3922 results for "*geneti*"

in 17.34 milliseconds.

• Genetic architecture of disease in Greenland is shaped by demographic history, fine-structure, and selection - MEGA chip data

  This file set has 1478 Greenlandic individuals scored on the Illumina MEGA array (1,748,250 sites). The data is in PLINK bed/bim/fam format. The individuals originate from the B2018 population survey.

  Dataset EGAD50000000934

• Genetic Analysis of Epidermal Inclusion Cysts

  The goal of this study was to identify a potential driver of epidermal inclusion cysts, or epidermoid cysts, (EICs) in human skin. Individuals were recruited at EIC excision appointments made a priori, with no exclusion criteria. Fresh biopsies and saliva were taken from consented patients, and DNA harvested immediately. Whole exome sequencing was performed (150-bp paired end reads) to 200X in tissue and 80X in saliva via Illumina NovaSeq 6000 sequencer. Identification of somatic mutations was performed using GATK best practices. Somatic variants in the NEK9 gene were found in 12/14 cases sequenced, with clustering of variants within the RCC1 and kinase domains of the protein.

  Study phs003776

• Common Fund (CF) Genotype-Tissue Expression Project (GTEx)

  Lay Description The aim of the Genotype-Tissue Expression (GTEx) Project is to increase our understanding of how changes in our genes affect human health and disease with the ultimate goal of improving health care for future generations. GTEx will create a database that researchers can use to study how inherited changes in genes lead to common diseases. GTEx researchers are studying genes in different tissues obtained from many different people. The GTEx project also includes a study of the GTEx donor consent process - this study will help ensure that the consent process and other aspects of the project effectively address the concerns and expectations of participants in the study. GTEx is a pioneering project that uses state-of-the-art protocols for obtaining and storing a large range of organs and tissues, and for testing them in the lab. Until now, no project has analyzed genetic variation and expression in as many tissues from the same person in such a large population as planned for GTEx. Scientific Description Understanding the role of variation in the human genome is crucial to elucidating genetic contributions to human health and disease. Despite the results of genome-wide association studies (GWAS) documenting strong statistical associations between genetic variation and human traits, the functional role for most of these variants is largely unexplained. Nearly 90% of these GWAS-implicated sites lie outside of protein-coding sequences, suggesting that these variants might regulate gene expression. The goal of the Genotype-Tissue Expression (GTEx) project is to establish a resource database and tissue biobank in which to study the relationship between genetic variation and gene expression and other molecular phenotypes in reference/non-diseased tissues. The ultimate resource will include approximately 960 post-mortem donors with several dozen tissues from each, a resource large enough to study both cis- and trans- gene expression quantitative trait loci (eQTLs). Some tissue will also be banked for additional molecular analyses. To request biosamples, please see the Biobank page for more information. GTEx was initially funded as a 2-year pilot project by the NIH Common Fund (CF) in 2010, and has been scaled up after demonstration of feasibility. The project will collect and analyze RNA levels in many different human tissues and each donor will be characterized for germline genetic variation through dense genotyping arrays and sequencing of either whole exomes or whole genomes. By treating RNA expression levels as quantitative traits, eQTLs will be identified as sites containing genetic variation that correlate with changes in RNA expression. Such eQTLs have been associated with 4%-12% of expressed human genes, and with common complex human diseases, including obesity, atherosclerosis, type 2 diabetes, Crohn's disease, and asthma. Additionally, few studies have examined the tissue specificity of eQTLs. A subset of banked tissue samples will also be analyzed for other molecular phenotypes, such as DNA methylation, DNaseI hypersensitivity sites, and proteomics. The GTEx project will thus serve as a resource database and tissue bank for many future studies, especially for understanding the functional basis of inherited susceptibility to disease. All GTEx releases since Version 5 follow the NIH Genomic Data Sharing (GDS) Policy whereby there are no restrictions on use or publication after release. Additional information about the GTEx Data Release and Publication policy can be found on the GTEx Data Portal at www.gtexportal.org.

