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• Whole Exome Sequencing Identifies

  Combined Immunodeficiency with Multiple Intestinal Atresias (CID-MIA) is a rare genetic disorder with the cause previously unknown. Symptoms of CID-MIA include atresias in the small and/or large intestine, as well as immunodeficiency. In this study, we sequenced the whole exome of 5 unrelated CID-MIA patients as well as their healthy family members, and identified deleterious homozygous or compound heterozygous mutations in the gene TTC7A in all the 5 patients, as well as in 3 additional patients with Sanger sequencing. Our findings strongly indicate TTC7A dysfunction to be the cause for CID-MIA.

  Study phs000641

• The Primary Open-Angle Glaucoma Genes and Environment (GLAUGEN) Study

  Background Information: Primary open-angle glaucoma (POAG) is an age-related, intraocular pressure (IOP)-dependent progressive optic neuropathy that ultimately leads to irreversible blindness. Vision loss from POAG is a condition of public health significance. Current evidence suggests that POAG is a polygenetic disease modified by environmental influences. Despite the fact that a positive family history of disease is an important risk factor for POAG, conventional linkage and candidate gene approaches have revealed less than 5% of the genetic component of the disease. Furthermore, there is no consensus on environment risk factors for POAG. Elevated IOP is the only modifiable risk factor for POAG; yet, lowering IOP slows, but does not halt the disease process. Study Objectives: The overall goal of our research is to elucidate the pathogenesis of POAG so that cost-effective disease detection and primary prevention strategies can be implemented. The primary aim of the Glaucoma Gene Environment Initiative (GLAUGEN), funded by the Human Genome Research Institute (NHGRI), is to discover genetic loci associated with POAG. The secondary aim of GLAUGEN, funded by NHGRI and the National Eye Institute, is to discover gene environment interactions in POAG. Methods and study populations: For this study, we have assembled cases and controls from three studies: the Nurses' Health Study (NHS), the Health Professionals Follow-up Study (HPFS) and the Genetic Etiologies of POAG (GEP) project based at Massachusetts Eye and Ear Infirmary (MEEI). This case-control group includes 1057 unrelated cases and 1272 controls. Members of the NHS and HPFS also have repeated environmental exposure data collected prior to a diagnosis of POAG. NHS - The Nurses Health Study started in 1976 under the direction of Dr. Frank E. Speizer. With funding from the NIH, registered nurses from 11 US states were invited to complete a detailed questionnaire regarding lifestyle and health biennially. Initially, 121,000 women responded to the baseline questionnaire. Currently, Dr. Susan Hankinson serves as the program director for the NHS. HPFS - The Health Professionals Follow-up Study began in 1986 under the direction of Drs. Walter Willett and Meir Stampfer. Under the auspices of the NIH, they enlisted 51,529 male health professionals from throughout the US to complete similarly designed biennial questionnaires. Beginning in 1990, questions regarding ocular health were added to biennial questionaires completed by health professionals participating in the NHS and HPFS. This allowed us to formulate (PI: S. Hankinson; NEI) and maintain (PI: L. Pasquale; NEI) a cohort at risk for POAG derived from the respective general cohorts who were under ophthalmic care. We then developed a definition of POAG that allowed us to identify cases from a population that was geographically dispersed. The centerpiece of this definition is the presence of reproducible visual field loss consistent with nerve fiber layer (NFL) dropout (the NFL contains the axons that comprise the optic nerve) on reliable tests. Reproducible visual field loss occurred in the context of anterior segment findings that did not suggest a secondary cause of elevated IOP and posterior segment findings that did not suggest a secondary cause of visual field loss. We selected controls from the cohort at risk for POAG on the basis of age, gender and time period when cases were identified. GEP - The Genetic Etiologies of POAG was initiated in 1996 with funding from the National Eye Institute under the direction of Dr. Janey Wiggs. The purpose of this work was to discover novel genetic loci associated with POAG. In the GEP, cases were derived predominantly from the Glaucoma Service at MEEI. The majority of cases had an examination by a glaucoma specialist and met the definition for POAG used in NHS and HPFS. Cases with only one reliable visual field consistent with NFL dropout were included if there was a cup-disc ratio of 0.7 or more. The majority of controls were patients who presented to the MEEI comprehensive ophthalmology service for routine eye examination or from spouses of MEEI patients with secondary forms of glaucoma. Other controls were identified from regional glaucoma screenings held throughout Massachusetts. Members of GEP have detailed ocular phenotype data but limited information on environmental exposures. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to primary open-angle glaucoma through large-scale genome-wide association studies of three well-characterized cohorts of cases and controls, some in matched pairs. Genotyping was performed at the Broad Institute of MIT and Harvard, a GENEVA genotyping center. Data cleaning and harmonization were performed at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000308

