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• Insights into Adult Gut Microbiota Composition Using the Estonian Cohort

  This study explores the gut microbiota of 1,826 adult individuals, predominantly from the Estonian population. The cohort includes 16S rRNA gene V4 region amplicon sequencing data along with metadata, such as age, sex, health status, anthropometric parameters, and stool consistency. The research aims to characterise the microbial community structure, identify distinct enterotypes, and examine their associations with host characteristics. The dataset provides a valuable reference for understanding gut microbiota composition and supports research in personalised nutrition and lifestyle-related health strategies.

  Study EGAS50000001611

• A targeted gene panel that covers coding, noncoding, and short tandem repeat regions improves the diagnosis of patients with neurodegenerative diseases

  Genetic testing for diagnosis of neurodegenerative diseases (NDs) is highly challenging because of genotype heterogeneity and overlapping clinical manifestations. Targeted-gene panels (TGPs), coupled with next-generation sequencing (NGS), can facilitate the profiling of a large repertoire of ND-related genes and help with differential diagnosis. Due to the technical limitations inherent in NGS and TGPs, short tandem repeat (STR) variations are often ignored. However, STR expansions are known to cause such NDs as Huntington’s disease and several types of spinocerebellar ataxias, such as SCA type 3 (SCA3).Here, we studied the clinical utility of a custom-made TGP that targets 199 NDs and 311 ND-associated genes on 118 undiagnosed patients. At least one known pathogenic or likely pathogenic variation was found in 54 (45.8%) patients; 27 (22.9%) of the 118 patients demonstrated clinical profiles that matched the variants. Based on the findings from the TGP, we refined the original diagnosis of 16 patients. A high concordance of single-nucleotide polymorphisms (99.9%) and small insertions and deletions (93.2%) were observed when comparing the results from TGP and whole-exome sequencing of four patients. We tested an in-house STR detection algorithm, and it reached a specificity of 0.88 and a sensitivity of 0.82 in our SCA3 cohort. This study also uncovered a trove of novel and recurrent variants that may enrich the repertoire of ND-related genetic markers. We propose that a combination of comprehensive TGPs and the bioinformatics analysis pipeline can improve the clinical diagnosis and accelerate the diagnosis of NDs.

  Study EGAS00001003737

• Submission FAQ

  Submission FAQ Before Submission Is the EGA the right archive for my data? TThe most suitable archive for your data is dependent on the type of data you are wishing to submit and whether the data require public or controlled access. Public access is defined as complete and open access to all submitted data. On the contrary, controlled access, exerted by the EGA, requires formal applications to be made to access the submitted data files and metadata. EGA only accepts human-derived data subject to controlled access. If your submission contains other types of data, please choose the appropriate repository for it (see image below): ENA, EVA, ArrayExpress, BioSD and GWAS catalog.  Should your submission be subjected to controlled access? Data access conditions are normally defined in the original informed consent agreements signed by the participants involved in your study. All data submitted to the EGA is subject to controlled access. These consents prevent the derived data files, potentially identifiable, from being dispersed by open and public access. Controlled access data often refers to human data derived from medical research and consortium projects. If in doubt, consult the informed consent agreements that apply to your study The EGA enables you to hold a submission before publication. What data types can be submitted to the EGA? Data types accepted by the EGA can be split into three categories: Sequences: both in generic and platform-specific formats. Array-based: from raw signal files to processed matrices. Phenotypes: all possible phenotype formats are accepted. All manufacturer-specific raw data formats derived from major next generation sequencing platforms are accepted. Also generic sequence formats: flat reads in a FASTQ file, aligned sequences (BAM or CRAM files) as well as sequence variation files in VCF format. All array-based technologies are accepted, including raw data, intensity and analysis files, without any restriction on data formats accepted. We also accept and distribute phenotype data (associated to the samples) in almost any format: from an image to a README file. How long does a submission take? Submissions to EGA come in a variety of formats and sizes, thus it is difficult for us to exactly predict how long a submission will take. We, therefore, advise all of our submitters to allow as much time as possible to make a submission. Based on previous records, we anticipate that the submission process may take at least one month. The submitter’s familiarity with the procedures, possible technical issues that may arise during submission and the amount of data that is being submitted are the main factors that will affect the length of the submission process. However, each step of a regular submission should be considered when estimating the time it would take: Encryption of the files Upload of the files Metadata submission Archival of the files Release of the study and datasets to EGA webpage For example, the upload of the files depends on the submission size, while metadata submission mainly relies on each submitter’s expertise. Further, some steps (e.g. answering to inquiries) depend on the EGA Helpdesk team, which may take some days to be processed during busy times Is data deposited in the EGA secure? The EGA set-up consists of a secure computing facility for data processing, a shared EBI set-up for data submissions and distribution of data via data requests made through the EGA website. Data is also copied in the Barcelona Supercomputing Center (BSC) infrastructure, where all stored and distributed data is encrypted Data is encrypted along the submission process and stored securely, granting its access to authorised users exclusively. During the download process, through our Python Client or Aspera, all requested data is downloaded over secure https connections. All data at the EGA is encrypted, and only accessible (for log-in and download) through secure protocols. For further information please, visit our security overview. What documentation do I need to provide? All submissions require policy’s documentation: 'Data Access Agreement (DAA)', 'Data Processing Agreement (DPA)' and 'Authorized Submitters Formulary'. The data processors (EGA) and the data owners will also sign the DPA. Will all metadata be public? Among the submitted metadata we need to make the distinction between identifiable and unidentifiable metadata: (1) the former may allow the identification of the human the sample derived from (e.g. detailed geographical providence, personal name, family ancestry…); (2) while the latter can be used to interpret the data without compromising the anonymity of the patients. The majority of the metadata submitted to the EGA corresponds to the unidentifiable category (e.g. sequencer's model). This type of metadata is publicly available on the EGA website and other EBI resources/partners’ websites. On the other hand, some parts samples’ metadata are subject to being identifiable, and thus only accessible by authorized data requesters, with the exception of: 5 submitter-defined attributes of the sample: alias, title, subject_id, gender and phenotype. It is the submitter’s responsibility not to submit sensitive metadata in these public fields. 3 anonymised fields that pinpoint the sample record in archivals: sample’s EGA stable ID (EGAN…), BioSample ID (SAMEA…) and submitter’s center name. During Submission Are there any sample specific requirements for EGA? All samples submitted to the EGA must include the attributes of biological sex, subject ID (anonymised individual identifier) and phenotype information. These are critical for data findability and its analysis, and we highly recommend using controlled ontology terms where applicable. For example: defining tumour and non-tumour samples and/or defining disease state. The EGA recommends using the Experimental Factor Ontology Database to find ontologized terms that describe your sample phenotypes. How do I get an accession number to use in my publication? You will receive your study accession number (EGAS…) upon complete your submission, either: Programmatically. As soon as the metadata is submitted and validated your study will be assigned an accession number that will be given in the submission’s response. Manually registering your study and relevant metadata using the online metadata submission tool: the EGA submitter Portal. How are files uploaded to the EGA? Data files are uploaded into private submission drop boxes (i.e. environments to which you are granted access and where you can transfer your files) using INBOX or FTP. These spaces are provided as part of the submission procedure. Before uploading any file, you must encrypt your files, . Only encrypted files shall be uploaded to the drop boxes. Why does data need to to encrypted for my submitted files? It is one of the security steps the EGA has implemented. In case of a security breach, people without the proper encryption key will not be able to read or use the information that could have been leaked. This measure is essential when working with sensitive data, such as controlled access human data. All submitters must use crypt4gh to create EGA compliant files prior to uploading them. This encryption is GPG-based, using EGA’s public key. Why are my files not available if I see them in the INBOX? There exists a time window between the data upload and the availability of such files via the Submitter Portal. For this reason, some metadata (run and analysis objects) cannot be registered until at least 24 hours after the files have been uploaded to your box. Why are MD5 sum values generated for my submitted files? We require pre- and post- encryption MD5 (message-digest) checksum values to be provided for all submitted files. These 128-bit values are computed using the content of each file, creating unique sequences that allow us to ensure that file integrity has been maintained during the transfer process. In other words, if the MD5 checksums we generate and those you generated match, we infer that the content of the transferred files is correct (i.e. files are not corrupted or truncated). MD5 checksums are computed automatically using the crypt4gh tool provided. Your submission will not be accepted and may be significantly delayed if you do not provide MD5 checksum values for all data files in the required format. How can I check if my files are correctly uploaded to the inbox? It is important to check the status pf your file so you know whether your files are in the inbox, being processed, or if there is any issue with one of them. In order to check this, you should: Look for the file locally. Drag and drop it to the file table. Then bars will appear on the table, which means that we are processing it. Green: The files checksum are correct and your file will move to “ingested files”. No further actions are needed from your end.. Red: The files checksum does not match and your file needs to be re-uploaded. Please re-upload relevant files to your inbox using the same path. After Submission How do I use my accession number in my publication? We suggest the use of the below template, using your study accession ID (EGAS…) : Data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found at https://ega-archive.org and "The European Genome-phenome Archive of human data consented for biomedical research" Your study ID will be the one that groups your whole submission, and thus its usage is recommended as such. Nevertheless, all metadata submitted to EGA hold a unique and persistent identifier (starting with EGA…) that can be used to identify specific records. For example, you could reference a specific dataset (EGAD…) or sample (EGAN…) in your publications (see full list of identifiers). How do I make my data searchable? Once you have finalized your submission, you can schedule the data release. Please take into account that the release process needs time for the files to be archived in our system, and for the Helpdesk team to validate your submission. Can I withdraw (meta)data from the EGA? We have methods in place for the secure removal of deposited (meta)data. Contact EGA-helpdesk for further details. EGA complies with FAIRness of (meta)data, and thus, even when the data is removed we keep an entry for their identifiers in our system. In other words, we execute a soft delete on canceled objects (e.g. a study): metadata is still stored in our systems, but it loses all links, cannot be queried and data files cannot be retrieved anymore. The reason behind this behaviour is so that queries using withdrawn data properly respond back (see example of a canceled study). What happens to the data once it has been submitted to the EGA? When the data is submitted, the submitter can choose either keep their data private or schedule the release of their data.