  Study phs000424

• Genome-Wide Association of Type 2 Diabetes in Africans: The AADM Study

  The Africa America Diabetes Mellitus (AADM) study is a genetic epidemiological study of type 2 diabetes in Sub-Saharan Africa. Study participants were enrolled through university medical centers in Nigeria, Ghana, and Kenya. Ethical approval for the study was obtained from the Institutional Review Board (IRB) of each participating institution. All subjects provided written informed consent for the collection of samples and subsequent analysis. The case definition of type 2 diabetes was based on the American Diabetes Association (ADA) criteria. After providing informed consent, participants underwent the same enrollment procedures, which included collection of demographic information, medical history, clinical examination and a blood draw. Genome-wide SNP genotyping was done on either the Axiom™ PanAFR SNP array (n=1,808) or the Multi-Ethnic Global Array (MEGA) (n=3,423). After appropriate quality control, in silico imputation was done using the African Genome Resources Haplotype Reference Panel (at the Sanger Imputation Service). Imputed genotypes were filtered for variants with minor allele frequency (MAF)≥ 0.01 and information score (info) ≥ 0.3 for genetic association analysis. Genome-wide association analysis between type 2 diabetes and the imputed genotype dosages was done using a generalized linear mixed model, which adjusted for age, gender, body mass index, the genetic relatedness matrix and the first three principal components (PCs) of the genotypes.Metabolomics profiling of plasma samples of type 2 diabetes (T2D) cases and controls in Nigerians (West Africa) was done in the AADM Study. Plasma metabolites were measured in a total of 580 individuals (N=310 for the discovery phase and N=270 for the replication stage) using the global/untargeted approach on the Metabolon platform and following the manufacturer's standard operation protocols. The analytic methods are described in detail in Doumatey et al. [Genome Med 2024]. The measured metabolites level represented by peak areas are relative values. The peak area data were batch-normalized to remove the instrument batch effects (batch variability) and the batch-normalized data correspond to the median-scaled raw data. For each identified metabolite, the minimum value across all batches in the batch-normalized was imputed for the missing values. The batch-normalized and imputed data are natural log-transformed and consist of 1116 metabolites for the discovery cohort and 1071 metabolites for the replication. Welch's two-sample t-test on the log-transformed data was used to identify metabolites differentially expressed between T2D cases and controls . All other statistical analyses conducted on both the replication and discovery cohorts used the log-transformed data. To merge the discovery and replication, the same quality control samples (bridge samples) were run with each batch of the experimental samples in both cohorts and used to correct for additional variability and uniformize the procedures. The resulting merged data is the QC-normalized and imputed data that contains only metabolites that were common to both cohorts and successfully bridged for all batches (n= 891 metabolites).

  Study phs001844

• CIDR: Molecular Pathological Epidemiology of Colorectal Cancer

  Colorectal cancer, the second most commonly diagnosed cancer is a biologically heterogeneous disease. To characterize the molecular attributes of colorectal tumors, we conducted targeted sequencing at the Center for Inherited Disease Research (CIDR) on 4,518 tumor and matched normal DNA samples from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colon Cancer Family Registry (CCFR). These consortia are based on cohort and population-based case-control studies.

  Study phs001905

• NHLBI TOPMed: Genomic Activities such as Whole Genome Sequencing and Related Phenotypes in the Framingham Heart Study

  The Framingham Heart Study (FHS) is a prospective cohort study of 3 generations of subjects who have been followed up to 65 years to evaluate risk factors for cardiovascular disease. Its large sample of ~15,000 men and women who have been extensively phenotyped with repeated examinations make it ideal for the study of genetic associations with cardiovascular disease risk factors and outcomes. DNA samples have been collected and immortalized since the mid-1990s and are available on ~8000 study participants in 1037 families. These samples have been used for collection of GWAS array data and exome chip data in nearly all with DNA samples, and for targeted sequencing, deep exome sequencing and light coverage whole genome sequencing in limited numbers. Additionally, mRNA and miRNA expression data, DNA methylation data, metabolomics and other 'omics data are available on a sizable portion of study participants. This project will focus on deep whole genome sequencing (mean 30X coverage) in ~4100 participants and imputed to all with GWAS array data to more fully understand the genetic contributions to cardiovascular, lung, blood and sleep disorders. Also available are aptamer proteomic profiling, RNAseq and 850K array DNA methylation data that predominantly overlap with participants with WGS data. Comprehensive phenotypic and pedigree data for study participants are available through dbGaP phs000007.

  Study phs000974

• Colorectal cancer functional annotation - ChIP

  ChIP-seq data for multiple colorectal cancer cell lines (H3K4me1, H3K4me3, H3K27ac, H3K27me3, H3K36me3, CTCF)

  Study EGAS50000000207

• scKaryoSeq of CRISPR-Cas9 edited primary human T cells

  To explore T cell-intrinsic driving forces of CRISPR-induced aneuploidy at the TRAC gene locus, we performed scKaryo-seq (Bolhaqueiro et al., Nature Genetics 2019). We compared the impact of the small molecule pifithrin-alpha in activated TRAC KO T cells. In a separate experiment, we determined the effect of pifithrin-alpha on non-activated T cells edited at the TRAC locus or treated with non-targeting Cas9 RNPs.

  Dataset EGAD50000000929

• Genetic subtyping by Whole Exome Sequencing across Diffuse Large B Cell Lymphoma and Plasmablastic Lymphoma.

  Whole exome sequencing was performed from 108 Diffuse Large B Cell Lymphoma cases. A library containing whole exome regions was used to isolate the DNA for sequencing (SureSelect XT Human All Exon V6 (Agilent technologies)). Sequencing on a NovaSeq 6000 instrument (Illumina, paired end, 2x100, mean 566Gb per FlowCell) was performed.

  Dataset EGAD50000000546

• Genetic subtyping by Whole Exome Sequencing across Diffuse Large B Cell Lymphoma and Plasmablastic Lymphoma.

  Whole exome sequencing was performed from 108 Diffuse Large B Cell Lymphoma samples (99 FFPE tumor tissue derived DNA and 9 circulating tumor DNA) and corresponding germline DNA of 38 controls. A library containing whole exome regions was used to isolate the DNA for sequencing (SureSelect XT Human All Exon V6 (Agilent technologies)). Sequencing on a NovaSeq 6000 instrument (Illumina, paired end, 2x100, mean 566Gb per FlowCell) was performed.

  Dac EGAC50000000261