• Distribution of data using Live Distribution

  Live Distribution Welcome to the documentation for using the Live Distribution feature for distributing data files securely through the EGA platform. This guide will walk you through the process of downloading encrypted files and decrypting them using Crypt4GH. Please follow the steps below to ensure a smooth experience. Before Downloading Create an EGA user. Make sure that you have the permissions to download the dataset of interest. In case you don’t have access, request access to the dataset. Add your Crypt4GH-compatible public key to your EGA account. Please allow a few hours for your public key to be synced with your profile. Afterwards, you will be able to connect to your EGA outbox using the SFTP protocol. Download Graphical User Interface (GUI) You can use any GUI that supports SFTP connections, such as FileZilla, an open-source FTP client. For Filezilla as your GUI, follow these steps to download files: Open FileZilla and access Site Manager (File > Site Manager). Create a new connection with the following settings (Figure 1): Protocol: SFTP - SSH File Transfer Protocol Host: __EGA_OUTBOX_DOMAIN__ Logon Type: Key file User: your EGA username Key file: path/to/your/private_key Figure 1: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the EGA outbox, ensuring seamless data transfers. Click Connect to access your Outbox. This folder serves as your storage space within the EGA cloud, containing files accessible for download in a secure way. Browse the remote directory on the right side of the FileZilla screen. Select the files you wish to download, right-click, and choose Download (Figure 2). Figure 2: Step-by-step process of manually downñoad files from __EGA_OUTBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can downñoad data from the EGA outbox to their local storage by following the depicted steps SFTP command line You can also use the SFTP command line to securely download files from the EGA Outbox. Using SFTP command line client in Linux/Unix Open a terminal window Enter the following command to connect: sftp username@hostname Enter your EGA password To see a list of available sftp commands type help sftp> put – Upload file sftp> get – Download file sftp> cd path – Change remote directory to ‘path’ sftp> pwd – Display remote working directory sftp> lcd path – Change the local directory to ‘path’ sftp> lpwd – Display local working directory sftp> ls – Display the contents of the remote working directory sftp> lls – Display the contents of the local working directory Type get command to download files. For example: get encrypted_file.c4gh Use the bye command to close the connection (SFTP session). Convenient SSH settings Include the following settings in your SSH config file, located in ~/.ssh/config Host __EGA_OUTBOX_DOMAIN__ EGA-outbox hostname __EGA_OUTBOX_DOMAIN__ User username IdentityFile path/to/the/private/key Replace username and path/to/the/private/key with the appropriate settings, and you will be able to connect to the __EGA_OUTBOX_DOMAIN__ simply using sftp EGA-outbox. How to decrypt Files archived at the EGA are encrypted based on Crypt4GH. Hence, to decrypt the files you need to install Crypt4GH. You can install a python implementation of it, with pip install crypt4gh or directly from the Github repository pip install git+https://github.com/EGA-archive/crypt4gh.git After installing Crypt4GH, decrypt files using the following command: crypt4gh decrypt --sk /path/private/key < encrypted_file.c4gh > decrypted_filename The command reads the encrypted file from stdin (with <) and output the decrypted version to stdout (with >). Replace encrypted_file.c4gh and decrypted_filename with the appropriate filenames but make sure to not use the same filename for both reading and writing because your SHELL would then truncate both files before you even read or write. Frequently Asked Questions What username should I use to log in to my outbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username, not your email address. Therefore, if you registered a username different from your email address when creating your EGA account, you must use that username to log in. If you have forgotten your registered username, please, contact our Helpdesk team for assistance. I see that some files in my dataset have 'unavailable' as extension. What should I do? Within your Outbox, you'll find a list of all the datasets available for download. Occasionally, certain files may be marked as "unavailable". These unavailable files can be identified by the "unavailable" extension added to their filenames (e.g. filename.fastq.gz.unavailable.c4gh). If you encounter an unavailable file that you need, please reach out to our Helpdesk. We'll promptly work on making the file accessible for download as soon as possible. Specific to using keys Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

