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• ELLIPSE Prostate Cancer Meta-Analysis and Genotyping

  The ELLIPSE Consortium is an international effort to discover risk loci for prostate cancer. It includes the meta-analysis of existing GWAS data as well as novel GWAS, exome, and iCOGS genotyping. The GWAS meta-analysis includes the following cases and controls from studies of European ancestry: UK GWAS stage 1 (Illumina Infinium HumanHap 550 Array: 1854 cases and 1894 controls), UK GWAS stage 2 (Illumina iSELECT: 3706 cases and 3884 controls), CAPS1 (Affymetrix GeneChip 500K: 474 cases and 482 controls), CAPS2 (Affymetrix GeneChip 5.0K: 1458 cases and 512 controls), BPC3 (Illumina Human610 Illumina: 2068 cases and 3011 controls), PEGASUS (HumanOmni2.5: 4600 cases and 2941 controls). The OMNI 2.5M genotyping was conducted for 977 prostate cancer cases from UKGPCS. The Exome SNP array genotyping was conducted for 4741 subjects from UKGPCS. The iCOGs genotyping was conducted for 10366 subjects which includes the Multiethnic Cohort (n=1648) and UKGPCS (n=8718). Below is a description of each study that contributed to the meta-analysis of men of European ancestry. Information about the studies that contributed to the multiethnic meta-analysis can be found on the associated study page and also in Conti et al (Nature Genetics, PMID:33398198).   UK GWAS Stage 1 (UK1) and Stage 2 (UK2): The UK Genetic Prostate Cancer Study (UKGPCS) was first established in 1993 and is the largest prostate cancer study of its kind in the UK, involving nearly 189 hospitals. We are based at The Institute of Cancer Research in Sutton, Surrey, and collaborate with the Royal Marsden NHS Foundation Trust. Our aim is to find genetic changes which are associated with prostate cancer risk. Our target is to recruit 26,000 gentlemen into the UKGPCS by 2017. Men are eligible to take part if they fit into at least one of the following groups: They have been diagnosed with prostate cancer at 60 years of age or under (up to their 61st birthday). They have been diagnosed with prostate cancer and a first, second or third degree relative where at least one of these men were diagnosed with prostate cancer at 65 years of age or under. They are affected and have 3 or more cases of prostate cancer on one side of their family. They are a prostate cancer patient at the Royal Marsden NHS Foundation Trust. We have to date recruited around 16,000 men on whom we have germline DNA and clinical data at diagnosis. The UK GWAS is based on genotyping of 541,129 SNPs in 1,854 individuals with clinically detected (non-PSA-screened) prostate cancer (cases) and 1,894 controls. 43,671 SNPs showing strong evidence of association in stage 1 were followed up by genotyping a further 3,268 cases and 3,366 controls from UK and Melbourne in stage2. CAPS1 and CAPS2: The CAPS (Cancer of the Prostate in Sweden) study represents a large Swedish population-based cancer study, comprising 3,161 cases and 2,149 controls, recruited between 2001 and 2003. Biopsy confirmed prostate cancer cases were identified and recruited from four out of six regional cancer registries in Sweden, diagnosed between July 2001 and October 2003. Clinical data including TNM stage, Gleason grade and PSA levels at time for diagnosis were retrieved through record linkage to the National Prostate Cancer Registry. Control subjects, who were recruited concurrently with case subjects, were randomly selected from the Swedish Population Registry and matched according to the expected age distribution of cases (groups of 5-year intervals) and geographic region. Whole blood was collected from all individuals for extraction of genomic DNA. A GWAS was conducted in two parts. In the first phase (CAPS1) 498 cases and 502 controls were genotyped, in the second phase 1,483 cases and 519 controls were genotyped. Genotyping was performed using the GeneChip Human Mapping 500K (CAPS1) and 5.0K (CAPS2) Array Set from Affymetrix (Santa Clara, CA). The National Cancer Institute Breast and Prostate Cancer Cohort Consortium, BPC3: BPC3 was a consortium of prospective cohort studies investigating genetic and gene-environmental risk factors for breast and prostate cancer. Each study selected cases and controls for this study as described below. The clinical criteria defining advanced prostate cancer (Gleason = 8 or stage C/D) were either obtained from medical records or cancer registries. The Gleason score source was either surgical specimens (radical prostatectomy or autopsy) or the diagnostic biopsy (needle biopsy or TURP). When multiple Gleason scores were available the surgical value was used. PLCO was removed from the analysis as the samples were included in the Pegasus GWAS described below. In total 2,473 advanced prostate cancer cases and 3,534 controls were included in the analysis following QC. ATBC, Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study: ATBC was a randomized, placebo-controlled primary prevention trial to investigate whether α-tocopherol or ß-carotene supplementation reduced the incidence of lung or other cancers in male smokers. Between 1985 and 1988, 29,133 men ages 50 to 69 years were enrolled in the trial from Finland and randomized to supplementation (50 mg α-tocopherol, 20mg ß-carotene, or both) or placebo. Men with a prior history of cancer, other than non-melanoma skin cancer or carcinoma in situ, were excluded from participating. Incident cancer cases are identified through linkage with the Finnish Cancer Registry, which has ~100% ascertainment of cancer cases nationwide. Cases included 249 men diagnosed with advanced prostate cancer (Gleason = 8 or stage C/D) from 1985 to 2003 with DNA available. Controls were 1,271 men selected previously for a GWAS of lung cancer in ATBC without a diagnosis of prostate cancer. CPSII, Cancer Prevention Study II: CPSII is a cohort study started in 1982 to investigate the relationship between dietary, lifestyle and other etiologic factors and cancer mortality. Approximately 1.2 million men and women enrolled in the study from 50 states in the U.S. In 1992, a subset of these participants (n= ~184,000) were enrolled in the CPSII Nutrition Cohort to examine the relationship between dietary and other exposures and cancer incidence. Blood samples were drawn from approximately 39,376 members of the Nutritional Cohort from 1998 to 2001, and buccal cells were collected from 69,467 members from 2001 to 2002. Cancer cases are identified by self-report through follow-up questionnaires followed by verification through medical records and/or linkage to state cancer registries as well as death certificates. A total of 660 advanced prostate cancer cases (Gleason = 8 or stage III/IV) with a source of DNA were identified for this study. Controls were 660 men matched on ethnicity, date of birth, sample collection date and DNA type. EPIC, European Prospective Investigation into Cancer and Nutrition: EPIC is a prospective study designed to investigate both genetic and non-genetic risk factors for different forms of cancer. Study participants were almost all white Europeans. Approximately 500,000 individuals (150,000 men) in EPIC were recruited between 1992 and 2000, from 23 centers in 10 European countries. Overall approximately 400,000 subjects also provided a blood sample at recruitment. The methods of recruitment and details of the study design are described in detail elsewhere. In brief, study participants completed an extensive questionnaire on both dietary and nondietary data at recruitment. The present study includes subjects from advanced prostate cancer cases (Gleason = 8 or stage III/IV) matched to controls based on study center, length of follow-up, age at enrollment (± 6 months), fasting and time of day of blood collection (± 1 hour). The advanced prostate cancer subjects were from 8 of the 10 participating countries: Denmark, Germany, Greece, Italy, the Netherlands, Spain, Sweden and the United Kingdom (UK). France and Norway were not included in the current study because these cohorts only included female subjects. All participants gave written consent for the research and approval for the study was obtained from the ethical review board from all local institutions in the regions where participants had been recruited for the EPIC study. HPFS, Health Professionals Follow-up Study: HPFS began in 1986 and is an ongoing prospective cohort study of 51,529 United States male dentists, optometrists, osteopaths, podiatrists, pharmacists, and veterinarians 40 to 75 years of age. The baseline questionnaire provided information on age, marital status, height and weight, ancestry, medications, smoking history, disease history, physical activity, and diet. At baseline the cohort was 97% white, 2% Asian American, and 1% African American. The median follow-up through 2005 was 10.5 years (range 2-19 years). Self-reported prostate cancer diagnoses were confirmed by obtaining medical and/or pathology records. Prostate cancer deaths are either reported by family members in response to follow-up questionnaires, discovered by the postal system, or the National Death Index. Questionnaires are sent every two years to surviving men to update exposure and medical history. In 1993 and 1994, a blood specimen was collected from 18,018 men without a prior diagnosis of cancer. Prostate cancer cases are matched to controls on birth year (+/-1) and ethnicity. Controls are selected from those who are cancer-free at the time of the case’s diagnosis, and had a prostate-specific antigen test after the date of blood draw.   