetes, 2008

  Cases with Type 1 Diabetes (T1D) in the UK, were part of the Wellcome Trust Case Control Consortium (WTCCC) - http://www.wtccc.org.uk - that first reported in 2007: Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661-678. [PubMed: 17554300] In this genome-wide association study (GWAS), funded by the NIH and JDRF, and sponsored by the Type 1 Diabetes Genetics Consortium (T1DGC), we were able to extend the case and control groups used in the WTCCC, with the intention of performing a well-powered meta-analysis. The study is written up as: Barrett, J.C., Clayton, D.G., Concannon, P., Akolkar, B., Cooper, J.D., Erlich, H.A., Julier, C., Morahan, G., Nerup, J., Nierras, C., Plagnol, V., Pociot, F., Schuilenburg, H., Smyth, D.J., Stevens, H., Todd, J.A., Walker, N.M., Rich, S.S. and The Type 1 Diabetes Genetics Consortium Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nature Genetics, PMID: 19430480. Resources - data: * Case data from this study is deposited here (i.e. in dbGaP) * Control data from this experiment - with subjects from the 1958 British Birth Cohort - is deposited with the European Genotype Archive (EGA): http://www.ebi.ac.uk/ega/ from where the WTCCC data is also available. * A complete description of how to request all components of the meta-analysis is available at: http://www.t1dbase.org/page/PosterView/display/poster_id/324 * Additional genetic data on the same case subjects, including some HLA types, are available from the Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory (JDRF/WT-DIL): http://www-gene.cimr.cam.ac.uk/todd/ Resources - samples: Case and control DNA samples are also available: * Case DNA samples are available from the JDRF/WT-DIL, as above, and will be available from the NIDDK Genetics Repository at Rutgers: https://www.niddkrepository.org * Control DNA samples are available from the 1958 British Birth Cohort (aka National Child Development Study): http://www.cls.ioe.ac.uk/studies.asp?section=000100020003 Use restrictions: There are access limitations to both data and samples, in order to keep their use in line with subjects' consent.

  Study phs000180

• MethylationEPIC BeadChip samples of pre- and post-5ZA-treated head and neck cancer patients refractory to anto-PD-1 therapy

  Background Immune checkpoint blockade (ICB) is an effective treatment in a subset of patients diagnosed with head and neck squamous cell carcinoma (HNSCC); however, most patients are refractory. Methods In a non-randomized, open-label phase 1b clinical trial, participants with recurrent and/or metastatic (R/M) HNSCC were treated with low-dose 5’azacytidine (5’aza) daily for either 5- or 10-days in combination with fixed doses of durvalumab and tremelimumab after progression on ICB therapy. The primary objective was to assess the biologically effective dose of 5’aza as determined by molecular changes in paired baseline and on-treatment tumor biopsies; the secondary objective was safety. Results Thirty-eight percent (3/8) of participants with evaluable paired tissue samples had a greater than 2-fold increase from baseline in interferon-gamma (IFN-ɣ) signature and CD274 (programmed cell death protein 1 ligand, PD-L1) expression within the tumor microenvironment (TME), which was associated with increased CD8+ T cell infiltration and decreased infiltration of CD4+ T regulatory cells. The mean neutrophil-to-lymphocyte ratio (NLR) decreased by greater than 50%, from 14.2 (SD 22.6) to 6.9 (SD 5.2). Median overall survival (OS) was 16.3 months (95% CI 1.9, NA) and 2-year OS rate was 24.7% (95% CI: 4.5%, 53.2%) with 58% (7/12) of treated participants demonstrated prolonged OS of greater than 12 months. Conclusion Our findings suggest that low-dose 5’aza can reprogram systemic host immune responses and the local TME to increase IFN-ɣ and PD-L1 expression. The increased expression of these established biomarkers correlated with prolonged OS upon re-challenge with ICB.