  Documentation submission/metadata/submission/FAQ

• CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium Summary Results from Genomic Studies

  GWAS have successfully identified genetic loci associated with a variety of conditions such as type 2 diabetes and coronary disease. The large number of statistical tests required in GWAS has posed a special challenge because few studies that have DNA and high-quality phenotype data are sufficiently large to provide adequate statistical power for detecting small to modest effect sizes. Even before the era of GWAS, the requirement for large sample sizes and the importance of replication have served as powerful incentives for collaboration. Meta-analyses combining summary data from multiple sources have improved the ability to detect new loci. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium was formed to facilitate GWAS meta-analyses and replication among multiple large and well-phenotyped cohort studies. The design of the CHARGE Consortium was formed initially from 5 prospective cohort studies from the United States and Europe: the Age, Gene/Environment Susceptibility (AGES) - Reykjavik Study, the Atherosclerosis Risk in Communities (ARIC) Study, the Cardiovascular Health Study (CHS), the Framingham Heart Study (FHS), and the Rotterdam Study (RS). Additional studies have expanded the CHARGE consortium based upon the phenotypes and willingness to share information across the research community. In order to facilitate investigators across the world to examine relationships between phenotypes and genetic markers within CHARGE published reports, an open site is made available on dbGaP that provides the rsID and the p-value for inspection. Access to detailed summary statistics (including minor allele frequency, odds ratio/effect size) requires approval of a Data Access Request (DAR).

  Study phs000930

• Same-day genomic and epigenomic diagnosis of brain tumors using realtime nanopore sequencing

  Molecular classification of cancer has entered clinical routine to inform diagnosis, prognosis and treatment decisions. At the same time, new tumor entities have been identified that cannot be defined histologically. For central nervous systems tumors, the current World Health Organization classification explicitly demands molecular testing, e.g. for 1p/19q- codeletion or IDH mutations, to make an integrated histomolecular diagnosis. However, a plethora of sophisticated technologies is currently needed to assess different genomic and epigenomic alterations and turnaround times are in the range of weeks, which makes standardized and widespread implementation difficult and hinders timely decision making. Here, we explored the potential of a pocket-size nanopore sequencing device for multimodal and rapid molecular diagnostics of cancer. Low-pass whole genome sequencing was used to simultaneously generate copy number (CN) and methylation profiles from native tumor DNA in the same sequencing run. Single nucleotide variants in IDH1, IDH2, TP53, H3F3A and the TERT promoter region were identified using deep amplicon sequencing. Nanopore sequencing yielded ~0.1X genome coverage within six hours and resulting CN and epigenetic profiles correlated well with matched microarray data. Diagnostically relevant alterations, such as 1p/19q codeletion, and focal amplifications could be recapitulated. Using ad hoc random forests, we could perform supervised pan-cancer classification to distinguish gliomas, medulloblastomas and brain metastases of different primary sites. Single nucleotide variants in IDH1, IDH2 and H3F3A were identified using deep amplicon sequencing within minutes of sequencing. Detection of TP53 and TERT promoter mutations shows that sequencing of entire genes and GC-rich regions is feasible. Nanopore sequencing allows same-day detection of structural variants, point mutations and methylation profiling using a single device with negligible capital cost. It outperforms hybridization-based and current sequencing technologies with respect to time-to diagnosis and required laboratory equipment and expertise, aiming to make precision medicine possible for every cancer patient, even in resource restricted settings.

