3335 results for "*geneti*"

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• Botswana 15 autosomal unlinked microsatellites

  Population structure was investigated in 990 Botswana individuals according to ethno-linguistics, Bantu and Khoisan, and geography (the nine administrative districts) using the AmpFLSTR Identifiler autosomal microsatellite markers. Genetic diversity and forensic parameters were calculated for the overall population, and according to ethno-linguistics and geography. The overall combined power of exclusion (CPE) was 0.9999965412 and the combined match probability 6,28 x 10-19. CPE was highest for the Khoisan Tuu ethnolinguistic group and the Northeast District at 0.9999582029 and 0.9999922652 respectively. CMP ranged from 6.28 x 10-19 (Khoisan Tuu) to 1,02 x 10-18 (Northwest district). Using pairwise genetic distances (FST), analysis of molecular variance (AMOVA), factorial correspondence analysis (FCA), and the unsupervised Bayesian clustering method found in STRUCTURE and landscape genetics software TESS, ethno-linguistics were found to have a greater influence on population structure than geography. FCA showed clustering between Bantu and Khoisan, and within the Bantu. This Bantu sub-structuring was not seen with STRUCTURE and TESS, which detected clustering only between Bantu and Khoisan. The patterns of population structure revealed highlight the need for regional reference databases that include ethno-linguistic and geographic location information. These markers have important potential for bio-anthropological studies as well as for forensic applications.

  Study EGAS00001002380

• Population_dynamics_in_abnormal_haematopoiesis

  Background: Our recent study has made direct insight into the clonal dynamics of blood cell production in an unperturbed setting in a human. Using whole genome sequencing to track acquired somatic mutations, we were able to construct a phylogenetic tree of different stem and progenitor cells and used this tree to determine the dynamics of mature blood cell production in longitudinal samples. These data set the baseline for understanding population dynamics in abnormal haematopoiesis, and how malignant clones outcompete their normal counterparts to drive haematological malignancies. Aim: This study will measure population dynamics in abnormal human haematopoiesis in patients with clonal blood stem cell disorders. Methods: From patient bone marrow or peripheral blood, we have isolated colonies grown from single blood stem cells/progenitors. Colony DNA will be analysed by whole-genome sequencing to build (or populate) a phylogenetic tree to determine clonal relationships and track stem cell contribution to mature blood cell production. These data will also reveal disease-associated genomic features (mutational burden and mutational signatures) in haematopoietic cells.

  Study EGAS00001003181

• Analysis of IDHwt-glioblastoma samples from paired primary and recurrent tumor samples

  IDHwt-glioblastomas rapidly recur after initial treatment. To study genetic evolution in glioblastoma, we analyzed whole genomes of 21 pairs of primary and recurrent tumor samples using next generation sequencing. In addition we sequenced RNA from 16 tumor pairs and a panel of 50 glioma-associated genes in tumor pairs from 43 patients (including 14 of the 21 patients included in the WGS discovery set). Glioblastoma subtypes were determined using 450k/EPIC methylation arrays. This study was supported within the e:med program of the German Ministry of Education and Research (BMBF) by the collaborative research project ‘SYS-GLIO - Systems-based prediction of the biological and clinical behavior of gliomas’ (https://www.sys-med.de/de/demonstratoren/sys-glio/) This study is part of the Heidelberg Center for Personalized Oncology - HIPO-043

  Study EGAS00001003184

• Low frequency and rare coding variation contributes to multiple sclerosis risk

  Multiple sclerosis is a common, complex neurological disease, where almost 20% of risk heritability can be attributed to common genetic variants, including >230 identified by genome-wide association studies. Multiple strands of evidence suggest that the majority of the remaining heritability is also due to the additive effects of individual variants, rather than epistatic interactions between these variants, or mutations exclusive to individual families. Here, we show in 68,379 cases and controls that as much as 5% of this heritability is explained by low-frequency variation in gene coding sequence. We identify four novel genes driving MS risk independently of common variant signals, which highlight a key role for regulatory T cell homeostasis and regulation, IFNγ biology and NFκB signaling in MS pathogenesis.

  Study EGAS00001003195

• Genetic characterization of B-cell prolymphocytic leukemia: a hierarchical prognostic model involving MYC and TP53 abnormalities - WXS

