4051 results for "*geneti*"

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• SNP array files, IDAT files, from 34 members of a Family with a high prevalence of psychosis

  We investigated the genetic causes of major mental disorders (MMDs) including schizophrenia, bipolar disorder I, major depressive disorder, and attention deficit hyperactive disorder, in a large family pedigree from Alpujarras, South of Spain, a region with a high prevalence of psychotic disorders. We applied a systematic genomic approach based on karyotyping (N=4), genotyping by genome-wide SNP array (N=33), whole-exome sequencing (N=4), and whole-genome sequencing (N=12). We performed genome-wide linkage analysis, family-based association analysis, and polygenic risk score estimates. Significant linkage was obtained at chromosome 9 (9q33.1-33.2, LOD score= 4.11), a suggestive region that contains five candidate genes ASTN2, BRINP1, C5, TLR4, and TRIM32, previously associated to MMDs. Comprehensive analysis associated the MMD phenotype with genes of the immune system with dual brain functions. Moreover, the psychotic phenotype was enriched for genes involved in synapsis. These results should be considered once studying the genetics of psychiatric disorders in other families, especially the ones from the same region, since founder effects may be related to the high prevalence.

  Study EGAS00001004592

• A GWAS for cutaneous leishmaniasis in Brazil

  Our goal was to identify genetic risk factors for cutaneous leishmaniasis (CL) caused by Leishmania braziliensis. Genotyping 2066 CL cases and 2046 controls using Illumina HumanCoreExomeBeadChips provided data for 4,498,586 imputed single nucleotide variants (SNVs). Genome-wide association testing using linear mixed models took account of genetic diversity/ethnicity/admixture. Post-GWAS positional, expression quantitative trait locus (eQTL), and chromatin interaction mapping was performed in FUMA. Transcriptional data were compared between lesions and normal skin, and cytokines measured using flow cytometry and Bioplex assay.

  Study EGAS00001004596

• A Universal Gut Metagenomic-Derived Signature Predicts Cirrhosis

  Dysregulation of the gut microbiome has been implicated in the progression of nonalcoholic fatty liver disease (NAFLD) to advanced fibrosis and cirrhosis. To determine the diagnostic capacity of this association, stool microbiomes were compared across 163 well-characterized participants encompassing non-NAFLD controls, NAFLD-cirrhosis patients and their first-degree relatives. Interrogation of shotgun metagenomic and untargeted metabolomic profiles using the Random Forest machine learning algorithm and differential abundance analysis identified discrete metagenomic and metabolomic signatures that were similarly effective in detecting cirrhosis (diagnostic accuracy 0.91, AUC). Combining the metagenomic signature with age and serum albumin levels accurately distinguished cirrhosis in etiologically and genetically distinct cohorts from geographically separated regions. Additional inclusion of serum aspartate aminotransferase levels, which are increased in cirrhosis patients, enabled discrimination of cirrhosis from earlier stages of fibrosis. These findings demonstrate that a core set of gut microbiome species may offer universal utility as a non-invasive diagnostic test for cirrhosis.

  Study EGAS00001004600

• The endometrial transcription landscape of MRKH syndrome

  The Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome (OMIM 277000) is characterized by agenesis of the uterus and upper part of the vagina in females with normal ovarian function. While genetic causes have been identified for a small subset of patients and epigenetic mechanisms presumably contribute to the pathogenic unfolding, too, the etiology of the syndrome has remained largely enigmatic. A comprehensive understanding of gene activity in the context of the disease is crucial to identify etiological components and their potential interplay. So far, this understanding is lacking, primarily due to the scarcity of samples and suitable tissue. In order to close this gap, we profiled endometrial tissue of uterus rudiments in a large cohort of MRKH patients using RNA-seq and thereby provide a genome-wide view on the altered transcription landscape of the MRKH syndrome.

  Study EGAS00001004601

• Prime editing for functional repair in patient-derived disease models

  Prime editing is a novel genome editing technology using fusion proteins of Cas9-nickase and reverse transcriptase, that holds promise to correct a wide variety of genetic defects. We succeeded in efficient prime editing and functional recovery of disease-causing mutations in patient-derived liver and intestinal stem cell organoids. Whole genome sequencing of did not detect off-target mutations or a mutational signature induced by prime editing.

