3450 results for "*geneti*"

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• Sudden Death in the Young Case Registry

  Knowledge gaps persist about the incidence of and risk factors for sudden death in the young (SDY). The SDY Case Registry is a collaborative effort between the National Institutes of Health, the Centers for Disease Control and Prevention, and the Michigan Public Health Institute. Its goals are to: (1) describe the incidence of SDY in the United States by using population-based surveillance: (2) compile data from SDY cases to create a resource of information and DNA samples for research; (3) encourage standardized approaches to investigation, autopsy, and categorization of SDY cases; (4) develop partnerships between local, state, and federal stakeholders toward a common goal of understanding and preventing SDY; and (5) support families who have lost loved ones to SDY by providing resources on bereavement and medical evaluation of surviving family members. Built on existing Child Death Review programs and as an expansion of the Sudden Unexpected Infant Death Case Registry, the SDY Case Registry achieves its goals by identifying SDY cases, providing guidance to medical examiners/coroners in conducting comprehensive autopsies, evaluating cases through child death review and an advanced review by clinical specialists, and classifying cases according to a standardized algorithm. The SDY Case Registry also includes a process to obtain informed consent from next-of-kin to save DNA for research, banking, and, in some cases, diagnostic genetic testing. The SDY Case Registry will provide valuable incidence data and will enhance understanding of the characteristics of SDY cases to inform the development of targeted prevention efforts. For cases where informed consent has been obtained for research genomic sequencing has been performed. On these cases genomic data, phenotype information and on a limited number of cases redacted autopsy reports will be available through dbGaP.

  Study phs003221

• Genetic Markers of Lipids in Indians: A Validation Study of Most Relevant Findings of Genome-Wide Association Studies

  We aimed to validate the GWAS loci (identified in European populations) related to serum lipid levels (total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and very low-density lipoprotein cholesterol (VLDL-C)) in the Indian population originally recruited to examine the 20-year trend of cardiometabolic risk factors in rural and urban areas around National Capital region in India. Isolated DNA was processed for genotyping of 12 established GWAS variants using multiplex Sequenom Mass ARRAY technology. We examined the association of these genetic variants with different lipid levels and found association of eight variants for lipid levels. We also derived weighted genetic risk scores (wGRAS) using the validated variants. The wGRS for triglycerides and VLDL-C was derived based on four associated variants (rs174546 at FADS1, rs17482753 at LPL, rs2293889 at TRPS1, rs4148005 at ABCA8, rs4420638 at APOC1) which was associated with 36.31 mg/dL elevated triglycerides and VLDL-C (β=0.95, SE=0.16, pTRPS1 and rs4147536 at ADH1B) was associated with 40.62 mg/dL higher total cholesterol (β=1.01, SE=0.23, pTRPS1, rs4147536 at ADH1B, rs4420638 at APOC1 and rs660240 at CELSR2). The wGRS derived from five associated variants (rs174546 and FADS1, rs17482753 at LPL, rs4148005 at ABCA8, rs4420638 at APOC1 and rs7832643 at PLEC) was associated with 10.64 mg/dL lower HDL-C (β= -0.87, SE =0.14, pThe phenotypic and genotypic data will be made available on the dbGaP portal for access.

  Study phs003469

• NHGRI GREGoR Consortium: Genomics Research to Elucidate the Genetics of Rare Disease

  The NHGRI GREGoR (Genomics Research to Elucidate the Genetics of Rare Disease) Consortium was established in June 2021 with the goal of developing novel tools and approaches to advance the discovery of the genetic basis of rare conditions. Numerous types of molecular data are generated in GREGoR and available on the AnVIL cloud platform via dbGaP application, including short- and long-read genome and exome sequencing, transcriptomics, metabolomics, methylomics, and proteomics. De-identified clinical and demographic data is obtained, with a focus on standardized ontologies. The Consortium comprises five Research Centers (RCs - see below), a Data Coordinating Center (DCC), and various partner members and external collaborators. Baylor College of Medicine Research Center (BCM-GREGoR) The Baylor College of Medicine GREGoR program, which is part of the GREGoR consortium, enrolls individuals, families, and cohorts with suspected rare disease across a range of syndromic and non-syndromic phenotypes. Subjects are enrolled from national and international collaborating physician referrals. Subjects provide written informed consent for future re-contact. Data generated and shared include family structure, detailed phenotypes, exome or short-read genome data, and in some cases long-read genome or RNA-sequencing, and these are shared upon completion of standard quality control checks and annotation. Broad Institute (Broad) The Broad Center for Mendelian Genomics, part of the GREGoR consortium uses next-generation sequencing (exome, genome, transcriptome, and long read sequencing), computational approaches, and functional studies to discover the variants and genes that underlie Mendelian conditions with a particularly focus on neuromuscular, neurodevelopmental, and syndromic phenotypes. Samples come from collaborators and direct enrollment through the Rare Genomes Project and we are committed to rapid data sharing without an embargo period. Children's National Hospital / Invitae - Pediatric Mendelian Genomics (CNH/Invitae) The Pediatric Mendelian Genomics Research Center, a joint collaboration between Children's National Hospital Research Institute and Invitae, is part of the GREGoR consortium to discover disease-causing variants. Our center uses a combination of short-read and long-read next-generation sequencing technologies (genomes and transcriptomes), computational approaches, and protein-level functional modeling to discover novel gene-variant associations for Mendelian disease. Research participants, and often family members, were consented and enrolled directly into our research study. Most of our participants are single families with a wide range of likely genetic phenotypes including congenital malformations, neurodevelopmental features, or connective tissue differences who have had negative standard of care genetic testing such as panels or exomes. Several specific phenotype cohorts of interest include holoprosencephaly, leukodystrophies, cardiomyopathies, differences of sexual development, congenital renal disease, and epilepsy. De-identified clinical phenotype and sequence data are linked to all samples. GREGoR Stanford Site (GSS) The goal of the GREGoR Stanford Site (GSS) is to provide a platform for functional genomics research and validation to improve diagnosis in Mendelian disease. Participants included individuals with undiagnosed suspected Mendelian disease who had non-diagnostic exome sequencing and their immediate family members. Participants and their family members provided written, informed consent and biological samples from which DNA, RNA, plasma, fibroblasts, PBMCs and other cell types were generated and stored. Samples from research participants and their immediate family members may have undergone short and long-read genome sequencing, transcriptome sequencing, metabolomics and/or lipidomics profiling, methyl-capture-sequencing and ATAC-sequencing. De-identified clinical data extracted from participant medical records are linked to the samples. University of Washington Center for Rare Disease Research (UW-CRDR) The goals of the University of Washington Center for Rare Disease Research are to: (1) maximize novel gene discovery for Mendelian conditions by recruitment, short- and long-read whole genome sequencing, transcriptome sequencing and analysis of families with rare conditions for which the gene is either unknown or the gene is known but no pathogenic variant can be identified via clinical testing; (2) develop new strategies for gene discovery for Mendelian conditions caused by variants that are difficult to detect using conventional testing strategies, variants of unknown function effect (e.g., regulatory, structural variants) or have unusual modes of inheritance; and (3) implement high-throughput screening and targeted follow-up functional studies to prioritize and validate candidate non-coding variants. De-identified data and phenotypic information are shared via MyGene2, ClinVar, and AnViL.