  Documentation access/download/files/live-outbox

• Advancing fast in the analysis of circulating tumour DNA

  In cancer patients, solid tumours also release DNA fragments into the blood, and they are called Circulating Tumours DNA (ctDNA). Nowadays, the standard procedure for cancer monitoring is analysis of surgically obtained biopsies. They generate a big amount of material, but the operation is highly invasive and provide medical doctors with a single-time snapshot of the tumours, which are often highly changeable. Analysis of ctDNA, on the other hand, can provide continuous information regarding the tumour itself without the need of invasive procedures. This is why interest in this method is growing, and with it, the need for its technological optimization. Guanhua Zhu and colleagues, at the Genome Institute of Singapore, developed a methodology to quantify ctDNA burden, which means to estimate the amount of tumours derived circulating DNA among all cell-free DNA in the blood of individuals. Their work, published in Nature Communications in april 2021 and summarized in a sketch below, uses state-of-the art methods to analyse cfDNA degradation patterns at promoters and exon regions, to discern it from the DNA coming from the tumours. The bright point of their approach is that simultaneously enable for profiling of actionable cancer mutations and quantitative estimation of ctDNA burden. Their sequencing data of ctDNA has been deposited in the EGA under the accession number EGAS00001004657. This study sums up to a total of 37 of cfDNA and 36 about ctDNA present up to date in the EGA archive. The existence of cfDNA was firstly described back in 2002, while the use of ctDNA analysis in the oncological field exploded only after 2016. The approach is rapidly evolving, moving in parallel with the ever-improving sequencing technologies. Especially, the possibility to obtain high quality sequences from minimal amount of material is a crucial factor. Being able to alleviate cancer patients of disturbing and often slow-recovery procedures, while at the same time improving the quality of diagnosis and disease monitoring seems like a lot to ask. Thankfully, we are blessed with brilliant and dedicated scientists from all around the world who collaborate to make this a reality, sharing their results, knowledge and data.

  Blog advancing-fast-in-the-analysis-of-circulating-tumour-dna

• RNA-Seq data_MM study cohort (n=73) and human myeloma cell lines

  Multiple myeloma (MM) represents approximately 10 % of all hematological malignancies and patients present with a variety of clinical manifestations and diverse cytogenetic backgrounds While MM patient survival has greatly improved due to novel treatments in the last years, the disease is generally considered incurable with a median survival of approximately 6 years. Therapy resistance remains as a major obstacle, causing almost all patients to eventually relapse, underlining the persisting need to identify novel therapeutic targets and biomarkers associated with disease progression.

  Study EGAS50000000392

• Identification of Targetable FGFR Gene Fusions in Diverse Cancers

  In this study, patients with advanced cancer across all histologies were enrolled in our IRB approved clinical sequencing program, called MI-ONCOSEQ, to go through an integrative sequencing which includes whole exome sequencing of the tumor and matched normal, and transcriptome sequencing. Four index cases were identified which harbor gene rearrangements of FGFR2 including two cholangiocarcinoma cases, a metastatic breast cancer case, and a metastatic prostate cancer case. After extending our assessment of FGFR rearrangements across multiple tumor cohorts, including TCGA, we identified FGFR gene fusions with intact kinase domains of FGFR1, FGFR2, or FGFR3 in cholangiocarcinoma, breast cancer, prostate cancer, lung squamous cell cancer, bladder cancer, thyroid cancer, oral cancer, glioblastoma, and head and neck squamous cell cancer. All FGFR fusion partners tested exhibit oligomerization capability, suggesting a shared mode of kinase activation. Overexpression of FGFR fusion proteins in vitro induced cell proliferation, and bladder cancer cell lines that harbors FGFR3 fusion proteins exhibited enhanced susceptibility to pharmacologic inhibition in vitro and in vivo. Due to the combinatorial possibilities of FGFR family fusion to a variety of oligomerization partners, clinical sequencing efforts which incorporate transcriptome analysis for gene fusions are poised to identify rare, targetable FGFR fusions across diverse cancer types.

  Study phs000602

• Establishment and genomic analysis of mixed phenotypic acute leukemia cell lines

  Among acute leukemias, mixed phenotype acute leukemias (MAPL; mixed phenotype acute leukemiae), which have both acute myeloid and acute lymphocytic leukemia traits, are generally considered resistant to chemotherapy, especially those with t(9;22) MPAL has a poorer prognosis than other forms of MPAL and is a challenge to overcome. MPAL accounts for less than 1% of all acute leukemias, and the rarity of this disease makes it difficult to conduct clinical studies to develop treatments for the disease. Therefore, we planned to establish cell lines based on acute leukemia cells (JMPAL-1) obtained from MPAL patients, and to analyze information such as gene mutations related to disease development based on these cell lines for therapeutic purposes.

