4002 results for "*geneti*"

in 34.67 milliseconds.

• Longitudinal Study of Fluoride and Other Factors Related to Dental Fluorosis, Dental Caries, and Bone Health

  This project is a combination of the Iowa Fluoride Study and the Iowa Bone Development Study from 1992 - 2019. The Iowa Fluoride Study (IFS) began in 1992 with recruitment of 1,882 mothers of newborns from eight Iowa hospital post-partum wards from March 1992 to February 1995. The focus of the IFS was to collect detailed fluoride, general dietary intake, and other data several times per year from age 6 weeks on and relate them to dental fluorosis and dental caries (cavities). Data collection occurred 3-5 times per year initially, and then twice per year after ~ age 4. Dental exams of the primary (baby) teeth were initiated at ~ age 5.The Iowa Bone Development Study (IBDS) grew out of the IFS in 1998 when we added a "normal bone development" focus, and soon after the age 5 dental exams, we began to collect genetic samples and accelerometry (motion sensor) data. All then-active parents of IFS-participating children were invited to have their child join the IBDS, and the majority agreed to do so. The majority of participating children received both ~ age 5 dental exams and bone densitometry assessments. The bone densitometry at age 5 was determined by dual-energy X-ray absorptiometry (DXA) of the hip, lumbar spine, and whole body.Dental exams for caries and fluorosis were performed again at age 8-9, along with DXA bone scans and accelerometry. DXA scans and accelerometry were performed again at ages 11 and 15 years, along with peripheral quantitative computed tomography (pQCT) scans of the distal tibia and radius, but no dental exams were performed.At ages 13 and 17, dental exams, DXA and pQCT scans, as well as accelerometry, were obtained.At age 19, no dental exams were conducted, but repeat DXA and pQCT scans and accelerometry were obtained, along with multi-slice detector CT (MDCT) of the distal tibia.At age 23, we conducted the final wave of exams, which included dental, DXA, pQCT, MDCT, and accelerometry assessments.In addition, parents were invited to participate once with DXA bone scans and to also provide limited questionnaire data about their own fluoride and dietary exposures and physical activity.Note: Please refer to the document Iowa Fluoride Study/Iowa Bone Development Study (IFS/IBDS) - dbGaP Data Tables: A User's Guide - Version 2.0 for a detailed reference manual on all of the clinical data submitted for this project.Note: No genetic data or specimens were submitted with this study. However, many of the child/parent participants in the study were genotyped. These genotyping data are linked to dbGaP Study Accession: phs000095 under Dr. Mary Marazita: "Dental Caries: Whole Genome Association and Gene X Environment Studies".<img style="width: 1155px;" src="data:image/png;base64,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

  Study phs002203

• Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) Center for Environmental Health Disparities

  MADRES is a prospective pregnancy cohort based in urban Los Angeles, recruiting predominantly low income Hispanic mothers-to-be as part of a children's health study, funded by Environmental Influences on Child Health Outcomes (ECHO). Participants were measured for a number of chemical exposures and biomarkers. Two epigenetic markers will be available through dbGaP: miRNA expression in pregnancy (measured via Nanostring human assay, v3) and DNA methylation from pregnancy and children's cord blood (measured via EPIC array). Principal findings of this research described differences in miRNA expression profiles between early and late pregnancy, as well as miRNA expression affected by psychosocial stress.

  Study phs003194

• MILK-Omics: Systems Biology of Human Milk and Its Links to Maternal and Infant Health

  The primary objective of the project is to generate a systems-level view of human milk in the context of healthy mothers and their term infants. The study uses biospecimens and clinical data from the Mothers and Infants LinKed for Healthy Growth Study (MILk Study), a prospective observational cohort of approximately 500 exclusively breastfeeding women and their infants who are followed to 6 months of age. The project focuses on mature milk samples collected at 1 month postpartum and infant gut microbiome samples collected at 1 and 6 months of age. Specific Aim 1 is to identify maternal genetic and clinical factors that shape human milk gene expression. Using bulk and single-cell RNA sequencing of the milk cell pellet and genotypes from maternal DNA, we will identify novel genetic determinants of the milk transcriptome and assess potential modification of these genetic associations by gestational weight gain, diet, and other clinical factors. Specific Aim 2 is to describe key features of the normative human milk biosystem and their interactions with one another. Specifically, machine learning techniques will be used to characterize interaction networks and correlational structures among the following features of human milk: transcriptomics, microbiomes, oligosaccharides, metabolomics, lipidomics, and milk macronutrient composition. Specific Aim 3 is to establish how the milk biosystem is related to variation in infant gut microbiomes and health. dbGaP will house the maternal genotype and milk transcriptomics data.The infant microbiome sequence data are associated with BioProject PRJNA1019702.

