3311 results for "*geneti*"

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• DAC for study Population Structure and Genetic Diversity in Argentinean populations

  Dac EGAC00001000426

• Division of Biomedical Genetics UMC Utrecht

  Dac EGAC00001000432

• Data Access Committee of National Institute of Genetics of Japan

  Dac EGAC00001000448

• Genetic predisposition to cancer, University of Tampere

  Dac EGAC00001000488

• Pacific Islands RHD Genetics Network Data Access Committee

  Dac EGAC00001000506

• Ludwig Center for Cancer Genetics and Therapeutics at Johns Hopkins

  Dac EGAC00001000507

• DAC for high resolution genomic data generated by the Gisselsson Group at the Department of Clinical Genetics, Lund University.

  Dac EGAC00001000534

• Simons Genome Diversity Project Data Access Committee

  Dac EGAC00001000541

• Epigenetic dysregulation in autism spectrum disorder

  Dac EGAC00001000547

• Clinical Genetics Pediatric Brain Tumor (CGPBT)-group at Gothenburg University.

  Dac EGAC00001000572

• Dr. Fatima Mechta-Grigoriou, PhD, DR1 Head of Stress and Cancer Laboratory Deputy Director Genetic and Biology of Cancer Dpt Institut Curie - Inserm U830

  Dac EGAC00001000581

• MeDIP-seq data in the Peri/postnatal Epigenetics Twins Study (PETS)

  Dac EGAC00001000596

• Human Genetics Wuerzburg

  Dac EGAC00001000654

• Cancer Pharmacogenetics Group at King's College London

  Dac EGAC00001000795

• DAC for Institute of Human Genetics, Bonn, AGA Project

  Dac EGAC00001000831

• DAC monitoring the usage of ultra-low-coverage MinION nanopore sequencing results of NA12877 and NA12878 from NIGMS Human Genetic Cell Repository.

  Dac EGAC00001000876

• DAC for present-day Central Asia genotype data at the Centre for GeoGenetics, Natural History Museum of Denmark.

  Dac EGAC00001000894

• DAC for the project on epigenetic dysregulation in tuberculosis

  Dac EGAC00001000909

• Laboratory of Medical Genetics - Department of Precision Medicine

  Dac EGAC00001000936

• DAC for Integrated Genomic, Epigenetic, and Expression Analyses of Ovarian Cancer Cell Lines

  Dac EGAC00001001101

• South Asia Rheumatic Heart Disease Genetics Network Data Access Committee

  Dac EGAC00001001161

• Control iPSC lines with clinically annotated genetic variants for versatile multi-lineage differentiation Committee

  Dac EGAC00001001226

• Control iPSC lines with clinically annotated genetic variants for versatile multi-lineage differentiation Committee (part II)

  Dac EGAC00001001332

• Genetics and transcriptomes of pediatric B cell precursor leukemia with gain of chromosome 21

  Dac EGAC00001001348

• TGA GENETICS DATA ACCESS COMMITTEE

  Dac EGAC00001001456

• Center for Medical Genetics

  Dac EGAC00001001466

• DAC for Sex-biased patterns shaped the genetic history of Roma

  Dac EGAC00001001477

• Data Access Committee LUMC-KFT pharmacogenetics

  Dac EGAC00001001486

• Center for Medical Genetics Ghent

  Dac EGAC00001001507

• Cologne University Hospital, Institute of Human Genetics, Pathomechanisms of Rare Kidney Diseases Research Group DAC

  Dac EGAC00001001549

• Genetics and transcriptomics of human acute erythroid leukemia

  Dac EGAC00001001553

• DAC for Phylogenetic reconstruction of breast cancer

  Dac EGAC00001001572

• Data Access Commitee for study Fine-Scale Genomic Analyses Of Admixed Individuals Reveal Unrecognized Genetic Ancestry Components In Argentina

  Dac EGAC00001001634

• VIB CCB Translational Genetics Data Access Commitee

  Dac EGAC00001001761

• VIB CCB Translational Genetics Data Access Commitee

  Dac EGAC00001001808

• Genetic structure of Norway Data Access Committee

  Dac EGAC00001001827

• Genetic makeup of agnospheres.