MEC, Multiethnic Cohort: The Multiethnic Cohort Study is a population-based prospective cohort study that was initiated between 1993 and 1996 and includes subjects from various ethnic groups - African Americans and Latinos primarily from Californian (great Los Angeles area) and Native Hawaiians, Japanese-Americans, and European Americans primarily from Hawaii. State drivers’ license files were the primary sources used to identify study subjects in Hawaii and California. Additionally, in Hawaii, state voter’s registration files were used, and, in California, Health Care Financing Administration (HCFA) files were used to identify additional African American men. All participants (n=215,251) returned a 26-page self-administered baseline questionnaire that obtained general demographic, medical and risk factor information. In the cohort, incident cancer cases are identified annually through cohort linkage to population-based cancer Surveillance, Epidemiology, and End Results (SEER) registries in Hawaii and Los Angeles County as well as to the California State cancer registry. Information on stage and grade of disease are also obtained through the SEER registries. Blood sample collection in the MEC began in 1994 and targeted incident prostate cancer cases and a random sample of study participants to serve as controls for genetic analyses. PHS, Physicians Health Study:PHS was a randomized trial of aspirin and ß carotene for cardiovascular disease and cancer among 22,071 U.S. male physicians ages 40-84 years at randomization; none had a cancer diagnosis at baseline. The original trial ended, but the men are followed. From 1982 to 1984, blood samples were collected from 14,916 physicians before randomization. Participants are sent yearly questionnaires to ascertain endpoints. Whenever a physician reports cancer, we request permission to obtain the medical records, and cancers are confirmed by pathology report. We obtain death certificates and pertinent medical records for all deaths. Follow-up for nonfatal outcomes in PHS is over 97% complete, and for mortality, over 99%. PLCO, Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial:PLCO is a multicenter, randomized trial to evaluate screening methods for the early detection of prostate, lung, colorectal and ovarian cancer. Between 1993 and 2001, over 150,000 men and women ages 55-74 years were recruited from ten centers in the United States (Birmingham, AL; Denver, CO; Detroit, MI; Honolulu, HI; Marshfield, WI; Minneapolis, MN; Pittsburgh, PA; Salt Lake City, UT; St. Louis, MO; and Washington, D.C.). Men randomized to the screening arm underwent prostate cancer screening with prostate-specific antigen (PSA) annually for six years and digital rectal exam annually for four years. Blood specimens were collected from participants randomized to the screening arm of the trial, and buccal cell specimens were obtained from participants randomized to the control arm. Cases included 754 men diagnosed with advanced prostate cancer (Gleason = 8 or stage III/IV) from either arm of the trial. Of these cases, 317 were genotyped previously as part of Cancer Genetic Markers of Susceptibility (CGEMS), a GWAS for prostate cancer. Controls included 1,491 men without a diagnosis of prostate cancer from the screening arm of the PLCO trial. All subjects provided informed consent to participate in genetic etiology studies of cancer and other traits. This study was approved by the institutional review boards at the ten centers and the National Cancer Institute. PLCO was removed from the meta-analysis of the BPC3 studies as a consequence of PEGASUS below. PEGASUS, Prostate cancer Genome-wide Association Study of Uncommon Susceptibility loci: Pegasus is a genome-wide association nested within the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. PLCO is a multicenter, randomized trial to evaluate screening methods for the early detection of prostate, lung, colorectal and ovarian cancer. Between 1993 and 2001, over 150,000 men and women ages 55-74 years were recruited from ten centers in the United States (Birmingham, AL; Denver, CO; Detroit, MI; Honolulu, HI; Marshfield, WI; Minneapolis, MN; Pittsburgh, PA; Salt Lake City, UT; St. Louis, MO; and Washington, D.C.). Men randomized to the screening arm underwent prostate cancer screening with prostate-specific antigen annually for six years and digital rectal exam annually for four years. Blood specimens were collected from participants randomized to the screening arm of the trial, and buccal cell specimens were obtained from participants randomized to the control arm. Cases included 4,598 men of European ancestry diagnosed with prostate cancer from either arm of the trial and controls included 2,941 men of European ancestry without a diagnosis of cancer from the screening arm, matched on age and year of randomization. All subjects provided informed consent, and the study approved by the institutional review board at the National Cancer Institute. Funding:This work was supported by the GAME-ON U19 initiative for prostate cancer (ELLIPSE): U19 CA148537. The BPC3 was supported by the U.S. National Institutes of Health, National Cancer Institute (cooperative agreements U01-CA98233, U01-CA98710, U01-CA98216, and U01-CA98758, and Intramural Research Program of NIH/National Cancer Institute, Division of Cancer Epidemiology and Genetics). The ATBC study and PEGASUS was supported in part by the Intramural Research Program of the NIH and the National Cancer Institute. Additionally, this research was supported by U.S. Public Health Service contracts N01-CN-45165, N01-RC-45035, N01-RC-37004 and HHSN261201000006C from the National Cancer Institute, Department of Health and Human Services. CAPS: The Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden was supported by the Cancer Risk Prediction Center (CRisP; www.crispcenter.org), a Linneus Centre (Contract ID 70867902) financed by the Swedish Research Council, Swedish Research Council (grant: K2010-70X-20430-04-3), the Swedish Cancer Foundation (grant: 09-0677), the Hedlund Foundation, the Söderberg Foundation, the Enqvist Foundation, ALF funds from the Stockholm County Council. Stiftelsen Johanna Hagstrand och Sigfrid Linnér’s Minne, Karlsson’s Fund for urological and surgical research. We thank and acknowledge all of the participants in the Stockholm-1 study. We thank Carin Cavalli-Björkman and Ami Rönnberg Karlsson for their dedicated work in the collection of data. Michael Broms is acknowledged for his skillful work with the databases. KI Biobank is acknowledged for handling the samples and for DNA extraction. Hans Wallinder at Aleris Medilab and Sven Gustafsson at Karolinska University Laboratory are thanked for their good cooperation in providing historical laboratory results. UKGPCS would like to acknowledge the NCRN nurses and Consultants for their work in the UKGPCS study. We thank all the patients who took part in this study. This work was supported by Cancer Research UK (grants: C5047/A7357, C1287/A10118, C1287/A5260, C5047/A3354, C5047/A10692, C16913/A6135 and C16913/A6835). We would also like to thank the following for funding support: Prostate Research Campaign UK (now Prostate Cancer UK), The Institute of Cancer Research and The Everyman Campaign, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. The MEC was supported by NIH grants CA63464, CA54281 and CA098758. 