  Study phs003408

• A Microwell Platform for High-Throughput Longitudinal Phenotyping and Selective Retrieval of Organoids

  In traditional bulk culture, organoids tend to overlap, hindering the analysis of individual organoid characteristics in a high-throughput manner. Additionally, variations in the local spatial properties of the bulk matrix make it challenging to distinguish whether phenotypic differences between organoids stem from inherent cellular disparities or disparities in the microenvironment. To address these challenges, we have developed a microwell-based technique that facilitates the quantification of image-based parameters for organoids grown from single cells. Furthermore, these organoids can be easily retrieved from their respective microwells for molecular profiling. By employing a deep-learning image processing pipeline, we conducted a comprehensive assessment of various phenotypic traits in two CRISPR-engineered human gastric organoid models. This analysis encompassed growth rates, cellular movement, and apical-basal polarity. Through our investigation, we successfully identified genomic alterations associated with increased growth rate, as well as changes in accessibility and expression patterns correlated with apical-basal polarity. This novel methodology offers a valuable tool for studying organoid behavior at a single-cell level, enabling researchers to gain deeper insights into organoid biology and associated genetic modifications.

  Study phs003315

• Center for Oral Health Research in Appalachia (COHRA) Genomic Studies of Oral Health and Disease

  The purpose of this project is to identify the genetic factors contributing to dental caries in children and adults. The datasets come from the Center for Oral Health Research in Appalachia (COHRA), which has the long-term goal of determining the sources of oral health disparities in a high risk, Northern Appalachian population so that effective preventive interventions can be designed and targeted. The Specific Aims of this project are to perform genome-wide association scans of dental caries of the (1) primary dentition in children, and (2) permanent dentition in adults, to identify novel risk variants and to replicate previously nominated risk variants. This project brings together samples from three cohorts: COHRA1 is a cross-sectional cohort comprising members of 862 northern Appalachian families; approximately 80% of the cohort has been previously genotyped by the Center for Inherited Disease Research with support from NIDCR using the Illumina Quad W array (see dbGaP Study Accession: phs000095.v3.p1). Dental SCORE is a cross-sectional cohort comprising approximately 550 unrelated individuals who underwent the same data collection protocol as COHRA1. COHRA2 is an ongoing longitudinal cohort that recruited approximately 1100 northern Appalachian women during pregnancy, and followed them and their children through their children's early childhood; the current project period will continue data collect though age 6 of the child. Phenotypes for this project were derived from intra-oral examinations performed by trained and calibrated research hygienists. In brief, each tooth was recorded as present or absent, and each surface of each present tooth was scored for evidence of decay. From these data, dental caries indices were generated. This project contains two phases of genotyping: (1) collection of exome SNP Chip data for the previously genotyped COHRA1 samples, and (2) collection of whole-genome SNP Chip data for the remaining COHRA1 samples and all Dental SCORE and COHRA2 samples. These data will support efforts to test hypotheses regarding the causal relationships of risk factors contributing to the unusually high rates of caries formation in the Appalachian population. Ultimately, these data may inform the development of an integrated model of caries risk, in which the effects of genetics, oral ecology, diet, and other environmental/psychosocial factors and behaviors are modeled in concert to explain the disparities, including the high rate of caries onset before age 6. The gene-mapping Aims of this project, which seek to identify the genetic factors that contribute to caries risk, are a requisite step in realizing this integrated model.

  Study phs001591

• NHLBI TOPMed: Cleveland Clinic Atrial Fibrillation (CCAF) Study

  The Cleveland Clinic Atrial Fibrillation Study consists of clinical and genetic data of patients with atrial fibrillation and control cohorts from the Cleveland Clinic CV/Arrhythmia Biobank, including the Cleveland Clinic Lone Atrial Fibrillation GeneBank. The Cleveland Clinic Lone AF GeneBank Study has enrolled patients with lone AF, defined as AF in the absence of significant structural heart disease. The CV/Arrhythmia Biobank has also enrolled participants with non-lone atrial fibrillation. All patients provided written informed consent.

  Study phs001189

• NHLBI TOPMed - NHGRI CCDG: AF Biobank LMU in the context of the MED Biobank LMU

  The Atrial Fibrillation Biobank Ludwig Maximilian University (AFLMU) Study contributes to the spectrum of disease by adding carefully characterized patients with atrial fibrillation. Atrial fibrillation, one of the most common human arrhythmias confers major morbidity, mortality and health care cost, and has been demonstrated to be caused and influenced by genetic and -omics factors. Particularly, AFLMU enrolled patients with an early onset of atrial fibrillation to increase the genetic burden on disease pathophysiology. All patients were recruited applying standardized protocols to maintain homogeneity in data and DNA quality.