  Study JGAS000721

• EM-seq datatset of primary human thymocyte subsets

  FASTQ-files of EM-seq experiments of 6 primary human thymocyte subsets, isolated by flow-cytometry (FACS) from neonatal thymus biopsies. All samples were sorted to be negative for the expression of the lineage surface markers CD11c, CD19, CD56, CD141 and CD303 and additionally had the following surface phenotypes: DN2 (CD4-CD8-CD7+CD1a-CD161-CD38+CD3-TCRab-); DN3 (CD4-CD8-CD7+CD1a+); ISP (CD4+CD8-CD3-TCRab-CD1a+); DPE (CD4+CD8+TCRab-CD69-); DPL (CD4+CD8+TCRab+CD69+); SP4 (CD4+CD8-). Samples were sorted from 1-2 pediatric patients undergoing heart surgery.

  Dataset EGAD50000001600

• RNA-seq of cells after injection into immunodeficient mice

  This dataset includes 73 samples profiled by high-throughput Illumina sequencing, in bam format, aligned to GRCh37. Normal human CD34+ cord blood (CB), bone marrow (BM), or post-natal thymus (PNT) cells were transduced with various combinations of T-ALL oncogenes, cultured in vitro on OP9-DL1 feeders for up to 25 days, and then transplanted into immunodeficient NSG or NRG mice. The samples were collected after development of frank leukemia in recipient mice.

  Dataset EGAD00001009751

• Myocardial Infarction Genetics Exome Sequencing Consortium: U. of Leicester

  MI cases were ascertained from two studies: (i) the British Heart Foundation Family Heart Study and (ii) the BRICCS Study. Control subjects were ascertained from the control subjects being recruited as part of the UK Aneurysm Growth Study (UKAGS). All exome sequencing was performed at the Broad Institute of Harvard and MIT; samples sequence capture was performed using Illumina&#39;s ICE Capture reagent and sequencing was performed on an Illumina HiSeq 2000 or 2500.

  Study phs001000

• Breast Cancer Family Registry Early-onset Breast Cancer GWAS

  In order to enhance our understanding of the genetic etiology of breast cancer, this study analyzed 1,265,548 Hapmap3 single-nucleotide polymorphisms (SNP) among a discovery set of 3,523 EOBC incident cases and 2,702 age-matched population control women, all of whom were age 50 or younger at enrollment. Subjects were recruited from the eight sites, some of which oversampled cases with a personal or family history of breast cancer.

  Study phs001589

• Transcriptomic analyses of a large cohort of adult B cell acute lyphoblastic leukemia

  Total mononuclear cells (MNC) were isolated from peripheral blood (PB) or bone marrow (BM) samples of 105 Ph-/-/- (B-cell acute lymphoblastic leukemia [B-ALL] triple negative) and 31 Ph+ B-ALL adult patients using Lymphosep (Biowest, Nuaillé, France). A total of 15 samples from healthy subjects were processed including hematopoietic stem-progenitor cells (CD34+) from bone marrow specimens (n = 3), and bone marrow mononuclear cell samples (n = 3) from STEMCELL Technologies (Vancouver, Canada). PB MNC samples (n=5) and cord blood samples (n = 4). CD34+ cells were enriched from cord blood samples by immunomagnetic separation (CD34 MicroBead Kit, Miltenyi Biotec, Bergisch Gladbach, Germany).Libraries were prepared using the TruSight RNA Pan-Cancer Panel Kit (Illumina, San Diego, California, USA), following the manufacturer’s protocol. Sequencing was performed using the Illumina MiSeq instrument.

  Dataset EGAD00001010837

• Whole exome sequencing for clarification of rare causes of axonal Charcot-Marie-Tooth disease (2017-08-16)

  The project is focused on the axonal forms of Charcot-Marie-Tooth (CMT) disease. We have selected 13 families (7 from Spain and 6 from Czech Republic) that have been indepth clinically assessed and previously tested for mutations in known CMT genes without causal variants characterised. In these patients we expect to discover several CMT2 genes. Thus, we requested for exome sequencing of 45 DNAs:27 exomes in families from Spain and 18 exomes in the families from Czech Republic. 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/ This dataset contains all the data available for this study on 2017-08-16.