  Study phs001120

• 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/µL in a final volume of 50 µL. The 96-well plates were designed to have a unique distribution pattern with x duplicates per plate to evaluate genotype concordance. Samples with <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 <0.98, 4. SNPs with Hardy Weinberg Equilibrium p-value <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

• 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 Alabama at Birmingham, 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 A. Bronchoscopy and Immunophenotyping Substudy The Bronchoscopy Substudy enrolled a total of 251 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. B. 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, was re-administered and new samples of blood, urine, saliva, and sputum was collected. Field center staff processed these biospecimen samples according to the same protocol. C. Exacerbation Substudy The Exacerbation Substudy is a prospective, longitudinal observational study of 204 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 phs001119

• 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

• Projects

  Projects Jointly managed by the European Bioinformatics Institute (EMBL-EBI) in Cambridge (UK) and the Centre for Genomic Regulation (CRG) in Barcelona, the EGA provides an invaluable service to the worldwide biomedical research community. The teams leading the EGA are involved in several international partnerships and consortia in numerous scientific fields, where they contribute to ambitious projects. In addition to the project listed below, the EGA is in a long-standing partnership with the Global Alliance for Genomics and Health (GA4GH), as described on the dedicated page. On-going projects Project Duration Domain Funder Tags CANDLE | CANDLE project aims to conceptualise and advance the development of National Cancer Data Nodes (NCDNs) in European countries. These NCDNs will boost the reuse of cancer data for research, innovation and policy making, in order to improve diagnostics and treatment for cancer patients, as well as prevention and early detection. 2025-2028 Cancer Horizon Europe DOCUMENTATION EASIGEN-DS | The EASIGEN-DS project aims to conduct a design study to establish a new European Research Infrastructure on Advanced Genomics Technologies, EASIGEN. To develop an excellent scientific, technological and operational design, we will conduct landscape studies, stakeholder consultations, and community surveying. 2025-2028 Genomic and health data Horizon Europe DATA MANAGEMENT DOCUMENTATION INFRASTRUCTURE Go-IMPaCT | Go-IMPaCT will contribute sequenced genomes and provide infrastructure as part of IMPaCT-Cohort, one of the three fundamental pillars of the Precision Medicine Infrastructure associated with Science and Technology (IMPaCT) program in Spain. Along with the Genome of Europe (GoE) project, around 18.000 people will have their genomes sequenced, also contributing to Spain's commitments in 1+MG. Go-IMPaCT will fund the development of an EGA node to manage and share this genomic and phenoclinic data, laying the foundations for regional and ethnic genomic variability in Spain to be available for research purposes. The IMPaCT cohort is created with the spirit of being an open research tool, compatible with the rest of the health research ecosystem, and other international initiatives. 2025-2027 Large-scale genomics and health data; personalised medicine Instituto de Salud Carlos III ACCESS DISCOVERY INFRASTRUCTURE METADATA STANDARDS FAIR-FEGA | This project seeks to accelerate data depositions into FEGA, significantly increasing the data flow in and from FEGA nodes. It will build capacity within the FEGA nodes and increase awareness in a wide range of stakeholders, thus altogether achieving the ultimate goal of enhancing data reuse. The project will be carried out by a strategic consortium comprising seven ELIXIR nodes and two ELIXIR communities. 2025-2026 Not applicable ELIXIR ACCESS DISCOVERY DOCUMENTATION INFRASTRUCTURE METADATA STANDARDS FEGA-Connect | A consortium of six ELIXIR nodes plus the Polish FEGA node (in-kind contribution) joining forces to build a solid base to develop solutions for effective multi-omic sensitive data integration between FEGA nodes and other infrastructures and specialised Data repositories. We aim to promote a more coherent data deposition, discoverability and retrieval of multi-omics datasets, providing FAIRer data and consequently accelerating research. 2025-2026 Multi-omics data ELIXIR ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE METADATA STANDARDS IMPaCT-Data 2 | IMPaCT-Data 2 will develop a digital platform for the integration and modelling of biomedical data associated with IMPaCT (Precision Medicine Infrastructure associated with Science and Technology) projects in Spain. It will deploy a sustainable infrastructure that facilitates the integration, standardisation, interoperability and analysis of clinical, genomic, molecular and medical imaging data. This platform will be aligned with European projects such as Genome of Europe (GoE), the first project to make use of the European Genomic Data Infrastructure (GDI), and EUCAIM. IMPaCT-Data 2 will benefit from advanced Artificial Intelligence and High Computing Capacity Systems capabilities, offering robust and accessible tools for researchers from the National Health System in Spain. 2025-2026 Large-scale genomics and health data; personalised medicine Instituto de Salud Carlos III ACCESS DISCOVERY INFRASTRUCTURE METADATA STANDARDS SenSec | This project aims to establish a mechanism for orchestrating secure access to sensitive data hosted by the EGA, whether in Central EGA or any Federated Node, from Galaxy, a popular open-source, community-driven VRE (Virtual Research Environment) for bioinformatics analysis. Building on a previous prototype that enabled Galaxy users within Trusted Research Environments (TREs) to decrypt sensitive data for workflow execution without sharing private encryption keys, SenSec will expand this prototype into a comprehensive solution for secure data analysis in Galaxy, facilitating encrypted data access and transfer from FEGA/EGA repositories to designated TREs. 2025-2026 Genomic and health data; trusted research environment ELIXIR ACCESS DATA ANALYSIS ERDERA | The European Rare Disease Research Alliance (ERDERA) takes over EJPRD to deliver concrete health benefits to rare disease patients in the next decade by advancing prevention, diagnosis and treatment research. To leave no one behind, over 170 organisations championed by the European Union and member states are working hand in hand to make Europe a world leader in rare diseases research and innovation. 