  Dac EGAC00001001846

• Center for Medical Genetics Data Access Commitee

  Dac EGAC00001001892

• VIB CCB Translational Genetics Data Access Commitee

  Dac EGAC00001001928

• VIB CCB Translational Genetics Data Access Committee

  Dac EGAC00001001975

• VIB CCB Translational Genetics Data Access Committee

  Dac EGAC00001002006

• International Genetics of Parkinson Disease Progression (IGPP) Consortium GWSS Summary Results Data Access Committee

  Dac EGAC00001002012

• Center of Medical Genetics Ghent

  Dac EGAC00001002063

• Data Access Committee for study Genetics of Preeclampsia at High Altitudes

  Dac EGAC00001002097

• Center of Medical Genetics Ghent

  Dac EGAC00001002102

• University College London, Department of Genetics and Genomic Medicine (UCL GGM)

  Dac EGAC00001002121

• Evolutionary predictability of genetic versus non genetic resistance to anticancer drugs in melanoma

  Dac EGAC00001002126

• VIB CCB Translational Genetics Data Access Committee

  Dac EGAC00001002157

• MRC Institute of Genetics and Cancer Data Access Committee

  Dac EGAC00001002163

• Department of Clinical Genetics of Maastricht University Medical Center

  Dac EGAC00001002259

• Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland

  Dac EGAC00001002346

• DAC Genetic variability in the glucocorticoid receptor NR3C1

  Dac EGAC00001002382

• Center for Medical Genetics Ghent

  Dac EGAC00001002384

• lupus genetic risk variant

  Dac EGAC00001002394

• Genetics of acne consortium

  Dac EGAC00001002400

• Lupus Genetic Risk Variant

  Dac EGAC00001002514

• Director of the Institute of Reproductive Genetics, Münster, Germany

  Dac EGAC00001002589

• UEA Genetics of Cholesteatoma DAC

  Dac EGAC00001002612

• Pharmacogenetics Team

  Dac EGAC00001002745

• University College London, Department of Genetics and Genomic Medicine (UCL GGM)

  Dac EGAC00001002813

• DLSM Epigenetics DAC

  Dac EGAC00001002822

• Differential Presence of Exons in Cell-Free DNA (NIMGenetics/IIS-FJD)

  Dac EGAC00001002885

• Center for Medical Genetics Ghent

  Dac EGAC00001002909

• VIB CCB Translational Genetics Data Access Commitee

  Dac EGAC00001002959

• COVID-19 Vaccine Genetics DAC

  Dac EGAC00001003037

• Data Access Commitee LUMC-KFT Pharmacogenetics

  Dac EGAC00001003046

• Southern African Genetic History

  Dac EGAC00001003149

• Center for Medical Genetics Ghent

  Dac EGAC00001003264

• Circulating tumor cells Exome sequencing from breast cancer

  Single cell technologies allow interrogating the extent of tumor heterogeneity, providing novel insights into tumor evolution and treatment resistance mechanisms. Here, we investigated the level of genetic heterogeneity among circulating tumor cells (CTCs) at the single nucleotide variation and copy number aberration (CNA) levels and compared it to synchronous matched primary tumor/metastatic lesion from 3 metastatic breast cancer patients with triple-negative (TNBC), HER2-positive (HER2+) and estrogen-receptor-positive (ER+) tumors. Whole exome sequencing was performed on 3 synchronous bulk tumor tissue and 21 single/pooled CTC samples. When comparing bulk tissue with CTCs from the same patient, we found similar CNA profiles and the same patient-specific driver mutations for the HER2+ and TNBC tumors. Different CNA profiles and driver mutations were found for the ER+ tumor, with two distinct clones being identified in the CTCs, one harboring an activating ESR1 mutation, the other one bearing a TP53 mutation. These data suggest that tumor tissue and CTCs provide in a subset of patients complementary clinically relevant information to map tumor heterogeneity and monitor tumor evolution. Further validation is needed.

  Study EGAS00001005228

• Dataset that contains runs of submission 70

  Description of runs dataset of submission 70

  Dataset EGAD50000000099

• Dataset that contains runs of submission 72

  Description of runs dataset of submission 72

  Dataset EGAD50000000103

• Whole-genome sequencing of Tibetans from China

  Twenty-seven Tibetan samples from China were whole-genome sequenced to investigate high-altitude adaptation, population genetics and demographic history.

  Dataset EGAD00001004797

• Oxidative phosphorylation is a key ontogenetic feature of monocyte immunometabolism promoting myeloid differentiation after birth

  The dataset consists of 12 samples of monocytes, which were analyzed using RNA-sequencing (RNA-seq). These samples were categorized into four distinct groups, each comprising three samples. The dataset was designed to investigate the transcriptomic and metabolic profiles of monocytes across different age groups and stimulation conditions. 3 neonatal controls, 3 neonatal LPS stimulated, 3 adult controls and 3 adult LPS stimulated samples.

