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• Knoll et al Identification of drug candidates targeting monocyte reprogramming in people living with HIV

  Introduction People living with HIV (PLHIV) are characterized by functional reprogramming of innate immune cells even after long-term antiretroviral therapy (ART). In order to assess technical feasibility of omics technologies for application to larger cohorts, we compared multiple omics layers. Methods Bulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivo drug stimulation was performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study. Results Single-cell RNA-seq analysis revealed that most immune cells in peripheral blood of PLHIV are altered in their transcriptomes and that a specific functional monocyte state previously described in acute HIV infection is still existing in PLHIV while other monocyte cell states are only occurring acute infection. Moreover, a reverse transcriptome approach on a rather small number of PLHIV was sufficient to identify drug candidates for reversing the transcriptional phenotype of monocytes in PLHIV. Discussion These scientific findings and technological advancements for clinical application of single-cell transcriptomics form the basis for the larger 2000-HIV multicenter cohort study on PLHIV, for which a combination of bulk and single-cell transcriptomics will be included as the leading technology to determine disease endotypes in PLHIV and to predict disease trajectories and outcomes.

  Study EGAS00001007460

• Single cell transcriptional characterization of human megakaryocyte lineage commitment and maturation

  Single cell RNA-sequencing of sternal bone marrow reciding Hematopoietic Stem Cells (HSCs) and Megakaryocytes (MKs) from individuals undergoing elective open heart valve replacement. HSCs were defined as Lineage-, CD34+, CD38-, CD45RA-, CD90+, CD49f+ cells. MKs where CD41a+, CD42b+ and ploidy was determined with Hoechst. A sternal bone marrow scraping was taken directly following median sternotomy using a Volkmann’s spoon. The sample was collected into an EDTA Vacutainer tube containing 1.8mg/ml EDTA. 4mL of Dulbecco’s phosphate buffered saline (PBS, Sigma) containing 10% human serum albumin (HSA, Gemini Bio Products) was added and the whole volume was resuspended by pipetting 2-3 times. The sample was then put on metallic thermal beads (ThermoFisher Scientific) at a temperature between 0-4°C and transported to the University of Cambridge for further processing. For HSC isolation the cells were stained with the following antibody cocktail: PECy5 conjugated anti-lineage specific antibodies: CD2 (BD), CD3 (BD), CD10 (BD), CD11b (BD), CD11c (BD), CD19 (BD), CD20 (BD), CD56 (BD), biotinylated CD42b (Pab5, NHS Blood and Transplant, International Blood Group Reference Laboratory [IBGRL]), biotinylated GP6 (Pab5, NHS Blood and Transplant, International Blood Group Reference Laboratory [IBGRL]) used in combination with PECy5 conjugated streptavidin (Biolegend). Alexa Fluor 700 conjugated anti-CD34 (BD), PerCP-Cy5.5 conjugated anti-CD38 (BD), Pacific Blue conjugated anti-CD45RA (Invitrogen), PECy7 conjugated anti-CD90 (BD),PE conjugated anti-CD49f (BD). After staining cells were kept at 4°C before sorting using a FACS Aria Fusion flow sorter (BD). Single HSCs defined as Lineage-, CD34+, CD38-, CD45RA-, CD90+, CD49f+ cells were sorted by FACS directly into individual wells of a 96-well plate. Index sort data was collected for each single cell. For MK isolation the cells were stained for surface MK markers with mouse anti-human CD41a APC conjugated antibody (BD) and mouse anti-human CD42b PE conjugated antibody (BD) and for ploidy analysis with 1ug/ml Hoechst 33342 (Invitrogen). After incubation at 37°C for 30 minutes, the cells were kept at 4°C before sorting using a FACS Aria Fusion flow sorter (BD). Single cells and MK pools of 20 cells were sorted by FACS according to ploidy level using a 100uM nozzle directly into individual wells of a 96-well plate. cDNA synthesis and poly(A) enrichment was performed following the G&T-seq protocol (Macaulay et al. 2015), a variation of the Smart-seq2 protocol1. ERCC spike-in RNA (Ambion) was added to the lysis buffer in a dilution of 1:4,000,000.

  Dataset EGAD00001006677

• RNA-sequencing of human skin samples obtained from SSc patients and healthy controls.

  Patient information SSc patients were recruited at the Department of Rheumatology of the Leiden University Medical Center (Leiden, The Netherlands). All patients met the American Rheumatism Association classification criteria for SSc (Subcommittee for scleroderma criteria 1980), and were classified according to LeRoy and Medsger criteria as either limited or diffuse cutaneous disease (LeRoy EC, Black C, Fleischmajer R, Jablonska S, Krieg T, Medsger TA Jr, Rowell N 1988). Institutional review board approval and written informed consent was obtained before patients entered this study. Two 4 mm skin biopsies were taken from a standardized location on the most proximal part of the lower arm, distal from the elbow. In 10 patients the skin biopsy came from a clinically affected area and in 4 patients the skin was locally unaffected. One sample was used for RNA sequencing and one sample was used for immunohistochemistry. Skin biopsies from healthy individuals were commercially sourced (Tissue Solutions, UK) and collected from donors undergoing skin resection surgery and after informed consent. To match the healthy skin with patients as much as possible, skin biopsies from healthy controls were also taken from a similar position (the under-arm (for 4 controls) and leg (for 2 controls)). Healthy skin donors were selected to match the age and sex of the SSc patient cohort. Biopsies from patients and controls were equally treated and were both stored at -80°C until RNA isolation was performed. RNA from frozen skin biopsies was isolated using RNeasy kit from fibrous tissue (Qiagen, the Netherlands). RNA quantity was determined by using SimplyNano 2000 and quality was assessed on Tapestation (Agilent, the Netherlands). All samples included in the study had a RIN score above 7.0. Transcriptome characterisation and analysis RNA sequencing was performed using polyA selection and a stranded protocol using Ion Torrent next generation sequencing technology (Service XS, The Netherlands). The Ion PI Template OT2 200 Kit v3 and Ion PI Sequencing 200 Kit v3 were used according to the manufacturer’s instructions. 20 samples were run on 11 PI chips. PI chip analyses, base calling and quality checks were performed using the Torrent Server Suite. An average of 42 million 100 bp reads was generated per sample. Following quality control, reads were aligned to the human genome (Homo sapiens GRh38.78) using Bowtie2 and STAR (Dobin et al. 2013; Langmead and Salzberg 2012). Reads were first aligned with STAR. For the unmapped reads from STAR, a second alignment step was performed using bowtie2 (local very sensitive options)

  Dataset EGAD00001003832

• RRBS data (cfSort study) from noncancer tissue DNA

  The RRBS libraries of the genomic DNA from the 521 tissue samples were constructed following the standard RRBS protocol. 100-200 ng of intact genomic DNA in the volume of 21.5 µl was used as input material. Restriction digestion was done with 2.5 µl 10xCutSmart buffer and 1 µl MspI (NEB) for 18 h at 37 oC and 20 min at 65 oC. 0.5 µl 10xCutSmart buffer, 0.3 µl dACGTP mixture (100 mM dATP, 10 mM dCTP, 10 mM dGTP), 1 µl Klenow (exo-, 5U/µl, NEB) and 2.6 µl RT-PCR water, 0.6 µl 50 mM DTT (ThermoFisher) was added to the mixture for end repair and A-overhang addition with the program 30 oC for 20 min, 37 oC for 1 h and 75 oC for 20 min. Adapter ligation was then performed with 1 µl 10xThermoFisher HC T4 ligase buffer, 0.4 µl 100 mM ATP (ThermoFisher), 0.2 µl 50 mM DTT, 1 µl ThermoFisher HC T4 DNA ligase (30 Weiss Unit/µl), 30 ng home-made duplex UMI adapter with all the cytosines methylated (protocol adopted from Kennedy et al.) at 16 oC for 20 h and 65 oC for 20 min. Bisulfite conversion of the adapter-ligated product was carried out with QIAGEN EpiTect plus DNA bisulfite kit following their protocol for two rounds of conversion. The converted product was purified with Qiagen MinElute spin column and eluted with 20 µl RT-PCR water. PCR amplification was done using the NEBNext Multiplex Oligos for Illumina (2.5 µl of universal and index primer each) and 25 µl KAPA HiFi HotStart Uracil+ ReadyMix (Roche) with the following cycling conditions: 98 oC for 45 s, 9 cycles of 98 oC for 15 s, 60 oC for 30 s and 72 oC for 30 s, followed by a final extension at 72 oC for 5 min. The PCR product was purified with 1x AmpureXP beads and eluted with 30 µl EB buffer. DNA concentration was measured by Qubit 1xdsDNA HS assay. 5% TBE-UREA PAGE and bioanalyzer assay was performed as quality control on each library before sequencing.

  Dataset EGAD00001010880

• Whole Genome Sequencing of clonal expansions of single healthy somatic cells (human, female)

  HSC73_clone: Bone marrow mononuclear cells from the healthy 73 years old female were thawed and labeled with Alexa-Fluor 488-conjugated anti-CD34 (581, Biolegend), Alexa-Fluor 700-conjugated anti-CD38 (HIT2, eBioscience), a cocktail of APC-conjugated lineage antibodies consisting of anti-CD4 (RPA-T4), anti-CD8 (RPA-T8), anti-CD11b (ICRF44), anti-CD20 (2H7), anti-CD56 (B159, all BD Biosciences), anti-CD14 (61D3), anti-CD19 (HIB19) and anti-CD235a (HIR2, all eBiocience) and 1 micro-gram/ml propidium iodide (Sigma). Using a BD FACSAria cell sorter, single Lin-CD34+CD38-PI- cells were individually sorted into low-adhesion 96-well tissue culture plates (Corning) containing 100micro-litre of StemSpan Serum-Free Expansion Medium (Stemcell technologies) supplemented with 100ng/ml of human SCF and FLT-3L, 50ng/ml of human TPO, 20ng/ml of human IL-3, IL-6 and G-CSF (all cytokines from Peprotech) and 50U/ml of penicillin and 50μg/ml of streptomycin (Sigma). Cells were incubated at 37 degrees C in a humidified atmosphere with 5% CO2 in air. After 5 days in culture, another 100micro litres of cytokine-containing medium were added. 13 days after seeding, clones B6 and G2 had expanded to approx. 105 cells and were selected for whole genome sequencing (2x101bp, paired-end, Illumina HiSeq2500) after tagmentation-based library preparation (see Extended Experimental Procedures) for clone B6 and standard library preparation for clone G2. For germline-control ~106 unsorted bone marrow mononuclear cells from the same donor were used for sequencing. An average of 30-fold sequence coverage for each the clones and the matching control were obtained.L4clone: A progenitor cell clone was raised from a peripheral blood sample of the 39 year old healthy female. Frozen peripheral blood mononuclear cells (PBMCs) were isolated from 2 ml heparinised peripheral blood via Ficoll Paque density centrifugation. A methylcellulose assay was performed as described earlier (Weisse et al., 2012). In brief, non-adherent mononuclear cells were incubated in the presence of the recombinant human cytokines IL-3, IL-5 and GM-CSF (R&D systems) over 14 days to induce colony formation. Colonies were detected under an inverted light microscope, and plucked by a pipette when colonies had approximately 10,000 cells/CFU. Each colony was washed three times in PBS and finally frozen as a cell pellet in -80 degrees C. Genomic DNA was isolated using the QIAamp DNA micro kit according to the instructions of the manufacturer (Qiagen, Hilden, Germany). Whole genome sequencing (2x101bp, paired-end, Illumina HiSeq2500) was performed for colony 4 after tagmentation-based library preparation and resulted in 15-fold sequence coverage for each the colony and the matching whole blood.

