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• Clinical Proteomic Tumor Analysis Consortium (CPTAC) Proteogenomic Confirmatory Study of Breast, Colon, Lung, and Ovarian Tumors

  Recently, significant progress has been made in characterizing and sequencing the genomic alterations in statistically robust numbers of samples from several types of cancer. For example, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and other similar efforts are identifying genomic alterations associated with specific cancers (e.g., copy number aberrations, rearrangements, point mutations, epigenomic changes, etc.). The availability of these multi-dimensional data to the scientific community sets the stage for the development of new molecularly targeted cancer interventions. Understanding the comprehensive functional changes in cancer proteomes arising from genomic alterations and other factors is the next logical step in the development of high-value candidate protein biomarkers. Hence, proteomics can greatly advance the understanding of molecular mechanisms of disease pathology via the analysis of changes in protein expression, their modifications and variations, as well as protein-protein interaction, signaling pathways and networks responsible for cellular functions such as apoptosis and oncogenesis. Realizing this great potential, the NCI launched the second phase of the CPTC initiative in September 2011. Renamed the Clinical Proteomic Tumor Analysis Consortium, CPTAC is beginning to leverage its analytical outputs from Phase I to define cancer proteomes on genomically-characterized biospecimens. The purpose of this integrative approach is to provide the broad scientific community with knowledge that links genotype to proteotype and ultimately phenotype. The data contained in this dataset are derived from samples designed to confirm CPTAC findings from the TCGA samples. These confirmatory samples contain breast, ovarian, colon, and lung tumors collected via a protocol optimized for proteomics. Specifically, ischemic time of the sample was controlled and restricted to less than 30 minutes. ACGT, Inc. produced whole exome, mRNAseq, and miRNAseq for these samples. Corresponding proteomic data are available at: https://cptac-data-portal.georgetown.edu/cptacPublic/ The study design was to profile colon, breast, ovarian, and lung tumors both genomically and proteomically. Germline DNA was obtained from blood. Normal control samples for proteomics varied by organ site: adjacent colon tissue for colon cases, contralateral breast tissue for some breast cases, and Fallopian tube fimbria for some ovarian cases. Lung cases had no normal control for proteomic analysis. All cancer samples were derived from primary and untreated tumors.

  Study phs000892

• Integrated genomic analyses identify ARID1A and ARID1B alterations in the childhood cancer neuroblastoma

  Neuroblastomas are tumors of peripheral sympathetic neurons and are the most common solid tumor in children. To determine the genetic basis for neuroblastoma we performed whole-genome sequencing (6 cases), exome sequencing (16 cases), genome-wide rearrangement analyses (32 cases), and targeted analyses of specific genomic loci (40 cases) using massively parallel sequencing. On average each tumor had 19 somatic alterations in coding genes (range, 3-70). Among genes not previously known to be involved in neuroblastoma, chromosomal deletions and sequence alterations of chromatin remodeling genes, ARID1A and ARID1B, were identified in 8 of 71 tumors (11%) and were associated with early treatment failure and decreased survival. Using tumor-specific structural alterations, we developed an approach to identify rearranged DNA fragments in sera, providing personalized biomarkers for minimal residual disease detection and monitoring. These results highlight dysregulation of chromatin remodeling in pediatric tumorigenesis and provide new approaches for the management of neuroblastoma patients.Data Provider: Johns Hopkins Sidney Kimmel Comprehensive Cancer Center (SKCCC)

  Study EGAS00001000369

• RNA-seq as a tool for evaluating human embryo competence

  The majority of embryos that are created through IVF do not implant. One possibility for this inefficiency is an incomplete understanding of the molecular factors affecting embryo competence. Currently, the process of selecting an embryo for uterine transfer utilizes an ad-hoc combination of morphological criteria, the kinetics of development, and genetic testing for aneuploidy. However, no single criterion can ensure selection of a viable embryo. In contrast, RNA-sequencing of embryos could yield highly dimensional data, which may provide additional insight and illuminate the discrepancies among current selection criteria. Indeed, recent advances enabling the production of RNA-sequencing (RNA-seq) libraries from single cells have facilitated the application of this technique to the study of some transcriptional events in early human development. However, these studies have not assessed the quality of their constituent embryos relative to commonly used embryological criteria. Here, we perform proof-of-principle advancement to clinical selection procedures by generating high quality RNA-seq libraries from a trophectoderm biopsy as well as the remaining whole embryo. We combine state-of-the-art embryological methods with low-input RNA-seq to develop the first transcriptome-wide approach for use in future predictive embryology studies. Specifically, we demonstrate the capacity of RNA-seq as a promising tool in preimplantation screening by showing that biopsies of an embryo can capture valuable information content available in the whole embryo from which they are derived. Furthermore, we show that this technique can be used to generate a RNA-based digital karyotype, and to develop a foundational dataset for identifying candidate competence-associated genes. Together, these data establish the foundation for a future RNA-based diagnostic in IVF.

