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• Bogalusa Heart Study (BHS-BioLINCC)

  Objectives: To investigate the early natural history of cardiovascular disease in a cohort of children and young adults in a biracial, semirural community.Background: The Bogalusa Heart Study has been a long-term epidemiologic study. The investigators have identified and followed black and white participants for nearly 40 years, and have described the incidence and prevalence of biologic and behavioral cardiovascular disease risk factors from childhood through adulthood. Their participation has enabled the study to not only document differences between males and females, but also between blacks and whites. The results from the Bogalusa Heart Study have clearly documented that the genesis of atherosclerosis has its basis in childhood, and that prevention can and must begin at the early ages.The Bogalusa Heart Study had been funded over the years by the Specialized Centers of Research (SCOR) Program. The SCOR program was initiated by NHLBI in 1970 to expedite the development and application of new knowledge essential for improved diagnosis, treatment, and prevention of arteriosclerosis, hypertension, pulmonary disease, and thrombosis. In 1984 a Demonstration and Education Component was added to the parent SCOR of the Bogalusa Heart Study in order to translate the experience gained in epidemiological studies into an intervention study designed to retard the development of cardiovascular risk factors in children. Beginning in 1997, the study was supported by the cooperative agreement mechanism. Participants: The Bogalusa dataset includes 11,796 participants that attended at least one of seven cross-sectional pediatric exams and/or the 1995-96 adult examination. Subjects ranged in age from 3 to 20 years at the pediatric exams and 20-37 at the time of the adult exam. Approximately 6,000 have more than one examination constituting a dynamic cohort. Design: The initial survey in 1973-1974 was restricted to children ages 2 1/2 to 14. A physical examination was conducted and information was collected on anthropometric data, hemoglobin, blood pressure, serum lipids, and health history. Over 3,500 children participated. The second cross-sectional survey of 1976-1977 and subsequent surveys expanded the eligible population to include all children ages 5-17 years. The second survey of over 4,000 children also included information on salt intake, smoking, health beliefs, and attitudes, and for girls ages 8-17, menstrual history and oral contraceptive use. The third survey of over 3,500 participants in 1978-1979 also collected anthropometric measurements on skinfold thickness and two measurements of heart rate. The fourth survey of over 3,300 participants in 1981-1982 added data on alcohol use, Type A behavior, peer networks and dieting habits.The Bogalusa Heart Study continued to use a cross-sectional and longitudinal design with the general cross-sectional survey of approximately 3,700 Bogalusa children ages five to seventeen in 1988-1989 in the sixth screen and additional longitudinal studies to recall children in defined subgroups for more intensive evaluation. Half of the 12,000 participants screened since 1973 had been studied three or more times. The Post High School Study examined young adults ages 21 through 30 who previously were examined as children ages five through fourteen in the first Bogalusa Heart Study screening in 1973-1974. The population included approximately 4,603 young adults originally screened and any other children or adolescents examined for the first time in any subsequent surveys. The cardiovascular phenotypes include obesity, blood pressure, lipids, lipoproteins, apoproteins, homocysteine, glucose-insulin, fibrinogen, plasminogen activator inhibitor-1 and von Willebrand Factor. Environmental risk factors consist of sociodemographic characteristics, tobacco and alcohol use, oral contraception, physical activity, cognitive and physical function, and quality of sleep and diet. Subclinical morbidity includes echo-Doppler measurements of cardiac-carotid structure and function.

  Study phs004173

• Isala Flow 1 DAC

  Dac EGAC00001003048

• EGAD00010000642

  CLL Expression Array

  Dataset EGAD00010000642

• A Pilot Study of Neoadjuvant Nivolumab, Ipilimumab and Intralesional Oncolytic Virotherapy for HER2-Negative Breast Cancer

  This study was a single arm phase 1 trial that assessed the regimen safety and feasibility as well as tumor response in patients with localized HER2-negative breast cancer treated with talimogene laherparepvec (T-VEC) in combination with nivolumab and ipilimumab. The primary objective was to evaluate the safety and adverse event profile of this treatment combination. Six patients were enrolled, four having relapses after prior neoadjuvant chemotherapy, and two who were previously untreated. One patient had a pathological complete response, three patients had pathological partial responses, one showed no significant response, and one had disease progression. Biopsies demonstrated increased immune cell infiltration in samples from patients who responded to therapy. In this study, the checkpoint blockade immunotherapy combined with T-VEC provided responses in patients with advanced or relapsed HER2-negative breast cancer at the expense of long-term toxicities.

