Search Results - EGA European Genome-Phenome Archive

Single cell eQTL map of T cell activation

We isolated naive and memory CD4+ T cells from 119 healthy individuals and stimulated the cells using anti-CD3/anti-CD28 coated beads. We profiled gene expression using single cell RNA-seq (10X-Genomics 3’ v2 kit) at resting state and three time points of activation (16h, 40h and 5 days post stimulation) and mapped expression quantitative trait loci.

Dataset EGAD00001008197

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

CNS Embryonal tumors

The dataset represents a total of 58 DNA samples from 16 male and 12 female pediatric patients affected with embryonal central nervous system tumors. The samples were subject to whole genome sequencing, WGS, [48 samples, (representing 12 male and 11 female individuals)] and whole exome sequencing, WES, [10 samples, (representing 4 male and 1 female individuals)]. One tumor tissue sample and one peripheral blood sample were analyzed from each of 26 patients, whereas two tumor tissue samples and one peripheral blood sample were analyzed from two patients. The WGS samples were sequenced 2x150 bp paired-end on an Illumina HiSeqX v2.5 instrument, and the WES samples were sequenced 2x100 bp paired-end on an Illumina HiSeq 2500 instrument. The FASTQ files generated were aligned to the human reference genome sequence GRCh38/hg38 using bwa-mem, with the ALT-aware option turned on. Sorting of reads and marking of PCR duplicates was performed with GATK. Base quality score recalibration and joint realignment of reads around insertions and deletions (indels) were conducted using GATK tools. The dataset consists of 58 files in the CRAM format (lossless compression) with a total file size of ~8,8 TB. All CRAM files but one, are derived from one sequence run and one sample. P4551_227N_P4552_112N is a CRAM file where 2 sequence runs (P4551_227N and P4552_112N) from peripheral blood samples from the same individual, P019, were aligned into one single CRAM file. Additional genomic and molecular data (FASTQ, BAM, IDAT, and VCF files) and limited clinical data can be requested by ethically approved projects conducting research in the field of pediatric cancer.

Dataset EGAD50000000298

Meningioma Exome

Dataset EGAD00001000099

CRLF2 sequencing project Exomes

Dataset EGAD00001000077

National Cancer Institute (NCI) TARGET: Therapeutically Applicable Research to Generate Effective Treatments

The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) Initiative seeks to accelerate research in novel marker and drug development, along with understanding the molecular basis of pediatric malignancy, through identification of genomic changes associated with the following childhood cancers: Acute Lymphoblastic Leukemia (ALL) - A fast-growing type of blood cancer in which too many immature white blood cells are found in the blood and bone marrow. Acute Myeloid Leukemia (AML), including AML Induction Failure cases (AML-IF) - Another type of blood cancer marked by too many myeloblasts, an alternate type of immature white blood cell, are found in the blood and bone marrow. Upon diagnosis, AML patients without high-risk genetic markers undergo standard chemotherapy regimen, called the primary induction phase, to eliminate most cancer cells and induce a remission state. Clear Cell Sarcoma of the Kidney (CCSK) - A rare type of kidney cancer, in which the inside of the cells looks clear when viewed under a microscope. Clear cell sarcoma can spread from the kidney to other organs, most commonly the bone, but also including the lungs, brain, and soft tissues of the body. Neuroblastoma (NBL) - Cancer of cells of the sympathetic nervous system. Cancer Model Systems (MDLS), including PPTP - MDLS includes acute leukemia cell lines and xenografts with matched primary and/or relapsed tumor information, NBL cell lines and xenografts with matched controls, kidney tumor cell lines, normal brain tissues, and embryonic stem cell lines that were sequenced for some TARGET disease projects. Osteosarcoma (OS) - A cancer of the bone that primarily affects children and adolescents. Rhabdoid Tumor (RT) - A rare, fast-growing cancer that usually forms in the kidney or central nervous system (the brain and spinal cord) but can also form in soft tissues in other areas of the body. Rhabdoid tumors tend to spread quickly, are hard to treat, and have a poor prognosis. Wilms' Tumor (WT) - A cancer of cells in the kidney that can spread to the liver, lung and lymph nodes. More information can be found about each TARGET subproject by following the links on the top of this page. Together these cancers account for the majority of the more than 10,000 childhood cancer cases diagnosed in the United States each year. TARGET has employed a set of advanced and complementary genome analysis technologies, including large scale 2nd and 3rd generation genome sequencing, to strategically characterize alterations in both gene expression and in genomic structure (such as deletions and amplification) that are involved in childhood cancers. The goal of this coordinated effort has been to create a comprehensive genomic and transcriptomic profile of each cancer. Integrated analysis of the TARGET data has identified those genes that are either altered in their expression level or mapped to the chromosome regions of deletion/amplification/translocation, as these genes represent strong candidates for therapeutic targeting. To learn more about the TARGET project, which is now completed, visit the website at https://www.cancer.gov/ccg/research/genome-sequencing/target. TARGET data is accessible at the NCI's Genomic Data Commons (GDC) via the GDC's Data Portal and from each of the TARGET Publication Pages at the GDC (Please see the "Supplemental Links" section of any TARGET publication's "Publication Information and Associated Data Files page" at the GDC.). Available datasets include raw sequencing data, datasets generated by the original TARGET research teams (fully annotated clinical information and higher-level/analyzed molecular characterization data - gene expression, copy number variation, epigenetics, targeted sequencing), as well as higher level data generated by the GDC. Please see the TARGET Use and Publication Guidelines for updated details on the sharing of any TARGET substudy data, including how to cite TARGET.