  Dataset EGAD00001000666

• NHLBI TOPMed: The Jackson Heart Study (JHS)

  Since there is a greater prevalence of cardiovascular disease among African Americans, the purpose of the Jackson Heart Study (JHS) is to explore the reasons for this disparity and to uncover new approaches to reduce it. The JHS is a large, community-based, observational study whose 5306 participants were recruited from among the non-institutionalized African-American adults from urban and rural areas of the three counties (Hinds, Madison, and Rankin) that make up the Jackson, MS, metropolitan statistical area (MSA). Jackson is the capital of Mississippi, the state with the largest percentage (36.3%) of African Americans in the United States. The JHS design included participants from the Jackson ARIC study who had originally been recruited through random selection from a drivers' license registry. Approximately six months before the JHS was to begin, an amendment to the federal Driver's Privacy Protection Act was passed that changed the level of consent for public release of personal information from driver's license lists from an "opt out" to an "opt in" basis. The Mississippi Highway Patrol was no longer able to release a complete listing of all persons with driver's licenses or state identification cards, which prevented its use in the JHS. New JHS participants were chosen randomly from the Accudata America commercial listing, which provides householder name, address, zip code, phone number (if available), age group in decades, and family components. The Accudata list was deemed to provide the most complete count of households for individuals aged 55 years and older in the Jackson MSA. A structured volunteer sample was also included in which demographic cells for recruitment were designed to mirror the eligible population. Enrollment was opened to volunteers who met census-derived age, sex, and socioeconomic status (SES) eligibility criteria for the Jackson MSA. In addition, a family component was included in the JHS. The sampling frame for the family study was a participant in any one of the ARlC, random, or volunteer samples whose family size met eligibility requirements. Eligibility included having at least two full siblings and four first degree relatives (parents, siblings, children over the age of 21) who lived in the Jackson MSA and who were willing to participate in the study. No upper age limit was placed on the family sample. Known contact information was obtained during the baseline clinic examination from the index family member with a verbal pedigree format to identify name(s), age(s), address (es), and telephone number(s). Recruitment was limited to persons 35-84 years old except in the family cohort, where those 21 years old and above were eligible. Only persons who otherwise met study criteria but were deemed to be physically or mentally incompetent by trained recruiters were excluded from study eligibility.1 1 Wyatt SB, Diekelmann N, Henderson F, Andrew ME, Billingsley G, Felder SH et al. A community-driven model of research participation: the Jackson Heart Study Participant Recruitment and Retention Study. Ethn Dis 2003; 13(4):438-455 (PMID: 14632263).

  Study phs000964

• Resuscitation Outcomes Consortium (ROC) Trauma Epidemiologic Registry (Trauma Epistry) (ROC-Trauma Epistry-BioLINCC)

  Data Access NOTE: Please refer to the "Authorized Access" section below for information about how access to the data from this accession differs from many other dbGaP accessions.Available Data: This dataset only includes traumatic injury participants. For ROC cardiac arrest Epistry data please see: ROC-Cardiac Epistry 1 and 2 and ROC-Cardiac Epistry 3.Objectives: To build a prospective population-based registry of participants with out-of-hospital traumatic injury responded to by Emergency Medical Services (EMS). Specific aims included: to establish whether the results of Resuscitation Outcomes Consortium (ROC) trials can be generalized to the larger population of people that experience traumatic injury; to more fully establish the burden of traumatic injury; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes.Background: Traumatic injury is a major public health problem generating substantial morbidity, mortality, and economic burden on society. The majority of seriously injured persons are initially evaluated and cared for by prehospital providers, however the effect of EMS systems, EMS clinical care, and EMS interventions on trauma patient outcomes is largely unknown. EMS factors such as service level, number of responding providers, use of procedures or drugs in the field, training, quality assurance/feedback, and response time intervals also vary significantly by region.The Resuscitation Outcomes Consortium (ROC) was established in 2004 to conduct clinical research in the areas of cardiopulmonary arrest and life-threatening traumatic injury with the overall goal of improving resuscitation outcomes. Previous trauma registries have generally focused primarily on hospitalized patients with limited prehospital information. Registries may also exclude trauma cases at far ends of the spectrum, such as those who die in the field or in a non-trauma center and/or patients that are treated and released. These limitations do not allow for detailed, outcome-based assessments of EMS system factors necessary to define prehospital resuscitation best practices. Therefore there was a need for standardized data collection of out-of-hospital traumatic injuries matched to hospital-based outcomes in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants: The registry included 13,830 traumatic events from 264 EMS agencies transporting to 287 acute care hospitals from the following regional centers: Birmingham, Alabama; Dallas, Texas; Iowa City, Iowa; Milwaukee, Wisconsin; Pittsburgh, Pennsylvania; Portland, Oregon; San Diego, California; Seattle/King County, Washington; Ottawa, Ontario; Toronto, Ontario; and Vancouver, British Columbia.Design: ROC Epistry collected standardized data regarding episode-specific factors, participant demographics, clinical information, pre-hospital interventions and disposition, hospital information, and participant outcome for all out-of-hospital traumatic injuries in the ROC regions. Cases were identified through review of emergency response system records including dispatch centers, EMS ground agencies, and air medical services. Out-of-hospital data were extracted from existing databases whenever possible and augmented with targeted review of EMS reports. Hospital data were abstracted directly from the hospital file in most cases. Sites submitted data using a web-based interface or batch uploads (Newgard, et al., 2008, PMID: 18482792).

  Study phs003809

• Best Endovascular vs. Best Surgical Therapy in Patients With Critical Limb Ischemia (BEST CLI-BioLINCC)

  Data Access NOTE Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives To compare the effectiveness of surgical treatment to the effectiveness of endovascular treatment in adults with chronic limb-threatening ischemia who are eligible for both treatment options. Background Peripheral artery disease (PAD) affects more than 200 million people worldwide. A portion of those individuals develop chronic limb-threatening ischemia (CLTI). CLTI is associated with severe health outcomes. CLTI is defined by ischemic foot pain at rest, ischemic ulcerations, or gangrene. Treatment for CLTI includes revascularization to improve limb perfusion and limit the risk of amputation. However, the choice of surgery or endovascular therapy for revascularization varies greatly. The extent to which this variability affects clinical outcomes in patients with CLTI is unknown. The BEST-CLI trial was initiated to determine whether endovascular revascularization was superior to surgical revascularization in patients with CLTI. Participants A total of 1434 participants with a single segment of great saphenous vein comprised cohort 1. Of these, 718 participants were randomly assigned to the surgical treatment group and 716 participants were randomly assigned to the endovascular therapy group. A total of 396 participants needing an alternative conduit comprised cohort 2. Of these, 197 participants were randomly assigned to the surgical treatment group and 199 participants were randomly assigned to the endovascular therapy group. Design BEST-CLI was an international, randomized, open-label trial. Participants were assigned to one of two cohorts based on the availability of a single segment of great saphenous vein. Participants in both cohorts were randomly assigned to either surgery or endovascular therapy. Follow-up was performed at 30 days after the procedure, 3 months, 6 months, and every 6 months thereafter up to 84 months after randomization. Telephone visits in lieu of clinic visits were planned at 30 months and every 12 months thereafter and at the end of the trial. Participants were followed for major adverse cardiovascular events (myocardial infarction, stroke, or death from any cause), quality of life, level of pain, and performance on the six-minute walk test. The primary outcome was a composite of major adverse limb events or death from any cause. A major adverse limb event was defined as above ankle amputation of the index limb or a major index-limb reintervention (new bypass, interposition graft revision, thrombectomy, or thrombolysis). Conclusions Among patients with CLTI who had an adequate great saphenous vein for surgical revascularization (cohort 1), the incidence of a major adverse limb event or death was significantly lower in the surgical group than in the endovascular group. Among the patients who lacked an adequate saphenous vein conduit (cohort 2), the outcomes in the two groups were similar. Farber A, Menard MT, Conte MS, et al. Surgery or Endovascular Therapy for Chronic Limb-Threatening Ischemia. N Engl J Med. 2022;387(25):2305-2316. PMID 36342173

  Study phs003844

• Inorganic Nitrite Delivery to Improve Exercise Capacity in Heart Failure with Preserved Ejection Fraction (INDIE-HFpEF): Heart Failure Network (HFN INDIE-Imaging)

  Data Access NOTE: Please refer to the "Authorized Access" section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objective: To determine the effect of inhaled, nebulized inorganic nitrite on exercise capacity in patients with heart failure with preserved ejection fraction.Background: Approximately half of patients with heart failure have a preserved ejection fraction (HFpEF). However, there are no proven effective medical treatments for this syndrome. Evidence suggests that impairments in nitric oxide availability have a potentially important role in the pathophysiology of HFpEF.Unlike organic nitrates, inorganic nitrite is converted to nitric oxide in the presence of hypoxia and acidosis, conditions that develop during exercise. Because the cardiac, vascular, and skeletal muscle abnormalities that limit physical capacity and contribute to symptoms in patients with HFpEF characteristically develop during exercise, inorganic nitrite may provide the best way to target nitric oxide delivery precisely at the time of greatest need. The HFN-INDIE trial was initiated to test the hypothesis that compared to placebo, longer-term use of inhaled, nebulized inorganic nitrite would enhance peak exercise capacity in patients with HFpEF. Participants: A total of 105 participants were randomized. 53 were randomized to receive nitrite first and 52 were randomized to receive placebo first.Design: HFN-INDIE was a multicenter, randomized, double-blind, placebo-controlled, crossover study. After enrollment, patients underwent baseline studies to determine eligibility. All patients were required to display objective exercise limitation, evidenced by reduced peak oxygen consumption (V̇o2) on cardiopulmonary exercise testing of less than 75% predicted, with a respiratory exchange ratio indicative of maximal effort (≥1.0). Following qualifying exercise testing, eligible participants received an open-label, single-dose run-in of inhaled, nebulized sodium nitrite (80 mg) to assess tolerability, symptoms, and orthostatic vital signs. Patients developing hypotension (systolic blood pressure The prespecified primary end point was peak V̇o2, measured as the highest 30-second average during upright cycle ergometry, during the 4-week period in which patients were receiving inorganic nitrite as compared with placebo. Accelerometry, health-related quality-of-life scores on the self-administered Kansas City Cardiomyopathy Questionnaire (score range, 0-100, with higher scores indicating better quality of life), echocardiographic indicators of cardiac filling pressures measured at trough drug levels (E/e′ ratio, estimated pulmonary artery systolic pressure, and left atrial volume index; lower scores indicate better health for all), ventilatory efficiency (VE/V̇co2, lower indicating better health), exercise time (higher indicating better health), and NT-proBNP levels (lower indicating better health) were also collected.Data Availability: Data available from this study includes transthoracic echocardiogram images from multiple timepoints. There are 199 echocardiographic exams available, totaling over 13,100 individual echocardiogram images. Conclusions: Among patients with HFpEF, administration of inhaled inorganic nitrite for 4 weeks, compared with placebo, did not result in significant improvement in exercise capacity.Reference: Borlaug et al., 2018, PMID: 30398602.