  Study EGAS00001003667

• Newborn Screening Translational Research Network (NBSTRN): Newborn Sequencing in Genomic Medicine and Public Health (NSIGHT2)

  Large-scale studies of whole genome sequencing (WGS) in neonates have largely been neglected. Thus, the clinical and social implications of this absence are largely unknown. The proposed study aims to increase scientific knowledge for acutely ill neonates. This population may stand to benefit largely from WGS given the severity of illness. The proposed clinical study is a prospective, randomized, partially-blinded study that has both quantitative and qualitative assessments of the risks and benefits of the use of STATseq, a 2-day genome test, in acutely ill neonates at Children's Mercy Hospital. Study participants include neonates, their parents, and clinicians caring for them. The overall aims will provide a nuanced understanding of the role of rapid diagnostic WGS for acutely ill neonates, including relative diagnostic sensitivity and change in management both for broad NICU populations and subpopulations, accuracy, reproducibility, and relative value of proband WGS and familial triads. The technological aims include improving diagnostic sensitivity without sacrificing the rapid turnaround time. The clinical aims include looking at increased diagnostic yield, shorter time to diagnosis and clinician perception of management changes that result from implementation of STAT-seq. Finally, the study will investigate the perceived value of STAT-seq in parents and clinicians using structural equation modeling. The studies also are designed for long-term continuation of assessment of the enrollees (beyond the current funding period) and in additional sample types (via a biorepository). Long-term continuation studies will test effects on long-term morbidity, mortality, quality of life and cost of care. We hope that the study results will be sufficient to provide an evidence base for physician adoption and provider reimbursement of WGS in Level 3 and 4 NICUs.

  Study phs002094

• Genomics of Substance Use Disorders in Latin American Populations

  This is a subsample of the National Survey of Drug, Alcohol, and Tobacco Use, administered in 2016 between August and October to participants aged 12-65 in urban and rural Mexican households, named Mexican Genomic Database for Addiction Research (MxGDAR). The objective of the survey was to evaluate the patterns of use of different psychoactive substances and mental health problems in Mexico. The sample universe was made up of primary sampling units (PSUs) that were the sum of basic statistical geographic areas, stratified by state and urban-rural communities. An adult aged 18-65 was chosen randomly from each household, and an adolescent aged 12-17 was also chosen randomly from the household which has at least one member under 18. The second administration of the survey included a subsample of 13,130 respondents who agreed to give a saliva sample for DNA analysis, to be used in analyzing the relationship of mental health, addiction, and genetics. This sample was weighted according to selection probability to be representative on the national level. Mental health symptomatology for these individuals was evaluated using the Diagnostic Interview for Psychosis and Affective Disorders (DI-PAD) using the operational criteria for psychotic disorders (OPCRIT v.4.0), administered by interviewers trained in its use. It includes the evaluation of symptomatology of psychosis, depression, anxiety, obsession/compulsion, mania/hypomania, and post-traumatic stress. The study seeks to replicate genetic associations with substance use (LINC01622), psychiatric disorders (PTPRM), or comorbidity between both (MGMT) reported in following paper: https://pubmed.ncbi.nlm.nih.gov/33762635/. Funding for genotyping was provided by the National Institute on Drug Abuse (NIDA), one of the National Institutes of Health (NIH).

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• Diagnostic yield and clinical utility of whole-exome sequencing in pediatric patients with rare and undiagnosed diseases in the Czech Republic

  In our single-center study, we have launched a pilot program for pediatric patients with undiagnosed diseases in the second-largest university hospital in the Czech Republic. WES was implemented as a first-line test after inclusion in the study as part of the diagnostic workflow.

  Study EGAS50000000442

• Highly prevalent NF-kappa B signaling pathway-activating somatic mutations in intracranial aneurysms

  Intracranial aneurysms (IAs) are a high-risk factor for life-threatening subarachnoid hemorrhage. Their etiology, however, remains mostly unknown. We conducted screening for somatic mutations in 65 IA tissues and identified 16 genes that were mutated with a high prevalence (92%). In particular, six genes, including PDGFRB, were mutated commonly in fusiform and saccular IAs and are linked to the NF-kappa B signaling pathway. Mutant PDGFRBs constitutively activate NF-kappa B signaling and induce inflammation-related gene expression in vitro. Spatial transcriptomics also detected similar changes in IA tissues. Virus-mediated expression of a mutant PDGFRB induces a fusiform-like dilatation of the basilar artery in mice. This study reveals a high prevalence of somatic mutations in NF-kappa B signaling genes in IAs and opens a new avenue of research toward development of its pharmacological interventions.