  Study EGAS00001004611

• Patient-tailored design for selective co-inhibition of leukemic cell subpopulations

  The extensive primary and secondary drug resistance in many cancer types requires rational approaches to design personalized and selective combinatorial therapies that do not only show synergistic effect in overall cancer cell killing but also result in minimal toxic side effects on non-malignant cells. To address the combinatorial explosion in the number of relevant combinations, we implemented a machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA-sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory AML patient cases, each with a different genetic background, our integrated approach accurately predicted patient-specific combinations that were shown to result not only in synergistic cancer cell co-inhibition but were also capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our data-driven approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of non-malignant cells, and highlight the relevance of considering cell heterogeneity for personalized cancer therapy.

  Study EGAS00001004614

• Understanding_hematopoietic_stem_cell_mobilization_and_engraftment_

  This study looks at patients undergoing gene therapy for genetic blood and immune disorders, in particular sickle cell disease. From each patient, single blood HSPC colonies are grown from patient blood mononuclear cells, bone marrow mononuclear cells, or CD34+ at multiple time points: pre gene transduction, post-transduction, and post-infusion (at several time points). Whole-genome sequencing will be performed on DNA from these colonies and somatically-acquired mutations will be called. Using these mutations, a phylogeny of these cells can be inferred and further inferences can be made about the gene therapy transduction and engraftment steps.

  Study EGAS00001004620

• Molecular classification improves risk assessment in adult B-lineage ALL: Patients on the international UKALLXII-ECOG2993 trial.

  Acute lymphoblastic leukemia (ALL) in adults is a rare disease with outcomes that remain significantly inferior to those of children with ALL. While genomic classification of pediatric B-lineage ALL (B-ALL) has led to improved risk assignment and therapeutic paradigms, similar advances in adults have been lacking. The international UKALLXII/E2993 (NCT00002514) trial accrued over 2000 adult (17-64 years old) patients over 13 years, including 1229 BCR/ABL negative B-ALLs. While 93% of B-ALL achieved a remission, 41% relapsed at a median time of 13 months (Range) and overall survival (OS) at 5 years was 42% (CI). Older age and higher white blood cell (WBC) count at presentation negatively affected OS. Patients with a sibling donor assigned to allogeneic transplantation had superior OS compared to patients without donor. Randomization of patients without a donor showed an advantage of chemotherapy over autologous transplantation in first remission. Transcriptome sequencing of 94% of E2993 patients enabled genomic subtyping. We found that one-third of patients belonged to favorable genotypes (DUX4-rearranged, ETV6-RUNX1, TCF3-PBX1, PAX5 P80R, high hyperdiploidy). Of 63.4% of patients classified as high-risk based age and WBC count at diagnosis, almost 40% harbored subtype-defining genetic associations associated with favorable- or intermediate outcomes. These patients did significantly better than expected from their protocol-defined risk assignment. Our data in a large adult B-ALL cohort treated on a single international trial show the important prognostic value of genomic analysis and the potential benefit for many of the novel genotypes.

  Study EGAS00001004638

• The proliferative history shapes the DNA methylome of B-cell tumor and predicts clinical outcome

  Here, we provide access to CLL and DLBCL DNA methylation and gene expression data in the context of a systematic analysis of the DNA methylation variability in 1,595 samples of normal cell subpopulations and 14 tumor subtypes spanning the entire human B-cell lineage. This approach showed that differential methylation among tumor entities relates to differences in cellular origin and to de novo epigenetic alterations, which allowed us to build an accurate machine learning-based diagnostic algorithm. We identify extensive patient-specific methylation variability in silenced chromatin associated with the proliferative history of normal and neoplastic B cells. Mitotic activity generally leaves both hyper- and hypomethylation imprints, but some B-cell neoplasms preferentially gain or lose DNA methylation. Subsequently, we construct a DNA methylation-based mitotic clock called epiCMIT, whose lapse magnitude represents a strong independent prognostic variable in B-cell tumors and is associated with particular driver genetic alterations. Our findings reveal DNA methylation as a holistic tracer of B-cell tumor developmental history, with implications in the differential diagnosis and prediction of clinical outcome.

  Study EGAS00001004640

• Genomewide detection of cytosine methylation by single molecule real-time sequencing

  5-methylcytosine (5mC) is an important type of epigenetic modification. In this study, we enhanced 5mC detection using SMRT sequencing by holistically analyzing kinetic signals of a DNA polymerase and sequence context for every base within a measurement window. We employed a convolutional neural network to train a methylation classification model. This methodology has provided a system for simultaneous genome-wide genetic and epigenetic analyses. 

  Study EGAS00001004642