  Study phs003316

• Kids First: Genomic Etiologies of CHARGE Syndrome, Related Conditions and Structural Anomalies

  Developmental disorders with structural birth defects account for the majority of morbidity and mortality in children's hospitals, and the genetic bases of many clinical phenotypes remain unknown. Genetic testing for individuals with structural malformations has uncovered the basis of many such birth defects; however, many more cases remain unsolved, posing challenges for diagnosis, treatment, and prevention. Multiple anomaly conditions are particularly challenging to diagnose, since they often present with unique combinations of clinical features that vary widely between affected individuals, even in the same family. CHARGE Syndrome (Coloboma of the eye, Heart Defects, Atresia of the choanae, Retardation of growth and development, Genital abnormalities including pubertal delay and infertility, Ear abnormalities with deafness and vestibular disorders) is a multiple anomaly condition that affects a wide variety of organ systems. CHARGE Syndrome is caused in most cases by monoallelic pathogenic variants in CHD7, the gene encoding ATP-dependent helicase chromodomain DNA binding protein 7. Both de novo and inherited variants in CHD7 have been reported in CHARGE, and a growing number of families present with individuals who test positive for a pathogenic CHD7 variant yet exhibit only mild features. Similarly, individuals with CHARGE Syndrome often exhibit broad variability and reduced penetrance of clinical features, consistent with pleiotropic roles for CHD7 during development and/or additional genetic contributors or modifying alleles. Recent work has also implicated RERE, KMT2D, EP300, and PUF60 in having a role in CHARGE Syndrome.We hypothesize that (1) some cases of CHARGE are due to other genetic etiologies including oligogenicity, and (2) genetic modifiers contribute to the broad clinical variability and reduced penetrance of CHARGE features. To address these hypotheses, we have generated a cohort of 223 deeply clinically phenotyped individuals with CHARGE Syndrome and related disorders and structural anomalies who tested negative by chromosomal microarray, single gene sequencing, next generation panel sequencing, or exome sequencing. These individuals exhibit clinical CHARGE-like features including structural birth defects affecting craniofacial, ocular, neurosensory, brain, heart, mediastinal, renal, genitourinary, and skeletal organs. Our cohort includes these 223 individuals as well as affected and unaffected family members who consented to clinical and research genetic testing and donated blood samples for DNA and RNA isolation and sequencing. Building on this valuable cohort, we propose to use exome and genome sequencing to identify novel genetic etiologies of CHARGE and related developmental disorders for which alternative genetic tests have been inconclusive. Identification of novel pathogenic genetic variants and contributing modifier alleles within the coding and non-coding portion of the genome of these individuals will improve genetic diagnosis and provide important insights toward understanding the developmental mechanisms of structural birth defects.

  Study phs002592

• CM067 WES - Clinical Outcomes by Tumor Mutational Burden and Inflammatory Gene Expression With Combined Nivolumab and Ipilimumab or Monotherapy in Advanced Melanoma

  Purpose Exploratory analyses of CheckMate 066 and 067 trials were conducted to investigate associations of tumor mutational burden (TMB), a 4-gene inflammatory gene expression signature, and BRAF mutation status with tumor response, progression-free survival (PFS), and overall survival (OS) in patients with advanced melanoma. Patients and Methods Patients with known programmed death ligand 1 (PD-L1) expression and BRAF mutation status received nivolumab (NIVO) or dacarbazine in CheckMate 066 and either NIVO, ipilimumab (IPI), or NIVO+IPI in CheckMate 067. Whole exome sequencing and RNA sequencing were used to determine TMB and inflammatory gene expression signature scores, respectively. These biomarkers were evaluated in terms of their association with PFS and OS. Results In the NIVO, NIVO+IPI, and IPI arms of CheckMate 067, longer survival was associated with high (> median) versus low (≤ median) TMB with hazard ratios (HRs) (95% confidence interval [CI]) for PFS of 0.45 (0.30–0.65), 0.55 (0.38–0.81), and 0.60 (0.43–0.82), and for OS of 0.46 (0.30–0.71), 0.53 (0.34–0.82), and 0.52 (0.36–0.74), respectively. For NIVO-treated patients, these results were confirmed in CheckMate 066. A survival benefit was observed with high TMB and absence of BRAF mutation. Survival was associated with high versus low inflammatory signature scores with HRs (95% CI) for PFS of 0.56 (0.34–0.94), 0.40 (0.23–0.72), and 0.43 (0.27–0.70), and for OS of 0.37 (0.20–0.66), 0.38 (0.19–0.74), and 0.46 (0.27–0.79), in the NIVO, NIVO+IPI, and IPI arms, respectively. Weak correlations were observed between PD-L1, TMB, and the inflammatory signature. Conclusions Combined assessment of TMB, inflammatory gene expression signature, and BRAF mutation status may be predictive for response to immunotherapy in advanced melanoma.

