4015 results for "*geneti*"

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• HGP-U-1

  Himalayan population genetic study raw data (Himalaya)

  Dataset EGAD00010001472

• 5074STDY-G-1

  Genetic Overlap between Metabolic and Psychiatric disease

  Dataset EGAD00010001484

• The sanger result of LAM disease

  Tumor biopsies from LAM disease were analyzed by sanger to characterize the alterations in patients ,to elucidate the landscape of genetic/genomic alterations. The dataset include 21 samples.

  Dataset EGAD00010001761

• The MLPA result of LAM disease

  Tumor biopsies from LAM disease were analyzed by MLPA to characterize the alterations in patients ,to elucidate the landscape of genetic/genomic alterations. The dataset include 44 samples.

  Dataset EGAD00010001757

• GWAS results from Danjou et al, Nature Genetics 2015

  GWAS results in epacts format for Danjou et al, Nature Genetics 2015

  Dataset EGAD00010001722

• Genetic and transcriptomic landscape of DLBCL

  Affymetrix SNP6.0 data for 341 DLBCL patients

  Dataset EGAD00010001980

• CentralAfricanCMC_Pemberton

  The CentralAfricanCMC_Pemberton dataset encompasses 153,798 SNPs from the Illumina Cardio-MetaboChip (Voight et al. 2012) genotyped in 406 individuals from 19 Central African Populations from Gabon, Cameroon, Centralafrican Republic and Uganda). Individual phenotypic and cultural information at the individual level for this data set encompass gender, lifestyle (hunter-gatherer or farmer), and, when available, stature phenotype (standing height in cm, sitting-height in cm, and weight in kg). Other cultural, linguistic, and geographical location information about the sampled populations can be found in Pemberton et al. , Human Genetics, 2018 (https://doi.org/10.1007/s00439-018-1902-3).This dataset can only be accessed and used for non-commercial research purposes with a finality complying with the informed consent provided by Central African donors for the study of human evolutionary history only.

  Dataset EGAD00010001584

• Kibbutzim Family study

  The Kibbutzim Family Study (KFS) aimed to investigate the environmental and genetic determinants of cardiometabolic traits (phenotype is LDL-C)

  Dataset EGAD00010001551

• Imputed_genetics

  Imputed genetic data for INTERVAL proteomics cohort

  Dataset EGAD00010001544

• thraruk-G-1

  Resolving the Genetic Architecture of Aseptic Loosening After Total Hip Replacement

  Dataset EGAD00010001289

• H3Africa AWIGEN Pilot MetaboChip

  Pilot study on the interplay between genetic, epigenetic, and environmental risk factors for obesity and related cardiometabolic diseases with 973 samples from South Africa genotyped on Illumina Human MetaboChip array.

  Dataset EGAD00010001258

• STOP-HCV_BOSON_HumanGeneticData

  Human genotyping data for patients infected by hepatitis C virus

  Dataset EGAD00010001202

• prcmd-G-1

  Genetic studies of pregnancy-related cardiometabolic disorders in Central Asian, Northern European, and Colombian populations

  Dataset EGAD00010001212

• EGAD00010000807

  Illumina HumanCoreExome genotyping data from the British Society for Surgery of the Hand Genetics of Dupuytren’s Disease consortium (BSSH-GODD consortium) collection

  Dataset EGAD00010000807

• EGAD00010000831

  BLUEPRINT EpiMatch: harnessing epigenetics for haematopoietic stem cell transplantation

  Dataset EGAD00010000831

• EGAD00010000538

  28 unlinked autosomal microsatellite loci for 20 African and 4 philippine populations

  Dataset EGAD00010000538

• EGAD00010000536

  21 unlinked autosomal microsatellite loci for 30 Central Asian populations

  Dataset EGAD00010000536

• Mapping the breast cancer metastatic cascade onto circulating tumour DNA using genetic and epigenetic clonal tracking

  Dataset EGAD00001005507

• TCR β-chain repertoire sequences of regulatory and conventional T cells in peripheral blood from breast cancer patients and healthy individuals

  Dataset EGAD00001004385

• Cambridge_INTERVAL_SomaLogic_pQTLs

  This dataset contains four data files relating to the Cambridge Interval SomaLogic pQTL study to go with the corresponding genetic data: (1) Genome-wide pQTL summary associations for each analyte. (2) Mapping table for the genetic variants analysed - containing rsID, position and allele information. (3) Normalised quantitative readouts for each analyte, along with covariates used for the pQTL analysis. (4) Table of SOMAmer analytes mapped to their protein targets. Please see the readme file in the dataset for more information.

  Dataset EGAD00001004080

• 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

• Clinical and genetic analysis of a rare syndrome associated with neoteny

  Dataset EGAD00001003593

• EGFR Mutant SCLC transformed exome seq

  Dataset EGAD00001001436

• Discovery of resistance mechanisms to the BRAF inhibitor vemurafenib in metastatic BRAF mutant melanoma

  Discovery of resistance mechanisms to the BRAF inhibitor vemurafenib in metastatic BRAF mutant melanoma by massively-parallel sequencing of tumour samples. Comparison of genomic characteristics of pretreatment 'sensitive' to recurrence 'resistant' tumours to identify the genetics of drug resistance.

  Dataset EGAD00001000707

• Combination therapies for personalized cancer medicine

  It is the ambition of the team formed by members of the Netherlands Cancer Institute (NKI) and the Cancer Genome Project at the Wellcome Trust Sanger Institute (WTSI) to unravel the genomic and phenotypic complexity of human cancers in order to identify optimal drug combinations for personalized cancer therapy. Our integrated approach will entail (i) deep sequencing of human tumours and cognate mouse tumours; (ii) drug screens in a 1000+ fully characterized tumour cell line panel; (iii) high-throughput in vitro and in vivo shRNA and cDNA drug resistance and enhancement screens; (iv) computational analysis of the acquired data, leading to significant response predictions; (v) rigorous validation of these predictions in genetically engineered mouse models and patient-derived xenografts. This integrated effort is expected to yield a number of combination therapies and companion-diagnostics biomarkers that will be further explored in our existing clinical trial networks.

  Dataset EGAD00001000869