  Study EGAS00001007998

• EGAD00010000658

  DLBCL 148 SNP 6.0 Cohort

  Dataset EGAD00010000658

• EGAD00010000704

  610k genotyping imputed on Hapmap 3 and 1000G Phase 1 CEU

  Dataset EGAD00010000704

• LP2100030-DNA_A03

  LP2100030-DNA_A03

  Dataset EGAD00001009878

• LP2100030-DNA_A02

  LP2100030-DNA_A02

  Dataset EGAD00001009872

• LP2100082-DNA_A01

  LP2100082-DNA_A01

  Dataset EGAD00001009873

• LP2100030-DNA_A08

  LP2100030-DNA_A08

  Dataset EGAD00001009874

• LP2100030-DNA_A01

  LP2100030-DNA_A01

  Dataset EGAD00001009875

• LP2100030-DNA_A07

  LP2100030-DNA_A07

  Dataset EGAD00001009876

• LP2100030-DNA_A06

  LP2100030-DNA_A06

  Dataset EGAD00001009877

• LP2100030-DNA_A09

  LP2100030-DNA_A09

  Dataset EGAD00001009879

• LP2100030-DNA_A05

  LP2100030-DNA_A05

  Dataset EGAD00001009880

• LP2100030-DNA_A04

  LP2100030-DNA_A04

  Dataset EGAD00001009881

• LP2100082-DNA_A02

  LP2100082-DNA_A02

  Dataset EGAD00001009882

• LP2100082-DNA_A03

  LP2100082-DNA_A03

  Dataset EGAD00001009892

• LP2100082-DNA_A04

  LP2100082-DNA_A04

  Dataset EGAD00001009893

• LP2100082-DNA_A05

  LP2100082-DNA_A05

  Dataset EGAD00001009894

• LP2100082-DNA_A06

  LP2100082-DNA_A06

  Dataset EGAD00001009895

• LP2100082-DNA_A07

  LP2100082-DNA_A07

  Dataset EGAD00001009896

• LP2100082-DNA_B01

  LP2100082-DNA_B01

  Dataset EGAD00001009897

• LP2100082-DNA_B02

  LP2100082-DNA_B02

  Dataset EGAD00001009898

• LP2100082-DNA_B03

  LP2100082-DNA_B03

  Dataset EGAD00001009899

• LP2100082-DNA_B04

  LP2100082-DNA_B04

  Dataset EGAD00001009900

• LP2100082-DNA_B05

  LP2100082-DNA_B05

  Dataset EGAD00001009901

• LP2100082-DNA_B06

  LP2100082-DNA_B06

  Dataset EGAD00001009902

• LP2100082-DNA_B07

  LP2100082-DNA_B07

  Dataset EGAD00001009903

• Lifelines-CH: core phenotypic variables

  Core phenotypic variables

  Dataset EGAD00001010145

• LP2100098-DNA_E05

  LP2100098-DNA_E05

  Dataset EGAD00001009954

• LP2100098-DNA_E07

  LP2100098-DNA_E07

  Dataset EGAD00001009955

• LP2100098-DNA_E09

  LP2100098-DNA_E09

  Dataset EGAD00001009956

• LP2100098-DNA_F01

  LP2100098-DNA_F01

  Dataset EGAD00001009957

• LP2100098-DNA_F03

  LP2100098-DNA_F03

  Dataset EGAD00001009958

• LP2100098-DNA_F05

  LP2100098-DNA_F05

  Dataset EGAD00001009959

• LP2100098-DNA_F07

  LP2100098-DNA_F07

  Dataset EGAD00001009960

• LP2100098-DNA_G01

  LP2100098-DNA_G01

  Dataset EGAD00001009961

• LP2100098-DNA_G05

  LP2100098-DNA_G05

  Dataset EGAD00001009962

• LP2100098-DNA_G07

  LP2100098-DNA_G07

  Dataset EGAD00001009963

• LP2100098-DNA_H01

  LP2100098-DNA_H01

  Dataset EGAD00001009967