3334 results for "*geneti*"

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• African American Multiple Myeloma GWAS

  The case set is from a case-control study designed to identify common genetic risk factors for multiple myeloma in African Americans, the population with the highest risk for this cancer. We conducted two GWAS and combined each of these with convenience controls consisting of unaffected African American participants in cohorts with existing GWAS data. We then conducted a meta-analysis of the two sets. Cases were persons of African ancestry with smoldering or active multiple myeloma identified at participating oncology clinics or through SEER registries, as part of the African American Multiple Myeloma Study (AAMMS) diagnosed from Jan 1, 1988 through July 31, 2016. The majority of the samples were collected from incident and prevalent cases diagnosed since Jan 1, 2008 (80.9%), with a minority obtained from biobanks from cases diagnosed prior to 2008 (19.1%). Additional samples were obtained from the Multiethnic Cohort (USC and University of Hawaii) (n=40), the University of California at San Francisco Multiple Myeloma Study (n=27), and from the Multiple Myeloma Research Consortium for secondary analysis (samples originally provided to MMRC by 8 additional sites (n=84)). We have identified the phenotype (smoldering myeloma, plasma cell multiple myeloma, or myeloma not otherwise specified (myeloma NOS) when myeloma phenotype was not known), sex and age at diagnosis in this data set. The initial GWAS set consisted of 1308 (1,305 passed QC) cases with DNA samples, with a GWAS performed on the Illumina Human Core. Controls consisted of 7,078 unaffected African American subjects who were participants in the African American Prostate and Breast Consortium, with existing GWAS data from the Illumina1M Duo BeadChip. The second GWAS set consisted of 529 African American multiple myeloma patients with samples (406 from University of Arkansas, results not contained in this dataset because NCI funds were not used for the collection and genotyping) with GWAS data resulting from the Illumina Mega-BeadChip v1.1. Controls were 2,390 unaffected African American participants in the Multiethnic Cohort with existing GWAS data from the same array. After QC and removal of duplicates within sets, sex mismatches, and removal of plasmacytoma cases (ICD-0 code 9731), this deposited data set contains the typed GWAS data for the Illumina Human Core (set 1) (n=1298), and for the MegaBead Chip v1.1 (set 2) (n=123), with the University of Arkansas samples removed, for a total of 1,421 case genotypes. Note that 13 samples that overlap set 1 and set 2 were included.

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• 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

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

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

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• Genomic Landscape of Cutaneous Diffuse Large B Cell Lymphoma

  Cutaneous diffuse large B cell lymphomas (DLBCL) are aggressive lymphomas with a poor prognosis. To elucidate their genetic bases, we performed whole exome capture and massively parallel sequencing on 19 tumor samples of diffuse large B cell lymphoma, leg type (DLBCL-LT) and 6 samples of secondary cutaneous diffuse large B cell lymphoma, not otherwise specified (DLBCL-NOS). Tumor specimens and their associated sequencing data were from multiple institutions, including Northwestern University (Chicago, IL), Massachusetts General Hospital (Boston, MA), and Medical University of Graz (Graz, Austria).

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• NHLBI TOPMed: Genetic Causes of Complex Pediatric Disorders - Asthma (GCPD-A)

  The Center for Applied Genomics (CAG) at the Children's Hospital of Philadelphia (CHOP) is focused on addressing the roles of both single nucleotide variants and structural copy number variants, and their functional impact, together with gene-environment interactions and their influence on asthma drug response.

  Study phs001661

• Varieties of Impulsivity in Opiate and Stimulant Users

  The study was conducted in Bulgaria as a collaboration between Virginia Commonwealth University (Richmond, VA, USA), the Bulgarian Addictions Institute (Sofia, Bulgaria), the Molecular Medicine Center/Medical University (Sofia, Bulgaria), and Indiana University (Bloomington, IN, USA). The overall aim of this study is to investigate the role of impulsivity as an endophenotype for drug addiction. Although impulsivity is considered one of the strongest candidate endophenotypes for addiction, progress in the field is hampered by the heterogeneity of impulsivity, characterized by multiple personality, psychiatric, and neurocognitive dimensions, rarely examined concurrently in the same population; and the heterogeneity of addiction phenotypes, due in part to the high rates of polysubstance dependence among substance users. To address these challenges, we have developed a program of addiction research in Bulgaria, a key transit country for heroin trafficking due to its strategic geographical location on the "Balkan Drug Route" and a major European center for production of synthetic amphetamine-type stimulants. This has allowed us to access rare populations of predominantly mono-substance dependent heroin and amphetamine users, many in protracted abstinence. Our preliminary results reveal a complex relationship between trait and neurocognitive (state) dimensions of impulsivity, often manifested in opposite directions in heroin and amphetamine dependent individuals. Pilot computational modeling analyses of decision-making, a central neurocognitive aspect of impulsivity, have proved particularly informative by indicating that different mechanisms may underlie the impaired decision-making of opiate and stimulant users. A different modeling approach, i.e. phenotypic modeling, holds significant promise to address the pervasive "missing heritability" problem in genetic studies. While genetic heterogeneity is often invoked as an explanation, the manner in which complex phenotypic traits are measured and modeled is equally important contributor to the missing heritability problem but has received much less attention in the literature. Despite the multidimensionality of traits measured by psychometric, diagnostic, and neurocognitive instruments, most GWAS studies typically use aggregate sum scores that do not reflect the underlying phenotypic multidimensionality. Therefore, at least part of the missing heritability problem may originate in misspecification of the phenotypic models. Consequently, sample sizes requirements may increase from ~800 subjects in correctly specified models to 6,000-16,000 subjects in incorrectly specified models. The current study aims to increase our understanding of the complex relationship between multiple putative impulsivity endophenotypes to help redefine endophenotypes as multi-level combination of measures that could inform multivariate multilevel models of complex phenotypes. The specific aims of the study are to: (1) Assess the utility of various personality, psychiatric, and neurocognitive indices of impulsivity (either individually or in combination) as candidate endophenotype(s) for drug addiction in general and for opiate and stimulant addictions in particular; (2) Evaluate the viability of computational model parameters modeling various neurocognitive dimensions of impulsivity as novel endophenotype(s) for addiction; and (3) Test the external validity of the optimal endophenotype(s) by evaluating their associations with HIV and other risk behaviors in opiate and stimulant users in protracted abstinence, a question of critical importance for prevention and intervention efforts in this much less-well understood stage of the addiction cycle.

