3413 results for "*geneti*"

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• Deciphering Developmental Disorders (DDD)

  The Deciphering Developmental Disorders (DDD) study is a research collaboration between the Wellcome Trust Sanger Institute, the NHS clinical genetics services and families across the UK and Ireland. The project aims to improve the diagnosis of children with developmental disorders by using high-resolution microarray and massively parallel sequencing technologies on 12,000 children and their parents. Genetic changes that explain the child's symptoms will be displayed in the DECIPHER database (https://decipher.sanger.ac.uk). Extended datasets generated by the DDD project will be available in the European Genome-Phenome Archive with access carefully managed by a Data Access Committee.

  Study EGAS00001000775

• Genomic DNA of tumor tissues, adjacent normal tissues, and peripheral blood were extracted using QIAamp DNA mini Kit (QIAGEN, cat. #51306)

  Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single cell resolution. Using optimized single cell multiomics sequencing together with multiregional sampling of the primary tumor, lymphatic and distant metastases, we provide insights beyond intratumoral heterogeneity. Genome wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 sequenced patients. The cancer cells’ DNA demethylation degrees clearly correlated with the densities of the heterochromatin-associated histone modification H3K9me3 of normal tissue, and those of repetitive element Long Interspersed Nuclear Element 1. Our work demonstrates the feasibility of reconstructing genetic lineages, and tracing their epigenomic and transcriptomic dynamics with single cell multiomics sequencing.

  Study EGAS00001003242

• Indonesian RNA-seq data

  Transcription data were collected to study patterns of functional variation across the broad geography of Indonesia. The region has extremely diverse populations, with very different demographic histories and multiple genetic ancestries. This dataset reports high-throughput RNA-sequencing data for individuals from three islands spanning this geographical region: Mentawai, Sumba and West Papua.

  Study EGAS00001003671

• Indonesian methylation data

  DNA methylation data were collected to study patterns of functional variation across the broad geography of Indonesia. The region has extremely diverse populations, with very different demographic histories and multiple genetic ancestries. This dataset reports Illumina Epic methylation chip data for individuals from three islands spanning this geographical region: Mentawai, Sumba and West Papua.

  Study EGAS00001003653

• Multimodal single-cell and bulk glioma analyses

  Glioma intratumoral heterogeneity enables adaptation to challenging microenvironments and contributes to therapeutic resistance. We integrated 914 single-cell DNA methylomes, 55,284 single-cell transcriptomes, and bulk multi-omic profiles across 11 adult IDH-mutant or IDH-wild-type gliomas to delineate sources of intratumoral heterogeneity. We show that local DNA methylation disorder associates with cell-to-cell DNA methylation differences, is elevated in more aggressive tumors, links with transcriptional disruption, and is altered in environmental stress response. Glioma cells under in vitro hypoxic and irradiation stress increased local DNA methylation disorder and shifted cell states. We identified a positive association between genetic and epigenetic instability that was supported in bulk longitudinally collected DNA methylation data. Increased DNA methylation disorder associated with accelerated disease progression, and recurrently selected DNA methylation changes were enriched for environmental stress response pathways. Our work identifies an epigenetically facilitated adaptive stress response process and highlights the importance of epigenetic heterogeneity in shaping therapeutic outcomes.

  Study EGAS00001005300

• Transcriptomic response of miRNAs of monocytes to bacterial and viral stimuli assessed by RNA-seq in Africans and Europeans

  Leveraging the miRNA transcriptome of 977 samples, the study aims to define the levels of miRNA and isomiR diversity across individuals and populations, explore the genetic sources of miRNA expression variability and miRNA-environment interactions, evaluate the effects of immune challenges upon miRNA and isomiR expression dynamics, and quantify the relative impact of transcription and miRNA-mediated degradation on gene expression variability.

  Study EGAS00001004192

• The genomic landscape of lung adenocarcinoma in East Asians

  We performed exome sequencing of 210 and transcriptome sequencing of 181 LUADs of Chinese patients. For more details, please see publication "Genomic landscape of lung adenocarcinoma in East Asians" by Chen J. et. al. in Nature Genetics 2020. https://www.nature.com/articles/s41588-019-0569-6.

  Study EGAS00001002941

• Bacterial SNPs in the human gut microbiome associate with host BMI

  Genome-wide association studies (GWAS) have revolutionized the field of genetics, providing numerous associations between human SNPs and health traits. Likewise, metagenome-wide association studies (MWAS) between bacterial SNPs and human traits can suggest mechanistic links between the microbiota and its host. However, very few such studies have been done to date, in part due to the large number of samples required to address the variable coverage of bacteria across samples and the fact that most bacteria are present in only a subset of the population. Here, we devised an MWAS framework to detect SNPs and associate them with host phenotypes systematically. We recruited and obtained gut metagenomic samples from a cohort of 7,190 healthy individuals and discovered 1,358 statistically significant associations between a bacterial SNP and host body mass index (BMI). To address the possible effects of population structure and linkage disequilibrium, we applied a clumping procedure, and found 40 independent associations between SNPs and host BMI. We uncovered BMI-associated SNPs in the genomes of 27 bacterial species, even though the relative abundance of 12 of these species was not associated with the phenotype. We separated genome-wide sub-species variations from local associations in individual SNPs, and demonstrated how this framework can highlight specific bacterial functions of interest. Our results demonstrate the importance of considering nucleotide-level diversity in microbiome studies and pave the way toward a better understanding of interpersonal gut microbiome differences and their health implications.

  Study EGAS00001007204

• UK10K COHORT ALSPAC

  The UK10K project proposes a series of complementary genetic approaches to find new low-frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome-wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein-coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will directly analyse quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The Avon Longitudinal Study of Parents and Children (ALSPAC) is a two-generation prospective study. Pregnant women living in one of three health districts in the former county of Avon with an expected delivery date between April 1991 and December 1992 were eligible to be enrolled in the study, and this formed the initial point of contact for the development of a large, family based resource. Information has been collected on the children and the mothers through retrieval of biological materials (e.g. antenatal blood samples, placentas), biological sampling (e.g. collection of cord blood, umbilical cord, milk teeth, hair, toenails, blood and urine), self-administered questionnaires, data extraction from medical notes, linkage to routine information systems and at repeat research clinics.

  Study EGAS00001000090

• UK10K COHORT TWINSUK

  The UK10K project proposes a series of complementary genetic approaches to find new low-frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome-wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein-coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will directly analyse quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The TwinsUK samples will be part of the cohort study and will undergo whole genome sequencing. For further information with regard to this cohort please contact Brent Richards (brent.richards@kcl.ac.uk) or Nicole Soranzo (ns6@sanger.ac.uk).

  Study EGAS00001000108