  Study phs003804

• cfMethyl-seq data (cfSort study) from serial plasma samples of NSCLC patients

  The cfMethyl-Seq libraries of the serial plasma cfDNA samples from the four NSCLC patients were constructed following the standard protocol. 10 ng of cfDNA in the volume of 25 µl was used as input material. 5’-end dephosphorylation was done with 3 µl 10xCutSmart buffer and 2 µl quick CIP from NEB (Ipswich, MA) at 37 oC for 30 min then heat-inactivated at 80 oC for 5 min. The 3’-end blocking was done with 0.5 µl 10xCutSmart buffer, 3 µl 2.5 mM CoCl2, 1 µl terminal transferase (all from NEB), and 0.5 µl 1 mM ddGTP at 37 oC for 2 h followed by 75 oC for 20 min. The mixture was then purified with 2x AmpureXP beads (Beckman Coulter, Indianapolis, IN) and eluted in 21.5 µl RT-PCR grade water (Thermo-Fisher, Waltham, MA). Restriction digestion was done with 2.5 µl 10xCutSmart buffer and 1 µl MspI (NEB) for 18 h at 37  oC and 20 min at 65 oC . 0.5 µl 10xCutSmart buffer, 0.3 µl dACGTP mixture (100 mM dATP, 10 mM dCTP, 10 mM dGTP), 1 µl Klenow (exo-, 5U/µl, NEB) and 2.6 µl RT-PCR water, 0.6 µl 50 mM DTT (ThermoFisher) was added to the mixture for end repair and A-overhang addition with the program 30  oC for 20 min, 37 oC for 1 h and 75 oC for 20 min. Adapter ligation was then performed with 1 µl 10xThermoFisher HC T4 ligase buffer, 0.4 µl 100 mM ATP (ThermoFisher), 0.2 µl 50 mM DTT, 1 µl ThermoFisher HC T4 DNA ligase (30 Weiss Unit/µl), 5 ng home-made duplex UMI adapter with all the cytosines methylated (protocol adopted from Kennedy et al.) at 16 oC for 20 h and 65 oC for 20 min. Bisulfite conversion of the adapter-ligated product was carried out with QIAGEN EpiTect plus DNA bisulfite kit following their protocol for two rounds of conversion. The converted product was purified with Qiagen MinElute spin column and eluted with 20 µl RT-PCR water. PCR amplification was done using the NEBNext Multiplex Oligos for Illumina (2.5 µl of universal and index primer each) and 25 µl KAPA HiFi HotStart Uracil+ ReadyMix (Roche) with the following cycling conditions: 98  oC for 45 s, 15 cycles of 98  oC for 15 s, 60  oC for 30 s and 72  oC for 30 s, followed by a final extension at 72  oC for 5 min. The PCR product was purified with 1x AmpureXP beads and eluted with 30 µl EB buffer. DNA concentration was measured by Qubit 1xdsDNA HS assay. 5% TBE-UREA PAGE and bioanalyzer assay was performed as quality control on each library before sequencing.

  Dataset EGAD00001010881

• Resource for Genetic Epidemiology Research on Adult Health and Aging (GERA)

  The Resource for Genetic Epidemiology Research on Aging (GERA) Cohort was created by a RC2 "Grand Opportunity" grant that was awarded to the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) and the UCSF Institute for Human Genetics (AG036607; Schaefer/Risch, PIs). The RC2 project enabled genome-wide SNP genotyping (GWAS) to be conducted on a cohort of over 100,000 adults who are members of the Kaiser Permanente Medical Care Plan, Northern California Region (KPNC), and participating in its RPGEH. The purpose of the RPGEH is to facilitate research on the genetic and environmental factors that affect health and disease by linking together clinical data from electronic health records, survey data on demographic and behavioral factors, and environmental data from various sources, with genetic data derived from biospecimens collected from participants. At the time of the award of the RC2 project in late 2009, the RPGEH had established a cohort of about 140,000 individuals who had answered a detailed survey, provided saliva samples for extraction of DNA, and given broad consent for the use of their data in studies of health and disease. To maximize the diversity of the resulting sample, the GERA cohort was formed by including all racial and ethnic minority participants with saliva samples (N = 20,925; 19%); the remaining participants were drawn sequentially and randomly from white non-Hispanic participants (89,341; 81%). A total of 110,266 participant samples were included to ensure that at least 100,000 were successfully assayed. The resulting GERA cohort is 42% male, 58% female, and ranges in age from 18 to over 100 years old with an average age of 63 years at the time of the RPGEH survey (2007). The sample is ethnically diverse, generally well-educated with above average income. Approximately 69% of the participants are married or living with a partner. Length of membership in KPNC averages 23.5 years. UCSF and RPGEH investigators worked with the genomics company Affymetrix to design four custom microarrays for genotyping each of the four major race-ethnicity groups included in the GERA Cohort, described in detail in Hoffmann et al., 2011a and 2011b. Following genotyping and quality control procedures, and after removal of invalid, discordant, or withdrawn samples, about 103,000 participants were successfully genotyped. The resulting genotypic data were linked to survey data and data abstracted from the electronic medical records. As described below, all RPGEH participants were mailed new consent forms with explicit discussion of the placement of data in the NIH-maintained dbGaP. About 77% of participants returned completed consent forms, resulting in a final sample size of 78,486 participants in the GERA Cohort with data for deposit into dbGaP. Origins of the RPGEH GERA Cohort The goal in creating the RPGEH GERA cohort was to create a large, multiethnic, and comprehensive population-based resource for research into the genetic and environmental basis of common age-related diseases and their treatment, and factors influencing healthy aging and longevity. The GERA Cohort consists of a diverse cohort of more than 100,000 adults who are members of the Kaiser Permanente Medical Care Plan, Northern California Region (KPNC), and participating in its Research Program on Genes, Environment and Health (RPGEH). KPNC is an integrated health care delivery system with a population of about 3.3 million people in northern California. The membership of KPNC is representative of the general population in the 14 county area in which facilities are located, although the membership is underrepresented for the extremes of income at both ends of the spectrum. The RPGEH utilizes the longitudinal electronic health records (EHR) of KPNC to obtain clinical, laboratory, imaging and pharmacy information on all cohort members, to which personal demographic, behavioral and health characteristics have been added through member surveys. The GERA Cohort comprises a subsample of the RPGEH participant cohort, and was created through the RC2 award from the NIA, NIMH, and NIH Common Fund as described above. GERA Study Design The GERA Cohort is a subsample, as described above, of the longitudinal cohort enrolled in the Kaiser Permanente RPGEH. The RPGEH cohort includes about 400,000 survey participants of whom about 200,000 have provided broad consent and a sample of saliva or blood for use in studies of genetic and environmental factors in health and disease. The GERA Cohort was developed from a mailed survey sent to all adult members of KPNC who had been members for two years or more in 2007. All survey respondents were contacted and asked to complete a consent form; those who completed consent forms were asked to provide a saliva sample. Additional male participants were added to the RPGEH through inclusion of the Northern California sample of the California Men's Health Study (CMHS) cohort of about 40,000 men from KPNC, ages 45-69 years old at the time of the CMHS survey in 2002-2003. The CMHS participants contributed about 15,400 saliva samples to the RPGEH and were eligible for inclusion in the GERA Cohort. CMHS participants were included according to the same sampling design as for the RPGEH cohort as a whole. Specifically, all minority participants were selected for inclusion in order to maximize representation of minorities in the GERA Cohort, and Non-Hispanic White participants were selected at random to complete the sample of 110,266 GERA Cohort participants. GERA Genotypic Data High-density genotyping was conducted at UCSF using custom designed Affymetrix Axiom arrays, as described in Hoffmann et al. (2011a; 2011b). To maximize genome-wide coverage of common and less common variants, four specific arrays were designed for individuals of Non-Hispanic White (EUR), East Asian (EAS), African-American (AFR), and Latino (LAT) race/ethnicity. There was broad overlap among the SNPs on the arrays, which were designed using a hybrid greedy imputation algorithm (Hoffmann et al., 2011b) applied to genotype information validated by Affymetrix from the 1000 Genomes Project. However, in order to capture low frequency variants specific to particular race-ethnicity groups, SNP content varies between arrays. A more detailed description of the process of genotyping and results is included in Genotyping of DNA Samples. Description of the analyses of population structure and development of principal components for adjustment of population structure is included in Population Structure Analysis. GERA Phenotypic Data RPGEH and CMHS Survey Data. The sources of data on demographic and behavioral factors deposited in dbGaP for the GERA Cohort are the RPGEH and CMHS surveys. Data on common demographic factors such as gender, race/ethnicity, marital status, and education and on behavioral factors such as smoking, alcohol consumption, and body mass index, have been cleaned, edited, reconciled between the two surveys, and compiled into summary indices, where appropriate, for deposition into dbGaP. A more complete description of the survey variables is included in Survey Variables Documentation. Please note that the terms of use of the GERA Cohort Data, as specified in the Data Use Certification (DUC), prohibit the use of survey variables as outcomes in analyses. For example, a genome-wide association study (GWAS) of education or smoking is not permitted as specified by the DUC. Only health conditions can be used as outcome variables in analyses. Health Conditions derived from Kaiser Permanente Electronic Medical Records. Data on the occurrence of health conditions in participants in the GERA Cohort have been derived from summarizing ICD-9 coded diagnoses in Kaiser Permanente's electronic medical records. An algorithm that aggregates specific ICD-9 codes into appropriate diagnostic groups for selected conditions is applied to outpatient and inpatient databases; see Disease and Conditions Definitions Documentation for details. The criterion for including a condition as "present" for a participant is the occurrence of two or more diagnoses within a diagnostic category occurring on separate days. Two or more is used as the criterion in order to reduce false positives due to mistakes or rule-out diagnoses. When compared with validated disease registries, the criterion of 2+ diagnoses yields high specificity and good sensitivity. ICD-9 codes in the electronic records are specified in several ways. For outpatient visits occurring during the period 1995 to 2006, diagnoses were assigned by the treating physician who endorsed specific diagnoses on an optically scanned list that varied by specialty. Beginning in 2006 with the advent of an integrated, fully electronic medical record, outpatient diagnoses are made by physicians/ providers using a pull down menu. Discharge diagnoses from inpatient stays are specified by physicians and coded by specially trained coders. Databases of ICD-9 codes for diagnoses assigned at outpatient visits, or as one of the discharge diagnoses following inpatient stays, are complete and available for all KPNC members dating back to 1995. Although the average length of KPNC membership among GERA cohort members is 23.5 years in 2007, not all have been members since 1995, so the history for some conditions, such as those that are not chronic or recurrent, may not be complete for all cohort members. The year of first membership in KPNC is included as a variable in the list of survey variables, enabling investigators to estimate the number of years of observation of each Cohort member. RPGEH Access and Collaborations Website and Procedures The RPGEH maintains a web portal for inquiries and applications for collaboration and access to data. The url is: https://rpgehportal.kaiser.org/. RPGEH has an application process and an Access Review Committee that reviews applications for collaboration and use. For more details, please contact RPGEH through the website.