  Study JGAS000591

• Genome-Wide Environment Interaction Study on Neurodevelopment in Children from Mexico and Bangladesh

  Cognitive development is known to be highly heritable, but various genome wide association studies (GWAS) have failed to find common single nucleotide polymorphisms (SNPs) which can explain a large proportion of the variance in cognitive ability. We hypothesized that an interaction between genes and exposure to potentially toxic metals may account for some proportion of this missing heritability. We genotyped 749 infants in Mexico and 636 infants in Bangladesh. Criteria for inclusion into the study are given below. We then performed a Genome Wide Environmental Interaction Study (GWEIS) with neurodevelopment outcomes. There were 552,487 SNPs in common between the two cohorts.

  Study phs000996

• Heart and Vascular Health Study (HVH)

  Objectives The Heart and Vascular Health Study (HVH) is a case-control study of risk factors for the development of myocardial infarction (MI), stroke, venous thrombosis (VT), and atrial fibrillation (AF). The study setting is Group Health, an integrated health care delivery system in Washington State. Background The HVH originated in 1988 with the examination of risk factors for MI. Over the ensuing years, the study has been funded by a series of grants, which have added case subjects with stroke, VT, and AF, and used a common control group. Study aims have focused on the associations of medication use with cardiovascular events. Starting in 1997, the study aims expanded to include genetic associations with cardiovascular disease. Participants recruited in 2009 or later and who provided blood samples for genetic analysis were asked for consent to deposit genetic and phenotype data in dbGaP. Design As part of the HVH study, case subjects were identified by searching for ICD-9 codes consistent with MI, stroke, VT, or AF, and medical records were reviewed to confirm the diagnosis. Control subjects were identified at random from the Group Health enrollment and were matched to MI cases. All subjects have an index date. For cases, the index date was assigned as the date that the cardiovascular event (MI, stroke, VT, or AF) came to clinical attention. For controls, the index date was a random date within the range of the case index dates. For both cases and controls, information was collected from the inpatient and outpatient medical record, by telephone interview with consenting survivors, and from the Group Health pharmacy and laboratory databases. Consenting participants provided a blood specimen. Genetic Research Genetic factors underlying cardiovascular disease are studied using DNA isolated from the blood samples.