  Dataset EGAD00001011340

• Oxidative phosphorylation is a key ontogenetic feature of monocyte immunometabolism promoting myeloid differentiation after birth

  Dac EGAC00001003420

• Genome-wide DNA methylation sequencing identifies epigenetic perturbations in the upper airways under long-term exposure to moderate levels of ambient air pollution

  While the link between exposure to high levels of ambient particulate matter (PM) and increased incidences of respiratory and cardiovascular diseases is widely recognized, recent epidemiological studies have shown that low PM concentrations are equally associated with adverse health effects. As DNA methylation is one of the main mechanisms by which cells regulate and stabilize gene expression, changes in the methylome could constitute early indicators of dysregulated signaling pathways. So far, little is known about PM-associated DNA methylation changes in the upper airways, the first point of contact between airborne pollutants and the human body. Here, we focused on cells of the upper respiratory tract and assessed their genome-wide DNA methylation pattern to explore exposure-associated early regulatory changes. Using a mobile epidemiological laboratory, nasal lavage samples were collected from a cohort of 60 adults that lived in districts with records of low (Simmerath) or moderate (Stuttgart) PM10 levels in Germany. PM10 concentrations were verified by particle measurements on the days of the sample collection and genome-wide DNA methylation was determined by enzymatic methyl sequencing at single-base resolution. We identified 231 differentially methylated regions (DMRs) between moderately and lowly PM10 exposed individuals. A high proportion of DMRs overlapped with regulatory elements, and DMR target genes were involved in pathways regulating cellular redox homeostasis and immune response. In addition, we found distinct changes in DNA methylation of the HOXA gene cluster whose methylation levels have previously been linked to air pollution exposure but also to carcinogenesis in several instances. The findings of this study suggest that regulatory changes in upper airway cells occur at PM10 levels below current European thresholds, some of which may be involved in the development of air pollution-related diseases.

  Study EGAS00001007374

• RNA-seq of Liver Cancer

  The present series corresponds to 161 RNA-seq samples from tumors with matched WES or WGS. Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Dataset EGAD00001003993

• Whole Genome Sequencing of HCC

  The present series corresponds to 24 whole genome sequencing (12 Tumoral/Non-tumoral pairs). Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Dataset EGAD00001003994

• Exome sequencing of advanced hepatocellular carcinoma

  The aim of our project is to decipher the genomic of advanced hepatocellular carcinoma using whole exome sequencing. To this purpose, we aim to compare genetic landscape of advanced hepatocellular carcinoma with early tumor in order to understand the mechanisms of tumor progression. This work will also help to identify new therapeutic targets potentially useful to treat patients at advanced stage. This dataset contain whole exome sequencing aligned reads for 41 tumor with matched normal samples

  Dataset EGAD00001004555

• Genetic landscape of hepatocellular carcinoma

  Genetic landscape of hepatocellular carcinoma

  Dataset EGAD00001000131

• Analysis of the Elements Involved in the Enrichment of a Panel of Genomic Regions by Nanopore Sequencing Using Adaptive Sampling

  DNA sequencing using nanopore technologies with the affordable MinION device is useful for the identification and characterization of structural variants, long haplotypes, sequencing of repetitive regions and identification of epigenetic modifications. The main limitation of this approach is the low coverage obtained, which might be avoided by adaptive sampling, a computationally controlled method of enrichment for targeted genomic regions. This study dissects the factors involved in the enrichment by adaptive sampling of a panel of 18 human genome regions containing 20 genes implicated in breast cancer sequenced in 16 patients with familial breast cancer negative for NGS screening. An average coverage of 2.0x was obtained for the whole genome and 5.1x for selected regions. Sequencing time was the main factor improving coverage. The selection of long reads (>1 Kb) did not improve the enrichment. The length of the selected region, which in our study ranged from 126 to 565 Kb, did not play a significant role in enrichment. However, the region containing PMS2 showed significantly lower coverage, which could be explained by the high number of PMS2 pseudogenes (N = 14), which were also enriched. Our study shows new evidence of enrichment obtained by adaptive sampling in a panel of genomic regions and shows parameters, the relevance of sequencing time and the role of pseudogenes, that improve the enrichment yield with no library reloading or GPU use, data useful for a more efficient application of this procedure in future studies.

