14480 results for "PLOS+ONE+Pennsylvania+German+ACS+language+code+pdc+open+access+study"

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• Medulloblastoma Hi-C Data Access Committee

  Contact Information Marco Gallo marco.gallo@bcm.edu

  Dac EGAC50000000357

• Data Access Committee Clinical Genetics at Karolinska Institutet (DAC-CG-KI)

  Dac EGAC00001002960

• Soskiclab data access committee

  Contact Information Blagoje Soskic blagoje.soskic@fht.org

  Dac EGAC50000000830

• Discovery of Colorectal Cancer Susceptibility Genes in High-Risk Families

  Colorectal cancer remains one of the most common cancers in the US with 146,970 new diagnoses and 49,920 deaths estimated for 2009. Colon cancer is also one of the most familial of cancers. Individuals with a first-degree relative with colon cancer have a 2- to 3-fold increased risk, and those with more than one first-degree relative with colon cancer, or a single first-degree relative affected at age 50 years, have a 3- to 6-fold greater risk than those with no family history. The most prominent high-risk colorectal cancer susceptibility genes, APC, MLH1, MSH2, MSH6, and PTEN, were all discovered more than a decade ago. Currently, mutation screening of these genes, plus a short list of additional genes that are responsible for a very small fraction of colorectal cancer, plays an important role in the clinical management of individuals with a strong family history of the disease or syndromic evidence for the presence of a gene mutation. At the other end of the risk spectrum, genome-wide association studies have identified a number of common alleles with very modest effects on colorectal cancer risk; their clinical utility has yet to be established. However, taken together, the known spectrum of genetic effects only explain about one quarter of the overall familial excess of colorectal cancer. It should be emphasized that, at present, the vast majority of individuals seen at familial cancer clinics are counseled on the basis of their family history alone because they do not have mutations in the known susceptibility genes. Accordingly, the long-term objective of this project is to identify the majority of genes responsible for the unexplained component of inherited colorectal cancer risk. Nextgen DNA sequencing is well suited for application to research questions in genetic susceptibility for which linkage analysis is confounded by extensive genetic heterogeneity. Taking advantage of unparalleled familial cancer genetics resources available through the Utah Population Database, candidate genes have been identified by sequencing all of the gene exons in the human genome from a series of colorectal cancer cases with a very strong family history of colorectal cancer not explained by one of the currently known high-risk susceptibility genes.

  Study phs001787

• Reading about genomic analysis of pan-neuroblastoma

  There are virtually no research fields that do not benefit from the large-scale genomic analysis that are so popular nowadays. For research into diseases with highly diverse clinical behavior like pediatric neuroblastoma, nevertheless, this type of approach has been a game-changer. Samuel W. Brady and colleagues, from St. Jude Children’s Research Hospital in Memphis, have gathered data from 702 neuroblastomas which were profiled by whole genome sequencing (WGS, n = 205), whole exome sequencing (WES, n = 539), and/or RNA-Seq. Less than half of the samples were generated during this study, while the others were obtained from previous work. Part of the WES data was indeed deposited in EGA in 2012 (EGA study EGAS00001000213). A scratchy summary about the huge work from Samuel W. Brady and colleagues, published in Nature Communications this October 2020 “These findings suggest that the sympathetic nervous system, the tissue from which neuroblastoma arises, is susceptible to different oncogenic insults at different times during development, which could be explored in future investigations using animal models.” Conclude the authors, highlighting the importance of their findings. Pediatric neuroblastomas are classified according to clinical and molecular features into low, intermediate, and high risk. Age plays a critical role in the classification, with younger children having an overall better prognosis. The samples included in this study belonged to all ages and risk-groups: the aim of such set up is the identification of age-associated molecular aberrations. The authors defined a mutational landscape of pediatric neuroblastoma including rare driver events, impossible to pick with a smaller data set. Interestingly, most driver mutations' frequency was found to be age-related. Those include alterations on MYCN, TERT, PTPRD, and Ras pathway, in addition to the previously reported ATRX. In addition, the large data set allowed the authors to define co-existence relations between the mutations (mutual exclusion or co-occurrence) which is a crucial puzzle piece to unravel the mechanisms driving the pathogenesis. Enjoy this really interesting article, open source on Pubmed.

  Blog genomic-analysis-of-pan-neuroblastoma

• 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

• Prediction of pigmentation phenotypes by SNP typing in a Northern German population

  Human pigmentation traits are of great interest to many research areas, from ancient DNA analysis to forensic science. We aimed to develop a gene-based predictive model for pigmentation phenotypes in a realistic target population for forensic case work from Northern Germany. Our aim was to determine whether better prediction accuracy can be achieved, or fewer genetic markers may suffice, than in previously studied, genetically more heterogeneous populations. We investigated the association between eye, hair and skin colour, and 12 candidate single nucleotide polymorphisms (SNPs) from six genes. Our study comprised two samples of 300 and 100 individuals from Northern Germany who were carefully characterized with regard to pigmentation phenotypes. The first sample was used to select trait-associated SNPs whereas the second sample served to estimate odds ratios (ORs) and to quantify the predictive capability of the respective SNP genotypes. SNP rs12913832 in HERC2 was found to be strongly associated with blue eye colour (OR=15.6, p<1.2•10-4) and to yield reasonable predictive power (90% sensitivity, 63% specificity). SNP associations with hair and skin colour were weaker and genotypes less predictive. A comparison to two recently published sets of markers to predict eye and hair colour revealed that the consideration of additional SNPs with weak to moderate effect increases the predictive power in Northern Germans for eye colour, but not for hair colour. In addition, fine phenotyping and differentiation of hair colour (light / dark and red tint / no red tint) were found to increase the number of significant genotype-phenotype associations.

  Study EGAS00001001174

• Berlin Institute for Medical Systems Biology - ccfDNA Methylation Data Access Commitee

  Dac EGAC00001003283

• The data access committee for Detecting Liver Cancer Using Cell-Free DNA Fragmentomes

  Dac EGAC00001003279

• Data Access Policy of the HCEMM - Semmelweis University, Molecular Oncohematology Research Group

  Dac EGAC00001003240