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• Data access policy for PDTX Breast Cancer data from Bruna et al (2016), Cell.

  Dac EGAC00001000540

• CIR-RIMLS committee on data access to Immunological mechanisms for celiac disease database

  Dac EGAC00001000557

• Data access committee for the pseudomyxoma peritonei exome sequencing vcf-files

  Dac EGAC00001000649

• DAC for "A Functional Network of Gastric-Cancer-Associated Splicing Events Controlled by Dysregulated Splicing Factors"

  Dac EGAC00001000675

• DAC for Hungarian human exome team (Department of Medical Biology, University of Szeged, Hungary)

  Dac EGAC00001000838

• Data access committee handling data access requests for biomarker data from the clinical trial IMvigor210.

  Dac EGAC00001000945

• DAC for study: "Circulating tumour DNA abundance and potential utility in de novo metastatic prostate cancer"

  Dac EGAC00001001099

• Control iPSC lines with clinically annotated genetic variants for versatile multi-lineage differentiation Committee (part II)

  Dac EGAC00001001332

• DAC Therapeutic vulnerabilities in the DNA damage response for the treatment of ATRX mutant neuroblastoma

  Dac EGAC00001001675

• The data access committee for Detection and characterization of lung cancer using cell-free DNA fragmentomes

  Dac EGAC00001002184

• Data Access Committee for the MAXOMOD Consortium - E-Rare / European Joint Programme on Rare Diseases

  Dac EGAC00001003287

• Integration of intra-sample contextual error modeling for improved detection of somatic mutations

  Study EGAS00001003806

• The WID-EC test for the detection and risk prediction of endometrial cancer

  Study EGAS00001005033

• Evaluating CRISPR-based Prime Editing for cancer modeling and CFTR repair in organoids

  Study EGAS00001005358

• NPY methylated ctDNA is a promising biomarker for treatment response monitoring in metastatic colorectal cancer

  Study EGAS00001006820

• MPIMG DAC

  DAC of the Max Planck Institute for Molecular Genetics

  Dac EGAC50000000149

• Stage-1 GWAS

  Summary statistics from Stage-1 GWAS for blood pressure phenotypes

  Dataset EGAD00010001569

• Quad samples for study EGAS00001001023

  Dataset EGAD00001001126

• TRAIP patients

  BAM files for two WES TRAIP patients

  Dataset EGAD00001001633

• Cancer Alliance WGS

  Whole genome sequencing for individualized cancer interpretation

  Dataset EGAD00001006233

• NHLBI TOPMed - NHGRI CCDG: The BioMe Biobank at Mount Sinai

  The IPM BioMe Biobank, founded in September 2007, is an ongoing, broadly-consented electronic health record (EHR)-linked clinical care biobank that enrolls participants non-selectively from the Mount Sinai Medical Center patient population. BioMe currently comprises >42,000 participants from diverse ancestries, characterized by a broad spectrum of longitudinal biomedical traits. Participants are enrolled through an opt-in process and consent to be followed throughout their clinical care (past, present, and future) in real-time, allowing us to integrate their genomic information with their EHRs for discovery research and clinical care implementation. BioMe participants consent for recall, based on their genotype and/or phenotype, permitting in-depth follow-up and functional studies for selected participants at any time. Phenotypic and genomic data are stored in a secure database and made available to investigators, contingent on approval by the BioMe Governing Board. BioMe uses a "data-broker" system to protect confidentiality. Ancestral diversity - BioMe participants represent a broad racial, ethnic and socioeconomic diversity with a distinct and population-specific disease burden. Specifically, BioMe participants are of African (AA), Hispanic/Latino (HL), European (EA) and other/mixed ancestry. BioMe participants are predominantly of African (AA, 24%), Hispanic/Latino (HL, 35%), European (EA, 32%), and other ancestry (OA, 10%). Participants who self-identify as Hispanic/Latino further report to be of Puerto Rican (39%), Dominican (23%), Central/South American (17%), Mexican (5%) or other Hispanic (16%) ancestry. More than 40% of European ancestry participants are genetically determined to be of Ashkenazi Jewish ancestry. With this broad ancestral diversity, BioMe is uniquely positioned to examine the impact of demographic and evolutionary forces that have shaped common disease risk. Phenotypes available in BioMe - BioMe has a high-quality and validated set of fully implemented clinical phenotype data that has been culled by a multi-disciplinary team of experienced investigators, clinicians, information technologists, data-managers, and programmers who apply advanced medical informatics and data mining tools to extract and harmonize EHRs. BioMe, as a cohort, offers a great versatility for designing nested case-control sample-sets, particularly for studying longitudinal traits and co-morbidity in disease burden. Biomedical and clinical outcomes: The BioMe Biobank is linked to Mount Sinai's system-wide Epic EHR, which captures a full spectrum of biomedical phenotypes, including clinical outcomes, covariate and exposure data from past, present and future health care encounters. As such, the BioMe Biobank has a longitudinal design as participants consent to make all of their EHR data from past (dating back as far as 2003), present and future inpatient or outpatient encounters available for research, without restriction. The median number of outpatient encounters is 21 per participant, reflecting predominant enrollment of participants with common chronic conditions from primary care facilities. Environmental data: The clinical and EHR information is complemented by detailed demographic and lifestyle information, including ancestry, residence history, country of origin, personal and familial medical history, education, socio-economic status, physical activity, smoking, dietary habits, alcohol intake, and body weight history, which is collected in a systematic manner by interview-based questionnaire at time of enrollment. The IPM BioMe Biobank contributed ~10,600 DNA samples for whole genome sequencing to the TOPMed program. Samples were selected for the Coronary Artery Disease (CAD) and the Chronic Obstructive Pulmonary Disease (COPD) working groups. Using a Case-Definition-Algorithm (CDA), we identified ~4,100 individuals with CAD (~50% women) and ~3,000 individuals as controls (65% women). In addition, we identified ~800 individuals with COPD (62% women) and 1800 individuals as controls (72% women). Another 600 BioMe participants with Atrial Fibrillation, all of African ancestry, were included.