Study phs000218

The Finland-United States Investigation of NIDDM Genetics (FUSION) Tissue Biopsy Study

The Finland-United States Investigation of NIDDM Genetics (FUSION) study is a long-term effort to identify genetic variants that predispose to type 2 diabetes (T2D) or that impact the variability of T2D-related quantitative traits (QTs). Most of the variants associated with T2D and related traits (glucose and insulin, anthropometrics, lipids) through genome-wide association studies (GWAS) occur in non-coding regions, suggesting a strong regulatory component to disease susceptibility. Regulatory element activity is often tissue-specific, which further complicates discovery of the causal/functional variation. Therefore, there is a critical need to identify the appropriate cell type, regulatory elements, target genes, and causal variants(s) in T2D-relevant tissues. We hypothesize that a subset of T2D and related variants alter gene expression regulation in skeletal muscle and adipose tissue - two major insulin target tissues and play key roles in insulin resistance. To that end, our study contains a comprehensive survey of genomics, epigenomics and transcriptomics in skeletal muscle and adipose tissue from individuals with glucose tolerance categories ranging from normal to T2D.For this FUSION Tissue Biopsy Study, we obtained RNA-Seq, microRNA (miRNA)-Seq, and DNA methylation (methyl)-Seq data on biopsy samples from 331 individuals from across the range of glucose tolerance: 124 normal glucose tolerance (NGT), 77 impaired glucose tolerance (IGT), 44 impaired fasting glucose (IFG), and 86 newly-diagnosed T2Ds. Participants completed two study visits, two weeks apart. First visits comprised most of the clinical phenotyping, including four-point OGTT (fasting, and 30, 60, and 120 minute post-load); BMI, WHR; lipids; blood pressure; and many other variables. Participants also completed FUSION health history, medication, and lifestyle questionnaires. On the second visit, we obtained ~250mg vastus lateralis skeletal muscle, ~750mg abdominal subcutaneous adipose, and a ~5x15mm section of abdominal skin. Visits were completed in March 2013. RNA isolation is ongoing in the Collins laboratory at the NIH, RNA and miRNA sequencing at the NIH Intramural Sequencing Center (NISC), and genotyping at the Center for Inherited Disease Research (CIDR). Individual-level data is available here for the 306 individuals who consented to data deposit. To focus on evaluation of gene expression and its regulation in skeletal muscle, we analyzed mRNA extracted from vastus lateralis skeletal muscle obtained from 271 of the 331 individual subjects from Finland, along with genome-wide genotypes. Individual-level data is available here for the 250 subjects who consented to the use of their data.Release phs001048.v2.p1 adds muscle data for an additional 42 subjects and data from adipose tissue for 276 subjects. Total RNA was isolated using Trizol extraction in the Collins laboratory at the NIH. The mRNA was poly-A selected, 24-plex libraries were generated using the Illumina TruSeq directional mRNA-seq library protocol and RNA sequencing was performed on HiSeq2000 sequencers using 101bp paired-end reads at NISC. miRNA libraries were prepared from total RNA from 296 muscle and 270 adipose samples, pooled and sequenced 50bp single-end reads on Illumina HiSeq2500. Data for 272 muscle and 251 adipose samples are available here for individuals with consent for data deposit. DNA was extracted from blood in the Collins laboratory, and genotyping on the Illumina Omni2.5M array was performed at CIDR. Genotypes were imputed using the HRC 2016 reference panel. In order to assess regions of open chromatin in skeletal muscle, we obtained muscle tissue from a commercial provider to perform ATAC-seq; these samples were sequenced at the University of Michigan DNA Sequencing Core.Release phs001048.v3.p1 adds single-nucleus (sn) RNA-seq and ATAC-seq data in 287 skeletal muscle samples out of the original 331 individuals. Individual-level data is available here for the 265 subjects who consented to the use of their data. The frozen tissue biopsy samples were processed in ten batches, each consisting of 40-41 samples. These batches were organized using a randomized block design to protect against experimental contrasts of interest including cohort, age, sex, BMI among others. Samples in each batch were pulverized, pooled together followed by nuclei isolation. The nuclei were processed on the 10X Genomics Chromium platform separately for snATAC-seq and snRNA-seq (v. 3.1 chemistry for snRNA-seq).Release phs001048.v4.p1 adds additional phenotypes and provides some corrections to several previous phenotypes.

