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• Transcriptional Profiling of PD-1+ and PD-1- Teff and Treg Cells in Glioblastoma and Health

  PD-1 is an important immune checkpoint inhibitor that shows great promise in the clinic, particularly for melanoma and lung cancers. Since PD-1 is also expressed on infiltrating CD4+ Treg and Teffector cells in glioblastoma, we sought to better understand the role of PD-1 on these infiltrating CD4+ Treg and Teffector cells. To this end, we performed functional and transcriptional profiling using CD4+ Treg and Teffector cells isolated from healthy donors and glioblastoma patients (from both tumors and blood).

  Study phs001079

• Feasibility study of enzymatic methylation sequencing of cell-free DNA from cerebrospinal fluid of pediatric brain tumors for classification

  Array-based DNA methylation profiling is the gold standard for molecular classification of brain tumors. However, it relies on significant amount of input DNA extracted from surgical tissue, thus limiting its use for tumors where biopsy is challenging or limited in quantity. Cell-free tumor DNA (cfDNA) in cerebrospinal fluid (CSF) presents alternative opportunities for brain tumor diagnosis and disease monitoring following treatment. Novel enzymatic DNA methylation sequencing (EM-seq) methods may allow us to overcome input DNA limitations and accurately quantify methylation from cfDNA for tumor classification.

  Study EGAS50000000871

• Genetic Analysis of Normal Human Facial Variation

  The purpose of this project is to identify genes associated with normal human quantitative facial variation. The motivation for this project stems from the fact that very little is known about how variation in specific genes relates to the diversity of facial forms commonly observed in humans. Viable candidates for these morphogenes originate from a number of sources: tissue expression studies, animal models with targeted or spontaneous mutations, and genetic syndromes with craniofacial manifestations. Importantly, understanding the genetic basis for normal facial variation also has important implications for health-related research. For example, this work has the potential to shed light on the factors influencing liability to common craniofacial anomalies such as orofacial clefts. There is now ample evidence that certain facial features (e.g., increased midfacial retrusion) characterize individuals genetically at-risk for orofacial clefts (e.g., biological relatives of affected cases). While these predisposing facial features are statistically over-represented in at-risk groups, they are also common in the general population. Since many of the current candidate genes for clefting are thought to play a critical role in facial morphogenesis, variation in these genes may also underlie normal variation in these facial features. These candidate genes, however, probably represent only a small fraction of the total number of loci influencing normal human facial variation. Phenotypes for this project were obtained from over 3000 healthy Caucasian subjects recruited through three separate studies. The majority of the subjects were recruited as part of the 3D Facial Norms Project, which is described in extensive detail here: (https://www.facebase.org/facial_norms/notes). The provided dbGaP phenotypes include a series of anthropometric craniofacial measurements (linear distances) primarily derived from 3D photographic facial surface scans (see previous hyperlink). The specific genotyping requested is described elsewhere in this document. Our analysis team is pursuing a variety of different analytic approaches to derive genetically informative phenotypes, including various shape-based morphometric methods. For those interested in pursuing more advanced phenotypic approaches, the original 3D surface scans and additional phenotypic traits are available to researchers through the FaceBase Consortium. This dataset has the potential to facilitate the discovery of new genetic loci with an important role in both normal and abnormal facial development. It may also serve as a dataset to test hypotheses regarding specific SNP associations (e.g. as a replication dataset) or as part of a larger meta-analysis.

  Study phs000949

• Raw sequencing data for donor and patient fecal samples

  Microbiome analysis was performed on the patient samples collected pre-FMT and on days after FMT, and on samples collected from the FMT donor. Genomic bacterial DNA was extracted from fecal samples using the QIAamp DNA Stool kit (Qiagen, Hilden, Germany), with the addition of a bead-beating lysis step. Genomic 16S ribosomal-RNA V4 variable regions were amplified and sequenced on the Illumina MiSeq platform.

  Dataset EGAD00001004371

• Genomic sequencing of Pediatric Rhabdomyosarcoma

  Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma of childhood accounting for approximately 350 newly diagnosed cases yearly in the United States. With the development of multimodal chemotherapy regimens, relapse-free survival rates have improved to 70-80% in patients with localized disease albeit with significant toxicity. Unfortunately, despite aggressive treatment, patients with metastatic or recurrent disease continue to suffer from high mortality. Further characterization of the genetic events underlying this tumor type is critical for the development of more effective diagnostic, prognostic and therapeutic strategies. In a collaborative effort between the National Cancer Institute, the Children's Oncology Group, and the Broad Institute, we use a combination of whole-genome and whole-exome sequencing to characterize the landscape of somatic alterations in 147 tumor/normal pairs.

  Study phs000720

• Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation

  Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expressions in unmeasured regions and tissues can enhance biologists��� histological interpretations. We developed the Deep learning model for Spatial gene Clusters and Expression, DeepSpaCE and confirmed its performance using the spatial-transcriptome profiles and immunohistochemistry images of consecutive human breast cancer tissue sections. For example, the predicted expression patterns of SPARC, an invasion marker, highlighted a small tumor-invasion region that is difficult to identify using raw data of spatial transcriptome alone because of a lack of measurements. We further developed semi-supervised DeepSpaCE using unlabeled histology images and increased the imputation accuracy of consecutive sections, enhancing applicability for a small sample size. Our method enables users to derive hidden histological characters via spatial transcriptome and gene annotations, leading to accelerated biological discoveries without additional experiments.