  Study EGAS00001002213

• Host Genetic Determinants of the Outcome of Staphylococcus Aureus Bacteremia by Whole Exome Sequencing

  In this case-control study, Complicated Staphylococcus aureus (case) is defined as bacteremia plus endocarditis or bone/joint infection, and Uncomplicated Staphylococcus aureus (control) has no such complications and was cured at follow-up. We used whole exome sequencing to examine the cumulative effect of coding variants in each gene on risk of complicated SAB in a discovery sample of 168 SAB cases (84 Complicated SAB and 84 Uncomplicated SAB, frequency matched by age, sex and bacterial clonal complex). Then we evaluated the 334 most significantly associated genes and 8 biological candidate genes in a replication sample of 240 SAB cases (122 complicated and 118 uncomplicated, frequency matched by age, sex and bacterial clonal complex). All participants were white, non-Hispanic of European descent. In the discovery sample, gene-based analysis using SKAT-O identified 334 genes associated with complicated SAB at p<3.5 x 10-3;. Gene-based analysis of the 342 genes in the replication sample using SKATO identified one gene, GLS2 (GeneID: 27165), significantly associated with complicated SAB (p=1.2 x10-4;) after Bonferroni correction. GLS2 is an interesting candidate gene for complicated SAB due to its role in regulating glutamine metabolism, a key factor in white blood cell proliferation and activation.

  Study phs001505

• Single Cell Analysis Program - Transcriptome (SCAP-T)

  This initiative is part of the Single Cell Analysis Program (SCAP) and is funded through the NIH Common Fund (See http://nihroadmap.nih.gov/), which supports cross-cutting programs that are expected to have exceptionally high impact. Common Fund initiatives address key roadblocks in biomedical research that impede basic scientific discovery and its translation into improved human health. In addition, these programs capitalize on emerging opportunities to catalyze progress across multiple biomedical fields. Single cell analysis has recently emerged as an important field of research because technologies have improved in sensitivity and throughput sufficiently to begin measuring and understanding heterogeneity in complex biological systems and correlating it with changes in biological function and disease processes. By profiling individual cells it is possible to resolve rare cells, transient cell states, and the influence of organization and environment on such cells and states, which cannot be described by ensemble measurements. The long-term goal of the SCAP is to accelerate this move towards personalizing health to the cellular level by understanding the link between cell heterogeneity, tissue function and emergence of disease through the discovery, development and translation of innovative approaches which will dramatically change the way cells are characterized. The SCAP will focus on research, which will systematically measure, analyze and model cell-to-cell variation, and identify crucial differences and rare biological states, which may have important functional consequences. Under SCAP, there are three studies to evaluate the cellular heterogeneity using transcriptional profiling of single cells (U01): University of Pennsylvania: Role of single cell mRNA variation in systems associated electrically excitable cells University of Southern California: Evaluation of cellular heterogeneity using patchclamp and RNA-seq of single cells University of California at San Diego: Single cell sequencing and in situ mapping of RNA transcripts in human brains The SCAP has been designed as a five-year program with several components: (1) the collection, analysis and sharing of comprehensive expression datasets to understand the role of heterogeneity in tissues and systemically and identify critical parameters and states; (2) the discovery of new, innovative tools for spatiotemporal imaging, manipulation, analysis and modeling of a biologically relevant population of cells with minimal perturbation; (3) milestone-driven validation and translation of technologies for characterizing single cells in situ meeting the needs of end-users; and (4) development and coordination of a multidisciplinary research community through workshops and other collective endeavors. Further details about the NIH SCAP-T program can be found here http://commonfund.nih.gov/singlecell/. The SCAP-T data sets include the detailed phenotype information, experimental protocols, QC information, RNA-sequencing data and NGS results from human heart and human brain cells. The first data release includes 697 single cells from human brain and heart. The second data release includes an additional 978 single cells from human brain and heart. The third data release includes an additional 1631 single cells from human brain and heart. The fourth data release is a correction to this study, but adds no new cells. The fifth data release includes an additional 2984 single cells from human brain and heart. The sixth data release includes an additional 944 single cells from human brain and heart. The seventh data release includes an additional 1977 single cells from human brain and heart. The SCAP-T data portal provides a customized interface for users to quickly identify and retrieve files by phenotypes, and data properties such as sequencing facility or coverage for all of these 9231 single cells. For more information about the SCAP-T study and the data portal, please visit http://www.scap-t.org.

  Study phs000833

• Measuring the level of relatedness between NGS datasets

  Sequencing technologies are providing increasingly detailed insight in genetic makeup are paving their way into molecular diagnostics. The field will benefit from rigorous and bias-free measures for the quality of sequence data and for the proper representation of the complexity of the original samples. While current methodologies rely on the availability of a well-characterized reference genome, we propose kMer profiling for alignment-free assessment of the quality, comparability, and complexity of sequencing datasets. We show that kMer detects technical artefacts such as high duplication rates, library chimaeras, and differences in library preparation protocols in whole-genome, whole-exome, and RNA sequencing data. Additionally, it successfully captures the complexity and diversity of microbiomes. Thus, kMer allows for a robust evaluation of the quality and complexity of sequencing data without relying on any prior information and opens the way to a more reliable biological reasoning.

  Study EGAS00001000600

• Center for Common Disease Genomics [CCDG] - Neuropsychiatric: SPARK Simons Foundation Powering Autism Research for Knowledge

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of New York Genome Center (NYGC). WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal.An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002511

• eMERGE Network's Multi-Center Pilot of Pharmacogenetic Sequencing in Clinical Practice