  B-PLL is a rare disease, whose molecular pathology is largely unknown. We report here the cytogenetic and molecular findings in a large series of 34 B-PLL. Karyotype (K) was complex (≥3 abnormalities) in 73%, and highly complex (HCK≥5) in 45%. The most frequent chromosomal aberrations were: translocation targeting the MYC gene [t(MYC)] (62%), 17p deletion including TP53 gene (38%), trisomy 18/18q (30%), 13q14 deletion (29%), trisomy 3 (24%), trisomy 12 (24%) and 8p deletion (23%). Whole-Exome Sequencing performed in 16 patients revealed recurrent mutations in TP53 (6/16, 38%), MYD88 (n=4), BCOR (n=4), MYC (n=3), SF3B1 (n=3), FAT1 (n=3), SETD2 (n=2), CHD2 (n=2), CXCR4 (n=2) and BCLAF1 (n=2). The main group of patients (21/34, 62%) had a t(MYC) associated with a higher percentage of prolymphocytes (p=0.03), CD38 expression (p<0.001), lower K complexity (p=0.0004), mutations in MYC and in genes involved in RNA metabolism and chromatin remodeling. Principal component analysis of gene expression data showed that patients with t(MYC) clustered together. A second group with MYC gain (5/34, 15%), was associated with HCK≥5 (p=0,01) and trisomy 3 (p=0,008). Altogether, 26/34 patients (76%) had a MYC activation, translocation or gain, that were mutually exclusive. We identified 3 distinct cytogenetic prognostic groups (p=0.0006): lower risk: absence of MYC activation (median not reached); intermediate risk: MYC activation without del17p (125 months); high risk: MYC activation + del17p (11 months). Our results show that cytogenetic analysis is a useful diagnostic tool in B-PLL that improves prognostic stratification.

  Study EGAS00001003275

• Genomics of enteropathy associated T cell lymphoma (EATL)

  Enteropathy-associated T cell lymphoma (EATL) is a lethal and the most common neoplastic complication of celiac disease. In this study, we applied whole exome sequencing in 69 EATL tumors to define the genetic landscape of EATLs. The most frequently mutated gene in EATL was SETD2 (32% of cases), with loss of function mutations comprising a large proportion (41%) of those mutations. The JAK-STAT pathway was the most frequently mutated signaling pathway, with mutations in STAT5B being the most common, followed by JAK1, JAK3, STAT3, and the negative regulator of the pathway, SOCS1. We also identified mutations in NRAS and KRAS, as well as TP53 and TERT. Interestingly, the two types of EATL had highly overlapping genetic alterations indicating shared mechanisms underlying their pathogenesis. Gene expression analysis of the two types of EATL identify robust differences in transcriptional profiles. We further modeled the effects of SETD2 loss in vivo by developing a T cell specific knockout mouse model. These mice manifested an expansion of γδ T cells that identify novel roles for SETD2 in T cell development and lymphomagenesis. Together, our data render the most comprehensive genetic portrait yet of this uncommon but lethal disease, and may inform future classification schemes.

  Study EGAS00001001954

• Pancreatic cancer organoids recapitulate disease and allow personalized drug screening

  We report derivation of 30 Patient-Derived Organoid lines (PDOs) from tumors arising in the pancreas and distal bile duct. PDOs recapitulate tumor histology and contain genetic alterations typical for pancreatic cancer. In vitro testing of a panel of 76 therapeutic agents revealed sensitivities currently not exploited in the clinic. The PRMT5 inhibitor EZP015556, shown to target MTAP negative tumors, was validated as such, but also appeared to constitute an effective therapy for a subset of MTAP+ tumors. Taken together, the work presented here provides a platform to identify novel therapeutics to target pancreatic tumor cells using PDOs.

  Study EGAS00001003369

• Analysis of error profiles in deep next-generation sequencing data

  BackgroundSequencing errors are key confounding factors for detecting low-frequency genetic variants that are important for cancer molecular diagnosis, treatment, and surveillance using deep next-generation sequencing (NGS). However, there is a lack of comprehensive understanding of errors introduced at various steps of a conventional NGS workflow, such as sample handling, library preparation, PCR enrichment, and sequencing. In this study, we use current NGS technology to systematically investigate these questions.ResultsBy evaluating read-specific error distributions, we discover that the substitution error rate can be computationally suppressed to 10-5 to 10-4, which is 10- to 100-fold lower than generally considered achievable (10-3) in the current literature. We then quantify substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing by using multiple deep sequencing datasets. We find that error rates differ by nucleotide substitution types, ranging from 10-5 for A>C/T>G, C>A/G>T, and C>G/G>C changes to 10-4 for A>G/T>C changes. Furthermore, C>T/G>A errors exhibit strong sequence context dependency, sample-specific effects dominate elevated C>A/G>T errors, and target enrichment PCR led to aprox 6-fold increase of overall error rate. We also find that more than 70% of hotspot variants can be detected at 0.1% - 0.01% frequency with the current NGS technology by applying in-silico error suppression.ConclusionsWe present the first comprehensive analysis of sequencing error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS analysis accuracy both experimentally and computationally, ultimately enhancing the precision of deep sequencing.

  Study EGAS00001003444

• MutWP1__CRUK_Grand_Challenge_Mutographs_of_Cancer__Colorectal_LCM

  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.

  Study EGAS00001003455

• Biological insights from the whole genome sequences of human embryonic stem cell lines

  Human embryonic stem cells (hESCs) are a powerful tool for the study of human development and can form the basis of cellular disease models or therapies. However, the genetic make-up and stability of hESCs has not been systematically studied at a genome-wide level with single nucleotide resolution. We therefore sequenced the whole genome and whole exomes of widely available hESC. This online resource will enable investigators to access raw sequencing data in order to interrogate cell lines for different disease- and trait-associated genetic variants.

  Study EGAS00001002400