892 results for "fc points pc 26 Besuche die Website Buyfc26coins.com. Schnelle Lieferung, guter Support..pxic"

in 8.52 milliseconds.

• Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - TargetedEMSeq

  Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.

  Dataset EGAD00001016060

• Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - PTA_WGS

  Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.

  Dataset EGAD00001016061

• Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - PTA_RNA

  Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.

  Dataset EGAD00001016063

• Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - LCMB_WES

  Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.

  Dataset EGAD00001016065

• Angelman, Rett, Prader-Willi Syndrome Consortium (ARP) Rett Syndrome Natural History Protocol

  This observational study will involve the comprehensive assessment of the clinical features of Rett syndrome with this Rare Diseases Clinical Research Network. A clinical database of 1350 individuals with Rett syndrome including MECP2 mutation status for the purpose of elaborating phenotype-genotype correlations will be established. Eligible participants will be enrolled in the natural history study for 11 years from the 4 clinic sites currently involved with Rett syndrome (BCM, GGC, and UAB, CHB) as well as from the several thousand registrants in the International Rett Syndrome Foundation registry who will be invited to the nearest consortium site involved in Rett syndrome. Potential participants are expected to have had determination of their MECP2 mutation status, but will not be excluded should no mutation be present. All individuals who meet criteria and are currently receiving care from study sites will be invited to participate (N>600). Additional participants will be invited through the assistance of the International Rett Syndrome Foundation (IRSF). All individuals with RS registered with IRSF will be invited to participate in this study via the IRSF website and the IRSF newsletter. Potential participants will be apprised of the participating study sites and invited to contact one of the sites based on feasibility of travel. Inasmuch as the enrollment goal of 1350 participants is 40% of the current IRSF registry, we believe this will be a representative sampling. Bias can certainly occur based on travel requirements, level of interest, and other factors. However, inclusion of such a large sample should minimize sampling bias. No travel expenses will be covered, so it is likely that participants will be those with geographic proximity to Houston, Greenwood, Birmingham, or Boston. From the sample pool described above, the aim is to conduct the observational longitudinal (natural history) study on up to 1350 participants with classic Rett syndrome and variant forms of Rett syndrome, that is, participants who fully meet the established clinical criteria for Rett syndrome and those who meet some but not all of these criteria. All participants expressing MECP2 mutations irrespective of phenotype will be eligible for enrollment. For each of the eight common genetic mutations as well as those with large scale deletions, the prevalence of the following RS characteristics will be estimated for each age category and overall: seizure presence and frequency, ambulation status, scoliosis, ECG abnormality, breathing irregularities while awake, feeding difficulties, growth as measured in percentiles from NCHS standards curves, the presence of osteopenia as evidenced by bone fracture, and the presence of gallbladder disease. Both incidence and prevalence estimates will be made using a combination of data obtained at the regularly scheduled clinical visits and that obtained from the medical history. Incidence estimates will be calculated using time-to-event analyses (e.g., Kaplan-Meier survival curves) with appropriate calculations of standard errors and 95% confidence intervals. Cox proportional hazard regression models will be used to allow control for other factors, and t-tests will be calculated on the coefficients themselves. Appropriate transformations on explanatory variables will be used as deemed necessary in Cox models. Prevalence estimates will be calculated from estimates of proportions with exact confidence intervals. For a sample size of 275, the standard error of the estimate will not exceed 0.03 (3%) and for a sample size of 60, the standard error of the estimate will not exceed 0.06 (6%).

  Study phs000574

• Add Health: Longitudinal Study of a Nationally Representative Sample of Adolescents in Grades 7-12 in the United States during the 1994-95 School Year, Followed into Adulthood with Five Interviews/Surveys in 1995, 1996, 2001-02, 2008, and 2016-18

  The National Longitudinal Study of Adolescent to Adult Health [Add Health] is an ongoing longitudinal study of a nationally representative U.S. cohort of more than 20,000 adolescents in grades 7-12 (aged 12-19 years) in 1994 followed into adulthood with five interviews/surveys in 1995, 1996, 2001-02, 2008, and 2016-18. Add Health was designed to understand how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. Add Health contains unprecedented environmental, behavioral, psychosocial, biological, and genetic data from early adolescence and into adulthood on a large, nationally representative cohort with unprecedented racial, ethnic, socioeconomic, and geographic diversity. Add Health has a large, multidisciplinary user base of over 50,000 researchers around the world who have published over 3,400 research articles. Add Health is housed at the Carolina Population Center of the University of North Carolina at Chapel Hill. Add Health datasets are distributed according to a tiered data disclosure plan designed to protect the data from the risk of direct and indirect disclosure of respondent identity. Add Health's large sample size, population diversity and rich longitudinal database of psychosocial, physical, and contextual data will permit investigation of an exceptionally broad range of phenotypes with known genetic variation. Prospective longitudinal measures are available to document change over time in each of these phenotypes, as well as change in the social environment and life experiences, making the Add Health sample ideal for understanding genetic linkages with health and behavior across the life course. The original design of Add Health included important features for understanding biological processes in health and developmental trajectories across the life course of young people, including an embedded genetic sample with more than 3,000 pairs of adolescents with varying biological resemblance (e.g., twins, full sibs, half sibs, and adolescents who grew up in the same household but have no biological relationship), testing of saliva and urine for sexually transmitted infections and HIV, and biomarkers of cardiovascular health, metabolic processes, immune function, renal function, and inflammation. Add Health therefore has critical objective indicators of health status and disease markers in young adulthood, well before chronic illness or its complications emerge in later adulthood. Because DNA has been collected on the full sample at Wave IV, it is possible to link genetic profiles with social, behavioral, and biological measures over time from adolescence into adulthood. Add Health sampled the multiple environments in which young people live their lives, including the family, peers, school, neighborhood, community, and relationship dyads, and provides independent and direct measurement of these environments over time. Add Health contains extensive longitudinal information on health-related behavior, including life histories of physical activity, involvement in risk behavior, substance use, sexual behavior, civic engagement, education, and multiple indicators of health status based on self-report (e.g., general health, chronic illness), direct measurement (e.g., overweight status and obesity), and biomarkers. No other data resource with this expanse of genotype and phenotype data on a large nationally representative longitudinal sample with race, ethnic, socioeconomic, and geographic diversity exists. A complete reference guide on study design and accomplishments can be found on the Add Health website: https://cdr.lib.unc.edu/concern/articles/6t053j27s.