Study phs001048

Genes and Blood Clotting Study (GABC)

Objectives: Use genome-wide approaches to identify genetic variants that influence common thrombosis and hemostasis factors, as well as selected common human traits. Design/Methods: The GABC study was a prospective sibling cohort design. Siblings were recruited by targeted email to the undergraduate and graduate student email lists at the University of Michigan. Healthy persons between 14 and 35 years old who had healthy siblings within the same age restriction were able to participate. Study participants agreed to an online informed consent and subsequently completed a 52-question online survey describing their specific bleeding traits as well as many common human traits. Fifty milliliters of blood was collected into a citrate-dextrose solution (ACD) from each participant. An aliquot of whole blood was used for an automated complete blood count analysis and the remainder was processed into platelet poor plasma and buffy coat portions. Plasma and buffy coat aliquots were snap frozen and stored in liquid nitrogen for future studies. 1189 individuals representing 507 sibships were collected between 06/26/2006 and 01/30/2009. Phenotyping Survey Details: To characterize individual bruising and bleeding history, the online survey recorded answers to questions based on a modified von Willebrand Disease (VWD) screening questionnaire. To characterize a collection of participant's common human traits, the survey recorded answers to questions about height, weight, presence of skin tags, history of acne, eye color, hair color, hair line characteristics, skin sunburn sensitivity, skin tanning ability, natural skin color, freckling, cheek dimpling, earlobe shape, shoe size, foot arch characteristics, hand fifth digit morphology, history of dyslexia, history of migraine headaches, history of seasonal allergies, history of apthous ulcers, tendency to sneeze while walking into a bright sunny place, history of dental caries, need for corrective eye lenses, handedness and like or dislike of strongly flavored foods. Biochemical phenotyping: Assays for plasma Von Willebrand Factor (VWF) antigen were performed using ELISA and "Alphalisa" techniques. Automated complete blood count analysis was performed on a Bayer Advia 120 on all participants (including WBC differential, RBC indices, and platelet count.) For the dbGaP v2 update, new biochemical phenotypes have been submitted and include von Willebrand Factor, von Willebrand Factor propeptide, plasminogen, gamma prime fibrinogen, ADAMTS 13, antithrombin III, protein C, and protein S. All new phenotypes were obtained using "Alphalisa" techniques. Genotyping Details: SNP genotyping was performed using genomic DNA extracted from peripheral blood at the Broad Institute, (MIT/Harvard). Genotyping was performed on the Illumina Omni-1 quad chip at the Broad Institute. For the dbGaP v2 update, genotyping data from the Illumina Human Exome was deposited. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to blood clotting through large-scale genome-wide association studies of siblings. Genotyping was performed at the Broad Institute of MIT and Harvard, a GENEVA genotyping center. Data cleaning and harmonization was performed by the primary investigators at the University of Michigan, Ann Arbor, and at the GEI-funded GENEVA Coordinating Center at the University of Washington. This study serves as a resource for investigators who are interested in the genetic determinants of specific plasma proteins in a healthy population. The sibling cohort design allows for linkage analysis in addition to association studies. Analysis of thrombosis and hemostasis related traits should help elucidate specific biochemical and genetic networks that maintain hemostasis. We hope to identify specific genetic determinants of VWF levels in order to better understand the factors that influence the development of VWD.