2024-2034 Rare diseases Horizon Europe; "La Caixa" Foundation cofunds CRG's contribution ACCESS DATA ANALYSIS DISCOVERY INFRASTRUCTURE SYNTHIA | The aim of SYNTHIA is to deliver validated, reliable tools and methods for synthetic data generation (SDG). The tools will cover multiple data types including lab results, clinical notes, genomics, imaging and m-health data. SYNTHIA also hopes to make possible the generation of longitudinal data. 2024-2029 Genomic and health data; multi-omics; AI solutions Innovative Health Initiative (IHI) DATA ANALYSIS DATA MANAGEMENT INFRASTRUCTURE GoE | The Genome of Europe initiative aims to build a European network of national genomic reference cohorts of at least 500.000 citizens. These reference cohorts will be selected to be representative of the European population. 2024-2028 Large-scale genomic and health data Horizon Europe ACCESS DISCOVERY INFRASTRUCTURE METADATA STANDARDS HEREDITARY | HEREDITARY aims to transform the way we approach disease detection, prepare treatment response, and explore medical knowledge by building a robust, interoperable, trustworthy, and secure framework that integrates multimodal health data (including genetic data) while ensuring compliance with cross-national privacy-preserving policies. 2024-2027 Neurodegenerative disorders, gut-brain interplay Horizon Europe DATA MANAGEMENT DATA ANALYSIS EOSC-ENTRUST | The mission of EOSC-ENTRUST is to create a European network of trusted research environments for sensitive data and to drive European interoperability by joint development of a common blueprint for federated data access and analysis. 2024-2026 Trusted Research Environment Horizon Europe INFRASTRUCTURE EBV-MS | "Targeting Epstein-Barr Virus Infection for Treatment and Prevention of Multiple Sclerosis". The ambitious goals of the project are to answer the questions why only a few EBV infected persons develop MS, and define the underlying mechanism of this process, as well as clarify if targeting the EBV infection can prevent MS or improve the disease course. 2023-2028 Viral-host genetics; immune response; disease modelling; disease prevention; AI/ML solutions Horizon Europe DATA MANAGEMENT DATA ANALYSIS WISDOM | WELL-BEING IMPROVEMENT THROUGH THE INTEGRATION OF HEALTHCARE AND RESEARCH DATA AND MODELS WITHOUT BORDER FOR CHRONIC IMMUNE-MEDIATED DISEASES aims to deploy novel approaches for data processing, harmonisation, management, and secure data sharing and federated access for diseases like multiple sclerosis. Using an end-user guided approach, it will facilitate responsible and critical assessment of the use of AI in healthcare. 2023-2028 Chronic immune-mediated diseases Horizon Europe DATA MANAGEMENT INFRASTRUCTURE EUCAIM | EUropean Federation for CAncer IMages is a project that will build a highly secure, federated and large-scale European cancer imaging platform, with capabilities that will greatly enhance the potential of Artificial Intelligence in oncology. 2023-2027 Cancer Digital Europe Programme (DIGITAL) DISCOVERY CONTAGIO | CONTAGIO (COhorts Network To be Activated Globally In Outbreaks) aims to create coordination mechanisms to rapidly react to infectious disease (re-)emergence in low- and middle-income countries (LMICs). 2023-2026 Infectious Diseases European Commission - Horizon Europe ACCESS DATA MANAGEMENT DISCOVERY Youth-GEMs | Youth-GEMS (Gene Environment Interactions in Mental Health TrajectorieS of Youth) will conduct research into the genetic and environmental factors of mental health in young European people. 2022-2027 Mental health European Commission - Horizon Europe DATA MANAGEMENT DISCOVERY GDI | The European Genomics Data Infrastructure project is enabling access to genomic and related phenotypic and clinical data across Europe. It is doing this by establishing a federated, sustainable and secure infrastructure to access the data. 2022-2026 Genomic and health data European Commission - Horizon Europe; "La Caixa" Foundation cofunds CRG's contribution DISCOVERY DOCUMENTATION INFRASTRUCTURE IMPaCT-T2D | The IMPaCT-T2D project aims at studying the complete genomes of a large cohort of patients with Type 2 Diabetes mellitus (T2D), using modern sequencing technologies and artificial intelligence (AI) in order to improve the stratification and pharmacological treatment in the context of precision medicine. 2022-2025 Cardiovascular and Complex Diseases Spanish Ministry of Science and Innovation; Instituto de Salud Carlos III ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE Completed projects Project Duration Domain Funder Tags EOSC4Cancer | EOSC4Cancer builds on existing projects, research outcomes and established community solutions to create the federated FAIR data, analysis and services infrastructure needed for European Cancer research programmes. 2022-2025 Cancer European Commission - Horizon Europe DISCOVERY EuCanImage | A European Cancer Image Platform Linked to Biological and Health Data for Next-Generation Artificial Intelligence and Precision Medicine in Oncology. 2020-2025 AI Solutions in Oncology European Commission - H2020 Programme; "La Caixa" Foundation cofunds CRG's contribution DATA MANAGEMENT METADATA STANDARDS GenoMed4ALL | A consortium built to empower personalised medicine in the field of haematological diseases through the use of AI and the pooling of genomic and clinical data. 2020-2025 Hematological diseases European Commission - H2020 Programme DISCOVERY METADATA STANDARDS BY-COVID | The BeYond-COVID project aims to make COVID-19 data accessible to scientists in laboratories but also to anyone who can use it, such as medical staff in hospitals or government officials. Going beyond SARS-CoV-2 data, the project will provide a framework for making data from other infectious diseases open and accessible to everyone. 2021-2024 Infectious diseases European Commission - H2020 Programme ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE IMPaCT-Data | IMPaCT-Data aims to create the infrastructure for secondary use of data from Spanish healthcare systems - electronic health records, medical imaging and genomic repositories - and contribute with the knowledge and methodology produced to the healthcare system. 2021-2024 Large-scale genomics and health dataSpanish Ministry of Science and Innovation; Instituto de Salud Carlos III ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE LaMarato | It is a project aimed at creating and developing a catalan interhospitalary network to interrogate genetic variants from thousands of genetic tests carried out in patients with rare diseases from the main catalan hospitals. 2021-2024 Genomic and health data Fundacio La Marato de TV3 (catalan foundation) DISCOVERY HealthyCloud | This consortium will contribute a Strategic Agenda towards the European Health Research and Innovation Cloud. The project will work in collaboration with a broad range of stakeholders to ensure that all voices are included and that the results are technically and ethically sound. 