  eMERGE-PGx is a multi-site test of the concept that sequence information can be coupled to electronic medical records (EMRs) for use in healthcare. The promise of personalized medicine - health care guided by each individual's biological characteristics - is being fostered by increasingly powerful and economical methods to acquire clinically relevant biomarkers from large numbers of people. One therapeutic area that seems especially ripe for an early test of the personalized medicine concept is pharmacogenomics (PGx) - the idea that individual variation in drug response includes a genomic component. Drug response variation is an accepted feature of virtually all drug treatments, and contemporary molecular biologic tools continue to identify key genes mediating drug metabolism, transport, and targets. Importantly, common variation in these genes is an increasingly well-recognized contributor, sometimes with large effects, to variation in drug responses. As a result, recommendations for genotype-guided therapy are increasing. These evidence-based recommendations, if implemented in health care practice, could reduce adverse drug events and improve time to therapeutic response. Through eMERGE-PGx, we are developing strategies for the optimal implementation of genetic sequence data into the clinical environment with the ultimate goal of improving patient care. Site and participants include: Children's Hospital of Pennsylvania (CHOP): The Center for Applied Genomics (CAG) at the Children's Hospital of Philadelphia (CHOP) is a high-throughput, highly automated genotyping and sequencing facility equipped with state-of-the-art genotyping and sequencing platforms. Children who are treated at the Children's Hospital Healthcare Network and their parents may be eligible to take part in a major initiative to collect more than 100,000 blood samples, covering a wide range of pediatric diseases. The PGx population selected for sequencing with the PGRNseq panel at CHOP is 1,650 children from CAG's biorepository with well-documented drug-related severe adverse events (SAEs) or EHR-based drug response profiles. SAEs were extracted from EPIC records and from CHOP's Adverse Event (AE) database, which documents every AE at CHOP. These AEs are classified by a medical review panel according to the causal relationship with the suspected drug into 'doubtful', 'possible', and 'probable'. Individuals with events classified as probable, severe and objective, were selected for sequencing. The drugs more frequently associated with adverse events are antibiotics, antineoplastics, immunosuppressants and psychotropic drugs. This cohort constitutes 50% of the target population. The remaining subjects were selected using EHR-based algorithms that we have developed and validated at CAG for identifying patients not responding to ADHD medication (primarily atomoxetine) and patients refractory to antiepileptic treatment from responders. Cincinnati Children's Hospital Medical Center/Boston's Children's Hospital (CCHMC/BCH): 811 CCHMC samples were obtained from children, adolescents or young adults exposed to medication or at risk for needing medication of study interest. 55% of participants were exposed to one or more opioids and their DNA source was a CCHMC study-specific biobank; while 27% of participants were at risk for needing an opioid for surgical pain management and were newly recruited. The remainder of the cohort was exposed to methylphenidate and their DNA samples were obtained from a CCHMC study-specific biobank. The focus of Boston Children's Hospital eMERGE PGx project is on individuals with epilepsy. Samples were taken from a current pharmacogenomics study already in place through which DMET analysis was run and used as confirmation for PGRN-Seq results. A total of 109 samples were sent for PGRN-Seq analysis at University of Washington. The remaining 141 epilepsy samples were from Children's Hospital of Philadelphia and underwent testing with PGRN-Seq at CHOP. Geisinger Health System: A research cohort of adult Geisinger Clinic patients was enrolled from community-based primary care clinics of the Geisinger Health System. Patients were eligible for enrollment if they were a primary care patient of a Geisinger Clinic physician and were scheduled for a non-emergent clinic visit. All data are from Geisinger patients who consent to participate in the MyCode project. MyCode participants agree to provide biological samples for broad research use, including genomic analysis, and for linking of sample data to information in the participant's Geisinger health record. The consent also permits sharing of de-identified data for research purposes. Group Health(GH)/University of Washington (UW): Potential GH participants for the PGx project were enrolled in the eMERGE Network through the Northwest Institute of Genetic Medicine (NWIGM) biorepository, and provided the appropriate consent to receive clinically relevant genetic results (N~6300). Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in GH's integrated group practice, and had completed an online Health Risk Appraisal. The selection algorithm was based on several data sources from the EHR at Group Health: 1. Demographics - participants with self-reported race as Asian or African ancestry were prioritized and selected to enrich for non-European ancestry; 2. Diagnosis and procedure codes - participants were selected if found to have a history of hypertension, atrial fibrillation (AF,) or congestive heart failure (CHF). Participants with a history of arrhythmia were added if the entire selection algorithm did not generate 900 individuals. We also enriched for participants with EHR evidence of actionable indications related to PGRNSeq genes. Participants were selected if found to have an ICD9 code for malignant hyperthermia, hypertension, atrial fibrillation, congestive heart failure or long QT syndrome (LQTS); 3. Laboratory values - if a participant had any laboratory event of creatine kinase (CK) > 1000, and were dispensed statins within 6 months of the event, then they were selected; and 4. Medications - participants were excluded if ever on carbamazepine or had a current regimen of warfarin. Essentia Institute of Rural Health, Marshfield Clinic, Pennsylvania State University (Marshfield): For this study, 750 subjects were selected and enrolled into PGx based on Vanderbilt's algorithm designed to enrich for patients who are most likely to receive one of three common drugs (Clopidogrel, Warfarin or Simvastatin) in the next 2-3 years. These patients were sent a letter of invitation and description of the PGx project. Follow-up phone calls were made, and interested subjects came in for a one time meeting to discuss the project and go through the informed consent with the research coordinator. If they were interested they signed the consent and HIPAA forms and gave blood. Subjects were chosen and enrolled into PGx independently of previous biobank participation. Mayo Clinic: The Right Drug, Right Dose, Right Time - Using Genomic Data to Individualize Treatment (The RIGHT Protocol) enrolled 1013 patients to test the hypothesis that prescribers could deliver genome-guided drug therapy at the point-of-care by using pharmacogenomic data preemptively integrated in the electronic medical record. Complete details regarding the study population have been previously described (Bielinski et al., 2014). Icahn School of Medicine at Mount Sinai School (Mt Sinai): Our study site is the Primary Care Associates (PCA) practice group of the Mount Sinai Faculty Practice Associates (FPA) of the Mount Sinai Medical Center in New York City. This practice has 12 physician providers. All patient encounters are documented and managed with EpicCare ambulatory electronic medical record. Active PCA Patients eligible for enrollment fulfilled the following criteria: a) age 50 or older receiving clinical care at Mount Sinai FPA PCA practice with at least one practice encounter within 18 months prior to commencement of enrollment; b) no history or current use of clopidogrel, warfarin, or simvastatin. Eligible patients were invited to participate through de novo recruitment by letter sent by their provider. Interested patients were screened for eligibility and enrolled to participate in the eMERGE PGX study on site by a dedicated research coordinator. In addition to de novo enrollment from clinical practice, patients of FPA PCA who had previously enrolled in Mount Sinai's BioMe Biobank program AND fulfilled eligibility criteria as stated under a) and b) were identified by chart review and samples sequenced at CIDR using PGRNseq platform (N=300). PGRNseq data from 291 samples passed stringent quality control and are included in the current data set. Furthermore, 56 of these patients carrying known and validated 'actionable' variants affecting prescribing of clopidogrel, warfarin, and/or simvastatin were enrolled in the eMERGE PGX study following invitation through recontacting by the Principal Investigator of the BioMe Program. Northwestern University: Participants for this study were recruited from the General Internal Medicine (GIM) clinic at Northwestern Medical Group (NMG). Patients were selected for invitation to participate if they had been seen a minimum of two times over the last four years, having a high likelihood to receive a prescription for warfarin, Plavix, or a statin, and are seeing a physician who has agreed to allow their patients to be contacted for the study. We utilized an algorithm developed at Vanderbilt and tailored to our population which uses our EHR to estimate the probability that individuals will receive a prescription for warfarin, Plavix, or a statin in the next three years. Participants were sent a letter explaining the study prior to their GIM appointment and offered participation at the time of their visit. Participants were consented on-site and blood drawn after consent was obtained. The GIM clinic consists of 39 primary care physicians who provide approximately 80,000 patient encounters per year. As with any large primary care clinic, a significant proportion of patients in GIM clinic suffer from a variety of chronic health conditions, such as diabetes, hypertension, and coronary artery disease. Over 50,000 individuals have been seen by GIM doctors in the past 5 years; 11,562 of these patients have evidence of a statin prescription in the EHR, 3,436 have evidence of a warfarin prescription, and 1,872 have evidence of a Plavix prescription. Vanderbilt University: The more than 1000 participants enrolled into Vanderbilt's eMERGE PGx study were newly recruited from the Cardiology and Internal Medicine Clinics and the Hillsboro Medical Group within Vanderbilt University Medical Center (VUMC). Patients were selected based on a predictive algorithm estimating the patient's likelihood of receiving Clopidogrel, Warfarin, and/or Simvastatin. The algorithm identifies primarily older middle-aged patients, and the mean age of the study group is 74. The cohort is approximately 45% female with 75% of subjects self-identified as EA and 24% as AA. Subjects were consented in person by study personnel following a routine clinic visit and an introduction to the study staff by their doctor. VUMC is a comprehensive health care facility dedicated to patient care, research, and the education of health care professionals. Translational research into the causes and treatment of disease as well as studying fundamental biological properties is the primary focus of discovery at Vanderbilt. Clinical research is conducted in Vanderbilt University Hospital, the Nashville Veterans Administration Hospital, Meharry General Hospital and in their associated outpatient clinics. These hospitals and clinics, all associated with the Vanderbilt system, each have full time Vanderbilt faculty and medical housestaff and provide clinical care and participate in research programs. The Vanderbilt Clinic is comprised of more than 95 adult outpatient specialty practices and received over 1.5 million ambulatory visits in 2012-13. The Vanderbilt Heart and Vascular Institute offers a comprehensive heart program offering diagnosis, medical treatment, minimally invasive therapies, surgical intervention and disease management, tailored to each individual's unique needs. All programs within the Vanderbilt Clinic have survival figures that surpass the national average.