  Study JGAS000290

• Single_cell_analysis_T_cell_activation

  We isolated naïve and memory T cells from healthy donors and cultured them in resting and stimulated conditions. We performed scRNA sequencing to assess T cell actiavtaion and perform sceQTL analysis. Major challenges for translating GWAS variants to function include identification of the disease relevant cell types in which the associated variants function. We have shown that immune-disease variants are functional in CD4+ T cell activation, and understanding how GWAS variants affect the activation efficiency is critical for development of new therapies. To perform sceQTL analysis of naïve and memory T cell activation, here we stimulated T cells and profiled single cell transcriptome at different time points following stimulation. We mapped sceQTLs affecting cell activation. 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 EGAS00001003479

• How are we funded?

  How are we funded? The EGA is provided infrastructure, administrative support, advisory input, and other relevant and necessary aspects by: CRG The EGA at the CRG is funded and supported by “La Caixa Foundation”, The Generalitat de Catalunya (Catalan Government), the Spanish Ministerio de Asuntos Económicos y Transformación Digital (Spanish Government) and the Instituto de Salud Carlos III. The Generalitat de Catalunya contributes with funding to the CRG and to the Barcelona Supercomputing Centre (BSC). The Barcelona Supercomputing Center is decisively contributing to EGA by providing the required compute, storage and networking resources, as well as key personnel for shared operations of EGA infrastructure. EMBL-EBI As part of the European Molecular Biology Laboratory (EMBL), the EGA at EMBL’s European Bioinformatics Institute receives funding from the governments of EMBL’s member states. The UK government, via UK Research and Innovation, also continues to support EMBL-EBI technical infrastructure, with EMBL supporting operational costs. A major source of funding for the EGA both at CRG and EMBL-EBI are competitive projects.The EGA resource continues to be funded through strategic engagement in collaborative partnerships and projects. Please see our projects page for more information. Other funders include the European Commission, the US National Institutes of Health, the Wellcome Trust, UK Research Councils, and our Industry Programme partners. Last updated on October, 2024.

  Documentation about/projects-and-funders/funders

• Genetic Progression of Head and Neck Squamous Cell Carcinoma

  These data are tumor/normal-matched exome sequencing results on 45 participants (median age of 58.6 years) treated for squamous cell carcinoma of the oropharynx at Mass Eye and Ear (MEE; Boston, MA, USA). Some tumors had two portions sequenced separately. All research complied with all relevant ethical regulations. Human subject research on patients at MEE was approved by the MEE Human Studies Committee under protocol HSC 11-024H, with informed consent obtained from participants. Participants were not compensated. Clinical data, including HPV status, were abstracted from patient records. Sequencing and clinical data were combined with TCGA data on 486 cases of head and neck squamous cell carcinoma (HNSCC). The PhylogicNDT suite of genomic analysis tools was used to infer the relative timing of genetic events during tumor progression for major molecularly defined classes of HNSCC: HNSCC negative for human papillomavirus (HPV), HPV-associated HNSCC, and HPV-negative HNSCC involving mutations in CASP8, NSD1, and NOTCH1. The timing of the development of whole-genome somatic events leading to tetraploidy or triploidy and to increased measures of intratumor genetic heterogeneity was determined. Associations of heterogeneity measures with clinical outcomes were assessed. The analysis identified genes with early mutations that likely play roles in HPV-tumor initiation (FBXW7, NOTCH1, CASP8, FAT1, NSD1, HRAS, EP300, CREBBP, KMT2D). Genes with mutations at later median relative timing (PK3CA, NFE2L2, HLA-A, SPEN, and KMT2C) were more likely to be important for progressing to later stages of the disease. Gain of chromosome arm 3q and loss of arm 11q were found to occur more frequent in HPV+ HNSCC cases. A high prevalence of 3p and 17p loss were also observed in HPV+ HNSCC cases.

  Study phs003139

• A New Reference Panel to Boost African American Genotype Imputation

  Modern genetic studies have been conducted predominantly in cohorts of individuals of European ancestry. By 2010, there were approximately ten times as many published genome wide association studies (GWAS) in people of European ancestry than studies in people of all other ancestries combined. This research disparity has led to an uneven understanding of the genetic basis underlying disease in Europeans and non-Europeans. 23andMe's web-based, large scale research model is ideal for scaling genetics research within non-European populations and thereby bringing more parity to genetics research. Our database is composed of genotypes and phenotypes of over 1,000,000 consenting customers, including over 200,000 individuals with non-European ancestry. The data derived from non-European individuals represent a particularly valuable resource for genetic discovery of novel variants that may not be found in the European population. However, research studies in non-European populations are weakened by the lack of availability of large-scale reference datasets and, in particular, genotype imputation panels. Genotype imputation is a statistical methodology that uses observations of genotypes in a large reference panel to infer unobserved genotypes in a target dataset. This methodology is widely used within GWAS, and allows novel genetic associations to be identified and refined. Due to this utility, very large reference panels have been constructed, containing thousands or tens of thousands of whole genome sequences. Unfortunately, the largest imputation panels are composed of predominantly European genomes, reflecting the modern bias towards European studies in GWAS. This proposal aims to address this imbalance by constructing an imputation panel specifically for the African American population. In doing so, we will expand 23andMe's ability to perform genetic discovery in non-European populations, and improve the understanding of global genetic variation underlying diseases and traits. Key commercial outcomes of the research include the identification of novel genetic targets for internal and external therapeutic development. The long-term aim is to improve understanding of disease in minority populations, which we hope may eventually lead to improved treatments of disease in these historically medically understudied groups.

  Study phs001798