2021-2023 Not Applicable European Commission - H2020 Programme DOCUMENTATION B1MG | Beyond 1 Million Genomes aims to create a network of genetic and clinical data across Europe. The project provides coordination and support to the 1+ Million Genomes Initiative (1+MG). This initiative is a commitment of 24 EU countries, the UK and Norway to give cross-border access to one million sequenced genomes by 2022. 2020-2023 Not applicable European Commission - Horizon Europe DATA MANAGEMENT INFRASTRUCTURE METADATA STANDARDS ELIXIR-CONVERGE | An alliance with the goal of Connecting and aligning ELIXIR Nodes to deliver sustainable FAIR life-science data management services. 2020-2023 Data Management and Infectious Diseases European Commission - H2020 Programme DATA MANAGEMENT INFRASTRUCTURE METADATA STANDARDS IHCC | The International HundredK+ Cohorts Consortium aims to create a global platform for translational research ? informing the biological and genetic basis for disease and improving clinical care and population health. 2020-2022 Translational research NIH; The Wellcome Trust; CZI INFRASTRUCTURE METADATA STANDARDS PPCG | The Pan Prostate Cancer Group aims to harmonise and interrogate Whole Genome DNA Sequence data generated around the world from over 2000 men with prostate cancer, with associated transcriptome and methylome data to include men from different clinical categories, and ethnicities. This project is about providing breakthrough advances through analysis of a very large series of Whole Genome DNA data from prostate cancer contributed by many of the leading scientists and clinicians working in prostate cancer genomics. 2019-2024 Cancer Cancer Research UK DATA MANAGEMENT CINECA | Consortium providing a Federated solution enabling population-scale genomic and biomolecular data accessible across international borders accelerating research and improving the health of individuals resident across continents. 2019-2023 Large-scale Genomics and Health Data European Commission - H2020 Programme ACCESS DATA MANAGEMENT DISCOVERY INFRASTRUCTURE EASI-Genomics | A project designed to provide easy access to cutting-edge DNA sequencing technologies to researchers from academia and industry, within a framework that ensures compliance with ethical and legal requirements, as well as FAIR and secure data management. 2019-2023 Next Generation Sequencing European Commission - H2020 Programme ACCESS EJP-RD | An European consortium built to create a comprehensive, sustainable ecosystem allowing a virtuous circle between research, care, and medical innovation. 2019-2023 Rare diseases European Commission - H2020 Programme ACCESS DATA MANAGEMENT DOCUMENTATION METADATA STANDARDS EOSC-Life | EOSC-Life brings together the 13 Life Science research infrastructures (LS RIs) to create an open, digital and collaborative space for biological and medical research. The project will publish 'FAIR' data and a catalogue of services provided by participating RIs for the management, storage and reuse of data in the European Open Science Cloud (EOSC). 2019-2023 Not applicable European Commission - H2020 Programme DOCUMENTATION EUCANCan | A federated network aiming at implementing a cultural, technological and legal integrated framework across Europe and Canada, to enable and facilitate the efficient sharing of cancer genomic data. 2019-2023 Cancer European Commission - H2020 Programme DATA MANAGEMENT METADATA STANDARDS The Federated EGA framework: supporting sensitive data management across the ELIXIR Nodes | This project is a direct continuation of the FHD IS with the goal to position the FEGA framework as the core infrastructure driver to support human data sharing for research. 2019-2023 Human genomic data ELIXIR INFRASTRUCTURE UK Biobank | UK Biobank is a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants. This project is to archive whole genome sequencing and other genetic data for UK Biobank participants. 2019-2023 Large-scale Genomics and Health Data The Wellcome Trust; UKRI; Amgen; AstraZeneca; GSK; Johnson & Johnson DATA MANAGEMENT INFRASTRUCTURE VEIS | The core mission of VEIS is to create an open ecosystem of technologies that will address and adapt to the requirements of the systems used to analyse and interpret -omics and clinical data in research and application environments in biomedicine. The aim of the project is to leverage the value of the EGA for both industry and society. 2019-2022 Oncology and Rare diseases Generalitat de Catalunya and European Regional Development Fund (ERDF) ACCESS DISCOVERY ELIXIR BEACON IS | This study follows on from a number of earlier activities that have established the ELIXIR Beacon Project. The main aim is to extend the Beacon protocol, developed at EGA, to become the reference ELIXIR Data Discovery product 2019-2021 Not applicable ELIXIR DISCOVERY ELIXIR FHD IS | This project coordinates the delivery of FAIR compliant metadata standards, interfaces, and reference implementation to support the federated ELIXIR network of human data resources. 2019-2021 Human genomic data ELIXIR INFRASTRUCTURE ELIXIR Rare Disease | The Rare Disease Community extends and generalises the system of access authorisation and high volume secure data transfer developed within the EGA. The goal of the Community is to create a federated infrastructure that will enable researchers to discover, access and analyse different rare disease repositories across Europe. It is doing this in partnership with other European infrastructure projects, namely RD-CONNECT, BBMRI-ERIC and E-Rare.2019-2021 Rare diseases ELIXIR INFRASTRUCTURE Solve-RD | Solve-RD - solving the unsolved rare diseases - is a research project funded by the European Commission. It echoes the ambitious goals set out by the International Rare Diseases Research Consortium (IRDiRC) to deliver diagnostic tests for most rare diseases by 2020. The current diagnostic and subsequent therapeutic management of rare diseases is still highly unsatisfactory for a large proportion of rare disease patients - the unsolved RD cases. For these unsolved rare diseases, we are unable to explain the etiology responsible for the disease phenotype, predict the individual disease risk and/or rate of disease progression, and/or quantitate the risk of relatives to develop the same disorder. 2018-2024 Rare diseases European Commission - H2020 Programme ACCESS DATA MANAGEMENT METADATA STANDARDS EuCanShare | An EU-Canada joint infrastructure for next-generation multi-Study Heart research. 2018-2022 Cardiovascular Diseases European Commission - H2020 Programme ACCESS METADATA STANDARDS