  Study phs000906

• Strabismus, CCDD and other anomalies

  Strabismus is one of the most common ophthalmological diseases affecting our population. It has many detrimental effects for affected individuals, including functional visual loss from amblyopia, secondary psychosocial difficulties, and limited employment opportunities. Strabismus also imposes a significant economical burden on society from the cost of screening programs, surgical correction, and lost productivity. Congenital incomitant strabismus is a broad term that encompasses congenital ocular motility disorders with restricted movement in one or more directions of gaze, and includes various forms of Duane's retraction syndrome, horizontal gaze palsy, congenital fibrosis of extraocular muscles, and Moebius syndrome. Incomitant strabismus together with a group of related disorders such as congenital ptosis and congenital facial palsy have been collectively redefined as congenital cranial dysinnervation disorders (CCDDs). This redefinition stemmed from previous studies in the Engle lab showing that these disorders can be caused by mutations in genes encoding transcription factors critical to ocular cranial motor neuron development and by genes that encode proteins essential to the proper growth and guidance of developing axons. Thus, while these disorders account for only a small percent of strabismus, understanding their genetic etiology has led to our ability to provide genetic testing and counseling, and has provided the first insights into the molecular etiologies of strabismus. While the Engle lab has identified all seven CCDD genes published to date, our cohort still includes >100 familial CCDD pedigrees that are mutation-negative. We continue to utilize co-segregation analysis and whole exome or whole genome sequencing technologies to identify new disease genes in this mutation-negative cohort.

  Study phs000478

• Juvenile Sjogren's Syndrome (JSS) Transcriptome Study

  Sjögren's disease (SjD) is a systemic autoimmune disorder with major clinical manifestations of dryness in the eyes and mouth, fatigue, and joint pain. Our interdisciplinary experts in SjD at the University of Florida newly discovered and established a cohort of SjD in children. Many of these patients with childhood SjD (cSjD) demonstrated a prominent feature of recurrent parotitis (RP). To understand the immunopathogenesis of childhood SjD (cSjD) for the first time in the field, we profiled the transcriptome of five groups of four PBMC samples by unbiased, high-throughput single-cell RNA sequencing (scRNA-seq): cSjD (n=4), non-cSjD (n=4), biopsy-positive non-cSjD (BP, n=4), biopsy-positive non-cSjD with RP (BPRP, n=4), and healthy controls (HC, n=4). In this study, RNA sequencing data, as well as all barcodes.tsv.gz, features.tsv.gz, and matrix.mtx.gz that may be used to create a Seurat object in R, will be availableTo protect the confidentiality of subjects, certain data were not assessed or collected in the subject phenotype dataset, such as race, age, height, weight, and education.