  Study phs001644

• DMET Genes, Nicotine Metabolism and Prospective Abstinence

  This study (DA033813; PI: Andrew W Bergen; PMID:26132489) includes samples from two laboratory studies of nicotine metabolism. The Pharmacokinetics of Nicotine Metabolism in Twins study (PKTWIN; PI: Gary E Swan; PMID: 15527659) was based on recruitment from a twin registry (PMID: 23084148). The Integrated Research Project on Tobacco Use and Dependence (IRP; PI: Gary E Swan; PMID: 14578134) was based on recruitment from a pedigree-based longitudinal study of risk factors for substance use, the Smoking in Families study (SMOFAM; DA03706; PI: Hy Hops). These two laboratory studies (PKTWIN and IRP/SMOFAM) served as the Stage I dataset to interrogate Drug Metabolizing Enzyme and Transporter genes with a targeted SNP array for association with the Nicotine Metabolite Ratio (NMR, ratio of trans-3'-hydroxycotinine and cotinine), an established biomarker of nicotine metabolism. In addition to the laboratory studies, samples from eight RCTs (PMID: 23249876) with the NMR and smoking-related measures used to test SNPs identified in Stage I (PMID: 26132489). In a third stage, a lung cancer meta-analysis database (PMID: 24880342) was used to assess association of SNPs identified in Stage II with lung cancer. The objectives of the study were to identify novel genes and SNPs contributing to nicotine metabolism (Stage I), and to validate PK SNPs associated with the NMR from individuals participating in a clinical laboratory protocol with the NMR obtained from treatment-seeking smokers, and then to investigate association with prospective smoking cessation (Stage II). This study built upon existing studies of nicotine metabolism and randomized trials of smoking cessation therapies. Enhanced knowledge of the genes influencing nicotine metabolism and prospective abstinence may help personalize smoking cessation treatment and risk assessment for smoking-related diseases. For Stage I, both subject [fixed-dose NMR, covariates (age, BMI, ethnicity, sex, smoking status, and hormone use), and pedigree relationships] and sample (common DMET SNP genotype, genotyping quality control) data are available in this accession. The analysis protocol, quality control summaries, summary genotype, summary phenotype, and analysis results are available for Stage I, II and III samples (PMID: 26132489). Extensive discussion of the prior CYP2A6 association literature with the NMR, abstinence, smoking heaviness and lung cancer risk is available (PMID: 26132489). The NMR has previously been associated with CYP2A6 activity, response to smoking cessation treatments, and cigarette consumption. We searched for drug metabolizing enzyme and transporter (DMET) gene variation associated with the NMR and prospective abstinence in 2,946 participants of laboratory studies of nicotine metabolism and of clinical trials of smoking cessation therapies. Stage I was a meta-analysis of the association of 507 common single nucleotide polymorphisms (SNPs) at 173 DMET genes with the NMR in 449 participants of two laboratory studies. Nominally significant associations were identified in ten genes after adjustment for intragenic SNPs; CYP2A6 and two CYP2A6 SNPs attained experiment-wide significance adjusted for correlated SNPs (CYP2A6 PACT=4.1E-7, rs4803381 PACT=4.5E-5, rs1137115, PACT=1.2E-3). Stage II was mega-regression analyses of 10 DMET SNPs with pretreatment NMR and prospective abstinence in up to 2,497 participants from eight trials. rs4803381 and rs1137115 SNPs were associated with pretreatment NMR at genome-wide significance. In post-hoc analyses of CYP2A6 SNPs, we observed nominally significant association with: abstinence in one pharmacotherapy arm; cigarette consumption among all trial participants; and lung cancer in four case:control studies. CYP2A6 minor alleles were associated with reduced NMR, CPD, and lung cancer risk. We confirmed the major role that CYP2A6 plays in nicotine metabolism, and made novel findings with respect to genome-wide significance and associations with CPD, abstinence and lung cancer risk. Additional multivariate analyses with patient variables and genetic modeling will improve prediction of nicotine metabolism, disease risk and smoking cessation treatment prognosis.

