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• Integrated genomic characterization of adrenocortical carcinoma

  Adrenocortical carcinomas (ACC) are aggressive cancers originating in the cortex of the adrenal glands. Despite the overall poor prognosis, ACC outcome is heterogeneous. CTNNB1 and TP53 mutations are frequent in these tumors, but the complete spectrum of genetic changes remains undefined. Exome sequencing and SNP array analysis of 45 ACC revealed recurrent alterations in known drivers (CTNNB1, TP53, CDKN2A, RB1, MEN1) and genes not previously reported to be altered in ACC (ZNRF3, DAXX, TERT and MED12), which were validated in an independent cohort of 77 ACC. The cell-surface transmembrane E3 ubiquitin ligase ZNRF36 was the gene the most frequently altered (21%), and appears as a potential novel tumor suppressor gene related to the ß-catenin pathway.Our integrated genomic analyses led to the identification of two distinct molecular subgroups with opposite outcome. The C1A group of poor outcome ACC was characterized by numerous mutations and DNA methylation alterations, whereas the C1B group with good prognosis displayed a specific deregulation of two miRNA clusters. Thus, aggressive and indolent ACC correspond to two distinct molecular entities, driven by different oncogenic alterations.

  Dataset EGAD00001000764

• Mechanisms of patient response to Dabrafenib in Melanoma

  Samples will be from the BRF113683 (BREAK-3) study which is a Phase III Randomized, Open-label Study Comparing GSK2118436 to Dacarbazine (DTIC) in Previously Untreated Subjects With BRAF Mutation Positive Advanced (Stage III) or Metastatic (Stage IV) Melanoma (n=250 enrolled)*NGS [Agilent capture (Sanger V2 panel): 360 genes and 20 gene fusions; Illumina HiSEQ Sequencing]*CNV: [via NGS or Affy SNP 6.0 or Illumina Omni (TBD)]Bioinformatics: Analysis will be performed using core Sanger informatics pipelines similar to those previously described (Papaemmanuil E et al. (2013) Blood. 22:3616 -3627). Briefly, copy number analysis will be performed using the ASCAT algorithm, and base substitutions, small insertions and deletions using the CAVEMAN and Pindel algorithms, respectively. Statistical approaches including generalized linear models will be used to predict clinical variables such as maximum clinical response and duration of response using genetic data. Sanger and EBI to conduct analysis; Raw data and correlation with clinical endpoints to be analyzed by both EBI/Sanger and GSK (unique pipeline analyses to increase call confidence)

  Dataset EGAD00001001375

• Somatic mutations and clonal dynamics in healthy and cirrhotic human liver

  Chronic liver injury predisposes to cirrhosis and hepatocellular carcinoma, but how somatic mutations accumulate in liver disease is unexplored. We sequenced whole genomes of 400 microdissections of 100-500 hepatocytes from 5 normal and 6 cirrhotic livers. Compared to normal liver, cirrhotic liver had higher mutation burden, especially structural variants, including chromothripsis. Cirrhotic nodules were oligoclonal; sometimes entirely derived from a single, recent common ancestor. Clonal expansions millimeters in diameter occurred in cirrhosis in the absence of known driver mutations. Endogenous mutational processes predominated, although signatures of polycyclic aromatic hydrocarbon and aristolochic acid exposure occurred in some samples. Up to 10-fold within-patient variation in activity of exogenous signatures existed between adjacent cirrhotic nodules, with both clone-specific and microenvironmental forces shaping this heterogeneity. Synchronous hepatocellular carcinomas drew from the same repertoire of mutational signatures as background cirrhotic liver, but with higher burden. Somatic mutations chronicle the exposures, toxicity, regeneration and clonal structure of liver tissue as it progresses from health to disease.