  Study phs003048

• Targeted sequencing of genes recurrently mutated in AML

  Background Massively parallel sequencing technology has transformed cancer genomics. It is now feasible, in a clinically relevant time-frame, for a clinically manageable cost, to screen DNA from patient tumours for mutations essentially genome-wide. The challenge for personalised medicine will be to increase the sample size to thousands or tens of thousands of well-characterised cases in order to attain sufficient statistical power to stratify patients accurately across the complexity and genomic heterogeneity expected for most of the common tumour types. Currently, whole genome sequencing on this scale is not feasible, and targeted sequencing of relevant portions of the genome will be required. Pilot data We have developed protocols for large-scale, multiplexed sequencing of 100-200 genes in thousands of samples. Essentially, using robotic technology, genomic DNA from the cancer specimen is processed into sequencing libraries with unique DNA barcodes, thereby allowing sequencing reads to be attributed to the sample they derive from. Currently, these sequencing libraries can be generated in a 96-well format using fully automated protocols, and we are exploring methods to expand this to a 384-well format. The sequencing libraries are pooled and hybridized to custom sets of RNA baits representing the genomic regions of interest. Sequencing of the pulled-down libraries is done in pools of 48-96 samples per lane of an Illumina Hi-Seq. This protocol is already implemented at the Sanger Institute. We have published proof that somatic mutations in novel cancer genes can be identified from exome-wide sequencing. In unpublished pilot data, we have established the feasibility of robotic library production, custom pull-down, and multiplexed sequencing of barcoded libraries for 100 known myeloid cancer genes across 760 myelodysplasia samples. Highlights of the data thus far analysed reveal that the coverage is remarkably even between samples; when 96 samples are run, average coverage per lane of sequencing is ~250, with 90-95% of targeted exons covered by >25 reads; known mutations can be discovered in the data set; and the protocol is amenable to whole genome amplified DNA. The bioinformatic algorithms for identification of substitutions and indels in pull-down data are well-established; we have pilot data proving that copy number changes, LOH and genomic rearrangements in specific regions of interest can also be identified by tiling of baits across the relevant loci. Proposal We propose to apply this methodology to 10000 samples from patients with AML enrolled in clinical trials over the last 10-20 years. Oncogenic point mutations and potentially genomic rearrangements will be identified, and linked to clinical outcome data, with a view to undertaking the following sorts of analyses: ? Identification of co-occurrence, mutual exclusivity and clusters of driver mutations. ? Correlation of prognosis with driver mutations and potentially gene-gene interactions ? Exploration of genomic markers of drug response Ultimately, we would like to be in a position to release the mutation data together with matched clinical outcome data to genuine medical researchers via a controlled access approach, possibly within the COSMIC framework (www.sanger.ac.uk/genetics/CGP/cosmic/). The vision here is to generate a portal whereby a clinician faced with an AML patient and his / her mutational profile can obtain a ?personalised? prediction of outcome, together with a fair assessment of the uncertainty of the estimate. With a sufficient sample size, there would also be the potential to develop decision support algorithms for therapeutic choices based on such data.

  Dataset EGAD00001000606

• PanCuRx Translational Research Initiative

  The goal of the PanCuRx TRI is to seek solutions to the high fatality rate of pancreatic adenocarcinoma (PDAC), the most common type of pancreatic cancer, by generating new knowledge about the genetics and biology of the disease, mechanisms of how tumours grow and tailored treatment options. PDAC is the fourth leading cause of cancer death in Canada and is expected to become the second-leading cause of cancer death within the next decade. The current five-year survival rate of eight per cent is the lowest of all epithelial cancers. There are major clinical challenges to treating the disease, including the fact that PDAC spreads to other parts of the body early so surgical removal of the tumour benefits few patients, that PDAC has proven relatively resistant to systemic therapies such as chemotherapy and that there has been limited benefit from the use of newer molecular targeted agents. The PanCuRx team has created the world’s first resource of tumour-purified PDAC whole genomes; more than 500 PDAC samples, with combined clinical annotation, have been successfully sequenced and analyzed at the Ontario Institute for Cancer research in Toronto. The resulting clinical genomic resource created from this data has led to multiple new findings that have established a framework to better understand PDAC biology and lays the foundation for new genomic approaches to better understanding the disease. The team also launched a clinical trial called COMPASS (Comprehensive Molecular Characterization of Advanced Ductal Pancreas Adenocarcinoma for Better Treatment Selection: A Prospective Study), which is recruiting patients with advanced PDAC (both locally advanced and metastatic) at The Princess Margaret Cancer Centre (PM) in Toronto. The study uses the molecular results from individual patient tumours to guide selection of better second line treatment on a case-by-case basis. The primary objective of the initial study was to assess the feasibility of collecting tumour materials from patients with advanced PDAC and delivering results within 56 days of biopsy. Having achieved this objective, the trial has expanded to other cancer centres across Canada. Pancreatic cancer is one of the most deadly forms of cancer and outcomes have not improved in four decades. The PanCuRx initiative is providing a better understanding of the disease and actively applying new knowledge to cancer patients through clinical trials. This knowledge will help to improve quality of life and prolong survival for pancreatic cancer patients worldwide.

  Study EGAS00001002543

• Whole genome sequencing of 198 epileptic individuals.

  This dataset includes whole genome sequencing of 198 epileptic individuals. Libraries preparation and whole-genome sequencing: gDNA was cleaned up using ZR-96 DNA Clean & ConcentratorTM-5 Kit (Zymo) prior to being quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit (Life Technologies) and its integrity assessed on agarose gels. Libraries were generated using the TruSeq DNA PCR-Free Library Preparation Kit (Illumina) according to the manufacturer’s recommendations. Libraries were quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit (Life Technologies) and the Kapa Illumina GA with Revised Primers-SYBR Fast Universal kit (Kapa Biosystems). Average size fragment was determined using a LabChip GX (PerkinElmer) instrument. The libraries were denatured in 0.05N NaOH and diluted to 8pM using HT1 buffer. The clustering was done on a Illumina cBot and the flowcell was ran on a HiSeq 2500 for 2x125 cycles (paired-end mode) using v4 chemistry and following the manufacturer's instructions. A phiX library was used as a control and mixed with libraries at 0.01 level. Bioinformatics: The Illumina control software was HCS 2.2.58, the real-time analysis program was RTA v. 1.18.64. Program bcl2fastq v1.8.4 was used to demultiplex samples and generate fastq reads. The filtered reads were aligned to reference Homo_sapiens assembly b37. Each readset was aligned to creates a Binary Alignment Map file (.bam).

  Dataset EGAD00001004062

• A non-canonical lymphoblast in refractory childhood T-cell leukaemia

  Refractory cancers may arise either through the acquisition of resistance mechanisms or represent distinct disease states. The origin of childhood T-cell acute lymphoblastic leukaemia (T-ALL) that does not respond to initial treatment, i.e. refractory disease, is unknown. Refractory T-ALL carries a poor prognosis and cannot be predicted at diagnosis. Here, we perform single cell mRNA sequencing of T-ALL from 58 children (84 samples) who did, or did not respond to initial treatment. We identify a transcriptionally distinctive blast population, exhibiting features of innate-like lymphocytes, as the major source of refractory disease. Evidence of such blasts at diagnosis heralds refractory disease across independent datasets and is associated with survival in a large, contemporary trial cohort. Our findings portray refractory T-ALL as a distinct disease with the potential for immediate clinical utility. This dataset contains 56 single-cell RNA sequencing data files with corresponding single-cell T cell receptor sequencing data files. The data here form the validation cohort in our paper.

  Dataset EGAD00001015673

• Proteogenomic discovery of a novel class of cancer antigens by HLA ligandome analysis of colon cancer tissues

  CD8+ T cells recognize peptides displayed by HLA class I molecules on cell surfaces, monitoring pathological conditions such as cancer. Recent advances in proteogenomic analysis of HLA ligandomes have demonstrated that cells present a subset of cryptic peptides derived from non-coding regions of the genome; however, the roles of cryptic HLA ligands in tumor immunity remain unknown. In the present study, we comprehensively and quantitatively investigated the HLA class I ligandome of a set of human colorectal cancer (CRC) and matched normal tissues, showing that cryptic translation products account for a small fraction of the HLA class I ligandome and serve as antigens for patient T cell surveillance against colon cancer.