  Study phs000931

• A Genome-Wide Association Analysis in Angiotensin-Converting Enzyme (ACE) Inhibitor-Associated Angioedema and ACE Inhibitor-Exposed Controls; A Collaboration between the NIH Pharmacogenomics Research Network and the RIKEN Yokohama Center for Genomic Medicine

  The purpose of this study is to identify genetic predictors of ACE inhibitor-associated angioedema. In addition to preventing the formation of the pressor angiotensin II, ACE inhibitors prevent the carboxyl-terminal degradation of the vasoactive substances bradykinin and substance P. Angioedema is hypothesized to result from defective amino-terminal degradation of bradykinin or substance P in patients in whom ACE is inhibited. For example, activity of dipeptidyl peptidase IV (DPP-IV), the enzyme responsible for the inactivation of substance P when ACE is inhibited, is decreased in patients with angioedema. In preliminary studies, we have identified SNPs in the DPP4 gene that associate with DPP-IV activity and, in blacks, with risk of angioedema. This project will use genome-wide genotyping to compare 250 cases and 568 ACE inhibitor-exposed control subjects (131 cases and 288 controls ascertained at Vanderbilt and 70 cases and 280 controls ascertained at the Marshfield Clinic). We plan a 2-stage analysis of associations between SNPs and angioedema - first, we will study DPP4 SNPs for association with angioedema and, second, we will explore associations using the full GWAS data set. Depending on the platform, additional DPP4 SNPs will be used to fully tag common genetic variants in both African American and European American samples. Based on the HapMap data, there are 14 tagging SNPs in people of European descent and 34 in Yoruba (selection criteria MAF>0.05 and r2>0.8). Cases were defined as having ACE inhibitor-associated angioedema if they had had swelling of the lips, throat, tongue or face while taking an ACE inhibitor but had never had angioedema while not taking an ACE inhibitor. For simplicity, intestinal edema was excluded. Control subjects were treated for at least 6 months with an ACE inhibitor without angioedema. Because black Americans are known to be overrepresented among patients with ACE inhibitor-associated angioedema, control subjects were prespecified to be 50% black American, 50% white American, and 50% female. At Vanderbilt, the medical history, including the history of angioedema, was confirmed by a research nurse or physician using a detailed case report form. Characteristics of Vanderbilt cases appear in the Table. At Marshfield, medical history will be confirmed by chart review. The Marshfield cohort is 98% white American and 57% female with a mean age of 47.2 years.

  Study phs000438

• DNA methylomes of monozygotic twins clinically discordant for multiple sclerosis

  Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system. Although genetic susceptibility is important, a modest concordance rate for MS in monozygotic (MZ) twins suggests that interaction with other risk factors is required to develop clinical symptoms. In this study, we examined whether DNA methylation differences contribute to the discordant clinical manifestation of MS in MZ twins, and studied the impact of MS treatments on the DNA methylome. Genome-wide DNA methylation profiles of peripheral blood mononuclear cells (PBMCs) of 45 MZ twins clinically discordant for MS were generated using the Illumina HumanMethylationEPIC array. Repetitive element methylation and selected differentially methylated positions (DMPs) were validated using targeted deep bisulfite sequencing (TDBS). In addition, we performed whole genome bisulfite sequencing (WGBS) to profile CD4+ memory T-cells of a subset of four MS discordant MZ twins. Our results show that interferon treatment causes robust DNA methylation changes and several epigenetic biomarkers for interferon treatment response were identified. However, overall the PBMC-based methylomes of the MS discordant MZ twins were highly similar, since large systematic methylation differences (>5%) were absent in the data. This suggests that previously reported large methylation changes are probably caused by genetic rather than epigenetic differences. In addition, our data does not support the hypothesis that the observed maternal parent-of-origin effect in MS is due to genomic imprinting errors, and no evidence was found that copy number variations explain the discordant phenotype in these MZ twins. Although not genome-wide significant, a couple of DMPs associated with the MS phenotype were identified and successfully technically replicated using TDBS, including a differentially methylated region (DMR) in the promoter of the transmembrane protein encoding gene TMEM232. Another MS-associated DMP, located in an enhancer in the gene body of the transcription factor ZBTB16, was also associated with medium-term glucocorticoid treatment history. WGBS analysis confirmed this DMP as a promising epigenetic biomarker for glucocorticoid treatment. In conclusion, this epigenome-wide association study (EWAS) in genetically identical twins identified a DMR in the TMEM232 promoter as a candidate loci associated with the clinical manifestation of MS. In addition, epigenetic biomarkers for MS treatments were identified, revealing that not only short-term, but also medium-term treatment effects are detectable in immune cells, which should be taken into account in future EWAS designs.

  Study EGAS00001003147

• Institute for Refractory Cancer Research (IRCR) Data Access Committee

  Dac EGAC00001000272