  Dataset EGAD00001004578

• Download Metadata

  Metadata Distribution Welcome to the realm of Metadata Distribution within the EGA ecosystem! Our Metadata REST API empowers you to effortlessly retrieve metadata from the expansive landscape of EGA. By utilising this API, you gain access to publicly available insights across various EGA domains, including studies, samples, experiments, runs, analyses, policies, DACs, and datasets. Furthermore, this API facilitates cross-referencing of objects, enabling you to gather, for example, all the datasets associated with a specific DAC, seamlessly. In addition, we have added the ability to query private data using the metadata API. If you possess the necessary permissions, you can access behind-the-login private data for a specified list of datasets. Metadata Distribution Index Identifiers Dataset Mappings Website Download Metadata API - Private Identifiers At the core of EGA's organisational structure are unique accessions that serve as essential tags for our diverse objects. Here's a quick overview of the accessions and their corresponding object types: EGA Accession ID EGA Object description EGAS EGA Study Accession ID EGAC EGA DAC Accession ID EGAP EGA Policy Accession ID EGAN EGA Sample Accession ID EGAR EGA Run Accession ID EGAX EGA Experiment ID EGAZ EGA Analysis Accession ID EGAD EGA Dataset Accession ID EGAB EGA Submission ID EGAF EGA File Unique Accession ID For further information check our metadata schema documentation. Dataset Mappings For authorised datasets, comprehensive mappings reveal meaningful connections: Sample_file: This file presents information about the linkage between samples and files available in the dataset. Study_experiment_run_sample: This file presents information about the linkage between studies, experiments, runs, and samples within the dataset. Study_analysis_sample: This file presents information about the linkage between studies, analyses, and samples contained within the dataset. Run_sample: This file presents information about the linkage between runs and samples within the dataset. Analysis_sample: This file presents information about the linkage between analyses and samples within the dataset. An empty file indicates the absence of corresponding information. Website Download Our website serves as your gateway to downloading metadata. Simply navigate to the dataset page, and you'll find a blue Metadata button. Once authenticated, you can click this button for authorised datasets. If you lack permissions for a particular dataset, request access by clicking the 'Request Access' button. For authorised datasets, choose your preferred metadata format: CSV, TSV, or JSON. Metadata API - Private Leverage the power of programmatic metadata downloads! Start by authenticating yourself with your credentials to obtain an access token. With this token, programmatically query private information. Queries mirror the structure of the Public Metadata API. However, behind the login, you can delve into specific mapping information (as mentioned above in dataset mappings) alongside object-level exploration. Authentication An active session is required to work with the API. Each time you log in with your credentials a new session is started, which is identified by an access_token. Below an example on how to obtain one using curl: curl https://idp.ega-archive.org/realms/EGA/protocol/openid-connect/token \ -d 'client_id=metadata-api' \ -d 'username=...' \ --data-urlencode 'password=...' \ -d 'grant_type=password' All responses from the API are in JSON format. A successful response should include a new token to be used for the session: {"access_token":"eyJhbGciOiJSUzI1NiIsInR5cCIgOiA...TNw", "expires_in":300, "refresh_expires_in":1800, "refresh_token":"eyJhbGciOiJIUzI1NiIsInR5cCIgOiA...pTX10", "token_type":"Bearer", ... } Save the access_token value and include it in the API call headers. Example query usage Below you can find some example of queries available behing authentication and authorisation. Querying study-experiment-run-sample mappings: curl https://metadata.ega-archive.org/datasets/{datasetID}/mappings/study_experiment_run_sample \ -H 'Authorization: Bearer access_token' Querying run-sample mappings: curl https://metadata.ega-archive.org/datasets/{datasetID}/mappings/run_sample \ -H 'Authorization: Bearer access_token' Querying study-analysis-sample mappings: curl https://metadata.ega-archive.org/datasets/{datasetID}/mappings/study_analysis_sample \ -H 'Authorization: Bearer access_token' Querying analysis-sample mappings: curl https://metadata.ega-archive.org/datasets/{datasetID}/mappings/analysis_sample \ -H 'Authorization: Bearer access_token' Querying sample-file mappings: curl https://metadata.ega-archive.org/datasets/{datasetID}/mappings/sample_file \ -H 'Authorization: Bearer access_token' You can get a different output format by adding one of these options to the curl command: -H 'Accept: text/tsv' -H 'Accept: application/json' -H 'Accept: text/csv' For more detailed information, refer to the Metadata API Specification.