  Study EGAS00001007375

• Capture-based NGS

  Capture-based NGS obtained using the "all-CLL" panel (also known as SOPHiA DDM (TM) Community CLL Clonality Solution). Library preparation was performed following SOPHiA GENETICS recommendations using 200 ng genomic DNA. Libraries were sequenced on a MiSeq instrument (2x300 bp, Illumina) aiming at a mean coverage of 1,000x.

  Dataset EGAD00001011151

• High-resolution, patient-level dissection of IL-23 blockade in cutaneous psoriasis

  Psoriasis vulgaris and other chronic inflammatory diseases improve markedly with therapeutic blockade of IL-23 signaling in T cells, but the genetic mechanisms of response remain poorly understood. We single-cell transcriptomically profiled CD45+ immune cells isolated from lesional psoriatic skin before and after IL-23 blockade. In clinically responsive patients, a psoriatic transcriptional signature in skin-resident memory T cells was sharply attenuated. In contrast, poorly responsive patients were distinguished by enduring T17 cell activation, a mechanism distinct from alternative cytokine signaling or downstream resistance. Spatial transcriptomic analysis suggests that successful IL-23 blockade requires dampening of > 90% of IL-17-induced signaling in lymphocyte-adjacent keratinocytes, an unexpectedly high threshold. We also detected a subset of persistent, disease-specific T17 abnormalities in responsive patients, revealing an irreversible cell identity that may necessitate ongoing IL-23 inhibition. Collectively, our data establishes a patient-level paradigm for dissecting response to immunomodulatory treatments.

  Study EGAS00001007373

• Massively parallel functional dissection of schizophrenia associated non-coding genetic variants

  Schizophrenia (SCZ) is a severe mental disorder affecting 1% of the world population. SCZ is characterized by an underlying genetic architecture that is highly polygenic. Genome wide association studies have identified thousands of genetic variants that are statistically linked to the disease. However, the translation of these associations into insights on the pathomechanisms has been challenging because the causal genetic variants, their molecular function, and their target genes remain largely unknown. To address these questions, we combined induced pluripotent stem cell technology with a massively parallel variant annotation pipeline (MVAP) to functionally characterize 35,000 SCZ associated non-coding genetic variants. This approach identified a set of 620 (1.7%) single nucleotide polymorphisms as functional on the molecular level in a highly cell type and condition specific fashion. Subsequent multi-modal integration of epigenomic data combined with CRISPR screening in human neurons enabled us to systematically translate SCZ variant associations into target genes, biological processes, and ultimately alterations of neuronal physiology. These results provide a new high-resolution map of functional variant-gene combinations and offer comprehensive biological insights into the developmental context and stimulus dependent molecular processes modulated by SCZ genetic variation beyond statistical association.

  Study EGAS00001007542

• An MTOR mutation hitchhikes through renal embryogenesis, driving multifocal, multiphenotypic tumours - WGS

  Embryogenesis is a vulnerable time. Mutations in developmental cells can result in the seeding of cells predisposed to disease within mature organs, creating a field effect. We characterise an embryonic cancer mutation that drives multifocal, multiphenotypic renal tumours in a 14-year-old girl. Their shared MTOR mutation, absent from normal tissues, increases protein flexibility which enables a FAT domain hinge to dramatically increase mTORC1 activity. Developmental mutations, not usually detected in traditional genetic screening, have vital clinical importance in guiding prognosis, targeted treatment, and family screening decisions for paediatric tumours.

  Dataset EGAD00001011645

• An MTOR mutation hitchhikes through renal embryogenesis, driving multifocal, multiphenotypic tumours - RNA

  Embryogenesis is a vulnerable time. Mutations in developmental cells can result in the seeding of cells predisposed to disease within mature organs, creating a field effect. We characterise an embryonic cancer mutation that drives multifocal, multiphenotypic renal tumours in a 14-year-old girl. Their shared MTOR mutation, absent from normal tissues, increases protein flexibility which enables a FAT domain hinge to dramatically increase mTORC1 activity. Developmental mutations, not usually detected in traditional genetic screening, have vital clinical importance in guiding prognosis, targeted treatment, and family screening decisions for paediatric tumours.