  Study phs001367

• A Genome-Wide Association Study in Patients Experiencing Breast Events While Receiving Adjuvant Aromatase Inhibitors for Early Breast Cancer on NCIC CTG Trial MA.27

  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 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 estrogen receptor (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. The final results of this study have been published, see Goss et al., 2013 (23358971). The patients in this analysis came from three cohorts: Cohort 1 consisted of 870 patients genotyped on the Illumina Human610-Quad BeadChip studied in a GWAS with the phenotype of musculoskeletal adverse event, see Ingle et al., 2010 (20876420), Cohort 2 consisted of 882 patients genotyped on the Illumina OmniExpress platform studied in a GWAS with the phenotype of fragility fractures, see Liu et al., 2014 (25148458), and the remaining 2913 patients were genotyped with the Illumina OmniExpressExome platform.

  Study phs001043

• Title: Divergent levels of CD112 and INKA1 define a distinct subset of human long-term hematopoietic stem cells

  The data provided here was critical in establishing that human long-term hematopoietic stem cells (LT-HSC), previously described as the most primitive HSC population, is actually composed of distinct subsets that can be prospectively isolated. Via mechanistic studies centering around the Rho-GTPase effector kinase PAK4 and its inhibitor INKA1, we identified the immune checkpoint ligand CD112 as a marker for hematopoietic stem and progenitor cells, that is highest expressed on LT-HSC. More importantly, CD112 can be used to stratify functionally distinct subsets within LT-HSC: In response to regeneration-mediated stress, the CD112low subset exhibits a transient restraint (termed latency) before contributing to hematopoietic reconstitution, while the CD112high subset is primed to respond rapidly. High resolution RNA-seq of the CD112 surface expression spectrum within rare LT-HSC subsets (human umbilical cord blood) demonstrated that more genes are differentially upregulated in the deeper quiescent and less metabolic active subset. Genes enriched in this subset centre around cell adhesion and Rho-GTPase signaling. This is in agreement with the scRNAseq data from human G-CSF mobilized peripheral blood (mPB) generated here that was used as an model of in vivo activation/priming revealing via RNA-velocity and pseudo-time analysis that INKA1high versus PAK4high, CDK6high and CD112high enrichment are either detected early or late in diffusion pseudotime indicative of quiescent versus primed cell status, respectively. RNAseq following INKA1 overexpression in LT-HSC and ST-HSC revealed by GSEA an overall stemness preserving phenotype and particularly in LT-HSC, but not in short-term HSC (ST-HSC), suppression of transcriptional programs linked to activation. Collectively, our data decipher the molecular intricacies underlying HSC heterogeneity and self-renewal regulation and point to latency as an orchestrated physiological response that integrates quiescence control with HSC fate choices to preserve a stem cell reservoir.

  Dataset EGAD00001006541

• 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

• Genome-Wide associations of Lung Health Study (LHS)

  The 'Genome-Wide Associations Environmental Interactions in the Lung Health Study' at Johns Hopkins University aims to test for association between lung function decline as a primary outcome associated with chronic obstructive pulmonary disease (COPD) using banked DNA and phenotype data on 4,287 European Americans from the longitudinal, multicenter Lung Health Study (LHS). The broad goals of the LungGO/ESP-GO falls into two general categories: (i) discovery of all variants (i.e., common and rare) in all protein-coding regions of the human genome (i.e., the exome) conferring risk to complex pulmonary diseases including COPD, in a subset of the LHS cohort. The Johns Hopkins University LHS cohort offers a unique opportunity to elucidate genetic variants that cause COPD. The Lung Health Study I was a randomized multicenter clinical trial with 5887 participants carried out from October 1986 to April 1994, designed to test the effectiveness of smoking cessation and bronchodilator administration in smokers aged 35 to 60 with mild lung function impairment. Participants were randomly assigned to one of three groups: usual care, who received no intervention smoking intervention with the inhaled bronchodilator ipratroprium bromide smoking intervention with an inhaled placebo. The effect of intervention was evaluated by the rate of decline of forced expiratory volume in one second (FEV1). For the GWAS, only the subset of European American LHS participants for whom lung function data from three time points or more are available. Thus, the GWAS represents 73% of the 5,887 volunteers who participated in the LHS study. Importantly, LHS subjects included had similar demographics (including age, gender and BMI) and rates of lung function decline (mean annual change in FEV1% predicted: -0.96 %/yr vs. -0.99 %/yr, p=0.57) compared with those not included in the GWAS, reflecting little selection bias for our primary outcome. They were, however, more likely to have quit smoking after 5 years. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to lung function through large-scale genome-wide association studies of smokers enrolled in a multicenter clinical trial. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000335