  Documentation access/download/metadata

• Demographically Diverse Substance Use Disorder Cohorts of Dr. Stanley H. Weiss

  The Demographically Diverse Substance Use Disorder Cohorts of Dr. Stanley H. Weiss, which constitute the Epidemiology of the Weiss Cohort Projects, consist of a series of inter-connected projects, building upon a set of cohort projects of various groups, mainly drug users from medication-assisted treatment programs, that Dr. Stanley H. Weiss first developed in the 1980’s plus several newer initiatives, each with an array of collaborators. Beginning in the 1980’s, Dr. Stanley H. Weiss started several long-term studies of persons who inject drugs (PWID) across the United States, ultimately enrolling over 10,000 participants through the early 1990’s with an average age then in their 30’s. About a quarter were enrolled from sites in New Jersey (NJ). These studies included the first testing of PWID for the human immunodeficiency virus (HIV) and the human T-cell lymphotropic viruses (HTLV I and HTLV II). Cumulative past support (initiation thru ~ 1999) for these cohort studies included ~ $20 million from intramural resources from the National Cancer Institute (NCI) and the National Institute on Drug Abuse (NIDA), plus multiple grants and in-kind support from the New Jersey Department of Health (NJDOH) totaling ~ $1 million. The Weiss Cohort Projects include the first large AIDS-era cohorts to include women at high risk for HIV. A high percentage of subjects in these studies are black or Latino. Thus, this is an ethnically diverse US cohort, with a high proportion of women included. These subjects are at high risk of parenteral and sexual infection from both drug use and sexual practices. Samples from other studies conducted by Dr. Weiss, in which detailed interviews were conducted, are included as controls (persons documented by us not to have a history of opioid drug use). As one of our groups of subjects have many persons of Haitian ancestry, we specifically included some Haitians who had never used opioids as controls. Our documentation includes such ancestry. These cohorts demonstrated high rates of HIV and HTLV-II infection in PWIDs, including one study initiated in 1981 with confirmation in the later cohorts. In the first two decades of these studies, among numerous publications was the first study showing a very high rate of hepatitis C infection among PWIDs. An example of how the studies’ long-time horizon proved essential was that it first became possible to test whether a person had ever been infected with hepatitis C virus (HCV), as well as how much HCV was in each person’s blood, many years after the specimens were collected. This allowed HCV amounts in blood to be compared for subjects who had died of liver disease early in the study versus those who survived. Then a sequence of published papers culminated in demonstrating, using a nested case-control design, that a high baseline HCV titer was predictive of early progression to death from end-stage liver failure. Outcomes related to HCV (end stage liver disease and hepatocellular carcinoma) remain under study. In the original cohort studies, the mean age at enrollment was ~ 33 years old, so that those still alive in 2022 are mainly now ~ 60 - 75 years old. Many participants have already died. The tincture of time has led to subjects reaching ages when many more are dying from a wide array of outcomes, including from many chronic diseases (including cancer) as well as from infectious agents (especially HIV, HCV) or drug overdose. Renewed collaboration with local drug treatment programs has led to new field-based studies, including examination of some currently evolving problems among drug users. Dr. Weiss joined the National Institute on Drug Abuse (NIDA) Genetics Consortium (NGC) in 2017, and through the NIDA project officer has had access to NGC contract resources (see below). NIH Certificate of Confidentiality, CC-DA-16-214 (attached) protects these studies. Past arrangements related to data on our subjects leads to restrictions on the use of data emanating from our study, such as potential commercialization and restrictions on whom may access and use these data. NIDA Genetics Consortium (NGC) resources further support these endeavors and will be used as part of the NGC analyses studying the genetics of substance use. Study participants signed informed consent for the information collected from them to be used with no time limit and for biologic specimens collected from them to be used without restriction in future research. Serum samples were collected from participants, and from many also plasma, white blood cells and/or urine samples. About 100,000 vials were stored. All specimens have been continuously preserved at sufficiently cold temperatures