  Dataset EGAD00001003565

• Hip OA Functional Genomics RNAseq (2017-06-09)

  From 9 patients undergoing hip joint replacement surgery for osteoarthritis, we collected 3 cartilage samples each: a low-grade sample (no obvious evidence of damage or fibrillation); a high-grade sample (damaged and fibrillated cartilage); an osteophytic sample (overlaid bony protrusions mainly around the margins of the articular surface). Multiplexed libraries were sequenced on Illumina HiSeq 2000 (75bp paired-end read length) and a cram file was produced for each sample. This dataset contains all the data available for this study on 2017-06-09.

  Dataset EGAD00001003354

• Study on the proliferation history of colorectal adenomas

  Mismatch repair (MMR) deficient colorectal adenomas are composed of transformed cells that descend from a common founder and progressively accumulate genomic alterations but their proliferation history is still largely unknown. Here we rebuilt the proliferation history of four MMR deficient colorectal adenomas from male individuals by deep sequencing the X chromosome and mapping the somatic mutations that were progressively acquired during tumor growth. We developed a novel method to call high and low frequency mutations, to cluster them according to their frequencies and rebuild the proliferation trees directly from these mutation clusters using a recursive algorithm. The trees of all four lesions were formed of a dominant subclone that coexisted with other genetically heterogeneous subpopulations of cells. Despite this similar hierarchical organization, however, the growth dynamics varied among and within tumors, likely depending on a combination of tumor-specific genetic and environmental factors. Our study provides insights into the biological properties of individual MMR deficient colorectal adenomas that may influence their growth and also the response to therapy. Extended to other solid tumors, our approach could inform on the mechanisms of cancer progression, thus providing insight into the best choice of cancer treatment methods.

  Study EGAS00001000883

• The Prediction and Prevention of Preeclampsia

  The Prediction and Prevention of Preeclampsia (PREDO) is a prospective birth cohort study of Finnish women who were pregnant between 2005 and 2010 and their children. The PREDO study cohort was set up to identify novel risk factors and biomarkers in pregnant women associated with the development of preeclampsia and intrauterine growth restriction (IUGR). Two groups of pregnant women were enrolled during the first trimester of their pregnancy: first, pregnant women with a known clinical risk factor status for preeclampsia and IUGR (N=1079; 969 of these women had at least one and 110 had none of the known risk factors for preeclampsia and IUGR), and second, pregnant women who volunteered to participate regardless of their risk factor status for preeclampsia and IUGR (N=3698). Both samples were monitored during pregnancy with four clinical visits and bi-weekly self-reports. The post-delivery follow-up has taken place at approximately 2 weeks, 6 months, and 3.5 years after the delivery. The most recent follow-up started in 2016 and is ongoing. In the high-risk sample, cord blood samples were collected and genome-wide genotyping has been conducted with Illumina OmniExpressExome 1.2 and genome-wide methylation status has been analysed with Illumina 450k HM array.

  Study EGAS00001001898

• Discovery of Novel Melanoma Predisposing Mutations by Exome Sequencing

  Melanoma risk shows clear heritability, but the inherited genetic factors remain largely unknown. Rare high risk mutations in CDKN2A, CDK4, BAP1 and TERT have been described but these variants, in aggregate, account for less than 50% of the kindreds reporting a family history of melanoma. This project will seek to identify new genes which may confer melanoma susceptibility. Identifying melanoma predisposing mutations can provide new biological insights and may impact important clinical decision making regarding treatment, surveillance and preventive approaches. Hereditary melanoma patients (defined as those individuals with multiple primary melanomas and those patients with a family history of melanoma) were collected from melanoma clinics at the Massachusetts General Hospital in Boston and the A. Sygrous Hospital in Athens, Greece. All exome sequencing was performed at the Broad Institute of Harvard and MIT; samples sequence capture was performed using Agilent SureSelect Human All Exon Kit v2 and sequencing was performed on an Illumina HiSeq 2000 or 2500.