  Study phs000674

• Implementation, Adoption, and Utility of Family History in Diverse Care Settings

  The purpose of this study is to address the key question of whether and how family health history (FHH) is adopted as a tool to more efficiently manage patients at risk for breast, colon, ovarian, and hereditary cancer syndromes as well as thrombophilia and coronary heart disease (CHD) and to provide evidence supporting clinical utility -- improved health behaviors in patients and physician screening recommendations. Five health care delivery organizations will participate in this demonstration project: Duke University, the Medical College of Wisconsin, the Air Force, Essentia Health, University of North Texas. Duke will serve as a coordinating center for this project (Pro00043372) as well as a site. Healthcare Effectiveness Data and Information Set (HEDIS) measures as intermediate clinical effectiveness measures for Coronary Heart Disease (CHD) and selected cancers as well as survey/formative data and electronic medical record (EMR) data will be used as outcomes measures. The research model is purposely designed to mimic clinical delivery as an important step toward widespread implementation and sustainability. In addition, a cost-effectiveness analysis comparing usual care to the FHH guided preventive health model will be used. The completion of this project will result in an optimal strategy for integration of FHH data collection and clinical decision support (CDS) tools into an EMR and demonstrate the utility of the FHH intervention among diverse primary care patients, their settings, their providers, and the health systems that deliver their care. Specific Aim 1: To optimize the collection of patient entered FHH in diverse clinical environments for coronary heart disease, thrombosis, and selected cancers Specific Aim 2: To export FHH data to an open clinical decision support (open CDS) platform and return CDS results to providers and patients (and to EMRs where relevant). To explore the integration of genetic risk and FHH data at selected sites. Specific Aim 3: To assess the clinical and personal utility of FHH using a pragmatic observational study design to assess reach, adoption, integrity, exposure, and sustainability, and to capture, analyze, and report effectiveness outcomes at each stakeholder level: patient, provider, and clinic/system. Specific Aim 4: To take a leadership role in the dissemination of guidelines for a FHH intervention across in diverse practice settings. AAA Hypertension Lupus Multiple sclerosis Obesity Osteoporosis Thyroid disease Rheumatoid arthritis Blood clotting (6): Blood Clot (high risk features) Factor V PT mutation AT III deficiency Protein S deficiency Protein C deficiency Brain Disorders (5): Dementia Hemorrhagic stroke Ischemic stroke Macrocephaly Seizure Cancer/Adenomas (32): Bone cancer Brain cancer Breast cancer Colon cancer Adrenal cortex tumor Neuroendocrine tumor Paraganglioma Pheochromoctyoma Pituitary adenoma Medullary thyroid cancer Non-medullary (follicular or papillary) thyroid cancer Don't know type of thyroid cancer Thyroid nodule Other type of endocrine cancer Esophageal cancer Kidney cancer Leukemia Lipoma Liver cancer Muscle cancer Ovarian cancer Pancreatic cancer Prostate cancer Rectal cancer Retinoblastoma Melanoma Non-melanoma skin cancer Do not know type of skin cancer Small bowel cancer Stomach cancer Uterine cancer Other type of cancer Cardiovascular/heart/artery disease (5): Atrial fibrillation Carotid stenosis Heart attack/coronary artery disease Peripheral arterial disease Other heart disease Diabetes (3): Gestational diabetes Diabetes type I Diabetes type 2 Digestive Disorders (5): Colon polyp Crohn's disease Irritable bowel syndrome Ulcerative colitis Other digestive disorder Eye Disorder (3): Blindness Glaucoma Macular degeneration Hereditary Cancer Syndromes (22): Birt-Hogg-Dube syndrome Cowden syndrome Familial adenomatous polyposis Hereditary breast and ovarian cancer syndrome Hereditary diffuse gastric cancer Hereditary leiomyomatosis and renal cell carcinoma syndrome Hereditary melenoma Hereditary papillary renal cancer syndrome Hereditary paraganglioma-pheochromocytoma syndrome Hereditary retinoblastoma Juvenile polyposis Li-Fraumeni syndrome Lynch syndrome Mutyh-associated polyposis Malignant hyperthermia susceptibility Multiple endocrine neoplasia type 1 Multiple endocrine neoplasia type 2 Nevoid basal cell carcinoma syndrome Peutz-Jeghers syndrome Tuberous Sclerosis complex Von-Hippel Lindau syndrome Other hereditary cancer Hereditary Cardiovascular syndromes (10): Long qt Brugada Catecholaminergic polymorphic ventricular tachycardia Hypertrophic cardiomyopathy Dilated cardiomyopathy Left ventricular non-compaction syndrome Arrhythmogenic right ventricular dysplasia Ehlers-Danlos syndrome Marfan syndrome Other hereditary cardiovascular syndrome High cholesterol (2): Hyperlipidemia Familial hypercholesterolemia Kidney Disease (6): Cystic kidney disease Diabetic kidney disease Kidney nephrosis Nephritis Nephrotic syndrome Other kidney disease Liver Disease (6): Alpha 1 antitrypsinase deficiency Auto-immune hepatitis Hereditary hemochromatosis Primary biliary cirrhosis Sclerosing cholangitis Wilson's disease Lung Disease (3): Asthma COPD/chronic bronchitis/emphysema Other lung disease Psychological disorder (14): Addiction ADHD Autism Bipolar Depression Eating disorder Intellectual disability Obsessive compulsive disorder Panic disorder Personality disorder PTSD Schizophrenia Social phobia Sickle cell/thalassemia (3): Sickle cell disease Sickle cell trait Thalassemia

  Study phs001641

• Childhood Cancer Survivor Study (CCSS)

  Background and Rationale for the Childhood Cancer Survivor Study (CCSS) Over the last several decades, advances in treatments for childhood and adolescent cancer have substantially improved survival following diagnosis. These improvements gave rise to the responsibility for investigating long-term treatment-associated morbidity and mortality. Early efforts to describe late effects were largely conducted through single-institution and limited consortia studies. However, by the mid-1980s, it became increasingly clear that these approaches had inherent limitations, including small sample size, convenience sampling, incompletely characterized populations, and limited length of follow-up. To overcome these limitations, the CCSS was proposed and funded by the National Cancer Institute (NCI) as a U01 grant in 1994. Subsequently, the strengths of the CCSS, including an efficient and extensive infrastructure, plus expanding database and biorepository, were recognized and appreciated. Thus, in consultation with the NCI, the CCSS was converted to a U24 (resource grant) funding mechanism to serve the scientific community in 2000. The overarching goal of the CCSS resource is to increase the conduct of innovative and high impact research related to pediatric cancer survivorship. CCSS has been used extensively by researchers from a wide range of disciplines to address a broad spectrum of topics. Strengths of the resource include its large size, comprehensive annotation of treatment exposures, ongoing longitudinal follow-up with characterization of a wide array of participant characteristics and outcomes, and an established biorepository. Design of the Childhood Cancer Survivor Study The Childhood Cancer Survivor Study (CCSS) is a multi-institutional, multi-disciplinary collaborative research resource comprised of a retrospective hospital-based cohort of survivors of childhood cancer and a comparison sibling cohort. Eligible survivors from 31 participating institutions were diagnosed between 1970 and 1999, prior to age 21 years, with selected common pediatric cancers (leukemia, central nervous system tumors, Hodgkin lymphoma, non-Hodgkin lymphoma, kidney tumors, neuroblastoma, soft tissue sarcoma, or bone tumors). All patients who survived five years from the date of diagnosis were eligible, regardless of disease or treatment status. The baseline questionnaire was completed by 24,368 survivors and 5,039 siblings recruited to serve as a comparison group. To date, participants have completed three general follow-up surveys, as well as a number of specialized surveys on specific topics (e.g. health care, insurance, screening practices, men's and women's health issues, adolescent health, sleep and fatigue). In addition, biological samples (buccal cells, saliva and/or blood) have been collected for over 11,000 participants. Full descriptions of the design and characteristics of the CCSS have been previously published (Robison et al; Leisenring et al.), and available data and samples are described at https://ccss.stjude.org/develop-a-study/gwas-data-resource.html. Treatment Data in the Childhood Cancer Survivor Study A key feature of CCSS is the availability of detailed treatment data, which were collected by abstraction of medical records for each individual member of the cohort. Detailed abstraction included dates of therapy, protocol information, and specific details regarding surgery, chemotherapy and radiation. Quantitative dose details were collected for 22 specific chemotherapeutic agents, including alkylating agents, anthracyclines, platinum compounds and epipodophyllotoxins. In addition to individual agent doses, algorithms have been created to calculate cumulative doses of all drugs in a specific class, such as anthracyclines (doxorubicin, daunomycin and idarubicin) or platinum agents (cisplatinum and carboplatinum). Data abstracted for surgeries included dates and both the names and corresponding International Classification of Diseases (9th revision) code. For radiation treatment data, all relevant records were sent to the Radiation Physics Center at M.D. Anderson Cancer Center for detailed abstraction and dosimetry. Initial body region dosimetry was performed for all participants, followed by more detailed dosimetry as needed for specific studies. Genomics Data in the Childhood Cancer Survivor StudyThe NCI's Division of Cancer Epidemiology and Genetics and CCSS investigators collaborated to conduct genomics studies (SNP array genotyping and whole exome sequencing) using samples from the CCSS Biorepository. Studies included all cohort participants with available DNA regardless of sex or ancestry when the genomics studies were initiated. Phenotype Data in the Childhood Cancer Survivor Study Vital status and cause of death for both participants and non-participants is determined via linkage with the National Death Index (NDI). Identification of subsequent neoplasms is based on self-report, followed by validation using medical records, or via NDI. A wide array of additional health outcomes have been ascertained via a comprehensive set of questions on the CCSS questionnaires, covering potential adverse events across a range of organ systems (hearing/vision/speech, urinary, hormonal, heart and circulatory, respiratory, digestive, brain and nervous systems). In addition to health outcomes, longitudinal data have been collected on demographics, health behaviors, family history, screening practices, insurance status, and a range of psychosocial and neurocognitive factors. A full listing of available variables and copies of the CCSS questionnaires are available at http://ccss.stjude.org. Research Areas in the Childhood Cancer Survivor Study Extensive use by the research community has resulted in over 265 published manuscripts on a wide range of topics, including associations between treatment factors and mortality, subsequent neoplasms, chronic health conditions, cardiac events, neurocognitive sequelae, psychosocial factors, fertility, and health status. Additional topics have included health behaviors, screening practices, health care access and utilization, statistical and exposure assessment methodology, and development of risk prediction models. A full listing of published manuscripts using CCSS data is available on the CCSS website at https://ccss.stjude.org/published-research/publications.html. The Childhood Cancer Survivor Study as a Resource for Investigators The CCSS is an NCI-funded resource (U24 CA55727) to promote and facilitate research among long-term survivors of cancer diagnosed during childhood and adolescence. Interested investigators are encouraged to develop research ideas and propose projects within CCSS, whether or not they are from a participating CCSS institution. The CCSS is now accepting proposals to collaborate with CCSS and NCI investigators in the use of genomics data and corresponding outcomes-related data to address innovative research questions relating to potential genetic contributions to risk for treatment-related outcomes. Any researcher, or group of researchers, qualified to conduct genetic research can submit a proposal. There are no restrictions relative to country, institution, or prior involvement in CCSS. A full description of the process for developing a proposal for genetic research in CCSS can be found at https://ccss.stjude.org/develop-a-study/gwas-data-resource.html, along with listings of approved proposals.