to prevent deterioration, and many subjects separated white blood cells were processed and frozen in such a way as to maintain viability. Detailed data from the participants has been accumulated over time, and in general, linkage has been retained in each sub-study in accordance with the consent forms and protocols. For some participants, specimens were collected at multiple times (that is, sequential specimens). Multiple specimens from a single person exist in this database, and efforts at de-duplication remain ongoing. Dr. Weiss should be contacted if an investigator requires unique individuals since: • Multiple phases of enrollment occurred, and as our prospective follow-up continues; Dr. Weiss may identify new instances of multiple enrollment. • Some persons are related to each other. • In general, in this dataset for dbGaP, only a single specimen/record form a given person is included. Advances in laboratory testing techniques now permit innovative new uses for our linked research biospecimen repository. The ongoing focus of an interdisciplinary research program based on these cohorts relates subjects’ diseases, behaviors, medical history, and outcomes with biological and exposure markers. Participants’ use of various substances was ascertained on study enrollments, many serially over time. Quantitative frequency of use data, also sometimes sequential over time, were ascertained. Active ascertainment of outcomes is being conducted, including matching to mortality and cancer databases. Investigators interested in collaborations on specific outcomes (which is not part of this dbGaP dataset) or in the use of our stored specimens are encouraged to contact the principal investigator, Dr. Weiss. The processing of the genomic data was done in conjunction with NIDA, and in accordance with some longstanding data cleaning steps used by NIDA in the NIDA Genetics Consortium (NGC), a group to which we shall be contributing these data for collaborative analyses. Since there is the potential for these steps to introduce certain types of potential biases, we summarize these here. Under contract from NIDA, cryopreserved sera or plasma (-80 C) or cells (in liquid nitrogen) were used, with most stored having been stored for 30 to 40 years in our biorepository. In the case of serum or plasma, in which only (largely) cell-free DNA fragments were available, DNA was extracted and restored prior to amplification. Industry standard DNA amplification techniques were done on all samples prior to genotyping in accord with established protocols of the NIDA Genetics Consortium. Our genotype data were run and processed on the Illumina Infinium OmniExpress_v_1.3 array. This array has 714,238 SNPs, and was designed many years ago. There were 628 SNPs on the array that do not correspond to any chromosome position, and these were removed. Genotype data were submitted by NIDA’s contracted genotyping laboratory in six batches over time to NIDA’s contracted dbGaP data management group, which conducted quality control (QC) analyses. QC analysis included an assessment of batch effects on for five of the six batches. (One of the batches, with only 12 samples, was too small for QC analysis of batch effects.) Standard NIDA Genetic Consortium cleaning was performed. Samples with a call rate <.85 were removed. Only one sample per person was retained. When more than one specimen was genotyped from one subject, only the sample with the higher call rate was retained (provided, of course, that that call rate was ≥ 0.85). We have retained some people we know are related, including some found to have been related through genotyping; the pedigree file describes those relationships. In summary, key cleaning steps include: 1. Using PLINK to check gender discrepancy. 2. Using PREST-PLUS and KING (Kinship-based Inference for GWAS) to check relatedness. 3. Using PEDCHECK and PLINK to check/zero-out Mendelian error. 4. Using PLINK to perform sample QC, SNP QC, along with KING to perform chromosome X and chromosome Y QC. 5. SNP-QC: Batch-effect: 5 Batches were compared (one batch, with few samples, was not). These five batches were compared to each other in all ten possible pairs, one batch vs. another batch, examining SNP allele frequency discrepancies by population (from GRAF), Fisher Exact Allelic test, with the criterion of p<5e-8 for removal. 6. SNP-QC: discordant SNPs in QC duplicates. Compared 25 QC duplicated samples with call rate > 0.95, removed SNPs with 3+ discordance. 7. There were 1,056 SNPs that were monomorphic; these have been retained so they can be included in analyses in which our dbGaP data are combined with those from other cohorts (in the latter of which those SNPs may not be monomorphic). The final cleaned dataset submitted has 8,898 samples and 606,793 SNPs.