  Documentation about/projects-and-funders/projects

• Providing safe access to sensitive human data across borders: Federated EGA becomes a reality

  Enabling discovery and access to sensitive data across national boundaries is vital for improving human health. It enables more powerful and efficient research by increasing the volume and diversity of data available for analysis. It allows us to better understand the causes of diseases – cancer, rare diseases, infectious diseases like COVID-19 – and develop new medicines and treatments. Sensitive human omics data are typically generated by research initiatives and shared using specialist repositories which provide services for data submission, discovery, and access. The EGA is one such repository. Established in 2008 at EMBL’s European Bioinformatics Institute (EMBL-EBI) in the UK, since 2012 the EGA (“Central EGA”) has been jointly managed by EMBL-EBI and the Centre for Genomic Regulation (CRG) in Spain. Many countries have emerging personalised medicine programmes which generate data from national initiatives. These programmes are driving a transition in human genomics from being research-driven to receiving funding through healthcare. Data generated in a clinical context are subject to stricter governance than research data and must follow national data protection legislation. To solve these challenges, the Federated EGA provides a network of connected resources to enable transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. In this way, the Federated EGA infrastructure supports the goals of European initiatives such as the 1+ Million Genomes initiative (1+MG), the European Health Data Space, and a number of EU-funded 1+MG implementation projects including Beyond 1 Million Genomes. The Federated EGA is made up of “Nodes” – typically nationally funded and operated – which store and manage data locally while allowing global discovery within the Federated EGA network. Since 2016, multiple parallel efforts, supported by ELIXIR and other transnational and national initiatives, have created technical and legal frameworks for establishing the Federated EGA. In 2022, the first five Nodes – Finland, Germany, Norway, Spain, and Sweden – officially joined the Federated EGA by signing Federated EGA Collaboration Agreements with Central EGA. The Finnish FEGA Node is operated by CSC - IT Center for Science and provides data management services according to national laws and the requirements of the EU General Data Protection Regulation (GDPR). These services provide tools and support for the whole life-cycle of sensitive research data from collating to analysis, publication, and authorised re-use. The development of the services has been a joint effort with the other Nordic nodes within NeIC's Tryggve and Heilsa projects, and funded by Finnish Ministry of Education and Culture and projects coordinated by ELIXIR Finland. Read more about Finnish FEGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. In Norway, a key component of the infrastructure is the services of The Sensitive Data Service (TSD) offered by USIT at University of Oslo. The Federated EGA Norway Node is developed by ELIXIR Norway and operated by the University of Oslo as the responsible legal entity. Core software modules are developed jointly with the other Nordic nodes in the NeIC Tryggve and Heilsa projects. Read more about FEGA Norway signing. The Spanish FEGA (es-FEGA) is a national service for storing sensitive biomedical data in Spain. Supported by the Spanish Institute of Bioinformatics (INB) in collaboration with Central EGA, sensitive research datasets are primarily hosted at the Barcelona Supercomputing Centre facilities. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. By providing a solution for secure and efficient management of human omics data, the Federated EGA aims to foster data reuse, enable reproducibility, accelerate biomedical research, and improve human health. Find out more Interested in setting up your own Federated EGA Node? Check out the FEGA Onboarding Knowledge Base for more information. The ELIXIR Federated Human Data Community is a great entry point for anyone interested in learning more about the Federated EGA. You can: Join the ELIXIR Federated Human Data Community mailing list (select “Human Data”) Attend the ELIXIR Federated Human Data Community calls

  Blog safe-access-to-sensitive-human-data-federated-ega

• From research to data sharing: exploring EGA user's experiences

  The Cell Plasticity and Regeneration Group at the Bellvitge Biomedical Research Institute-IDIBELL focuses on the process of recruitment of macrophages that takes place in the small intestine during injury and healing. They recently published a paper titled “Mucosal Macrophages Govern Intestinal Regeneration in Response to Injury" in Gastroenterology Journal. As part of the research, some experiments were conducted using human intestinal organoid lines. These cells were processed for RNA sequencing, and the sequencing data were deposited at the EGA to be made available to the scientific community. When dealing with human genomic information, repositories must ensure the availability of the datasets while ensuring data protection. In this context, the European Genome-phenome Archive stands as a service for secure archiving and sharing of genetic, phenotypic and clinical data resulting from biomedical research. Following the recent publication of their paper, we took the opportunity to talk to Ilias Moraitis, first author, and Jordi Guiu, group leader, to find out about their experience with data sharing. Could you explain the focus of your research? In the lab, we study intestinal regeneration and how immune cells participate in this process. We use several techniques: engineered mouse models, image tracing, as well as mouse and human cells intestinal organoids. What challenges do you face regarding data management? We didn’t have a lot of problems. Always the informatic part, the data analysis, can give problems. But everything was smooth and working. We think it’s very important to deposit the data in repositories. For the mouse data it is very straightforward, but for us it was the first time depositing human data, which is sensitive because it comes from patients, and this is legally regulated. That’s why we thought about the EGA, and it was our first time. Why do you think it is important to submit data to repositories such as EGA? Because we think data is important for the science. Nowadays, we are sequencing a lot everywhere worldwide and having these resources shared with the scientific community it’s not only good for science, but it is also saving money and reducing costs. And there is so much data in there that can be used for other projects and for other questions. And it saves time! How was the process of submitting the data to the EGA? The communication worked very well but it was slower than we would like. But I think this is something we learn on the way. People usually wait until the last minute to do this before publication but if you know it in advance, you can start the process earlier. If you had to repeat the process, would you do anything differently? It’s like everything, isn’t it? For the first time you don’t know, you aren’t sure, but for the second time you know the steps, you know what to do and it’s faster. Of course, we would to do it earlier. Also, there were some things related to the control access that we didn’t know how to manage, but it was something more internal to us. Who is going to receive the communications and when, is our ethical committee going to evaluate this? All these technicalities that we didn’t know before starting this process, so we had to think about all these things. Next time it will be faster on our side. Do you have any suggestions for us to improve the submission process? Maybe we’ll need to ask our bioinformatician who did it! He was in charge of this part. Besides that, we think if the process were faster, it would be better for everyone. Also, in science a lot of times you have to do a lot of things at the very last minute, because of the nature of the experiments, or the need to accomplish for submitting a paper. Being faster in the process would be a plus. On your side, we went through the process and were able to find some information, but there were aspects we weren't aware of and weren't sure how to handle. I'm not sure if other institutes have different protocols for managing data requests or how they handle these internally. We believe the main issue lies in how the forms are filled out. Do you have any recommendations for other submitters? The main advice is to do this with enough time. Submit the data when you have the sequences, and don’t wait until the last minute. And then it can be under embargo, so you don’t need to make it public at that moment. As soon as you sequence, upload it and then it will be there for whenever you need to publish. How do you think we could encourage other researchers to submit their data? There are different layers here. One is that it is mandatory. You have to do this. The other is that we belong to a research community, to the same community and sharing this is making our research community stronger and more efficient. And then, also because this gives you visibility. The data is there, other people can analyse your data, and this also will bring citations to your papers. It has many advantages.

  Blog from-research-to-data-sharing

• Better Outcomes for Children: GWAS from Cincinnati Children's Hospital Medical Center (CCHMC) - eMERGE Phase II data - (dbGaP Deposit 1)