  Study phs001327

• California Pacific Medical Center Research Breast Health Cohort

  Description of Cohort: The California Pacific Medical Center (CPMC) Breast Health Cohort is a cohort study based at CPMC and is linked to the San Francisco Mammography Registry, one of the sites of the NCI-funded Breast Cancer Screening Consortium (U01CA063740). CPMC is a community hospital in San Francisco, which has one of the highest volumes for mammography in San Francisco. Between September 2004 and June 2007, >90,000 mammograms were performed at CPMC. The CPMC breast health cohort collects demographic and risk factor data on women receiving mammography through participation in the San Francisco Mammography Registry, as part of the Breast Cancer Screening Consortium (U01CA063740). The SFMR database collects information from all sources, including a questionnaire on demographic and risk factor information, the clinical results of the breast examination, the measures of breast density by Dr. John Shepherd and the women who agreed to donate a blood sample. By merging these various sources of information we have very efficiently developed a large sample of women who have donated blood and have had a measure of mamographic density. Blood Collection: Dr. Steve Cummings is leading an effort to collect and archive blood samples from women who are receiving mammography screening. All women who are sent for a screening mammogram at CPMC are considered eligible. Since the cohort began collecting blood samples in July 2004 until June 2007, samples have been collected from over 11,000 women. Measurement of Breast Density: Dr. John Shepherd is currently measuring breast density in a large fraction of the cohort using an automated approach with single X-ray absorptiometry. Dr. Shepherd has established a link with the CPMC mammography center that allows him to collect routine digital mammography information. Using the data from the mammogram, Dr. Shepherd and his group have developed the single X-ray absorptiometry (SXA) technique for measuring density which is described in more detail below. The table demonstrates the distribution of demographic variables and some breast cancer risk factors of women who donated blood and had a breast density measurement in the CPMC breast health cohort. Nearly 80% of the participants are Caucasian and most of the women are post-menopausal with a median age of ~52. Since it will be difficult to accrue a large enough sample from each ethnic group, our study will focus only on Caucasian women. Table: Demographic variables, reproductive history and family history of breast cancer among 2962 women participating in the CPMC cohort study who contributed blood samples between 1994-1997. Variable Median/Percentage Age (Median/IQR) 52 (46-59) Ethnicity Caucasian/White 0.76 Asian/Pacific Islander 0.141 Hispanic 0.029 Mixed Race/Ethnicity 0.039 African American/Black 0.022 American Indian 0.001 Other 0.009 First degree relative with breast cancer 0.17 Age at first birth Nulliparous 0.39 Age<20 0.043 Age>40 0.032 Age<30, ≥20 0.251 Age>30, ≤40 0.282 Measurement of Breast Density in Cohort: Measurement of breast density is accomplished using an automated technique for all mammograms obtained by Dr. Shepherd using Single X-Ray absorptiometry (SXA). SXA measurement of breast density is done on approximately 30% of all screening mammograms. Below we describe the method for measurement of breast density by SXA by Dr. Shepherd's group and its validation and association with breast cancer. As we demonstrate below, breast density, as measured by SXA, is an automated, highly reproducible measure of the density of breast tissue and is associated with breast cancer risk. SXA for Quantifying Breast Density: Single x-ray absorptiometry (SXA) was initially developed for measuring bone density. SXA can determine the fraction of each of two densities simultaneously using the fact the sample is a constant thickness, the thickness in known, and the total attenuation is known. In applying this technique to breast density, we assume a two compartment model: fat and non-fatty (fibroglandular tissue). We use a reference material composed of various concentrations of two materials: one which is the same density as fat and another which is the same density as fibroglandular tissue. The reference material (phantom) is placed in the X-ray field with each mammogram. We have been able to implement this in a way that is unintrusive to the patient and technologist at CPMC. Assuming this two-compartment model and a constant known breast thickness, we can then calculate the percent density at any region of the breast based on the assumption that % pixel grey-scale is proportion to the mass fractions of breast fat and lean tissue. If reference materials (a phantom) of fat and fibroglandular tissue are imaged with the patient's breast and the reference materials have the same thickness as the patient's breast, then the breast's grey-scale values can be converted to fat/fibroglandular mass fractions by interpolating between those two references. The total percent density is found by averaging the volume fraction over all breast pixels. The phantom being used for breast density assessment at CPMC began to be used in September 2004. The phantom does not have to be manipulated by the technologist and stays attached on the mammography device during standard craniocaudal (CC) views. Thus it creates minimal to no interference with the clinical mammogram. Reproducibility of breast density measures: Traditional measures of mammographic density require some human interpretation. A human reader outlines the area perceived to be dense and a computer then calculates the percent area outlined as a percent of the entire image. Thus, while traditional mammographic density is associated with breast cancer risk, it has some limitations. In a study by Drs. Shepherd, Kerlikowske, et al., the correlation coefficient (Pearson's R) between different readers was 0.8-0.9. In contrast to the traditional mammographic density measurement, the SXA measurement is fully automated and, therefore, the reproducibility of the measurement is higher. Dr. Shepherd and colleagues have performed a replication study of SXA as a measurement of breast density. They have estimated the correlation coefficient of the SXA measurement of breast density to be >0.98. Thus, as expected for an automated measure, SXA is a highly reproducible measure of mammographic breast density. Drs. Shepherd and Kerlikowske have recently analyzed the association between breast cancer risk and breast density as measured by SXA (Shepherd et al., Cancer Epi Biomarkers and Prev, 2011, PMID: 21610220). They found that women in the highest quintile of % volumetric density had an odds ratio of 4.1 (95% CI: 2.3 - 7.2) for breast cancer risk compared to women in the lowest quintile of volumetric density. Thus volumetric density appears to be a highly reproducible, automated measure of breast cancer.

  Study phs000395

• Myeloma Genome Project Targeted Panel Validation dataset

  372 samples consisting of 185 patient paired CD138+ tumor and non-involved DNA pairs, plus 5 Horizon Diagnostic known mutation standards (HD). Samples were processed using the KAPA HyperCap protocol and hybridized onto a targeted panel for multiple myeloma and associated diseases. Reference Sudha et al Clinical Cancer Research, 2022.

  Dataset EGAD00001008689

• Serrated Colorectal Cancer: An Emerging Disease Subtype

  This dataset represents two combined study populations. Serrated Colorectal Cancer: An Emerging Disease Subtype (called the Advanced Colorectal Cancer of Serrated Subtype Study or ACCESS Study) was a grant awarded to investigate a newly-recognized, biologically-distinct subtype of colorectal cancer (CRC) called “serrated CRC.” The objective of this project was to characterize factors related to the genetic predisposition, clinical presentation, and prognosis of serrated CRC. The study recruited incident invasive CRC cases diagnosed between April 2016 and December 2018, aged 20-74 years at diagnosis. Cases were identified through the Surveillance, Epidemiology and End Results (SEER) cancer registry serving 13 counties in western Washington State. Eligibility for all individuals was limited to those who were English-speaking and could consent. Participation included completing a baseline epidemiologic questionnaire shortly after diagnosis, optional donation of a saliva sample for genetic analysis, and optional consent to release of medical records and tissue specimens related to their diagnosis. Tumor specimens were tested for serrated CRC-defining molecular characteristics. Further, we have vital status on all participants and cause of death on those that have died since enrollment. Hormones and Colon Cancer: Epigenetic Subtypes, Risks, and Survival (called the Post-Menopausal Hormones Study or PMH Study) was a grant awarded to investigate the impact of post-menopausal hormone use on colon cancer risk, tumor molecular characteristics, and outcomes. Eligible cases were females, newly diagnosed with invasive colorectal adenocarcinoma between October 1998 and February 2002, aged 50 to 74 years. Cases were residents of 10 out of the 13 counties in western Washington State served by the Surveillance, Epidemiology and End Results (SEER) cancer registry. Eligibility for all individuals was limited to those who were English-speaking with available telephone numbers, in which they could be contacted. Unrelated population-based controls were randomly selected according to age distribution (in 5-year age intervals) of the eligible cases by using lists of licensed drivers from the Washington State Department of Licensing (for individuals aged 50 to 64 years) and rosters from the Health Care Financing Administration (now the Centers for Medicare and Medicaid, for individuals older than 64 years). Participation included completing a baseline epidemiologic questionnaire, optional donation of a saliva sample for genetic analysis, and (for cases only) optional consent to release of medical records and tissue specimens related to their diagnosis. Tumor specimens were tested for epigenetic and other molecular characteristics. The ACCESS study was supported by funding from the National Cancer Institute of the National Institutes of Health (NCI/NIH) (R01CA196337, PI: Newcomb, PA), as was the PMH Study (R01CA076366, PI: Newcomb, PA). Additional support for the PMH Study came from the Seattle site of the Colon Cancer Family Registry (SCCFR) (U01CA167551, PI: Jenkins, M, and U01/U24CA074794, PI: Newcomb, PA). Additional support for case ascertainment was provided by the Cancer Surveillance System of the Fred Hutchinson Cancer Center, which is funded by Contract Number HHSN261201300012I; NCI Control Number: N01 PC-2013-00012; Contract Number HHSN261201800004I; and NCI Control Number: N01 PC-2018-00004 from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute with additional support from the Fred Hutchinson Cancer Center and the State of Washington. This research was also supported by the Genomics and Bioinformatics, Comparative Medicine, Specialized Pathology, Collaborative Data Services, and Experimental Histopathology Shared Resources of the Fred Hutch/University of Washington Cancer Consortium (P30 CA015704).Tumor marker testing was performed using formalin-fixed paraffin-embedded diagnostic tumor tissue specimens, and DNA extracted from those specimens. Testing for microsatellite instability (MSI) was based on either a 10-gene panel (BAT25, BAT26, BAT40, MYCL, D5S346, D17S250, ACTC, D18S55, D10S197, BAT34C4) or a 4-marker immunohistochemistry panel of DNA mismatch repair proteins (MLH1, MSH2, MSH6, PMS2). CpG island methylator phenotype (CIMP) testing was based on a validated quantitative DNA methylation assay using a five-gene panel (CACNA1G, IGF2, NEUROG1, RUNX3, SOCS1) or eight-gene panel (CACNA1G, IGF2, NEUROG1, RUNX3, SOCS1, MLH1, CRABP1, CDKN2A). Somatic p.V600E BRAF mutation status was tested for using a fluorescent allele-specific PCR assay. KRAS mutations in codons 12 and 13 were also assessed through forward and reverse sequencing of amplified tumor DNA. DNA was extracted from blood/saliva samples using conventional methods. The genotyping panel completed was the Build37 OncoArray500K-C, including 1%-6% blinded duplicates to monitor the quality of the genotyping. Quality control procedures were performed to 1) make sure that there were no patterns of missing data by batch, study, or plate, 2) check for gender discrepancies and kinship, 3) complete Principal Component Analysis, and 4) test for Hardy-Weinberg equilibrium (HWE). Samples were excluded based on call rate, heterozygosity, unexpected duplicates, gender discrepancy, and unexpectedly high identity-by-descent or unexpected genotypic concordance (>65%) with another individual. In addition, variants were excluded based on call rate (98%), lack of HWE in controls (P

