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• Early-onset Colorectal Cancer Study TOGETHER Data Access Committee

  The Early-onset Colorectal Cancer Study TOGETHER Data Access Committee (DAC) is responsible for reviewing requests to access sequencing and clinical data from the study. Access to the data is restricted to researchers with approved ethical clearance and scientifically valid research projects. Each request will be evaluated by the DAC to ensure compliance with participants’ informed consent and all applicable data protection regulations.

  Dac EGAC50000000752

• Detection of uniparental disomy from genome sequencing of family trio

  We created a bioinformatics pipeline to detect uniparental disomy from whole genome sequence data of family trios. We use inheritance patterns of single nucleotide polymorphisms to identify uniparental disomy events.

  Study EGAS00001006154

• Esophageal Adenocarcinoma Organoid Genomics

  Single cell RNA-seq (scRNA-seq) of esophageal adenocarcinoma organoids to benchmark variant calling from 10X Genomics scRNA-seq data. Five EAC organoids were subjected to scRNA-seq and two included matched exome data.

  Study EGAS00001005224

• Methylation profiling of osteoblastomas and their mimics

  Here, we set out to comprehensively characterize a series of 77 osteoblastomas by immunohistochemistry, fluorescence in-situ hybridization as well as copy number and methylation profiling and compared our findings to histologic mimics.

  Study EGAS00001005932

• Denisova admixture in Southeast Asia and Oceania

  Genome-wide data from populations from south Asia, Southeast Asia, and Oceania were analyzed for signatures of Denisova admixture to infer the history of human migration to this part of the world

  Study EGAS00001006132

• Molecular Classification of Hormone Sensitive and Castration Resistant prostate cancer, using non-negative matrix factorization molecular subtyping

  We aim to identify molecular subtypes of prostate cancer using consensus non-negative matrix factorization and correlate these with existing biomarkers to inform future immunotherapeutic strategies.

  Study EGAS00001006204

• GeneSTAR (Genetic Study of Atherosclerosis Risk) NextGen Consortium: Functional Genomics of Platelet Aggregation Using iPS and Derived Megakaryocytes

  The causal mechanisms of common diseases and their therapies have been only marginally illuminated by genetic variants identified in genome wide association studies (GWAS) utilizing single nucleotide polymorphism (SNPs). Platelet activation pathways reflecting hemostasis and thrombosis are the underlying substrate for many cardiovascular diseases and related acute events. To overcome GWAS limitations, genomic studies are needed that integrate molecular surrogates for platelet-related phenotypes assayed in cell-based models derived from individuals of known genotypes and phenotypes. In our GWAS study of native platelet aggregation phenotypes and aggregation in response to low dose aspirin in 2200 subjects (GeneSTAR, Genetic Study of Aspirin Responsiveness), important genome wide "signals" (p<5x10-8) associated with native platelet aggregation and important "signals" associated with platelet responsiveness to aspirin were identified and replicated. Although we are currently performing functional genomics studies to elucidate our most promising findings in known genes (PEAR1, MET, PIKC3G), most "signals" occurred in intergenic regions or in introns. Mechanistic interpretation is limited by uncertainty as to which gene(s) are up- or down-regulated in the presence of most SNP modifications. In this 3 phase proposal, we will (1) create pluripotent stem cells (iPS) from peripheral blood mononuclear cells, and then differentiate these stem cells into megakaryocytes (2) develop an efficient strategy to produce iPS and megakaryocytes using a novel pooling method, and (3) produce iPS and megakaryocytes from 250 subjects in GeneSTAR (European Americans and African Americans), selected based on specific hypotheses derived from GWAS signals in native and post aspirin platelet function; characterize genetic mRNA transcripts using a comprehensive Affymetrix array; measure protein expression for transcripts of interest using mass spectrometry; examine mRNA and protein expression patterns for each GWAS signal to determine the functional pathway(s) involved in native platelet phenotypes; and examine the functional genomics of variations in responsiveness to aspirin using our prior genotyped and phenotyped population. Precise information about the exact functional processes in megakaryocytes and platelets may lead to innovative and tailored approaches to risk assessment and novel therapeutic targets to prevent first and recurrent cardiovascular and related thrombotic events.