  This submission includes genotyping data from 6 separate cohorts (named A-E here), each is described in separate paragraphs below. Cohort A) Autism is a neurodevelopmental disorder that affects 1.5 million people in the United States, at an estimated lifetime cost of $4 million. It is part of Pervasive Developmental Disorder characterized by impairments in communication, language and reciprocal social interaction, and by unusual patterns of restricted and repetitive interests or behaviors. Autism, or autistic disorder, is the most severe form of the disease which has a wide range of symptom severity encompassed by the more inclusive term, Autism Spectrum Disorder (ASD). The prevalence of Autistic Disorder is currently estimated to be 4 per 1000, affecting 4 times more boys than girls. Susceptibility to autism is clearly attributable to genetic factors but the extensive clinical and genetic heterogeneity within the disorder have hindered efforts at genetic dissection. Identifying the genetic variants that contribute to this highly heritable disease is crucial to advancing research on autism. Reported concordance rates among monozygotic twins range from 60% − 90% and heritability of autism has been estimated to be 90% or greater. Identifying genetic variants that contribute to this highly heritable disease is crucial to advancing research on autism. Cohort B) The long-term objective of this research is to determine the molecular genetic basis of Cardiovascular (CV) disease in the young; this study seeks to identify gene mutations that cause "structural heart defects". Every year in the USA, 32,000 individuals are born with CV malformations; approximately 20% die during the first year of life. Another 30,000 young people develop a disturbance of cardiac rhythm or conduction. In addition, the life expectancy of another 40,000 young people is shortened by other forms of CV disease. It is becoming more apparent that in spite of the success in diagnosis and treatment, very little is known about the causes of CV disease in the young; understanding the causes will permit insight into the pathophysiologic basis of disease and allow definition of disease risk - two critical elements for disease prevention. Disease prevention and/or risk modification promises to be the new frontier in the management of CV disease in the young. Cohort C) Eosinophilic Esophagitis (EE) is one of the manifestations of eosinophilic gastrointestinal inflammation which have profound effects on a patient's health and development. Results of epidemiologic studies performed through our center demonstrate that eosinophil-associated gastrointestinal disease is not an uncommon entity. While the epidemiology of eosinophilic esophagitis has not been thoroughly studied until recently, there appears to be a significant increase in the diagnosis of EE in the last decade. Based on our research, this mainly reflects increased disease recognition, but there is also a bona-fide increase in disease incidence which coincides with the increasing incidence of asthma and allergic diseases in the industrialized world. In addition, many patients with intractable symptoms thought in the past to represent atypical GERD or other disorders are now being recognized as having EE. Diagnosis of EE requires endoscopy and biopsies to document the characteristic histologic findings of esophageal eosinophilia. In general, this study proposed to elucidate the mechanisms underlying eosinophil growth, survival, migration, and function, and to investigate and further characterize the pathophysiology of, clinical manifestations of, and spectrum of disease severity of eosinophilic esophagitis in humans. Cohort D) Juvenile idiopathic arthritis (JIA) is a debilitating complex genetic disorder characterized by inflammation of the joints and other tissues and shares histopathological features with other autoimmune diseases. It is considered a complex genetic disorder. There are more than 50,000 children with JIA in the USA, approximately 1 per 1000 births, which is about the same incidence as juvenile diabetes. It is believed that genes in the major histocompatibility complex (MHC) play a role in defining genetic risk, and it can be hypothesized that loci in other chromosomal regions are involved in conferring risk in JIA. These candidate chromosomal regions can be identified using genome wide association analyses. The long-term goal is a comprehensive understanding of the genetic basis of these disabling arthropathies for which the molecular basis is not presently understood. The results of recent GWAS for Caucasian JIA were recently published in Arthritis Rheum. 2012 Feb 21 (Published online, PMID=22354554). Cohort E) Childhood Absence Epilepsy (CAE) is characterized by very frequent (several to many per day) absence seizures in an otherwise normal child with an EEG usually demonstrating 3 Hz bilateral, synchronous, symmetrical spikewaves pattern with normal background activity. CAE occurs in 10% to 15% of all children with epilepsy with an annual incidence of 6.3 to 8/100,000 in children <15 years of age. Females are more affected than males. Epilepsy onset is typically between the ages of 4 to 8 years with a peak incidence of 6 to 7 years. CAE does not start after age 13 years. The primary objective of this study is: To identify the optimal anticonvulsant (i.e. the antiepileptic drug (AED) with highest rate of seizure control and lowest incidence of treatment limiting toxicity) used for the initial treatment of children with Childhood Absence Epilepsy (CAE). The secondary objectives are: To determine the pharmacogenetic and other non-heritable factors underlying the interindividual variation in anticonvulsant response efficacy and toxicity. To define and contrast the effects of ethosuximide, lamotrigine, and valproic acid monotherapy on cognition (attention), behavior and quality of life in children with Childhood Absence Epilepsy. Cohort F) Cincinnati Controls. These are local healthy children selected to be representative of the population of Cincinnati with respect to gender and ancestry. Cohort G) Cytogenetics cases are ascertained by the clinical request to obtain cytogenetic analysis. The platform used included genotyping data which are then available for genome wide association analysis

  Study phs000494

• Institut Curie Neuroblastoma Whole Genome Sequencing Diagnosis Relapse

  Neuroblastoma, a clinically heterogeneous pediatric cancer, is characterized by distinct genomic profiles but few recurrent mutations. As neuroblastoma is expected to have high degree of genetic heterogeneity, study of neuroblastoma's clonal evolution with deep coverage whole-genome sequencing of diagnosis and relapse samples will lead to a better understanding of the molecular events associated with relapse. Samples were included in this study if sufficient DNA from constitutional, diagnosis and relapse tumors was available for WGS. Whole genome sequencing was performed on trios (constitutional, diagnose and relapse DNA) from eight patients using Illumina Hi-seq2500 leading to paired-ends (PE) 90x90 for 6 of them and 100x100 for two. Expected coverage for sample NB0175 100x100bp was 30X for tumor and constitutional samples. For the seven other patients expected coverage was 80X for tumor samples with PE 100x100, 100X in the other tumor samples and 50X for all constitutional samples (see table 1). Following alignment with BWA (Li et al., Oxford J, 2009 Jul) allowing up to 4% of mismatches, bam files were cleaned up according to the Genome Analysis Toolkit (GATK) recommendations (Van der Auwera et al., Current Protocols in Bioinformatics, 2013, picard-1.45, GenomeAnalysisTK-2.2-16). Variant calling was performed in parallel using 3 variant callers: GenomeAnalysisTK-2.2-16, Samtools-0.1.18 and MuTect-1.1.4 (McKenna et al., Genome Res, 2010; Li et al., Oxford J, 2009 Aug; Cibulskis et al., Nature, 2013). Annovar-v2012-10-23 with cosmic-v64 and dbsnp-v137 were used for the annotation and RefSeq for the structural annotation. For GATK and Samtools, single nucleotide variants (SNVs) with a quality under 30, a depth of coverage under 6 or with less than 2 reads supporting the variant were filter out. MuTect with parameters following GATK and Samtools thresholds have been used to filter our irrelevant variants. .SNVs within and around exons of coding genes overlapping splice sites.. Then,variants reported in more than 1% of the population in the 1000 genomes (1000gAprl_2012) or Exome Sequencing Project (ESP6500) have been discarded in order to filter polymorphisms. Finally, synonymous variants were filtered out. MuTect focuses on somatic by filtering with constitutional sample. Mpileup comparison between constitutional and somatic DNAs allowed us to focus also on tumor specific SNVs with GATK and Samtools. Finally, every SNV called by our pipeline and also supported in any constitutional samples were filtered our in order to prevent putative constitutional DNA coverage deficiency. Then we analyzed CNVs (copy number variants) with HMMcopy-v0.1.1 (Gavin et al., Genome Res, 2012) and control-FREEC-v6.7 (Boeva et al., Bioinformatics 2011) with a respective window of 2000bp and 1000 bp, and auto-correction of normal contamination of tumor samples for Control-FREEC. Finally we explored Structural variants (SVs) including deletions, inversions, tandem duplications and translocations using DELLY-v0.5.5 with standard parameters (Rausch et al., Oxford J, 2012). In tumors, at least 10 supporting reads were required to make a call and 5 supporting reads for the sample NB0175 with a coverage of only 40X (see table 2). To predict SVs in constitutional samples for subsequent somatic filtering, only 2 supporting reads were required in order not to miss one. To identify somatic events, all the SVs in each normal sample were first flanked by 500 bp in both directions and any SVs called in a tumor sample which was in the combined flanked regions of respective normal sample was removed (see graph 1). Deletions with more than 5 genes impacted or larger than 1Mb and inversions or tandem duplications covering more than 4 genes, were removed. We focused on exonic and splicing events for deletions, inversions, and tandem duplications. For translocation, we keep all SVs that occurred in intronic, exonic, 5'UTR, upstream or splicing regions. Bioinformatics detection of variations with Deep sequencing approach Once PE reads merged and adaptors trimmed by SeqPrep with default parameters, merged reads were aligned via the BWA (Li H. and Durbin R. 2009 PMID 19451168) allowing up to 1 differences in the 22-base-long seeds and reporting only unique alignments. Only reads having a mapping quality 20 or more have been further analysed. Variant calling software was not used, since we aimed to predict variations at low frequencies, observed in less than 1% of reads. Such variants require a custom approach. Using DepthOfCoverage functions of the Genome Analysis Toolkit (GATK) v2.13.2 (McKenna A, et al., 2010 Genome Research PMID: 20644199), we focused on high quality coverage of bases A, C, G and T at the targeted variant position. Depth of coverage of each base following a mapping quality higher than 20 and a base quality higher than 10 have been taken into account in order to focus only on high quality data. Aiming to determine the background level of variability at the studied regions, 10 control samples were included in the analysis. The same approach and filtering criteria have been applied as introduced above over the entire amplicons. In order to highlight variants, for each sample the frequencies of each bases at each amplicon position were then compared to those observed in the set of controls. Statistical analyses were performed with the R statistical software (http://www.R-project.org). Fisher’s exact two-sided tests with a Bonferroni correction were performed to compare percentages of bases between the data sets, i.e. for a given base between a case and the controls. Finally, significant variations were filtered-in once (i) a significant increase in the percentage of avariant base and (ii) a significant decrease in the percentage of it's reference base following our p.values criteria was observed (p.val < 0.05).