  Study JGAS000280

• Single-cell ATAC-seq analysis for COVID19 patients

  While SARS-CoV-2 infection causes mild respiratory disease in most individuals, a small group of patients develops severe COVID-19. Dysfunctional innate immune responses have been identified to contribute to differences in COVID-19 severity, but the key regulators are still unknown. Here, we present an integrative single-cell epigenetics, transcriptomic, and genetics analysis of peripheral blood mononuclear cells from hospitalized and convalescent COVID-19 patients. In classical monocytes, we identified 41.3% of significantly up-regulated genes in hospitalized COVID-19 patients potentially induced by differential chromatin accessibility. Sub-clustering and motif-enrichment analyses of monocytes reveal disease condition-specific regulation by transcription factors, such as C/EBPs and SPI1, and their targets, including a long-noncoding RNA LUCAT1, which further regulates interferon responses and is associated with the need for oxygen supply of COVID-19 patients. The interaction between C/EBPs and LUCAT1 was validated through loss-of-function experiments. Finally, we investigated genetic risk variants that exhibit allele-specific open chromatin (ASoC) in promoters/enhancers of COVID-19 patients. Integrating our data with publicly available expression quantitative trait loci and chromosomal interactions indicates that ASoC SNP rs6800484-C is associated with lower expression of CCR2, which may contributeto higher viral loads in lungs and higher risk of COVID-19 hospitalization. Altogether, our study highlights the diverse genetic and epigenetic regulators that contribute to the innate immune responses of different COVID-19 patients.

  Study EGAS00001006559

• Single-cell RNA-seq analysis for COVID19 patients

  While SARS-CoV-2 infection causes mild respiratory disease in most individuals, a small group of patients develops severe COVID-19. Dysfunctional innate immune responses have been identified to contribute to differences in COVID-19 severity, but the key regulators are still unknown. Here, we present an integrative single-cell epigenetics, transcriptomic, and genetics analysis of peripheral blood mononuclear cells from hospitalized and convalescent COVID-19 patients. In classical monocytes, we identified 41.3% of significantly up-regulated genes in hospitalized COVID-19 patients potentially induced by differential chromatin accessibility. Sub-clustering and motif-enrichment analyses of monocytes reveal disease condition-specific regulation by transcription factors, such as C/EBPs and SPI1, and their targets, including a long-noncoding RNA LUCAT1, which further regulates interferon responses and is associated with the need for oxygen supply of COVID-19 patients. The interaction between C/EBPs and LUCAT1 was validated through loss-of-function experiments. Finally, we investigated genetic risk variants that exhibit allele-specific open chromatin (ASoC) in promoters/enhancers of COVID-19 patients. Integrating our data with publicly available expression quantitative trait loci and chromosomal interactions indicates that ASoC SNP rs6800484-C is associated with lower expression of CCR2, which may contributeto higher viral loads in lungs and higher risk of COVID-19 hospitalization. Altogether, our study highlights the diverse genetic and epigenetic regulators that contribute to the innate immune responses of different COVID-19 patients.

  Study EGAS00001006560

• All you need to know about the new Submitter Portal

  In September we launched new services for all EGA users, including a new version of the former Submitter Portal. Our main objective with this Portal is to transform the metadata submission in a simplified and user-friendly method. Although it is possible to find documentation about the Submitter Portal on our website, we wanted to highlight the new features in this article. Enjoy! Credentials: who needs to create a new user to access the Submitter Portal and who doesn't? Users with an old submission account (ega-box) can already access the new Submitter Portal. If it is the first time that you want to access, it is time to request a submitter role linked to the EGA User. In the form, it is mandatory to explain which type of data you want to deposit, the size of your submission, add any comment you’d like us to read, and manage the EGA Data Processing Agreement (DPA) document. This document should be checked by the legal department of your institution and signed by a person with authority to sign contracts on behalf of the institution. Note that we provide a DPA template for new Users; it can be found and downloaded in the Submitter Request section. With the DPA signed and the Submitter Request sent, it is time to wait for our Helpdesk team to validate the request. Metadata submission now can be collaborative It is possible to add collaborator(s) to your submissions. Only registered EGA Users will appear on the search bar. When adding a collaborator, it is mandatory to define the permission granted: Read only: collaborators with this permission can check if the information of the submission is correct but cannot modify anything. Read & write: collaborators with this label can register new objects and modify the registered ones. We wanted to save time for submitters that usually rely on the same collaborators. For that, we have implemented the possibility of adding the same collaborators from previous submissions; all you need to do is to search the submission, and the exact set of EGA users will have access to your submission. You can check the collaborators and their permissions at any point of the submission by going to the collaborators section. Even adding more people to complete the submission is allowed by following the same steps: looking for the EGA User and defining the permission granted (please, note that during the submission it is not possible to add collaborators from previous submissions). Use external links to complement the study information When filling the information about the study we recommend users to enrich the submission with complementary fields such as PubMeds IDs, custom tags and external links. External links are used to connect a study to an external repository. We have implemented an external link to Euro-Bioimaging for a European Project (EuCanImage). Now, we are looking forward to keeping increasing the list of external repositories! Choose the expected release date for your metadata to be searchable on our website Once all the metadata objects are registered it is time to finalise the submission so that the Helpdesk team can approve it. In the new version of the Submitter Portal users can choose the expected release date for the metadata to be searchable on the EGA website. Please, note that the selected date will have a minimum of one week margin from the day of finalisation. This time is necessary for your files to be archived and for the submission to be validated by the Helpdesk Team. Are you looking for the Array submission within the Submitter Portal? Our Submitter Portal does not support array submissions. The submission of Array-based metadata must be done using the EGA programmatic submission. You will find our Array-based format template and all the necessary information on our website, and you can find all the steps in our submission quickguide! More information about the submission process at the EGA Our Team has developed an extended documentation related to metadata submission. A good first step is to take a closer look at the Submission FAQ. To have a general overview of all the metadata submission process we recommend taking a glimpse of our take-the-tour or watching the tutorial available on our YouTube Channel. Don't miss our section on EGA Schemas, to learn more about how the EGA is built. It can be useful to fully ensure that your submissions comply with the EGA's standards and contribute to a valuable and accessible genomic resource.

  Blog new-submitter-portal

• Genetic Analysis of Limb Malformation Disorders: Miller Syndrome Sequencing Study (LMD-MS)

  The overall goal of this project is to investigate the etiology and pathogenesis of malformations (i.e., birth defects) of the limb, concentrating on abnormalities of limb patterning such as limb deficiency/duplications and multiple congenital contractures. The exome sequences of two siblings and two unrelated individuals were obtained by massively parallel DNA sequencing. The four individuals were affected with Miller syndrome (OMIM: 263750). Additionally, the whole-genome sequences of a family of four were obtained with the method of Complete Genomics Incorporated (CGI). The two offspring were both affected with Miller syndrome and is the same sibling pair mentioned previously from whom exome sequences were also obtained.