  Study phs000823

• Epigenetic landscape of mixed phenotype leukemias

  Leukemias with ambiguous lineage comprise a number of loosely defined entities, often without a clear mechanistic basis. Here, we investigated a group of such leukemias with a CpG Island Methylator Phenotype (CIMP), previously identified as CEBPA-silenced AML. Transcriptomics and epigenomics analyses revealed a hybrid myeloid/lymphoid epigenetic landscape, whereas genetic alterations were heterogenous. This suggests that CIMP leukemias are defined by their shared epigenetic profile rather than a common genetic lesion. Gene expression enrichment showed strong similarity with ETP-ALL and an early lymphoid progenitor cell of origin. Accordingly, integration of differential methylation and expression revealed widespread silencing of myeloid transcription factors (TFs), among which CEBPA was key for differentiation arrest. Hypermethylation also resulted in loss of CTCF binding, accompanied by a few changes in chromatin interactions involving critical TFs like KLF4. In conclusion, epigenetic dysregulation, and not genetic lesions, explain the mixed phenotype of a group of CIMP leukemias resembling ETP-ALL.

  Study EGAS00001007094

• Chemotherapy induces canalization of cell state in childhood B-cell precursor acute lymphoblastic leukemia

  Comparison of intratumor genetic heterogeneity in cancer at diagnosis and relapse suggests that chemotherapy induces bottleneck selection of subclonal genotypes. However, evolutionary events subsequent to chemotherapy could also explain changes in clonal dominance seen at relapse. We therefore investigated the mechanisms of selection in childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL) during induction chemotherapy where maximal cytoreduction occurs. To distinguish stochastic versus deterministic events, individual leukemias were transplanted into multiple xenografts and chemotherapy administered. Analyses of the immediate post-treatment leukemic residuum at single-cell resolution revealed that chemotherapy has little impact on genetic heterogeneity. Rather, it acts on extensive, previously unappreciated, transcriptional and epigenetic heterogeneity in BCP-ALL, dramatically reducing the spectrum of cell states represented, leaving a genetically polyclonal but phenotypically uniform population, with hallmark signatures relating to developmental stage, cell cycle and metabolism. Hence, canalization of the cell state accounts for a significant component of bottleneck selection during induction chemotherapy.

  Study EGAS00001004407

• Longitudinal single-cell transcriptomic study in patient-derived xenografts of pediatric T-ALL

  In this study, we conducted single cell full length total RNA sequencing (VASA-seq) on 13 matched pediatric T-ALL PDX samples at initial diagnosis and relapse, along with 5 non-relapsing PDX samples collected at initial diagnosis. We identified a subpopulation of T-ALL cells with stem-like cell features that expands substantially at relapse indicating resistance to first-line therapy. Chemotherapy resistance was further validated through functional testing: Two samples were subjected to in-vitro drug testing, treated with Cytarabine for three days, followed by single-cell transcriptomic analysis using 10x Genomics. Additionally, in-vivo drug testing was conducted on one sample, involving re-engraftment into mice and treatment with a combination of Vincristine, Doxorubicin, and Dexamethasone. Single-cell transcriptomic analysis using 10x Genomics was performed after 0, 15 and 30 days of treatment.

  Dataset EGAD50000000831

• National Eye Institute Glaucoma Human Genetics Collaboration (NEIGHBOR) Consortium Glaucoma Genome-Wide Association Study: Whole Exome Resequencing in Glaucoma