  Study phs002140

• Jackson Heart Study (JHS) Cohort

  The Jackson Heart Study (JHS) is a large, community-based, observational study whose participants were recruited from urban and rural areas of the three counties (Hinds, Madison and Rankin) that make up the Jackson, MS metropolitan statistical area (MSA). Participants were enrolled from each of 4 recruitment pools: (1) random, 17%, (2) volunteer, 30%, (3) currently enrolled in the Atherosclerosis Risk in Communities (ARIC) Study, 31%, and (4) secondary family members, 22%. The final cohort of 5,306 participants included 6.59% of all African American Jackson MSA residents, aged 35-84, during the baseline exam (N-76,426, US Census 2000). Among these, approximately 3,600 gave consent that allows genetic research and deposition of data into dbGaP. Major components of three clinic examinations (see study history) include medical history, physical examination, blood/urine analytes, and interview questions on areas such as physical activity, stress, coping and spirituality, racism and discrimination, socioeconomic position, and access to health care. Extensive clinical phenotyping includes anthropometrics, electrocardiography, carotid ultrasound, ankle-brachial blood pressure index, echocardiography, CT chest and abdomen for coronary and aortic calcification, liver fat, and subcutaneous and visceral fat measurements, and cardiac MRI. At 12-month intervals after the baseline clinic visit (Exam 1), participants have been contacted by telephone to update information, confirm vital statistics, document interim medical events, hospitalizations and functional status, and obtain additional sociocultural information. Questions about medical events, symptoms of cardiovascular disease and functional status are repeated annually. Ongoing cohort surveillance includes abstraction of medical records and death certificates for relevant International Classification of Diseases (ICD) codes and adjudication of nonfatal events and deaths. CMS data are currently being incorporated into the dataset. Note: Regarding date variables warehoused in the Jackson Heart Study (JHS) Cohort on dbGaP, the coordinating center has developed an algorithm that will systematically review and de-identify any of the (nearly 100) date-related variable types stored in the data package. To simultaneously minimize (i) de-identifiability of the data and (ii) impact on analyses utilizing sensitive data elements, a participant-level random number was generated to avoid the necessity of sharing any potentially sensitive data. The coordinating center maintains an archived linkage of these data in their raw form and regularly reviews ad hoc requests to utilize the raw data on a project-by-project basis. The JHS Cohort is utilized in the following dbGaP substudies. To view genotypes, other molecular data, and derived variables collected in these substudies, please click on the following substudies below or in the "Substudies" section of this top-level study page phs000286 JHS Cohort. phs000402 ESP Heart GO JHS phs000498 JHS Allelic Spectrum Seq phs000499 JHS CARephs001069 MI Gen JHS phs001098 T2D GENES Exome Seq phs001356 Exome Chip phs002256 Cardiometabolic Renal Proteomics phs002369 Metabolomics Insulin Resistance

  Study phs000286

• PREDICT-HD Huntington Disease Study

  The purpose of this project is to make clinical measurements from the PREDICT-HD consortium available through the dbGaP mechanism. The phenotype data will first be converted into a community open standard and subsequently exported to dbGaP for archival and open access distribution of the results of the studies. This will permit members of the scientific community to utilize a permanent resource for investigating the interactions of phenotypes upon an international cohort of early Huntington Disease. In version 2 cut of the data we provided HD CAG repeat lengths for both alleles as well as enrollment age of all participants. We have also generated unique identifiers prospectively compatible with the larger initiative GWAS in Huntington's Disease project (also on DbGaP). As such, the version 1 cut of the data was mainly proof of concept and should be deprecated. Going forward, all updates will add-on to version 2 cut of the data. In version 3 cut of the data, we provided baseline or the first usable MRI T1-weighted imaging analysis subcortical and cortical segmentations and cortical parcellations based on a customized Freesurfer 5.2 pipeline developed at The University of Iowa. The customizations to the standard pipeline were mainly to improve bias field correction and image normalization such that segmentation of gray, white, internal csf, dura and surface CSF are optimized for the Freesurfer pipeline. There are 1111 subjects with results in this data release. In version 4 cut of the data, we provided all longitudinal clinical measurements for all subjects (total of 1476) assessmented or enrolled through the end of 2013. Additionally, we are providing measurements on 39 baseline FDG PET images spatially normalized by SPM5 into MNI space, relative regional metabolic values computed in 120 volumes of interest (VOI) defined in the Automated Anatomical Labeling (AAL) Atlas (Tzourio-Mazoyer et al. 2002), and global metabolic values calculated by SPM standard mean voxel value (within per image fullmean/8 mask). This project is a funded ancillary study of PREDICT-HD. In version 5 cut of the data, we provided the first of many forthcoming results from ancilliary studies of PREDICT-HD. In this data cut, we provide individual subject results derived from structural MRI data. The earliest MRI session for each subject was used. The results summarized represent source based morphometry loading coefficients for 23 components (see: "Patterns of Co-Occurring Gray Matter Concentration Loss across the Huntington Disease Prodrome", Ciarochi et al., 2016, Front Neurol. 2016; 7: 147, Published online 2016 Sep 21. doi: 10.3389/fneur.2016.00147]. In this version 6 cut of the data, we provide a full set of derived data, more than 10,000 raw MRI images, and ancillary study data sets. For sample information please link to: PREDICT-HD Biospecimen Resources