  Study phs002171

• Distribution of data using Live Distribution

  Live Distribution Welcome to the documentation for using the Live Distribution feature for distributing data files securely through the EGA platform. This guide will walk you through the process of downloading encrypted files and decrypting them using Crypt4GH. Please follow the steps below to ensure a smooth experience. Before Downloading Create an EGA user. Make sure that you have the permissions to download the dataset of interest. In case you don’t have access, request access to the dataset. Add your Crypt4GH-compatible public key to your EGA account. Please allow a few hours for your public key to be synced with your profile. Afterwards, you will be able to connect to your EGA outbox using the SFTP protocol. Download Graphical User Interface (GUI) You can use any GUI that supports SFTP connections, such as FileZilla, an open-source FTP client. For Filezilla as your GUI, follow these steps to download files: Open FileZilla and access Site Manager (File > Site Manager). Create a new connection with the following settings (Figure 1): Protocol: SFTP - SSH File Transfer Protocol Host: __EGA_OUTBOX_DOMAIN__ Logon Type: Key file User: your EGA username Key file: path/to/your/private_key Figure 1: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the EGA outbox, ensuring seamless data transfers. Click Connect to access your Outbox. This folder serves as your storage space within the EGA cloud, containing files accessible for download in a secure way. Browse the remote directory on the right side of the FileZilla screen. Select the files you wish to download, right-click, and choose Download (Figure 2). Figure 2: Step-by-step process of manually downñoad files from __EGA_OUTBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can downñoad data from the EGA outbox to their local storage by following the depicted steps SFTP command line You can also use the SFTP command line to securely download files from the EGA Outbox. Using SFTP command line client in Linux/Unix Open a terminal window Enter the following command to connect: sftp username@hostname Enter your EGA password To see a list of available sftp commands type help sftp> put – Upload file sftp> get – Download file sftp> cd path – Change remote directory to ‘path’ sftp> pwd – Display remote working directory sftp> lcd path – Change the local directory to ‘path’ sftp> lpwd – Display local working directory sftp> ls – Display the contents of the remote working directory sftp> lls – Display the contents of the local working directory Type get command to download files. For example: get encrypted_file.c4gh Use the bye command to close the connection (SFTP session). Convenient SSH settings Include the following settings in your SSH config file, located in ~/.ssh/config Host __EGA_OUTBOX_DOMAIN__ EGA-outbox hostname __EGA_OUTBOX_DOMAIN__ User username IdentityFile path/to/the/private/key Replace username and path/to/the/private/key with the appropriate settings, and you will be able to connect to the __EGA_OUTBOX_DOMAIN__ simply using sftp EGA-outbox. How to decrypt Files archived at the EGA are encrypted based on Crypt4GH. Hence, to decrypt the files you need to install Crypt4GH. You can install a python implementation of it, with pip install crypt4gh or directly from the Github repository pip install git+https://github.com/EGA-archive/crypt4gh.git After installing Crypt4GH, decrypt files using the following command: crypt4gh decrypt --sk /path/private/key < encrypted_file.c4gh > decrypted_filename The command reads the encrypted file from stdin (with <) and output the decrypted version to stdout (with >). Replace encrypted_file.c4gh and decrypted_filename with the appropriate filenames but make sure to not use the same filename for both reading and writing because your SHELL would then truncate both files before you even read or write. Frequently Asked Questions What username should I use to log in to my outbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username, not your email address. Therefore, if you registered a username different from your email address when creating your EGA account, you must use that username to log in. If you have forgotten your registered username, please, contact our Helpdesk team for assistance. I see that some files in my dataset have 'unavailable' as extension. What should I do? Within your Outbox, you'll find a list of all the datasets available for download. Occasionally, certain files may be marked as "unavailable". These unavailable files can be identified by the "unavailable" extension added to their filenames (e.g. filename.fastq.gz.unavailable.c4gh). If you encounter an unavailable file that you need, please reach out to our Helpdesk. We'll promptly work on making the file accessible for download as soon as possible. Specific to using keys Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

  Documentation access/download/files/live-outbox

• IDH-wildtype untreated human glioblastoma samples (GB-UK cohort), published in Noorani & Haughey et al 2025.

  Extrachromosomal DNA (ecDNA) oncogene amplification is associated with treatment resistance and shorter survival in cancer. Currently, the spatial dynamics of ecDNA, and their evolutionary impact, are poorly understood. Here, we investigate the spatiotemporal evolution of ecDNA by integrating computational modeling with samples from 94 treatment-naive human IDH wild-type glioblastoma patients. Random ecDNA segregation combined with ecDNA-conferred fitness advantages induce distinct spatial ecDNA copy number patterns which depend on ecDNA oncogenic makeup. EGFR-ecDNAs often reach high copy numbers and confer strong fitness advantages. In contrast, PDGFRA-ecDNAs reach lower copy numbers and confer weaker fitness advantages. EGFR structural variants occur exclusively on ecDNA, arise from and are intermixed with wild-type EGFR-ecDNAs. Computational modeling suggests wild-type and variant EGFR-ecDNAs often accumulate before clonal expansion. Using a genetically engineered mouse model, we confirm that the accumulation of oncogenic ecDNA prior to clonal expansion under strong positive selection is observable both in vivo and in vitro in mouse neural stem cells. Intermixed wild-type and variant ecDNAs remain prevalent at diagnosis, as less fit wild-type ecDNAs hitchhike on strongly selected variant EGFR-ecDNAs. A dN/dS analysis in the Genomics England cohort further suggests positive selection of variants on EGFR-ecDNAs but not PDGFRA-ecDNAs in glioblastoma. Overall, our results suggest ecDNA oncogenic makeup determines unique evolutionary trajectories in glioblastoma. New concepts like ecDNA tumor clonality, ecDNA heteroplasmy and multi-species ecDNAs require a refined evolutionary interpretation of genomic data in a large subset of glioblastoma patients and likely other solid cancers.

  Study EGAS00001008126

• ProHealth: Kaiser Permanente Genome-wide Association Study of Prostate Cancer

  A genome-wide association study (GWAS) of prostate cancer (PCa) was conducted in Kaiser Permanente (KP) Northern California health plan members (7,783 cases, 38,595 controls; 80.3&#37; non-Hispanic white, 4.9&#37; African-American, 7.0&#37; East Asian, and 7.8&#37; Latino) [PMID: 26034056]. The data for these members were drawn from three KP cohort studies: Research Program in Genes, Environment and Health (RPGEH) ProHealth, and California Men&#39;s Health Study (CMHS) (described further under Study History). Four custom arrays were designed for genotyping, one for each of the four major race-ethnicity groups in the RPGEH cohort: African Americans, East Asians, Latinos, and Non-Hispanic Whites. The number of SNPs and SNP content varied by array, with SNP content designed to maximize the genome-wide coverage of low frequency and more common variants specific to the different race-ethnicity groups, including newly identified SNPs from sequencing projects, and SNPs with established associations with disease phenotypes and risk factors [PMIDs: 21565264, 21903159]. Within the total study cohort, n&#61;34,736 completed a consent which permitted deposition of data to NIH. Genotyping followed the same general procedure described in [PMIDs: 26092718, plus additional quality control (QC) steps for the additional men, in order to control for potential batch and kit effects, described in [PMID: 26034056. Briefly, we first repeated the filters described in [PMID: 26092718] for all four arrays (EUR, LAT, EAS, AFR). Then, on an array-wise basis, we removed SNPs with MAF&#60;0.01, with a call rate&#60;95&#37;, or with Hardy-Weinberg Equilibrium (HWE) p-value in homogeneous groups&#60;1x10&#710;-5. Furthermore, on the EUR array, to adjust for potential kit effect, we conducted a GWAS of kit, and removed those kit associated SNPs with p&#60;1x10&#710;-6; we also re-genotyped each of the new samples (those not genotyped with the original GERA data) with some of the original GERA data, and removed SNPs with &#62;13&#47;1,268 (1&#37;) mismatches. For the AFR array, to adjust potential plate batch issues, we conducted a GWAS of whether an individual was in the original GERA data vs. in the newly genotyped data and removed those batch-associated SNPs with p&#60;0.05 (we used a stronger threshold than that used for the EUR array because there were fewer individuals on the AFR array); we also re-genotyped each of the new samples with the original GERA data and removed SNPs with &#62;2&#47;78 (2.6%). After the QC described above, imputation was performed as described in [PMID: 26034056]. Imputation was performed on an array-wise basis, pre-phasing with SHAPE-IT v2.5 [PMID: 22138821], and imputing from the 1000 Genomes Project October 2014 release as a cosmopolitan reference panel with IMPUTE2 [PMID: 22384356]. In addition to the GWAS described above, a nested exome-wide association study (EWAS) of PCa was also conducted (7,489 cases, 7,323 controls; 78&#37; non-Hispanic white, 9&#37; African-American, 3&#37; East Asian, 6&#37; Latino, 4&#37; Other). A custom EWAS array primarily focused on rare variants was designed for genotyping that complemented the GWAS arrays [PMID: 26034056]. The EWAS array content included missense and loss-of-function mutations, and rare exonic mutations from The Cancer Genome Atlas (TCGA) and dbGaP prostate cancer tumor exomes [PMID: 26544944; PMID: 26544944]. Much of the EWAS array design content overlapped with the probesets on the UK Biobank Affymetrix Axiom array [PMID: 30305743]. Genotyping and QC steps taken to filter out samples exhibiting low quality and variants with low call rates are described in Emami et al., 2020 [biorXiv]. The resulting EWAS array genotypes are provided here.

  Study phs001221

• The UCSD / O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension

  Study Design: The UCSD / O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension contains N=797 participants. At the time of recruitment individuals ranged from 15-84 years of age and had varying ethnic backgrounds (Whites, African-Americans and Hispanics). The cohort was developed to study the genetic basis of autonomic dysfunction in human essential hypertension (reviewed in Zhang et al. Curr Hypertens Rep. 2011 13: 36-45. PMID: 21104344) and contains cross-sectionally collected phenotypes related to blood pressure regulation.&nbsp; Phenotypes: Phenotype data specifically focuses on the sympathetic branch of the autonomic system, which is a key regulator of blood pressure. Data includes circulating concentrations of the secretory "quantum" (catecholamines, neuropeptides, chromogranins) which influence vascular responses to sympatho-adrenal activation resulting in blood pressure. Other measures on the twins and a subset of family members include a standard urine panel with absolute concentrations of potassium, sodium, uric acid, phosphorous; DynaPulse acquired heart rate and blood pressure measures including systolic and diastolic blood pressure, mean arterial pressure, heart rate, Left Ventricular (LV) ejection time, LV contractility, cardiac output, stroke volume data including; and absolute concentrations of plasma aldosterone, endothelin, leptin, and insulin. Phenotype data is included for the 697 individuals sequenced here and 100 family members.&nbsp; Whole Genome Sequence Data Generation: Whole genome sequencing (20X coverage) was conducted on plasma-derived DNA. The WGS was generated using the Illumina HiSeq X Ten sequencing platform with variants called by Human Longevity, Inc (HLI; Ref 1). Preparation of sample libraries was conducted by mechanical shearing of gDNA (100-800 bp), isolation of DNA fragments 300-500bp in size, followed by library PCR amplification employing high-fidelity, low-bias PCR to amplify library fragments consisting of appropriate adapter sequences on both ends. BAM files were created by alignment of sequence reads to the reference genome (GRCh38) using iSAAC-01.14.02.06 (Python2.7). Note: The user may want to realign the sequence data and recall variants. In total, the WGS dataset contains 30 monozygotic twin pairs represented by one member of the twin pair, 133 monozygotic twin pairs with both individuals sequenced, 7 dizygotic twin pairs represented by one member of the twin pair, 67 dizygotic twin pairs with both individuals sequenced, 8 twin pairs with undetermined zygosity (both individuals sequenced) and 1 triplet with mother (all individuals sequenced). The dataset also contains sequences of 260 family members of twins including their mother, father, non-twin sibling, and offspring. Table 1 describes the composition of families ("sibling" refers to a non-twin sibling in the cohort). Note: We suggest users check gender and family relatedness. Table 1: Description of the families with sequence data &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;Units with:Twin PairsTwin (no pairs)No twinSibshipsMother and Father511Mother only1222Father only113One or more siblings22133Mother and siblings132Father and siblings(s)211Offspring of twins11Non twin siblings2Sib with unknown relative1Only one member of8The UCSD/O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension is now being managed at the UCSD School of Medicine's Institute for Genomic Medicine (IGM). The TSP Resource has collected a wide range of information (phenotypic, genotypic, demographic, metabolomic) and reagents (blood, plasma, urine, DNA, human induced pluripotent stem cells). For additional information about the resource, including other data types available as well as references and links to data from studies with previous collaborators, please go to the resource website at IGM [http://igm.ucsd.edu/research/twins-resource.shtml] or contact Dr. Brinda Rana at UCSD [bkrana@ucsd.edu].&nbsp;