  This is a study of primary open angle glaucoma (POAG) conducted through exome sequencing of cases and comparison of variant frequencies with general population frequencies available in dbGAP and controlled for sequencing platform artifact to minimize false positives. POAG is an intraocular pressure (IOP) related progressive optic neuropathy that ultimately leads to blindness. This study builds upon the efforts of an on-going collaborative consortium that studied 2,170 POAG cases and 2,347 controls with a unified definition of POAG (the NEIGHBOR consortium: NEI Glaucoma Human genetic collaBORation). The case definition for NEIGHBOR was harmonized with an additional 976 cases and 1140 controls from the NHGRI supported GENEVA (gene-environment) study of glaucoma (GLAUGEN) (NIH/NHGRI U01HG004728, Pasquale PI). In NEIGHBOR, cases and controls were recruited from ophthalmology clinics and were examined by ophthalmologists. For cases, the clinical exam included intraocular pressure measurements, optic nerve assessment and visual field evaluation. Controls had no family history of glaucoma, normal intraocular pressure and normal optic nerves. Cases and controls were also drawn from two clinical trial populations: Advanced Glaucoma Intervention Study (AGIS, NEI U10EY006827, D. Gaasterland PI) and Collaborative Initial Glaucoma Treatment Study (CIGTS, NEI U10 EY009149, P. Lichter PI). The Glaucoma Exome Sequencing study has one Principal Investigator: Theresa Gaasterland (UCSD) and two Co-Investigators: Robert Weinreb, MD, and Kang Zhang, MD, PhD, all of whom are part of the NEIGHBOR, subsequently NEIGHBORHOOD, consortium, which in turn has two Co-Principal Investigators: J. Wiggs (Harvard, MEEI), and M. Hauser (Duke). NEIGHBOR collaborators who contributed samples and/or expertise to the Glaucoma Exome Sequencing study included the following: Harvard Medical School (Massachusetts Eye and Ear Infirmary) (J. Wiggs, L. Pasquale); Duke University Medical Center (M. Hauser, E. Hauser, R. Allingham, S. Schmidt); University of Michigan (J. Richards, S. Moroi, P. Lichter); University of Miami (M. Pericak-Vance, R. Lee, D. Budenz); Vanderbilt University (J. Haines); University of California San Diego (K. Zhang, R. Weinreb; T. Gaasterland); University of Pittsburgh (J. Schuman, G. Wollenstein); University of West Virginia (A. Realini, J. Charlton, S. Zareparsi); Johns Hopkins University (D. Friedman); Stanford University (D. Vollrath, K. Singh), Eye Doctors of Washington (D. Gaasterland), Marshfield Clinic (Cathy McCarty). Hemin Chin serves as the NEI Staff Collaborator. This national collaborative study is supported by multiple NIH grants: NEI R01 EY015543 (Allingham); NEI U10 EY006827 (D. Gaasterland); NHLBI R01 HL073389 (E. Hauser); NEI R01 EY13315 (M. Hauser); NEI U10 EY009149 (Lichter); NEI R01 EY015473 (Pasquale); NEI U10 EY012118 (Pericak-Vance); NEI R03 EY015682 (Realini); NEI R01 EY011671 (Richards); NEI R01 EY09580 (Richards); NEI R01 EY013178 (Schuman); NEI R01 EY015872 (Wiggs); NEI R01 EY009847 (Wiggs); NEI R01 EY010886 (Wiggs); NEI R01 EY144428 (Zhang); NEI R01 EY144448 (Zhang); NEI R01 EY18660 (Zhang). Funding support for genotyping through exome sequencing, which was performed at the University of California, San Diego, was provided by the National Eye Institute (RC2 EY020678-01).

  Study phs000558

• Natural History, Pathogenesis and Outcome of Melorheostosis

  Melorheostosis is a rare osteosclerotic disease resulting in exuberant excessive bone growth with a characteristic radiographic appearance often described as "dripping candle wax". As a result of these bony formations, patients report mild to moderate pain that interferes with their routine activities. It is usually diagnosed on radiographs but bone biopsy may be performed to exclude other osteosclerotic diseases and/or osteosarcoma. Deformities, limb-length discrepancy, muscle atrophy, neurological deficit have been reported as complications. A subset of patients have somatic mutations in MAP2K1. The cause of this disease is not known in all patients, the natural history poorly described and there is no clearly-defined systemic therapy. We propose a prospective observational study to investigate the natural history and pathogenesis of the disease. Subjects will undergo standardized initial evaluation and medically indicated testing. Skin biopsies may be performed to test for known mutations related to melorheostosis, and if negative affected bone and/or skin may be sent for genetic testing for acquired somatic mutations in genes that control bone homeostasis. Enrolled subjects will be followed every two to three years for assessment of disease progression and receive clinically indicated testing and treatment. The study of this rare bone disease offers the potential to generate new insights, provide answers as well as generate new questions into the biology of the skeletal and mineral metabolism.

  Study phs001976

• CNV detection in targeted NGS panel data

  We present targeted NGS panel data from 170 samples that were processed using the TruSightTM Cancer (TSC) panel (Illumina, San Diego, CA, USA), which targets 94 genes and 284 SNPs associated with a predisposition towards cancer. The samples are enriched for CNVs in the genes of interest. All CNVs have previously been assessed with MLPA and can therefore be considered as confirmed.