  Study phs003047

• Whole-Exome Sequencing Study of Diabetic Nephropathy

  To identify rare variants of diabetic kidney disease (DKD), we conducted a whole-exome sequencing (WES) study. This study employed an extreme case–control design, which included a selection of the 593 most rapidly progressing DKD cases from the Chronic Renal Insufficiency Cohort (CRIC) Study (phs000524). In this two-stage WES study of DKD, participants with DKD from the CRIC were compared with control groups of participants without DKD from another cohort in the first stage. Suggestive signals identified in the first stage were confirmed in the second stage analysis among a diverse population with existing whole-genome sequencing (WGS) data. Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test implemented within our mixed model framework. As a result, we identified four novel gene loci, DIS3L2, KRT6B, ERAP2 and NPEPPS, that achieved exome-wide significance and may play an important role in the DKD disease course. This large WES study provides novel insights into DKD's molecular mechanisms.

  Study phs003429

• Genetic Predictors of Ibrutinib-Related Cardiovascular Side Effects in Patients with Chronic Lymphocytic Leukemia

  The goal of this study was to identify genetic polymorphisms associated with ibrutinib-related cardiovascular side effects (CVSEs). This is an observational non-interventional pharmacogenomic study of 50 patients with newly diagnosed or relapsed refractory chronic lymphocytic leukemia (NDCLL or RRCLL) receiving ibrutinib 420 mg for at least six months. The ibrutinib-related CVSEs included atrial fibrillation and hypertension, which occurred in 20% of patients. DNA obtained from buccal swabs was genotyped for 40 single nucleotide polymorphisms (SNPs) in GATA4, SGK1, KCNQ1, KCNA5, NPPA, and SCN5A using a customized next generation sequencing panel. It was found that GATA4 rs804280 AA (P =.043), KCNQ1 rs163182 GG (P =.036) and KCNQ1 rs2237895 AA (P =.023) were associated with the ibrutinib-related CVSEs. Patients with a high-risk score had at least two of the three significant risk genotypes identified in univariate analysis for GATA4 rs804280 A>C, KCNQ1 rs162182 G>C and KCNQ1 rs2237895 A>C, and patients with a low-risk score had either one or none of these genotypes. In multivariate analysis, 18 patients with at least two of the three high-risk genotypes had an 11.5-fold higher odds of CVSEs (P =.019; 1.79-119.73 at 95% CI).

  Study phs003370

• 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;" 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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

• NextGen Consortium: The iPSCORE (iPSC Collection for Omic Research) Resource for Studying the Impact of Genetic Variation on Molecular and Physiological Phenotypes - Whole Genome Sequence

  The iPSCORE Resource of human induced pluripotent stem cells (hiPSCs) was created as part of the Next-Gen Consortium funded by the NHLBI. The overarching purpose of the iPSCORE Resource is to provide a large collection of hiPSCs for use in studying the impact of genetic variation on molecular and physiological phenotypes. This Resource is being used in a number of ongoing studies for which genomic data will be generated and deposited into public repositories and linked through dbGaP. A total of 273 individuals have participated in the study for which 222 have had hiPSCs generated from fibroblasts (available through WiCell (http://www.wicell.org/)). Of the 273 individuals, 181 are part of 55 families that include 24 monozygotic twin pairs and 5 dizygotic twin pairs, allowing for the incorporation of familial relationships into genetic analyses. Germline DNA has been sequenced from blood or fibroblast samples for all 273 individuals (this study) and other genomic data (RNA-seq, DNA methylation, and genotype arrays) has been generated from the 222 hiPSCs derived from a subset of these individuals (phs000924). Current studies include differentiation of these hiPSCs to other cell types, including cardiomyocytes and retinal pigment epithelium, and the generation of additional genomic data.

  Study phs001325