Study phs000304

eMERGE Network Imputed GWAS for 41 Phenotypes

The electronic Medical Records and Genomics (eMERGE) Network is a consortium of ten participating sites (Cincinnati Children's Hospital Medical Center/Boston Children's Hospital, Children's Hospital of Philadelphia, Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University, Geisinger Clinic, Group Health Cooperative/University of Washington, Mayo Clinic, Icahn School of Medicine at Mount Sinai, Northwestern University, Vanderbilt University Medical Center) funded by the NHGRI to investigate the use of electronic medical record (EMR) systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 55,000 individuals using EMR-derived phenotypes and DNA from linked Biorepositories. Using electronic phenotyping methods, the consortium used DNA samples from all participating sites to explore the genetic determinants of over forty phenotypes, including Abdominal aortic aneurysm; Ace-Inhibitor/Cough; Attention Deficit Hyperactivity Disorder; Age-related macular disease; Appendicitis; Asthma; Atopic Dermatitis; Autism; Benign Prostatic Hyperplasia; Carotid artery disease as a Quantitative Measure; caMRSA; Cataract; Clostridium difficile colitis; Extreme Obesity; Chronic Kidney Disease; Chronic Kidney Disease and Type 2 Diabetes; Chronic Kidney Disease, Type 2 Diabetes and Hypertension; Colon Polyps; Cardiorespiratory Fitness; Dementia; Diverticulosis; Diabetic retinopathy; Gastroesophageal Reflux Disease; Glaucoma; Height; Heart failure; Hypothyroidism; Lipids; Ocular hypertension; Peripheral Arterial Disease; QRS duration; Red blood cell indices; Remission of Diabetes after ROUX-EN-Y gastric bypass surgery; Resistant hypertension; MACE while on Statins; Type 2 Diabetes; Venous Thromboembolism; White blood cell indices; and Zoster virus infection, as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes. Sites 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. A large majority of participants consenting to prospective genomic analyses also consent to analysis of their de-identified electronic medical records (EMRs). EMRs are longitudinal, with a mean duration of 6.5 years. Cincinnati Children's Hospital Medical Center/Boston's Children's Hospital (CCHMC/BCH): Cincinnati Children's Hospital Medical Center (CCHMC) and Boston Children's Hospital (BCH) are pediatric institutions dedicated to improving health and welfare of children and to the shared purpose of discovery and practical application of new genomic information to the ordinary care of children. The CCHMC/BCH site has been built on a five-year history of collaboration, particularly in patient electronic record (ERM)-related informatics, the basis of much of eMERGE II. CCHMC and BCH together bring an extraordinary faculty to eMERGE II who are committed to diseases that afflict children, specifically phenotypes that focus upon diseases of children in ways that will leverage the available eMERGE adult GWAS and EMRs to discover meaningful use results. CCHMC/BCH plans to demonstrate real-time execution of phenotypic selection across their two distinct pediatric institutions as a model for ensuring phenotypic standardization and for national scalability. They will also look carefully at parents' responses to results and use of their children's research results and better understand the factors that influence their decisions about learning incidental findings. In addition to patient and parent perceptions CCHMC/BCH will also explore clinician perceptions of pharmacogenetic research results after EMR integration. 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 participants provided written informed consent and HIPAA authorization. Consenting patients agreed to provide blood samples for broad biomedical research use, and permission to access data in their Geisinger electronic medical record for research. The enrollment rate was 90% of patients approached. The demographics of the cohort approximate those of the Geisinger Clinic outpatient population. Research blood samples were collected during an outpatient clinical phlebotomy encounter. Research blood samples are coded and stored in a central biorepository. Samples are linkable to clinical data in a de-identified