  Study phs001013

• GAW16 Framingham and Simulated Data

  Important links to apply for individual-level data Genetic Analysis Workshop Instructions to Request Authorized Access Data Use Certification Requirements (DUC) Apply here for controlled access to individual level data Research Use Statement Questions regarding GAW16 should be directed to Vanessa Olmo at vanessa@business-endeavors.com. Problem 2: Description of the Framingham Heart Study In GAW16, we use data drawn from the Framingham Heart Study. The Framingham Heart Study — under the direction of National Heart, Lung, and Blood Institute; NHLBI — began in 1948 with the recruitment of adults from the town of Framingham, Massachusetts. At the time, little was known about the general causes of heart disease and stroke, but the death rates for cardiovascular disease (CVD) had been increasing steadily since the beginning of the 20th century and had become an American epidemic. The Framingham Heart Study is now conducted in collaboration with Boston University. The objective of the Framingham Heart Study was to identify the common factors or characteristics that contribute to CVD by following its development over a long period of time in a large group of participants who had not yet developed overt symptoms of CVD or suffered a heart attack or stroke. Between 1948 and 1953 the researchers recruited 5,209 subjects (2,336 men and 2,873 women) between the ages of 29 and 62 from the town of Framingham, Massachusetts and began the first round of extensive physical examinations and lifestyle interviews that they would later analyze for common patterns related to CVD development. Subjects were recruited from lists of addresses recorded for the town. Two out of every three households were approached for participation in the study. While there was no intention to recruit families for family studies, the plan was to recruit all household members in the ages 30-60 within each house that was selected for study. Hence, many biologically related individuals were recruited, including 1644 spouse pairs. Since 1948, these participants have returned to the study every two years for a detailed medical history, physical examination, and laboratory tests. Now in 2008 at 60 years of follow up, there remain about 500 participants from this cohort. Between 1971 and 1975 the study enrolled a second-generation group — 5,124 of the original participants' children and the spouses of these children — to participate in similar examinations. 2,616 subjects are offspring of the original spouse pairs and 34 are stepchildren. A total of 898 offspring are children of cohort members where only one parent was a study participant and 1,576 are spouses of the offspring. The Offspring Cohort has been followed every four years through 2001 (except between Exams 1 and 2 with an intervening 8 years) using protocols similar to those used for study of the Original Cohort. Between 2002 and 2005 the study enrolled the third generation (Gen3) of the Framingham Heart Study - 4095 offspring of the second generation. None of their spouses were recruited. An additional 103 parents of this third generation, who were not recruited between 1971 and 1975, were also recruited at this time. The latter group is not included in the GAW16 data. With the recruitment of this third generation, the study has increasingly focused on genetic factors associated with the development of cardiovascular disease and its associated risk factors. To date, there is only one examination of this generation of participants. A description of the recruitment of this third generation and comparison with the earlier generations at their initial recruitment is presented in Splansky GL et al., 2007. Further information on the Study can be found at http://www.nhlbi.nih.gov/about/framingham/index.html. Genome-wide Dense SNP Scan in Framingham Heart Study Genetic studies did not begin in the FHS until the 1990s. In the late 1980s and through the 1990s DNA was extracted from blood samples of surviving FHS participants. In 2007, the FHS entered a new phase with the conduct of genotyping for the FHS SHARe (SNP Health Association Resource) project, for which dense SNP genotyping was performed using approximately 550,000 SNPs (GeneChip® Human Mapping 500K Array Set and the 50K Human Gene Focused Panel) in 10,775 samples (some duplicates) from the three generations of subjects (including over 900 pedigrees). Affymetrix conducted all genotyping for the FHS SHARe project, using the 250K Sty, 250K Nsp, and the supplemental 50K platforms. Eighty-nine percent of the DNA samples were collected during the 1990s. To maximize the power of the study, we also extracted DNA from 1133 blood samples, drawn from subjects who had no DNA, to include in the SHARe project. These samples had been sitting in our refrigerators for some time, a few as far back as the 1970s. We refer to these DNA samples as the legacy samples. These samples had a higher failure rate in the genotyping process (40%) than the other eighty-nine percent (3%). Affymetrix invoked its own criteria for a sample to succeed in genotyping. All non-legacy samples must succeed on all three platforms, while legacy samples needed to pass on at least one platform. When a sample failed, additional attempts were made. Samples that repeatedly failed 2-4 times were called failures. Other samples failed due to issues of genotyped sex identification not matching our records or low SNP concordance among SNPs common across arrays or contamination. Eighty-nine percent of the legacy samples for which genotyping results are available passed all three platforms. The genotyping data from the 10,043 samples from 9354 subjects that passed the Affymetrix criteria were additionally checked for gender consistency and consistency with family structure, resulting in genotyping data for 9,274 participants in FHS SHARe. Genotype calls were made with the BRLMM algorithm. The SHARe database is housed at the National Center for Biotechnology Information database of genotypes and phenotypes (NCBI dbGaP) and contains all ~550,000 SNPs. This genome-wide dense SNP scan and a subset of phenotypes from the Framingham Heart Study are the focus of the Genetic Analysis Workshop 16. Further information on the specific variables in the Problem 2 dataset can be found by clicking on the Documents tab at the top of the page. Problem 3: Description of the Simulated Data Set The focus of this simulation is gene discovery in genome-wide association scans (GWAS). The Framingham Heart Study data set (distributed as "Problem 2") is the basis for the FHS* simulated data. The pedigree structures are derived from the data distributed for Problem 2, and we distribute an accompanying triplet file (triplet_sim) containing person ID, father ID, mother ID, to ensure the identical subjects, pedigrees, and singletons are used in the simulated data analysis. Consistent with standard practice, founders and singletons are designated as subjects with both fshare and mshare equal to zero (missing). The simulated data includes a total of 6,479 subjects with both phenotype and genotype data, in 942 pedigrees distributed among 3 generations and 188 singletons. Data inclusion is consistent with the subjects' consent for use by both for-profit and not-for-profit researchers. The genotypes for all Problem 3 replicates are fixed as measured and distributed for Problem 2 for both the genomewide scan and the additional candidate gene SNPs, for a total of approximately 550,000 SNPs (GeneChip® Human Mapping 500K Array Set and the 50K Human Gene Focused Panel). Thus, to analyze the Problem 3 simulated data you also will need to download the Problem 2 genotypes. Note that there are slight discrepancies in counts between Problems 2 and 3 due to a change in consents between the two datasets. Several phenotypes that contribute to coronary heart disease (CHD) were simulated for all individuals with genotypes across three different time points, 10 years apart. All genotyped individuals have complete data; the effects of missing values can be investigated by user-specified missing value patterns. There are 200 longitudinal datasets created, based on the generating model, and each replication is found in a separate dataset. We suggest that if only one replication is to be analyzed, that it be replication 1 to enable more precise comparisons among analytical approaches. The 'shareid' will allow you to merge the simulated phenotype data with the Problem 2 genotype data, and reconstruction of the pedigrees using the distributed 'triplet_sim' file or for larger families' relationships with the triplet distributed with the Problem 2. The simulated data problem is further described in the associated readme file, and a data dictionary is provided defining all the variables. For disclosure of the generating model for these data, please contact Jean MacCluer at jean@sfbrgenetics.org.

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