  Dataset EGAD00001011646

• An MTOR mutation hitchhikes through renal embryogenesis, driving multifocal, multiphenotypic tumours - scRNA

  Embryogenesis is a vulnerable time. Mutations in developmental cells can result in the seeding of cells predisposed to disease within mature organs, creating a field effect. We characterise an embryonic cancer mutation that drives multifocal, multiphenotypic renal tumours in a 14-year-old girl. Their shared MTOR mutation, absent from normal tissues, increases protein flexibility which enables a FAT domain hinge to dramatically increase mTORC1 activity. Developmental mutations, not usually detected in traditional genetic screening, have vital clinical importance in guiding prognosis, targeted treatment, and family screening decisions for paediatric tumours.

  Dataset EGAD00001011647

• Low-coverage whole genome sequencing for a highly selective cohort of severe COVID-19 patients

  Background Despite advances in identification of genetic markers associated to severe COVID-19, the full genetic characterisation of the disease remains elusive. Imputation of low-coverage whole genome sequencing (lcWGS) has emerged as a competitive method to study such disease-related genetic markers as they enable genotyping of most common genetic variants used for genome wide association studies. This study aims at exploring the potential use of imputation in lcWGS for a highly selected severe COVID-19 patient cohort. Findings We generated an imputed dataset of 79 variant call format (VCF) patient files using the GLIMPSE1 tool, each containing, on average, 9.5 million single nucleotide variants. The validation assessment of imputation accuracy yielded a squared Pearson correlation of approximately 0.97 across sequencing platforms, showing that GLIMPSE1 can be used to confidently impute variants with minor allele frequency up to approximately 2% in Spanish ancestry individuals. We conducted a comprehensive analysis on the patient cohort, examining hospitalisation and intensive care utilisation, sex and age-based differences, and clinical phenotypes using a standardised set of medical terms specifically developed to characterise severe COVID-19 symptoms for this cohort. Conclusion This dataset highlights the utility and accuracy of lcWGS imputation in the study of COVID-19 severity, setting a precedent for other applications in resource-constrained environments. The methods and findings presented here may be leveraged in future genomic projects, providing vital insights for health challenges like COVID-19.

  Study EGAS00001007573

• Low-coverage whole genome sequencing for a highly selective cohort of severe COVID-19 patients

  We generated a dataset consisting of 79 VCF files, and respective FASTQ and CRAM files, methodically generated using the GLIMPSE1 imputation algorithm leveraging the 1000 Genomes Project Phase 3 dataset as the reference panel of haplotypes. In total this dataset is composed of approximately 325 GB of FASTQ data, 156 GB of CRAM data, and 6 GB of VCF data. Our samples were specifically derived from sequenced DNA from a highly selective cohort of patients, mostly comprised of Iberian Populations in Spain (IBS) individuals but also containing some individuals with other genetic backgrounds, who presented severe COVID-19 symptoms during the initial wave of the SARS-CoV-2 pandemic in Madrid, Spain. On average, each VCF file in this rich dataset contains 9.49 million high-confidence single nucleotide variants [95%CI: 9.37 million - 9.61 million].

  Dataset EGAD00001011363

• Submission 36 - study_title 1

  Submission 36 - description 1

  Study EGAS50000000206

• Submission 36 - study_title 2

  Submission 36 - description 2

  Study EGAS50000000207

• Dataset that contains runs of submission 36

  Description of runs dataset of submission 36

  Dataset EGAD50000000204

• Dataset that contains runs of submission 37

  Description of runs dataset of submission 37

  Dataset EGAD50000000206

• VIB CCB Translational Genetics Data Access Committee

  Dac EGAC00001003415

• Comprehensive Whole-Genome Sequence Annotation to Resolve the Genetic Architecture of Cerebral Palsy

  Study EGAS00001006724

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Kidney (1)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Dataset EGAD00001006427

• MutWP1 CRUK Grand Challenge Mutographs of Cancer Colorectal

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Dataset EGAD00001007510

• Therapeutic vulnerabilities in CCA from different etiologies identified using integrative multi-omics enhancer activity profiling

  Study EGAS00001007309

• Dataset that contains runs of submission 157

  Description of runs dataset of submission 157

  Dataset EGAD50000000246

• Peripheral blood RNA-sequencing in 4,732 participants of the INTERVAL cohort

  We performed bulk RNA-sequencing on peripheral blood collected from 4,732 blood donors recruited as part of the INTERVAL study. Using these data, we mapped gene expression and splicing quantitative trait loci (QTLs). Then, we integrated these data with protein, metabolite and lipid QTLs in the same individuals. The study aimed to identify the shared genetic etiology across transcriptional phenotypes, molecular traits and health outcomes in humans.

  Dataset EGAD00001008015

• Genetic Mechanisms of Disease DAC

  Dac EGAC00001003409