  Dataset EGAD00001003400

• Ghana Prostate Study

  Participants were recruited through the Ghana Prostate Study-a population-based component, and a clinical component. The population-based component was a probability sample designed using the 2000 Ghana Population and Housing Census data in an attempt to recruit approximately 1,000 men aged 50-74 years in the Greater Accra region (~3 million people), which successfully recruited 1,037 healthy men between 2004 and 2006 with a response percentage of 98.8 %. Consented individuals underwent an in-person interview, and within 7 days had a digital rectal examination (DRE) and provided an overnight fasting blood sample for prostate-specific antigen (PSA) testing, biomarker assays, and genetic analysis. Subjects who had a positive screen by PSA (&#62;2.5 ng/ml) or DRE underwent a transrectal ultrasound-guided biopsy. A total of 73 histologically confirmed prostate cancer cases were identified through the population-based screening component of the Ghana Prostate Study and were included in the case population in the published GWAS (Cook et al., Human genetics, 2013). From the remaining 964 screen-negative individuals, 836 had at least 20 &#956;g DNA extracted and available for analysis, and 500 of these were matched to cases for analysis by age (in 5-year categories). In the Ghana Prostate Study, we recruited 676 prostate cancer cases at Korle Bu Teaching Hospital in Accra, Ghana, between 2008 and 2012. All consented cases were interviewed and provided an overnight fasting blood sample. At the time of selection for this analysis we had recruited 582 prostate cancer cases, from which we selected 427 for analysis. Combined with the 73 cases diagnosed through the population-based component of the study, this yielded 500 available prostate cancer cases for analysis.

  Study phs000838

• T2D-GENES Project 2: San Antonio Mexican American Family Studies

  The Type 2 Diabetes (T2D) Genetic Exploration by Next-generation sequencing in Ethnic Samples (T2D-GENES) Consortium is a collaborative international effort to identify genes influencing susceptibility to T2D in multiple ethnic groups using next generation sequencing. T2D-GENES Project 2 is a complex pedigree-based study designed to identify low frequency or rare variants influencing susceptibility to T2D, using whole genome sequence (WGS) information from 1,043 individuals in 20 Mexican American T2D-enriched pedigrees from San Antonio, Texas. The major objectives of this study are to identify low frequency or rare variants in and around known common variant signals for T2D, as well as to find novel low frequency or rare variants influencing susceptibility to T2D. The sampled individuals are obtained from two studies: the San Antonio Family Heart Study (SAFHS) and the San Antonio Family Diabetes/Gallbladder Study (SAFDGS), collectively referred to as the San Antonio Mexican American Family Studies (SAMAFS). The strategy is to sequence approximately 600 individuals at an average of 50x coverage across the entire genome, then impute genome wide genotypes for about 440 additional family members. The 600 sequenced individuals are specifically chosen for their value in imputing sequence information into other family members. By studying large pedigrees, we expect to find multiple individuals carrying each genetic variant, even if this variant is very rare in the population at large. Thus, a pedigree-based approach provides an excellent opportunity for identifying rare novel variants influencing risk of T2D and quantitative variation in T2D-related phenotypes. The whole genome sequencing has been done commercially by Complete Genomics, Inc. (CGI). The final data set includes whole genome sequence data for 607 individuals. After quality control, 585 sequenced individuals provide data for family based imputation, using Merlin linkage analysis software, into approximately 440 additional family members for whom chip based genotypes are available to indicate which parental haplotype is transmitted. Extensive phenotype data is provided for 1048 individuals. These include 5 sequenced individuals who do not belong to any of the 20 large pedigrees. Phenotype information was collected between 1991 and 2011 in the two contributing longitudinal studies. SAFHS participants may have information from up to 5 visits, and SAFDGS participants may have up to 4 visits. The clinical variables reported are coordinated with T2D-GENES Project 1 (multi-ethnic exome sequencing) and include T2D status and age at diagnosis, glycemic traits (fasting and 2 hour glucose and insulin), blood pressure, blood lipids (total cholesterol, HDL cholesterol, calculated LDL cholesterol and triglycerides), clinical chemistry (cystatin c, glutamic acid decarboxylase antibody titer (GadAb), creatinine, adiponectin and leptin). Glycated hemoglobin (HbA1c) was not measured for these individuals and insulin C-peptide is not included in this data set. Additional phenotype data include the medication status at each visit, classified in four categories as any current use of diabetes, hypertension or lipid-lowering medications, and, for females, current use of female hormones. Anthropometric measurements include age, sex, height, weight, hip circumference, waist circumference and derived ratios. Each phenotype variable has an initial summary column containing the most recent non-missing measurement for each individual, followed by the five potential time points for each individual, the number of non-missing measurements, and the age and year for the most recent non-missing measurement. For historical reasons, the order in which variables are presented on the dbGaP web site differs from their order in the data download file. When reading the comment fields for each variable, please note that commas are omitted to support data exchange in .csv format.

  Study phs000462