  Study phs000222

• CIDR: NINDS High Throughput Genotyping Resource Access for Structural Hindbrain Disorders

  Recessive forms for many structural brain disorders (SBDs), including cerebellar hypoplasia (CBH), corpus callosum hypoplasia (CCH), cobblestone lissencephaly (COB), classical lissencephaly (LIS), microcephaly (MIC), and pontocerebellar hypoplasia (PCH), have been described. Patients usually present to the clinic with neurodevelopmental disorders (NDDs), including autism, epilepsy, intellectual disability, and cerebral palsy. Subsequent brain MRI usually leads to the diagnosis of an SBD. While a handful of genes that cause these diseases have been identified, most patients do not have mutations in these genes, suggesting that there is still much work to be done in identifying the genetic causes of these disorders. We have recruited a cohort of families with inherited SBDs, most from families with documented parental consanguinity with more than one affected member. Current approaches utilizing exome sequencing of SBD affected members still leave most cases unsolved, in part because there are too many possible variants to consider. Linkage analysis will allow us to extract all the useful genetic information from the family, taking advantage of each genetically informative meiosis in each family. We propose to analyze up to 100 SBD families for 500 samples per year for 4 years, through the CIDR SNP Genotyping Service, in parallel with already-funded exome sequencing of affects, leveraging off an already funded Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) effort to identify new causes of SBDs. Understanding the pathogenetic mechanisms underlying recessive pediatric SBDs will lead to better diagnostic and therapeutic opportunities for patients, and may shed light on complex NDDs. Ultimately, this work will identify new genetic disease loci to provide insight into pediatric SBD, with the hope to mitigate the impact of SBD worldwide.Public Health Significance: NDDs affect approximately 4-­6% of the general population, most notably children, and are evident in disorders, such as intellectual disability, epilepsy, and autism, with estimated yearly costs of $51.2B for intellectual disability, $11.5B for cerebral palsy, and $2.5B for visual impairment, making up >5% of total health care costs. Recessive NDDs impose enormous personal, social, and economic costs because of the early onset and the lifetime of medical dependence that often ensues. Genetic testing is critical to the diagnosis and treatment of these diseases. Many of the diseases under study have no treatment or cure. Structural brain diseases (SBDs) underlie a large percentage of these diseases, and are defined loosely as any condition in which defects in the structure or volume of a key brain component. SBDs are divided into major categories based upon the anatomical brain location. The major types involve the anatomical structures of the developing neural tube, forebrain, hindbrain, cerebral cortex, and those affecting overall brain size. For the purposes of this application, we have excluded SBDs resulting from environmental causes (i.e. prematurity-­induced), those below the brainstem, or those unlikely to be due to a germline or de novo mutation (i.e. focal SBDs or simplex disease).