  Study phs002230

• A Genome-Wide Association Study in Patients Experiencing Musculoskeletal Adverse Events on NCIC CTG Trial MA.27 Evaluating Aromatase Inhibitors as Adjuvant Therapy in Early Breast Cancer. A Collaboration Between the NIH Pharmacogenetics Research Network and the RIKEN Yokohama Institute Center for Genomic Medicine.

  Source of Patients The source of the patients for this genome-wide case control study was MA.27, which was conducted as a multi-cooperative group effort under the auspices of the NCI Breast Cancer Intergroup of North America. The NCIC Clinical Trials Group (CTG) serves as the coordinating group, with participation by the NCI-sponsored North Central Cancer Treatment Group, Eastern Cooperative Oncology Group (ECOG), Southwest Oncology Group, and Cancer and Leukemia Group B (CALGB). MA.27 involved postmenopausal women with histologically confirmed and completely resected invasive breast cancer with surgical margins clear of invasive carcinoma and DCIS in the following TMN categories (AJCC Version 6): pT1, pT2, pT3; pNx, pN0, pN1, pN2, pN3 (only when the sole basis is presence of 10 or more involved axillary nodes); MO. The primary tumor must have been ER and/or progesterone receptor positive. Patients were stratified by lymph node status at diagnosis, prior adjuvant chemotherapy, and trastuzumab use and were randomized to 5 years of adjuvant therapy with anastrozole or exemestane. The trial was activated on May 26, 2003, and reached its accrual objectives on July 31, 2008, after the randomization of 6827 North American patients, with the majority (79%) providing DNA and consent for genetic testing. Non-North American patients were also entered by the International Breast Cancer Study Group but they did not contribute DNA. From 2003 to December 21, 2004, patients also underwent a second randomization to celecoxib 400 mg twice daily or placebo but, after the entry of 1,622 patients, this treatment was discontinued because of reports of increased cardiovascular risk associated with celecoxib. Case Definition for Musculoskeletal Adverse Events Cases were defined to have had at least one of the following six MSAE): 1. pain - joint, 2. pain - muscle, 3. pain - bone, 4. arthritis, 5. joint - function, 6. musculoskeletal - other. Cases were required to have at least grade 3 toxicity, according to the NCI Common Terminology Criteria for Adverse Events v3.0, or go off treatment for any grade of the six MSAEs; and were required to have had an MSAE within the first two years (i.e., a MSAE occurring after 2 years was not be considered a case). Subjects were excluded from the case group who met the case definition while on celecoxib or in the three months after stopping celecoxib but subjects were allowed to be cases if they developed their first MSAE more than three months after stopping celecoxib. Study Design The design was a nested matched case-control study with two controls selected for each case. Random matching from the potential control group (i.e., risk-set sampling such that controls had at least the same length of followup as the timing of the case event) was done by matching the case on the following factors: 1. treatment arm (exemestane vs. anastrozole, blinded), 2. prior adjuvant chemotherapy (yes vs. no), 3. age at start of treatment (+/- 5 years), 4. Celecoxib allocation (yes vs. no). Attempts were made to exactly match each case with two controls according to the above criteria. If, however, controls could not be exactly matched, "close" matching was used to define a distance between each case and all potential controls in an optimal matching algorithm. Race is a potential confounding factor, and 94% of the MA.27 population is Caucasian, 3% are Black, 1% Asian, 1% American Indian, and 1% are of unknown race. Because there are so few non-Caucasian participants, was restricted to only Caucasians. Following identification of cases and controls, the list was sent to the CALGB Pathology Coordinating Office (PCO) at Ohio State University Medical Center for plating of the samples. Other patient factors that might influence the association of SNP genotypes with a MSAE, either as confounding factors or as effect modifiers, were included as investigational covariates. These include 1. body mass index (BMI), 2. bisphosphonate use, 3. fractures in past 10 years, 4. baseline ECOG performance status, 5. prior hormone replacement therapy, 6. prior adjuvant radiotherapy, and 7. prior taxane treatment. An Honest Broker Process was utilized to ensure patient confidentiality. Genotype Assays and Quality Control A GWAS was performed with the Illumina Human610-Quad at the RIKEN Center for Genomic Medicine. The DNA for all of the samples were adjusted to a concentration of 50 to 100 ng/&#181;L in a final volume of 50 &#181;L. The 96-well plates were designed to have a unique distribution pattern with x duplicates per plate to evaluate genotype concordance. Samples with &lt;98% call rate were removed from analyses. SNPs with ambiguous call will be excluded using the following criteria: 1. low signal intensity of each allele, 2. low cluster separation score, 3. SNP call rate &lt;0.98, 4. SNPs with Hardy Weinberg Equilibrium p-value &lt;0.001 were excluded. X-chromosome SNPs were used to validate female sex and quality control measures were examined across the different submitting centers. For all pairs of subjects, the percentage of matching genotypes were computed to screen for unexpected duplicate samples. All SNPs found to be associated with MSAE were subjected to visual inspection of the cluster plots to verify that the genotype clustering was adequate.

  Study phs000210

• Osteoporotic Fractures in Men (MrOS)

  The Osteoporotic Fractures in Men Study (MrOS) is a multi-center prospective, longitudinal, observational study of risk factors for vertebral and all non-vertebral fractures in older men, and of the sequelae of fractures in men. The original specific aims of the study include: (1) to define the skeletal determinants of fracture risk in older men, (2) to define lifestyle and medical factors related to fracture risk, (3) to establish the contribution of fall frequency to fracture risk in older men, (4) to determine to what extent androgen and estrogen concentrations influence fracture risk, (5) to examine the effects of fractures on quality of life, (6) to identify sex differences in the predictors and outcomes of fracture, (7) to collect and store serum, urine and DNA for future analyses as directed by emerging evidence in the fields of aging and skeletal health, and (8) define the extent to which bone mass/fracture risk and prostate diseases are linked. The MrOS study population consists of 5,994 community dwelling, ambulatory men aged 65 years or older from six communities in the United States (Birmingham, AL; Minneapolis, MN; Palo Alto, CA; Monongahela Valley near Pittsburgh, PA; Portland, OR; and San Diego, CA). Inclusion criteria were designed to provide a study cohort that is representative of the broad population of older men. The inclusion criteria were: (1) ability to walk without the assistance of another, (2) absence of bilateral hip replacements, (3) ability to provide self-reported data, (4) residence near a clinical site for the duration of the study, (5) absence of a medical condition that (in the judgment of the investigator) would result in imminent death, and (6) ability to understand and sign an informed consent. To qualify as an enrollee, the participant had to provide written informed consent, complete the self-administered questionnaire (SAQ), attend the clinic visit, and complete at least the anthropometric, DEXA, and vertebral X-ray procedures. There were no other exclusion criteria. The baseline examination was completed over a 25-month period from March 2000 to April 2002. Participants completed questionnaires regarding medical history, medications, physical activity, diet, alcohol intake, and cigarette smoking. Objective measures of anthropometric, neuromuscular, vision, strength, and cognitive variables were obtained at a clinic visit. Skeletal assessments included DEXA, calcaneal ultrasound, and vertebral radiographs. Vertebral and proximal femoral QCT was performed on a subset (65%) of participants. Serum, urine, and whole blood specimens were collected. During the study period, participants complete a tri-annual questionnaire every four months that obtains information concerning the occurrence of incident falls and fractures. Follow-up is over 99% complete. All reported fractures are confirmed using physician review of radiology reports or study radiologist review of x-rays. All deaths are also centrally adjudicated using death certificates and recent hospitalization records. In additional to the baseline study visit and tri-annual questionnaires, all surviving active participants were invited to follow-up visits and questionnaires. A second comprehensive clinic visit was completed between March 2005 and May 2006 or about 4.5 years after baseline. Most baseline measures, including vertebral radiographs, were repeated at the second visit. A third comprehensive clinic visit was completed between March 2007 and March 2009 or about 7.0 years after baseline. Many baseline measures were repeated at the third visit. A measure of energy expenditure was also obtained utilizing an accelerometer based armband device. Surviving participants have also completed two extensive mailed questionnaires designed to updated information obtained at clinic visits. The first questionnaire was mailed between baseline and visit 2 between July 2002 and March 2004. The second questionnaire was mailed after the third questionnaire between March 2009 and February 2011. The MrOS Sleep Study, an ancillary study of the parent MrOS cohort, was conducted between December 2003 and March 2005 and recruited 3,135 MrOS participants for a comprehensive sleep assessment. Men were screened for nightly use of mechanical devices during sleep including pressure mask for sleep apnea (CPAP or BiPAP), mouthpiece for snoring or sleep apnea, or oxygen therapy and were generally excluded. The sleep visit included objective measures of sleep using actigraphy and polysomnography. Other measures at the sleep visit include neuropsychiatric measures, performance measures, health related questionnaires, routine exam measures previously completed at MrOS clinic visits and a urine and serum specimen collection. During the sleep study period, participants complete a tri-annual questionnaire every four months that contains information about incident cardiovascular events. All reported events are centrally adjudicated utilizing hospital records, study ECGs and other supporting documentation. The Hip Osteoarthritis study, another ancillary study of the parent MrOS cohort, was completed at the second clinic visit between March 2005 and May 2006. Participants were asked for consent for a hip x-ray and osteoarthritis measurements were obtained from these x-rays. DNA was extracted from baseline whole blood samples for use in genetic research. Consent for use of DNA was obtained through written consent. Raw GWAS data is housed at the study&#39;s coordinating center and not released as part of the dbGAP release. The raw data can be requested from the study&#39;s coordinating center. Only QC cleaned Plink files are available on dbGAP.