  Study phs001074

• ALS Compute

  Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive loss of brain and spinal cord motor neurons. Half of ALS patients display cognitive symptoms of frontotemporal dementia (FTD); reciprocally, about 40% of FTD patients show motor neuron deficits, and approximately 15% develop overt ALS. The clinical overlap between ALS and FTD means that the two conditions are thought to represent a disease spectrum (ALS/FTD). In recent years, the identification of several genetic causes of ALS/FTD has contributed significantly to our understanding of disease pathogenesis. Unfortunately, one-third of the underlying genetic causes of familial cases and ~90% of sporadic cases of ALS/FTD remain unexplained. As such, there is a dire need to identify additional genetic factors contributing to ALS/FTD. Such studies require huge cohorts of harmonized whole genome sequencing (WGS) data sets from cases and controls. Currently, there are several major ongoing sequencing efforts for ALS patients. Numerous centers lead to inefficiency, especially in terms of overall costs. At a minimum, this includes the cost of high-performance computing, the storage of large data files, and the duplication of effort between groups. This lack of data harmonization between groups precludes sharing of genetic information and weakens collaborative efforts. The cost and logistics are also a barrier to attracting talented investigators to the ALS/FTD field. To overcome this unmet need, we have founded the ALS Compute project. We are centralizing the storage of ALS/FTD WGS data from every significant sequencing effort in the United States and beyond within a single Cloud environment. This approach will facilitate data harmonization and improve accessibility to the data. To accomplish this, we have made the data and the computational infrastructure available via the Terra platform hosted by NHGRI's Genomic Data Science Analysis, Visualization, and Informatics Lab-Space (AnVIL). This will allow researchers worldwide to access this wealth of data, develop new theories of the disease, and yield breakthroughs in our understanding of ALS/FTD.

  Study phs003184

• Northwestern NUgene Project: Type 2 Diabetes

  The ability to correlate genetic variation with disease susceptibility and response to drug therapy depends on genotype or sequence analysis of large numbers of richly characterized DNA samples. Eight years agoWe are a part of NHGRI's electronic Medical Records and Genomics (eMERGE) Network, whose goal is to conduct genome-wide association studies in thousands of individuals using EMR-derived phenotypes and DNA from linked biorepositories. For eMERGE, Northwestern University (NU) is studying type 2 diabetes as a phenotype. In addition, in order to explore race differences in the prevalence of type 2 diabetes, NU collaborated with Vanderbilt University to study a mix of both Caucasian and African-Americans. Northwestern University: In 2002, Northwestern committed to the development of a DNA repository to serve as a platform for the identification and validation of genotype-phenotype associations that will impact healthcare. 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 questionnaire (2 versions were used, 1 before and 1 after February 2006, both are included) 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 phs000237

• Exceptional Responders Initiative

  The Exceptional Responders Initiative is a pilot study to investigate the underlying molecular factors driving exceptional treatment responses of cancer patients to drug therapies. Study researchers will examine molecular profiles of tumors from patients either enrolled in a clinical trial for an investigational drug(s) and who achieved an exceptional response relative to other trial participants, or who achieved an exceptional response to a non-investigational chemotherapy. An exceptional response is defined as achievement of either a complete response or a partial response for at least 6 months duration in a trial or treatment where the overall response rate is < 10%. The hope is to discover underlying molecular features that can be further investigated and may eventually predict benefit from a given drug or class of drugs for a particular patient. This pilot project will successfully characterize approximately 100 cases of tumor tissue and, when available, case-matched germline DNA. All samples will undergo whole exome sequencing, and cases with sufficient nucleic acids will undergo additional analyses (e.g. whole genome sequencing, mRNA-sequencing, mi RNA sequencing, promoter methylation analysis, SNP etc). Each case will be annotated with demographic and clinical information, along with follow-up information minimally sufficient to correlate molecular profiles with response. Both retrospective and prospective collections will be considered. The project will also accept sequencing data and clinical data from patients who have had sequencing performed outside of this project. All data will be de-identified and placed in a controlled access database so other investigators may use them for additional insights. Clinically annotated tissue specimens meeting the criteria will be provided by groups participating in the Exceptional Cases Initiative to a Biospecimen Core Resource (BCR), which will perform quality control on the tissues, and will use a standard operating procedure to isolate nucleic acids. The nucleic acids will be shipped to a sequencing center to perform whole exome sequencing and analysis. These findings will be made available to the broader cancer research community in a controlled access database.

  Study phs001145