  Dataset EGAD00001007573

• University of Miami Study on Genetics of Autism and Related Disorders (AutismDisorders)

  Autism spectrum disorders (ASD) are neurodevelopmental in origin and characterized by impairments in reciprocal social interaction and communication accompanied by restricted and repetitive patterns of interest or behavior. With improved surveillance and a broadening of the diagnostic criteria, the most recent prevalence studies suggest that ASD may affect as many as 1/166 children in the US. Treatments are limited resulting in little impact on the profound morbidity. Little is known about the etiology of ASD, but there is a strong genetic component. Genetic studies over the past decade have failed to identify definitive ASD genes, but have clearly shown that the underlying genetics are more complex than anticipated with the likelihood that multiple genes are acting independently and/or interactively. With this realization the field of ASD genetics is at a critical juncture. To move forward we must embrace new and creative paradigms to successfully dissect the genetic etiology of this disease. Specifically the current study will: Extend our ascertainment to the full range of ASD. We will increase our ASD dataset by collecting 600 families who meet clinical diagnostic criteria for the three most common ASDs: autism (AUT), Asperger disorder (ASP) and Pervasive Developmental Disorder-NOS (PDD-NOS). Phenotype assessment will be expanded to include the Social Responsiveness Scale (SRS) and detailed seizure data. Ascertainment will focus on parent-child trios. Further characterize the ASD-associated variations in the Gamma-Aminobutyric Acid (GABA) receptor (GABR) subunits. We will complete our GABR subunit variant analysis, including coding, UTR, regulatory, and conserved intronic sequence, concentrating on GABRA4 and GABRB1. We will also investigate the role of variation in other GABR subunits. All AUT specific findings will be examined in the entire ASD dataset. Identify the chromosome 19p ASD gene. Multiple genome-wide screens have provided significant evidence for linkage to Chromosome 19p and we have found evidence for significant association in the linked families within an ~3 Mb region. We will genotype 768 SNPs spaced at ~4 kb to refine the candidate region and delineate specific candidate genes for further testing. We will screen the strongest associated candidate genes for additional variants and evaluate evidence for association in the entire ASD dataset (SA5). Develop and apply the Phenotypic Homogeneity Distinction (PHD) algorithm for identification of the phenotypic signature of linkage and/or association signals. The PHD algorithm identifies phenotypic covariates that discriminate a target sample subset (e.g. the positively linked families or families carrying the associated risk allele) from its complement. The resulting PHD discriminators will define a homogeneous subset from our newly-ascertained ASD dataset to use, as a confirmatory dataset and for localization within genes under study in our other aims. Test for gene-gene interactions. To fully explain the spectrum of autism risk, we will test for interactions between genetic variations associated with ASD (such as those in GABR subunit genes). We will build upon our experience using the MDR method and extend our analyses across biological pathways using the entire ASD data set.

  Study phs000436

• DAC of the BriTROC-1 project

  Dac EGAC00001003291

• ADPKD Data Access Commitee 1

  Dac EGAC00001002838

• FFA_dataset

  Case_control_meta_analysis

  Dataset EGAD00010001683

• Total RNA sequencing of fibroblasts from unmethylated full mutation carrier 1

  Total RNA sequencing of fibroblasts from an individual with a fragile X syndrome related unmethylated full mutation (UFM1). Libraries were prepared with total RNA with Ribo-Zero ribosomal RNA depletion (illumina), and sequenced paired-end, 75bp on a NextSeq 550 system. This dataset contains three RNA-sequencing replicates from the same cell line.

  Dataset EGAD50000000918

• Shallow whole genome sequencing on stage 1 epithelial ovarian cancer

  BAM files (aligned against the hg38 genome) from a shallow whole-genome sequencing experiment (median depth 0.5X) on 218 samples from Stage 1 epithelial ovarian cancer biopises. Samples labeled "bis" or "tris" with the same ID are relapses; "left" or "right" samples indicate, in the case of bilateral tumor, from which ovary the sample was taken.