  Study phs000373

• NHLBI TOPMed: SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS)

  Subpopulations and Intermediate Outcome Measures in COPD Study Description Subpopulations and intermediate outcome measures in COPD study (SPIROMICS) supports the prospective collection and analysis of phenotypic, biomarker, genetic, genomic, and clinical data from subjects with COPD for the purpose of identifying subpopulations and intermediate outcome measures. It is funded by the National Heart, Lung, and Blood Institute and is coordinated by the University of North Carolina at Chapel Hill. Molecular fingerprinting and extensive subject phenotyping will be performed to identify disease subpopulations and to identify and validate surrogate markers of disease severity, which will be useful as intermediate outcome measures for future clinical trials. Secondary aims are to clarify the natural history of COPD, to develop bioinformatic resources that will enable the utilization and sharing of data in studies of COPD and related diseases, and to create a collection of clinical, biomarker, radiographic, and genetic data that can be used by external investigators for other studies of COPD. Participating Institutions: Research participants for SPIROMICS will be enrolled, phenotyped, and followed at twelve SPIROMICS Clinical Centers: Columbia University, Temple University, Johns Hopkins University, Wake Forest University, University of Michigan, University of Illinois at Chicago, University of Iowa, University of Utah, National Jewish Health, University of California at San Francisco, and University of California at Los Angeles. The University of North Carolina at Chapel Hill serves as the Genomics and Informatics Center. The Radiology Reading Center is based at the University of Iowa. The PFT Reading Center is based at the University of California at Los Angeles. Study Design: SPIROMICS is a prospective cohort study that enrolled approximately 2,981 participants at twelve clinical centers over five years. Participants are distributed across four enrollment strata (i.e., Never-smokers, Smokers without COPD, Mild/Moderate COPD, and Severe COPD). Study Visits: Participants have up to four in-person visits (Baseline and Annual Clinic Visits at years 1, 2, 3 after Baseline). Study questionnaires and spirometry are completed at all main study visits. Blood and urine samples are collected at visits 1, 2, and 4. Sputum samples are collected at Visit 1. The CT scans are completed and Baseline and Visit 2. Participants also receive quarterly follow-up calls to assess health status and determine if an exacerbation has occurred. Substudies Bronchoscopy and Immunophenotyping Substudy The Bronchoscopy Substudy will enroll 50 subjects per site, for a total of 300 participants. These participants will be recruited across all four study strata. This substudy includes two study visits. During the first visit, sputum samples are collected for Immunophenotyping analysis at the Immunophenotyping Core Lab based at the University of Michigan. Participants then return for a second visit during which samples are collected via bronchoscopy, including bronchoalveolar lavage, epithelial brushings and bronchial biopsies. Immunophenotyping analysis is also conducted on BAL and blood collected during the bronchoscopy study visit. Repeatability Substudy The entire baseline clinic visit was repeated on 98 volunteers to determine reliability of measurement procedures. All baseline study-related procedures and questionnaires, including the CT scan, were re-administered and new samples of blood, urine, saliva, and sputum were collected. Field center staff processed these biospecimen samples according to the same protocol. Exacerbation Substudy The Exacerbation Substudy is a prospective, longitudinal observational study of up to 400 participants, which will allow the assessment of participant-driven health care utilization (HCU) events and symptom-defined exacerbation events over time. HCU-driven events are defined by change in medical treatment in response to a perceived COPD Exacerbation. Symptom-based events will be defined by using EXACT-PRO (EXacerbations of Chronic Pulmonary Disease Tool - Patient Reported Outcome), a daily symptom diary developed to measure the frequency, severity, and duration of exacerbations in clinical trials. All participants are provided with a PDA on which to complete the diary. Participants reporting a possible COPD exacerbation will be brought into the study clinic for a study visit to collect biological specimens and questionnaire data. The overall objectives of the Exacerbation Substudy are to: Obtain clinical data and specimens on SPIROMICS participants before and during an acute COPD exacerbation defined by Health care utilization triggered by the subject, or Symptomatic change (triggered by an EXACT defined threshold) Describe symptomatic changes occurring around HCU and symptom-defined (EXACT) events and their relationship to clinical and specimen data, Characterize the non-exacerbation "stable" state in COPD using the EXACT, including: Symptom variability (EXACT), Clinical data and specimen parameters during a stable state (baseline), The relationship between clinical and specimen data and symptom severity and variability. Explore the characteristics of stable patients, defined as those who do not experience HCU or symptom-defined (EXACT) events during the sub-study period, using baseline clinical data and specimens, and compare these characteristics with patients who experience HCU and/or symptom-defined events. Examine the relationship between HCU and symptom-defined exacerbation frequency during the first one-year period of follow-up for exacerbations and health outcomes, including 12-month change in lung function and COPD health status, and longer-term morbidity and mortality, with the latter derived from long-term data from the larger SPIROMICS study.

  Study phs001927

• eMERGE Network Study of the Genetic Determinants of Resistant Hypertension

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of five participating sites (Group Health Seattle, Marshfield Clinic, Mayo Clinic, Northwestern University, and Vanderbilt University) funded by the NHGRI to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 19,000 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 resistant hypertension. Treatment resistant hypertension is a common health problem in the clinical setting. A basic definition of resistant hypertension included subjects with uncontrolled blood pressure despite use of three antihypertensive medications or subjects requiring four or more medications to maintain control. Other conditions, such as secondary causes of hypertension, were exclusions. Site and participants include: Vanderbilt University: BioVU, Vanderbilt&#39;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&#39;s EMR, which forms the basis for phenotype definitions used in genotype-phenotype correlations. Marshfield Clinic: 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. 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&#39; 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. Group Health(GH)/University of Washington (UW): Aging and Dementia eMERGE study biorepository leverages rich population-based longitudinal data from both electronic medical records and in-depth research data to explore genome wide associations. Participants include Seattle-area members of GH (a large integrated health care system in Washington State) consented and enrolled in 1) the UW Alzheimer&#39;s Disease Patient Registry (ADPR) and 2) the Adult Changes in Thought (ACT) study. The ADPR (PI: Eric B. Larson; NIH/NIA U01 AG 006781) is a population-based registry of incident dementia cases designed to identify all new Alzheimer&#39;s Disease cases within GH from 1987 to 1996. Medical history, physical, laboratory testing, and neuropsychological testing were performed on all consenting potential cases for determination of dementia status by a consensus conference. The study base of the ADPR population was stable with an attrition rate of less than 1%/year. The ACT study (PI: Eric B. Larson; NIH/NIA U01 AG 006781) is an ongoing community-based cohort study of aging and dementia. The original cohort of 2,581 randomly selected dementia-free members age 65 and older was enrolled in 1994-1996 and expanded by 811 in 2000-2002. Continuous enrollment to maintain a cohort of 2,000 dementia free persons began in 2005. Participants receive biennial assessment including cognitive status determination. The ACT sub-sample is stable; for the original cohort, median enrollment in GH was 19 years prior to joining the ACT study, and 85% of the cohort has &#8805; 10 years of GH enrollment. DNA for the ADPR participants were obtained through a companion study, Genetic Differences in Cases and Controls (PI: Walter Kukull; NIH/NIA R01 AG007584). DNA obtained through both studies were extracted from blood using Gentra Systems Puregene methods. DNA concentration is determined by UV optical density. All samples are checked for quality by 260/280 ratio. For long-term storage, samples are aliquoted and stored at -70&#176;C. 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 &lt;1%. Data regarding risk factors for atherosclerosis such as diabetes, dyslipidemia, hypertension, and smoking are ascertained from the EMR.

  Study phs000297

• Broccoli Sprouts Extracts Trial (BEST-COPD-BioLINCC)

  Data Access NOTE: Please refer to the &#8220;Authorized Access&#8221; section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives: To establish a safe and tolerable dose of sulforaphane that effects in vivo antioxidants via Nrf2 for development as a potential novel treatment for participants with Chronic Obstructive Pulmonary Disease (COPD).Background: Chronic Obstructive Pulmonary Disease (COPD), caused primarily by smoking, is the third leading cause of death in the United States and world-wide. Surprisingly, there are few treatments available to address the pathobiology of COPD other than cessation of smoking. The development and progression of COPD are associated with increased inflammatory response(s) and increased oxidative stress in the lung. Thus, one approach to the development of novel therapies is the stimulation of endogenous antioxidant defense mechanisms.Nuclear factor erythroid-2-related factor 2 (NFE2L2/Nrf2) is a transcription factor activated by oxidative stress. Nrf2 activity promotes anti-oxidant enzymes, and anti-oxidant enzymes play key roles in cellular defenses. Sulforaphane, a derivative of broccoli and other cruciferous vegetables, has been shown to stimulate Nrf2 activity in both in vivo and in vitro experiments. For example, activation of Nrf2 protected mice from developing emphysema after chronic smoke exposure and decreased oxidative stress. Similarly, activation of Nrf2 in human COPD lung cells resulted in decreased oxidative stress. Therefore, this study was designed to assess whether daily ingestion of sulforaphane by COPD participants for four weeks increased Nrf2 activity in alveolar macrophages and bronchial epithelial cells. Participants: There were a total of 89 participants randomized to one of three treatment arms. Of these, 31 participants were randomized to the placebo arm, and 29 participants were randomized to the each of the sulforaphane arms. One participant withdrew from the placebo arm, therefore, a total of 88 participants completed the study.Design: Participants were assigned to receive sulforaphane at 25 micromoles (4.4mg), sulforaphane at 150 micromoles (26.6 mg), or placebo (microcellulose) once daily by mouth. Computer-generated treatment assignments were blinded to participants, clinical staff, and study staff. Doses were back-filled with methylcellulose and presented in similar capsules to compensate for appearance and weight differences in sulforaphane and placebo treatments arms.There were a total of five study visits over the six-week study period. Prior to randomization, participants were assessed for eligibility, which included baseline data collection. Participants provided medical histories, underwent a physical examination, pre-and-post bronchodilator spirometry, lung volume measurements, carbon monoxide diffusing capacity (DLCO), and pulse oximetry over the six-week study period. Follow up data and biospecimens were collected at the final visit, which was targeted for four weeks after randomization. Two fiberoptic bronchoscopies were performed under sedation to collect endobronchial brushings and bronchoalveolar lavage used to isolate alveolar macrophages and bronchial epithelial cells. The first bronchoscopy was performed on the day of randomization, and the second bronchoscopy was performed the day after the final visit. In addition, nasal brushings were obtained prior to bronchoscopy to isolate nasal epithelial cells. The primary outcomes were changes in Nrf2 target gene expression at four weeks in alveolar macrophages and bronchial epithelial cells. The target genes for the primary outcome were NQ01, H01, AKR1C1, and AKR1C3. Secondary outcomes included the evaluation of: expression of other genes in the Nrf2/Keap1 pathway (e.g. Nrf2/NFE2L2, KEAP1, and SLPI), markers of oxidative stress (e.g., isoprostane and thiobarbituric acid reactive substances) in plasma and expired breath condensate, and cytokine profiles in bronchoalveolar lavage fluid. Conclusions: Sulforaphane administered at four weeks doses of 25umoles and 150umoles to participants with COPD did not significantly increase Nrf2 target gene expression in alveolar macrophages or bronchial epithelial cells. In addition, changes in oxidative stress markers and the expression of other genes in the Nrf2/Keap1 pathway were not statistically significant between the treatment groups.Wise RA, Holbrook JT, Criner G, et al. Lack of Effect of Oral Sulforaphane Administration on Nrf2 Expression in COPD: A Randomized, Double-Blind, Placebo Controlled Trial. Vij N, ed. PLoS ONE. 2016; 11(11):e0163716. doi:10.1371/journal.pone.0163716 (PMID: 27832073; PMID: 28350841).

  Study phs004022