  Study phs000244

• Similarity and diversity of the tumor microenvironment in multiple metastases: critical implications for overall and progression-free survival of high-grade serous ovarian cancer.

  The tumor microenvironment is pivotal in influencing cancer progression and metastasis. Different cells co-exist with high spatial diversity within a patient, yet their combinatorial effects are poorly understood. We investigate the similarity of the tumor microenvironment of 192 local metastatic lesions in 61 ovarian cancer patients. An ecologically inspired measure of microenvironmental diversity derived from multiple metastasis sites is correlated with clinicopathological characteristics and prognostic outcome. We demonstrate a high accuracy of our automated analysis across multiple sites. A low level of similarity in microenvironmental composition is observed between ovary tumor and corresponding local metastases (stromal ratio r = 0.30, lymphocyte ratio r = 0.37). We identify a new measure of microenvironmental diversity derived from Shannon entropy that is highly predictive of poor overall (p = 0.002, HR = 3.18, 95% CI = 1.51-6.68) and progression-free survival (p = 0.0036, HR = 2.83, 95% CI = 1.41-5.7), independent of and stronger than clinical variables, subtype stratifications based on single cell types alone and number of sites. Although stromal influence in ovary tumors is known to have significant clinical implications, our findings reveal an even stronger impact orchestrated by diverse cell types. Quantitative histology-based measures can further enable objective selection of patients who are in urgent need of new therapeutic strategies such as combinatorial treatments targeting heterogeneous tumor microenvironment.

  Study EGAS00001002065

• Genome-Wide Association Study of Hypertriglyceridemia in Mexicans

  Coronary artery disease (CAD) is the number one cause of death and mortality world-wide. High levels of serum triglycerides (TGs) and low levels of serum high-density lipoprotein cholesterol (HDL-C) are major risk factors for CAD. Previous epidemiological studies have shown that these two lipid disturbances are highly common dyslipidemias in Mexicans. However, the genetic factors underlying high serum TGs and low HDL-C are underinvestigated and poorly identified in Mexicans. As the Mexican-American and the genetically related Latin-American populations represent the fastest growing minority in the United States, elucidation of the unknown genetic factors influencing the increased susceptibility of Mexicans to these common dyslipidemias is of great relevance to these U.S. minorities and the American healthcare system. This study is a research collaboration between investigators at the University of California, Los Angeles (UCLA) and Instituto Nacional de Ciencias Médicas y Nutrición (INCMN), Mexico City, to perform a two-stage genome-wide association study (GWAS) for hypertriglyceridemia and low high-density lipoprotein cholesterol (HDL-C) in Mexicans (Weissglas-Volkov et al. 2013, PMID: 23505323). This study was funded by an RO1 grant from NIH/NHLBI (PI Paivi Pajukanta; PI of the subaward Teresa Tusie-Luna). In stage 1 of the GWAS, we tested all GWAS SNPs from the Illumina Infinium Human610-Quad platform that passed quality control for association in ~2,200 hypertriglyceridemia cases and controls. In stage 2, we genotyped the ~1,200 positive signals of stage 1 in additional ~2,200 hypertriglyceridemia cases and controls, and performed a combined analysis of the two stages to identify genome-wide significant variants. The subjects were recruited at the INCMN. The DNA isolation and clinical laboratory measurements of the case-control study samples were performed at INCMN using standardized procedures. Genotyping and statistical analyses of the data were performed at UCLA. A summary of this GWAS can be found in Weissglas-Volkov et al. 2013 Journal of Medical Genetics, PMID: 23505323. Briefly, using the two-stage GWAS design, we identified a novel Mexican-specific genome-wide significant locus for serum TG levels close to the Niemann-Pick type C1 protein (NPC1) gene (Weissglas-Volkov et al. 2013, PMID: 23505323). Of the European lipid GWAS loci, three TG loci (APOA5, GCKR, and LPL) and four HDL-C loci (ABCA1, CETP, LIPC and LOC55908) resulted in genome-wide significant signals in Mexicans. Furthermore, our cross-ethnic mapping narrowed three European TG GWAS loci, APOA5, MLXIPL, and CILP2 that exhibited long range LD and a large number of candidate SNPs in the European GWAS scan. In the apolipoprotein A1C3A4A5 gene cluster region, the cross-ethnic fine mapping and LD comparisons reduced the number of candidate variants to one SNP, rs964184. We also found that although 52% of the associations from European lipid GWAS meta-analysis could be generalized to Mexicans, in 82% of the European GWAS loci, a different variant other than the European lead SNP provided the statistically strongest evidence of association in the Mexican scan (Weissglas-Volkov et al. 2013, PMID: 23505323). Taken together, our Mexican GWAS for lipids identified a novel Mexican-specific locus for high serum TGs; restricted three European GWAS loci; and investigated which European lipid GWAS variants extend to the Mexican population (Weissglas-Volkov et al. 2013, PMID: 23505323). This NHLBI sponsored RO1 project aimed to identify genetic variants contributing to serum lipid levels in Mexicans. In addition to de-identified genome wide genotypic data, a selected list of de-identified phenotypic data related to hypertriglyceridemia and related metabolic traits are also available in dbGaP.

  Study phs000618

• Multi-modal single-cell analysis of salivary glands from patients with Sjogren's syndrome collaboration

  Sjogren's syndrome (SS) is an autoimmune disease that causes salivary gland dysfunction due to immune-mediated destruction. While autoantibodies such as anti-SSA and anti-centromere (CENT) are associated with distinct clinical manifestations, the underlying mechanisms remain to be elucidated. In this study, we apply multi-modal single-cell technologies-single-cell RNA sequencing, T- and B-cell receptor sequencing, and spatial transcriptomics-to salivary gland lesions, aiming to uncover common and unique cellular and transcriptional signatures linked to different autoantibody profiles.

  Study JGAS000773

• Chromosome contacts in activated T cells identify autoimmune disease-candidate genes

  Autoimmune disease-associated DNA variants are preferentially found in regions with putative regulatory function in immune cells, particularly CD4+ T cells. Linking such regulatory elements to gene promoters in disease-relevant cells and contexts is essential to identify disease candidate genes. Here we show that the activation of CD4+ T cells invokes changes in the activity of regulatory elements and transcription of RNAs that correspond to changes in the expression of their interacting genes identified by promoter capture Hi-C (PCHi-C).

  Study EGAS00001001961