manner for research via an IRB-approved data broker process. For genomic analysis, DNA is extracted from EDTA-anticoagulated whole blood. Group Health(GH)/University of Washington (UW): 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 participants 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): The Marshfield Clinic Personalized Medicine Research Project is a population-based biobank in central Wisconsin with more than 20,000 adult subjects who provided written, informed consent to access their medical records and provided a blood sample from which DNA was extracted and plasma and serum stored. In addition to an average of 30 years of medical history data, a questionnaire about environmental exposures, including a detailed food frequency questionnaire, is available to facilitate gene/environment studies. Mayo Clinic: The Mayo biobank is a disease-specific biobank for vascular diseases including peripheral arterial disease (PAD). PAD patients were identified from individuals referred to the non-invasive vascular laboratory for lower extremity arterial evaluation. Since 1997, laboratory findings have been recorded into an electronic database employing an in-house software package for data archiving and retrieval; this data becomes part of the Mayo EMR. Patients referred to the center with suspected PAD undergo a comprehensive non-invasive evaluation including the ankle-brachial index (ABI) - the ratio of blood pressure measured in the upper arms divided by blood pressure measured at the ankles. Controls subjects are identified from patients referred to the Cardiovascular Health Clinic for stress ECG. The prevalence of PAD in patients with normal exercise capacity who do not have inducible ischemia on the stress ECG , was <1%. Data regarding risk factors for atherosclerosis such as diabetes, dyslipidemia, hypertension, and smoking are ascertained from the EMR. Icahn School of Medicine at Mount Sinai School (Mt. Sinai): The Institute for Personalized Medicine (IPM) Biobank Project is a consented, EMR-linked medical care setting biorepository of the Mount Sinai Medical Center (MSMC) drawing from a population of over 70,000 inpatients and 800,000 outpatient visits annually. MSMC serves diverse local communities of upper Manhattan, including Central Harlem (86% African American), East Harlem (88% Hispanic Latino), and Upper East Side (88% Caucasian/white) with broad health disparities. IPM Biobank populations include 28% African American (AA), 38% Hispanic Latino (HL) predominantly of Caribbean origin, 23% Caucasian/White (CW). IPM Biobank disease burden is reflective of health disparities with broad public health impact: average body mass index of 28.9 and frequencies of hypertension (55%), hypercholesterolemia (32%), diabetes (30%), coronary artery disease (25%), chronic kidney disease (23%), among others. Biobank operations are fully integrated in clinical care processes, including direct recruitment from clinical sites, waiting areas and phlebotomy stations by dedicated Biobank recruiters independent of clinical care providers, prior to or following a clinician standard of care visit. Recruitment currently occurs at a broad spectrum of over 30 clinical care sites. Northwestern University: The NUgene Project is a repository with longitudinal medical information from participating patients at affiliated hospitals and outpatient clinics from the Northwestern University Medical Center. Participants' DNA samples are coupled with data from a self-reported questionnaire and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 2 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators. Vanderbilt University: BioVU, Vanderbilt's DNA databank, is an enabling resource for exploration of the relationships among genetic variation, disease susceptibility, and variable drug responses, and represents a key first step in moving the emerging sciences of genomics and pharmacogenomics from research tools to clinical practice. BioVU acquires DNA from discarded blood samples collected from routine patient care. The biobank is linked to de-identified clinical data extracted from Vanderbilt's EMR, which forms the basis for phenotype definitions used in genotype-phenotype correlations.