  Study EGAS00001001184

• Submitter Portal API

  Submitter Portal API The metadata submission process can be difficult and time-consuming. For this reason, the EGA has developed the Submitter Portal, a tool that was created to offer a simplified and user-friendly method of registering metadata. Our portal provides features that are intended to make the input of metadata easier, ensuring that your data is registered correctly and effectively. Our page is divided into logical sections and includes a helpful video instruction to further assist you as you complete the submission process. With the Submitter Portal, you can rest assured that the submission of your data is in good hands. For those that want a more flexible and automated approach, we also provide a programmatic approach using the Submitter Portal AP in addition to the user interface of the Submitter Portal. With the help of our API, you can quickly include the submission of metadata into your own workflow for a more effective and individualised experience. Previous steps Create your EGA account To submit data you first need to create your EGA user. Then, once your account has been verified, you will have to request a submitter role and sign the EGA Data Processing Agreement (DPA). When the DPA is signed, you must send it to EGA Helpdesk for further validation. Please, note that if you already have an EGA account, or you have an ega-box (submission account), you can skip this step.  Upload your files Please note that all your files must be encrypted using the Crypt4GH tool before upload them.As soon as you are assigned with a submitter role, you will be able to connect to the EGA inbox and upload your files. Understand the EGA metadata schema It's crucial to comprehend the EGA metadata schema, a set of rules that specify how data is organised, described, and shared inside the EGA, in order to get the most out of this resource. Learn all about EGA metadata schema! Register your DAC and policy There are two objects in the EGA metadata schema that are registered in a separate portal. All DACs and policy objects are registered using the DAC Portal, a tool developed by EGA to help data controllers manage their data stored at the EGA. You can find the relevant information in the DAC Portal Guide. Programmatic submission All the calls to the API need to be Authenticated. We use the OpenID Connect protocol. API Usage Flow: Click here to check the API usage flow schema Obtain Access and Refresh Token: The first step to using the API is to obtain an access and refresh token. These tokens are required for authentication and authorisation of API requests. To obtain these tokens, you need to log in using your EGA credentials. Example: curl "https://idp.ega-archive.org/realms/EGA/protocol/openid-connect/token" \ -d "grant_type=password" \ -d "client_id=sp-api" \ -d "username=your-username" \ --data-urlencode "password=your-password" Use Access Token in API Calls: Once you have obtained the access token, you must use it in all the calls to the API. The access token is valid for a limited time period. When it expires, you will need to use the refresh token to obtain a new access token. Create Submission Object: After authentication, you can start creating a submission object. This object will be used to store all the metadata objects and files related to the submission. Example: curl "https://submission.ega-archive.org/api/submissions" \ -H "Authorization: Bearer your-access-token" \ -H "Content-Type: application/json" \ -d "{ \"title\":\"My submission title\", \"description\":\"My submission description\" }" Create EGA Metadata Objects: Within the submission object, you can create other metadata objects required for the submission. These objects may include information about the submitter, study, sample, experiment, and run. Example of study creation: curl "https://submission.ega-archive.org/api/submissions/{submisison-id}/studies" \ -H "Authorization: Bearer your-access-token" \ -H "Content-Type: application/json" \ -d "{ \"title\":\"My study title\", \"description\":\"My study description\", \"study_type\":\"Metagenomics\" }" If you would like to reuse already registered objects in new submissions, bear in mind that they must have an accession which you must use to reference them. You cannot reuse objects that have not been finalised and accepted yet.For example, when creating a new run which is reusing a previously submitted experiment, instead of using experiment_provisional_id, you must use experiment_accession_id. Enums There are some fields that only accept specific values, i.e. study_type. The API provides several enumerations to list these values. You can list all the enumerations, or a specific one, for example, study_types. Obtain IDs of files You can list the files available in your inbox and filter by a prefix by adding the prefix param, i.e. &prefix=/my_folder/abc Example: curl "https://submission.ega-archive.org/api/files?status=inbox&prefix=/my_folder/abc" \ -H "Authorization: Bearer your-access-token" \ -H "Content-Type: application/json" To improve performance and manageability, we have limited the number of files that the Submitter Portal API can return in a response. If a given call exceeds this limit, user will be asked to narrow down the search by using the query parameters already available in this endpoint. Finalise Submission: Once all the metadata objects are created and linked to the submission, you can finalise the submission. Finalising the submission sends it to the Helpdesk team for review. Please, note that all files linked to the submission must be ingested, and all objects created in a submission must be linked before you can finalise the submission. API Reference: For a full API reference, please check our specification documentation.

  Documentation submission/metadata/submission/sequencing-phenotype/submitter-portal-api