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• Center for Education and Drug Abuse Research (CEDAR)

  Under its programmatic name, the Center for Education and Drug Abuse research (CEDAR), this Center of Excellence project was completed in 2015. The CEDAR sample consists of 775 nuclear families comprising an adult male (proband), his spouse/mate, and their biological child who is 10-12 years of age at recruitment. This child is designated as index child (IC), and is followed through age 30. Minimal exclusionary criteria were applied to maximize the sample's representativeness. Follow-up evaluations on ICs were conducted at ages 14, 16, 19, 22, 25, 27 and 30. Interim questionnaires were mailed annually beginning at age 20. CEDAR utilizes the family/high-risk paradigm. The offspring thus comprise high average risk (HAR) and low average risk (LAR) groups, constituting the majority of the CEDAR children sample: 250 HAR males, 100 HAR females, 250 LAR males and 94 LAR females. The remainder of the IC sample, 50 males and 31 females, are the offspring of fathers who have a lifetime non-SUD psychiatric dis-order. The follow-up rate from baseline to the final assessment is ~65%. The cohort of children is 76% White and 22% Black. The representation of females among ICs is lower than males, due to the later start of recruit-ment (2nd funding cycle; the study originally focused on males). The CEDAR genetic study has been augmented by the inclusion of its sample into the whole-genome genotyping effort conducted under the Genes, Environment and Development Initiative (GEDI). DNA from the blood samples of target children (index cases) and their siblings submitted to the NIDA Genetics Depository a Rutgers University was obtained and genotyped on Illumina Human660W-Quad Beadchips, with a high average genotyping rate of 99.8%. The sample comprised 158 females and 271 males (37 and 63 %), ~70:30 European- and African- American, with approximately 1:1 ratio of children of affected fathers and normal controls.

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• Genetic and Genomic Stability Across Lymphoblastoid Cell Line Expansions

  This study was focused on exploring genetic and genomic stability across cell line expansions. We used DNA and cell lines sampled from six apparently healthy research participants. We assayed genome-wide genetic variants and inferred structural variants for DNA extracted from blood, from transformed cell cultures, and from three generations of expansions.

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• Whole Exome Characterization of Squamous Cell Lung Cancers (Lung SQCC) from Appalachian Kentucky (APPKY)

  This is a pilot study of squamous cell lung cancer and matched normal tissue pairs collected (convenience sample) from patients from Central Appalachia, in Southeastern Kentucky. This study performed whole exome sequencing on tumor and matched normal DNA samples from 51 lung squamous cell carcinoma (SQCC) subjects from Appalachian Kentucky (AppKY). Somatic genomic alteration profiles were compared between the AppKY and TCGA SQCC cohorts. We found that IDH1 and PCMTD1 had higher mutation rates in AppKY versus TCGA. These findings suggest population differences in the genetics of lung SQCC between AppKY and US populations that have implications for personalized treatment for this disease.

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• Bayesian-Based Noninvasive Prenatal Diagnosis of Single-Gene Disorders

  In the last decade, non-invasive prenatal diagnosis (NIPD) has emerged as an effective procedure for early detection of inherited diseases during pregnancy. This technique is based on using cell-free DNA (cfDNA) and fetal cfDNA (cffDNA) in maternal blood, and hence, has minimal risk for the mother and fetus compared with invasive techniques. NIPD is used today for identifying chromosomal abnormalities (in some instances) and for single-gene disorders (SGDs) of paternal origin. However, for SGDs of maternal origin, sensitivity poses a challenge that limits the testing to one genetic disorder at a time. Here we present a Bayesian method for the NIPD of monogenic diseases that is independent of the mode of inheritance and parental origin. Furthermore, we show that accounting for differences in the fragment length distribution of fetal- and maternal-derived cfDNA results in increased accuracy. Our model is the first to predict inherited insertions-deletions (indels). The method described can serve as a general framework for the NIPD of SGDs; this will facilitate easy integration of further improvements. One such improvement that is presented in the current study is a machine learning model that corrects errors based on patterns found in previously processed data. Overall, we show that next generation sequencing (NGS) can be used for the NIPD of a wide range of monogenic diseases, simultaneously. We believe that our study will lead to the achievement of a comprehensive NIPD for monogenic diseases. (Reprinted from Bayesian-based noninvasive prenatal diagnosis of single-gene disorders, with permission from Genome Research)

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