Study phs000888

Genetic Basis of Early Onset Bicuspid Aortic Valve Disease

The study protocol was approved by the Committee for the Protection of Human Subjects at the University of Texas Health Science Center at Houston (HSC-MS-11-0185). Study recruitment began on July 1, 2017, and concluded on March 30, 2022. After written informed consent, we enrolled probands with early onset BAV disease (EBAV), which we defined as individuals with BAV who were under the age of 30 at the time of first clinical event. Clinical events were defined as aortic replacement, aortic valve surgery, aortic dissection, moderate or severe aortic stenosis or aortic regurgitation, large aneurysm (Z > 4.5), or intervention for BAV-related conditions. Those with hypoplastic left heart, known genetic mutations, genetic syndromes, or complex congenital heart disease were excluded. Samples were collected and genotyped as previously reported. For comparison, we analyzed a cohort of older individuals of European ancestry with sporadic BAV disease selected from the International BAV Consortium (BAVGWAS). Phenotypes were derived from record review with confirmation of image data whenever possible [25-26]. The computational pipeline for CNV analysis of Illumina single nucleotide polymorphism (SNP) array data included three independent CNV detection algorithms.GenomeStudio was used to exclude samples with indeterminate sex or more than 5% missing genotypes, and single nucleotide polymorphisms (SNPs) with GenTrain = 0. Principal component analysis was used to remove outliers that did not cluster with European ancestry. Prior to CNV analysis, each dataset was trimmed by selecting a common set of 650,000 SNPs that were genotyped on each of the microarrays used in this study.Three independent algorithms (PennCNV, cnvPartition, and QuantiSNP) were used to generate CNV calls and sample-level quality statistics from SNP intensity data. PennCNV and QuantiSNP were run on Unix clusters and cnvPartition data were exported from GenomeStudio. The analysis was run using default configurations. PennCNV was used to generate QC data and remove CNV calls that intersect with polymorphic genomic regions. Samples that met any of the following criteria were excluded, standard deviation of the LogR ratio (obtained from PennCNV) > 0.35 or number of CNVs > 2 standard deviations above the mean for each data set. CNV calls less than 20 Kilobases in length and/or spanned by fewer than 6 probes were excluded. The overlap function for rare CNVs in PLINK was used to construct CNV regions (CNVRs) after adjacent regions were merged using PennCNV. LogR ratio (LRR) and B allele frequency (BAF) data at CNVRs and calls of interest were visualized in GenomeStudio for validation. For segregation analysis, GenomeStudio was used to determine the presence of CNVs in relatives.A total of 22,014 unselected control Illumina Genotypes obtained from the Database of Genotypes and Phenotypes were analyzed using identical methods (S1 Table). The Wisconsin Longitudinal Study (WLS) includes data on a cohort of 10,300 individuals who graduated from Wisconsin high schools in 1957. The Health and Retirement Study (HRS) includes data on 37,000 individuals aged 50 above from 23,000 households across the United States. Principal component analysis was used to select European ancestry genotypes from these datasets for analysis. Datasets were paired for case-control analysis based on the concordance of log-transformed sample-level quality control statistics (number of CNV calls and standard deviation of logR ratios). Chi-squared or Fisher exact tests were used to compare CNV frequencies in cases and controls.Rare CNV functions in PLINK (v1.7) were used to perform permutation-based burden tests or gene set-based enrichment tests. Case control burden tests were restricted to CNVs that were longer than 110 Kb and less than 0.1% in frequency. CNV overlap functions in PLINK were used to identify rare CNVs that intersect between datasets or involve specific BAV or CHD genes . The list of candidate genes included 190 CHD genes that have strong cumulative evidence to cause BAV or related congenital malformations from human or animal model data. Genome Reference Consortium Human Build 37 was used for CNV annotation [34].

Study phs003705

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