  Study phs002621

• The Human Virome in Children and its Relationship to Febrile Illness

  Definition of the human microbiome is an important scientific priority. This study will expand the scope of the investigation to include viruses, which account for a substantial proportion of infectious disease morbidity and mortality, especially in children. The long-term goal of this project is to describe the human virome in children and to investigate its relevance to febrile illnesses in children. The project will also seek to understand the relationship of the immune system to the composition of the virome. Thus, the project's specific aims are 1) To elucidate the spectrum of viruses that can be detected using non-biased, high throughput sequencing on samples of blood, respiratory, and gastrointestinal secretions from healthy children and to use this information as a basis for understanding the role of viruses in acute febrile illnesses without an obvious source, and 2) to investigate the effect of various forms of immunosuppression on the spectrum of viruses detected in children, and to use this information as a basis for understanding the role of viruses in acute febrile illnesses occurring in these children. Our preliminary studies show that diverse viruses can be detected in children having undiagnosed fever. To carry out the specific aims, well children will be enrolled prior to having elective surgery, and febrile otherwise well children will be enrolled from the Emergency Department at St. Louis Children's Hospital. Immunocompromised children will be recruited from hematopoietic stem cell and solid organ transplant clinics, the HIV/AIDS clinic, and the rheumatology/immunology clinic from the same hospital. Children with fever will have samples obtained at the time of the febrile illness and at 1 and 6-month follow-up visits. Selected samples from each study group will be analyzed at the Genome Center at Washington University (GCWU) using next generation 454 high throughput sequencing to detect and sequence all viral sequences present. We anticipate detecting and sequencing a broad range of viruses, including previously unrecognized agents. A variety of techniques will be used to investigate the significance of viruses detected. Virus-specific PCR assays will be used to determine the frequency and extent of viruses detected by sequencing, using the full range of samples collected. Host response to the detected viruses will be investigated using serologic analysis, cytokine profiling, and microarrays to characterize host gene expression. These studies will take advantage of follow-up samples to compare the acute response with the response in the convalescent period. This study will draw upon the expertise and technological assets of one of the world's most powerful sequencing centers to provide the research community with a comprehensive sequence data base of the viruses that are present in children, which can be used to improve our understanding of the causes of febrile illnesses in young children, many of which are currently undiagnosed.

  Study phs000264

• Genome-Wide Association of Type 2 Diabetes in Africans: The AADM Study

  The Africa America Diabetes Mellitus (AADM) study is a genetic epidemiological study of type 2 diabetes in Sub-Saharan Africa. Study participants were enrolled through university medical centers in Nigeria, Ghana, and Kenya. Ethical approval for the study was obtained from the Institutional Review Board (IRB) of each participating institution. All subjects provided written informed consent for the collection of samples and subsequent analysis. The case definition of type 2 diabetes was based on the American Diabetes Association (ADA) criteria. After providing informed consent, participants underwent the same enrollment procedures, which included collection of demographic information, medical history, clinical examination and a blood draw. Genome-wide SNP genotyping was done on either the Axiom™ PanAFR SNP array (n=1,808) or the Multi-Ethnic Global Array (MEGA) (n=3,423). After appropriate quality control, in silico imputation was done using the African Genome Resources Haplotype Reference Panel (at the Sanger Imputation Service). Imputed genotypes were filtered for variants with minor allele frequency (MAF)≥ 0.01 and information score (info) ≥ 0.3 for genetic association analysis. Genome-wide association analysis between type 2 diabetes and the imputed genotype dosages was done using a generalized linear mixed model, which adjusted for age, gender, body mass index, the genetic relatedness matrix and the first three principal components (PCs) of the genotypes.Metabolomics profiling of plasma samples of type 2 diabetes (T2D) cases and controls in Nigerians (West Africa) was done in the AADM Study. Plasma metabolites were measured in a total of 580 individuals (N=310 for the discovery phase and N=270 for the replication stage) using the global/untargeted approach on the Metabolon platform and following the manufacturer's standard operation protocols. The analytic methods are described in detail in Doumatey et al. [Genome Med 2024]. The measured metabolites level represented by peak areas are relative values. The peak area data were batch-normalized to remove the instrument batch effects (batch variability) and the batch-normalized data correspond to the median-scaled raw data. For each identified metabolite, the minimum value across all batches in the batch-normalized was imputed for the missing values. The batch-normalized and imputed data are natural log-transformed and consist of 1116 metabolites for the discovery cohort and 1071 metabolites for the replication. Welch's two-sample t-test on the log-transformed data was used to identify metabolites differentially expressed between T2D cases and controls . All other statistical analyses conducted on both the replication and discovery cohorts used the log-transformed data. To merge the discovery and replication, the same quality control samples (bridge samples) were run with each batch of the experimental samples in both cohorts and used to correct for additional variability and uniformize the procedures. The resulting merged data is the QC-normalized and imputed data that contains only metabolites that were common to both cohorts and successfully bridged for all batches (n= 891 metabolites).

  Study phs001844