  Dataset EGAD00001006881

• FFPE CRC sequence data and somatic variants

  We included 3 BAM files of the genome sequencing data: 2 of 3 are from tumour samples, namely 1 repaired-FFPE and 1 unrepaired FFPE; the third BAM file is from normal tissue of FFPE block. There is also a VCF file containing all somatic mutations in the dataset.

  Dataset EGAD00001007723

• ctDNA monitoring using patient-specific sequencing and integration of variant reads - Breast cohort

  Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to individual parts per million. This resulted in median Area Under the Curve (AUC) values of 0.98 in advanced cancers, and 0.80 in early stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that the INVAR may be applied without requiring personalized sequencing panels, so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

  Dataset EGAD00001006293

• EGAD00010000791

  Illumina HumanOmni2.5-8 BeadChip

  Dataset EGAD00010000791

• EGAD00010000764

  Ovarian tumor samples using Illumina

  Dataset EGAD00010000764

• EGAD00000000042

  GenomEUtwin Finnish (FIN) samples

  Dataset EGAD00000000042

• EGAD00000000041

  GenomEUtwin Swedish (SWE) samples

  Dataset EGAD00000000041

• EGAD00000000040

  GenomEUtwin Danish (DK) samples

  Dataset EGAD00000000040

• EGAD00000000033

  NcOEDG Helsinki 2 samples

  Dataset EGAD00000000033

• EGAD00000000034

  NcOEDG Helsinki 3 samples

  Dataset EGAD00000000034

• EGAD00000000035

  NcOEDG Helsinki 4 samples

  Dataset EGAD00000000035

• EGAD00000000037

  NcOEDG Stockholm 2 samples

  Dataset EGAD00000000037

• EGAD00000000038

  NcOEDG Stockholm 3 samples

  Dataset EGAD00000000038

• EGAD00000000039

  NcOEDG Malmo - Lund samples

  Dataset EGAD00000000039

• Type 1 Diabetes Genetics Consortium (T1DGC): Case-only RNA-Seq Study

  Peripheral blood mononuclear cells (PBMC) from 82 T1D cases from the T1DGC study were fractionated by positive selection on magnetic beads into CD4+ T cells, CD8+ T cells and CD19+ B cells. RNA purification, library preparation and sequencing to an average of 50 million reads per sample were performed by the HudsonAlpha Genome Services Laboratory. The resulting dataset includes 82 subjects with data from at least one cell type; sequence data from all 3 cell types is available from 79 subjects. The individuals in this study can be linked to the phs000911 T1DGC ImmunoChip study via subject ids to obtain both genotypic and phenotypic information, including genotypes used for expression quantitative trait locus analysis.

  Study phs001426

• Tumour sample for patient SA667

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA667

  Dataset EGAD00001009210

• Tumour sample for patient SA668

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA668

  Dataset EGAD00001009211

• Tumour sample for patient SA669

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA669

  Dataset EGAD00001009212

• Tumour sample for patient SA671

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA671

  Dataset EGAD00001009213

• Tumour sample for patient SA672

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA672

  Dataset EGAD00001009214

• Tumour sample for patient SA535

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA535

  Dataset EGAD00001009215

• Normal sample for patient SA1035

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1035

  Dataset EGAD00001009216

• Normal sample for patient SA604

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA604

  Dataset EGAD00001009217

• Normal sample for patient SA605

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA605

  Dataset EGAD00001009218

• Normal sample for patient SA609

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA609

  Dataset EGAD00001009219

• Normal sample for patient SA218

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA218

  Dataset EGAD00001009220

• Normal sample for patient SA219

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA219

  Dataset EGAD00001009221

• Normal sample for patient SA272

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA272

  Dataset EGAD00001009222

• Normal sample for patient SA274

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA274

  Dataset EGAD00001009223

• Normal sample for patient SA275

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA275

  Dataset EGAD00001009224

• Normal sample for patient SA276

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA276

  Dataset EGAD00001009225

• Normal sample for patient SA279

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA279

  Dataset EGAD00001009226

• Normal sample for patient SA283

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA283

  Dataset EGAD00001009227

• Normal sample for patient SA287

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA287

  Dataset EGAD00001009228

• Normal sample for patient SA394

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA394

  Dataset EGAD00001009229

• Normal sample for patient SA395

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA395

  Dataset EGAD00001009230

• Normal sample for patient SA398

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA398

  Dataset EGAD00001009231

• Normal sample for patient SA402

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA402

  Dataset EGAD00001009232

• Normal sample for patient SA404

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA404

  Dataset EGAD00001009233