  Study phs001543

• Evolutionary Analysis of Chronic Lymphocytic Leukemia Cells During Relapse After Allogeneic Hematopoietic Stem Cell Transplant

  We investigated the molecular dynamics driving relapse of chronic lymphocytic leukemia (CLL) following allogeneic hematopoietic stem cell transplant (allo-HSCT) through an integrated analysis of whole exome, bulk transcriptome, bulk methylome and single cell transcriptional data from paired leukemic samples representing 19 patients. We found that their clinical kinetics corresponded to distinct genetic and epigenetic evolutionary trajectories and suggest that the selection pressures of the post-transplant, immunologic bottleneck are distinct from those imposed by chemotherapy.In order to discover cancer antigens derived from annotated and unannotated protein-coding regions of the genome, we carried out ribosome profiling (Ribo-seq) and RNA-sequencing on CLL samples and isolated primary B cells from healthy donors. We discovered novel or unannotated open reading frames (nuORFs), their expression levels, as well as somatic mutations within them to predict potential neoantigens.

  Study phs001998

• Genome Studies in Hereditary Spastic Paraplegia

  Next-generation sequencing technology is opening up new opportunities to rethink the way we identify disease causing genetic variation. An early application, whole exome sequencing, has now been established by a small number of research labs, including ours. Exome sequencing allows obtaining a near complete set of protein coding genomic variation in single individuals for less than $5,000. Promising targets for exome sequencing studies are Mendelian diseases, such as hereditary spastic paraplegias (HSP). HSP comprise a genetically very heterogeneous set of neurological disorders with currently 39 different HSP chromosomal loci being reported; yet, the identified genes explain only 60% of the genetic effect at best. Traditional methods of gene identification require linkage analysis of large families, but face increasing difficulties to identify such extended pedigrees for rare HSP forms. However, the innovative approach described in this application will overcome some of these limitations and utilize relatively small pedigrees for highly effective gene identification. We will apply exome sequencing, which will characterize all coding changes and flanking exonic variation in two individuals of a family. We have developed a multi-tiered strategy to reduce the number of identified novel variants to the very causative change in an individual family. We propose to study at least 60 HSP families, which are too small to yield conclusive results with linkage analysis. If the developing technology permits we will consider a larger sample or perform whole genome sequencing. Beyond the important benefit to genetics of HSP, this study will allow us to further establish this new method, which will benefit a large range of additional disease studies.

  Study phs001080

• University of Michigan Clinical Sequencing Exploratory Research (CSER)

  Overview. The personalization of therapy for cancer will require molecular characterization of unique and shared genetic aberrations. In particular, patients who have a sarcoma or other rare cancers and are candidates for clinical trials could potentially benefit by identifying eligibility for "targeted" drugs based on the "actionable" genes in their specific tumor. Growing technological advances in genomic sequencing has now made it possible to consider the use of sequence data in a clinical setting. For instance, comprehensive testing that includes whole exome and transcriptome sequencing may identify biomarkers for predictive or prognostic purposes and thereby inform treatment choices and prevention strategies. Thus, the translation of high throughput next generation sequencing would support a "personalized" strategy for cancer. However, the translation of clinical sequencing bears unique challenges including identifying patients who could benefit, developing informed consent and human subjects protections, outlining measurable outcomes, interpreting what results should be reported and validated, and how results should be reported. In addition, we know very little about how patients and clinicians will respond to the potentially confusing and overwhelming amount of information generated by genomic sequencing, and we lack model processes for clinically evaluating and presenting these data. For the promise of our innovative biotechnologies to be realized, "translational genomics" research that evaluates genomic applications within real-world clinical settings will be required. This proposal brings together expertise at the University of Michigan including clinical oncology, cancer genetics, genomic science/bioinformatics, clinical pathology, social and behavioral sciences, and bioethics in order to implement this clinical cancer sequencing project. Three integrated Projects have the following themes: Project 1) "Clinical Genomic Study" will identify patients with a rare cancer (i.e., 15 out of 100,000 individuals per year) who are eligible for clinical trials, consent them to the study, obtain biospecimens (tumor tissue, germline tissue), store clinical data, and assemble a multi-disciplinary Sequencing Tumor Board to deliberate on return of actionable or incidental genomic results; Project 2) "Sequencing &#38; Analysis" will process biospecimens and perform comprehensive sequencing and analysis of tumors to identify point mutations, copy number changes, rearrangements/gene fusions, and aberrant gene expression; Project 3) "Ethics &#38; Psychosocial Analysis" will observe the expert review process for evaluating sequence results and will examine the clinician and patient response to the informed consent process, delivery of genomic sequence results, and use of genomic results.

  Study phs000673