scholarly journals SNP Variants at 16p13.11 Clarify the Role of the NDE1/miR-484 Locus in Major Mental Illness in Finland

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Vishal Sinha ◽  
Alfredo Ortega-Alonso ◽  
Liisa Ukkola-Vuoti ◽  
Outi Linnaranta ◽  
Amanda B Zheutlin ◽  
...  

Abstract Through copy number variations, the 16p13.11 locus has been consistently linked to mental disorders. This locus contains the NDE1 gene, which also encodes microRNA-484. Both of them have been highlighted to play a role in the etiology of mental illness. A 4-SNP haplotype spanning this locus has been shown to associate with schizophrenia in Finnish females. Here we set out to identify any functional variations implicated by this haplotype. We used a sequencing and genotyping study design to identify variations of interest in a Finnish familial cohort ascertained for schizophrenia. We identified 295 variants through sequencing, none of which were located directly within microRNA-484. Two variants were observed to associate with schizophrenia in a sex-dependent manner (females only) in the whole schizophrenia familial cohort (rs2242549 P = .00044; OR = 1.20, 95% CI 1.03–1.40; rs881803 P = .00021; OR = 1.20, 95% CI 1.02–1.40). Both variants were followed up in additional psychiatric cohorts, with neuropsychological traits, and gene expression data, in order to further examine their role. Gene expression data from the familial schizophrenia cohort demonstrated a significant association between rs881803 and 1504 probes (FDR q < 0.05). These were significantly enriched for genes that are predicted miR-484 targets (n = 54; P = .000193), and with probes differentially expressed between the sexes (n = 48; P = .000187). While both SNPs are eQTLs for NDE1, rs881803 is located in a predicted transcription factor binding site. Based on its location and association pattern, we conclude that rs881803 is the prime functional candidate under this locus, affecting the roles of both NDE1 and miR-484 in psychiatric disorders.

2021 ◽  
Author(s):  
Joseph Boen ◽  
Joel P. Wagner ◽  
Noemi Di Nanni

Copy number variations (CNVs) are genomic events where the number of copies of a particular gene varies from cell to cell. Cancer cells are associated with somatic CNV changes resulting in gene amplifications and gene deletions. However, short of single-cell whole-genome sequencing, it is difficult to detect and quantify CNV events in single cells. In contrast, the rapid development of single-cell RNA sequencing (scRNA-seq) technologies has enabled easy acquisition of single-cell gene expression data. In this work, we employ three methods to infer CNV events from scRNA-seq data and provide a statistical comparison of the methods' results. In addition, we combine the analysis of scRNA-seq and inferred CNV data to visualize and determine subpopulations and heterogeneity in tumor cell populations.


2021 ◽  
Author(s):  
Richard R Green ◽  
Renee C Ireton ◽  
Martin Ferris ◽  
Kathleen Muenzen ◽  
David R Crosslin ◽  
...  

To understand the role of genetic variation in SARS and Influenza infections we developed CCFEA, a shiny visualization tool using public RNAseq data from the collaborative cross (CC) founder strains (A/J, C57BL/6J, 129s1/SvImJ, NOD/ShILtJ, NZO/HILtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ). Individual gene expression data is displayed across founders, viral infections and days post infection.


Author(s):  
Jirí Kléma ◽  
Filip Železný ◽  
Igor Trajkovski ◽  
Filip Karel ◽  
Bruno Crémilleux

This chapter points out the role of genomic background knowledge in gene expression data mining. The authors demonstrate its application in several tasks such as relational descriptive analysis, constraintbased knowledge discovery, feature selection and construction or quantitative association rule mining. The chapter also accentuates diversity of background knowledge. In genomics, it can be stored in formats such as free texts, ontologies, pathways, links among biological entities, and many others. The authors hope that understanding of automated integration of heterogeneous data sources helps researchers to reach compact and transparent as well as biologically valid and plausible results of their gene-expression data analysis.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2841-2841 ◽  
Author(s):  
Yosaku Watatani ◽  
Yasunobu Nagata ◽  
Vera Grossmann ◽  
Yusuke Okuno ◽  
Tetsuichi Yoshizato ◽  
...  

Abstract Myelodysplastic syndromes (MDS) and related disorders are a heterogeneous group of chronic myeloid neoplasms with a high propensity to acute myeloid leukemia. A cardinal feature of MDS, as revealed by the recent genetic studies, is a high frequency of mutations and copy number variations (CNVs) affecting epigenetic regulators, such as TET2, IDH1/2, DNMT3A, ASXL1, EZH2, and other genes, underscoring a major role of deregulated epigenetic regulation in MDS pathogenesis. Meanwhile, these mutations/deletions have different impacts on the phenotype and the clinical outcome of MDS, suggesting that it should be important to understand the underlying mechanism for abnormal epigenetic regulation for better classification and management of MDS. SETD2 and ASH1L are structurally related proteins that belong to the histone methyltransferase family of proteins commonly engaged in methylation of histone H3K36. Both genes have been reported to undergo frequent somatic mutations and copy number alterations, and also show abnormal gene expression in a variety of non-hematological cancers. Moreover, germline mutation of SETD2 has been implicated in overgrowth syndromes susceptible to various cancers. However, the role of alterations in these genes has not been examined in hematological malignancies including myelodysplasia. In this study, we interrogated somatic mutations and copy number variations, among a total of 1116 cases with MDS and myelodysplastic/myeloproliferative neoplasms (MDS/MPN), who had been analyzed by target deep sequencing (n=944), and single nucleotide polymorphism-array karyotyping (SNP-A) (n=222). Gene expression was analyzed in MDS cases and healthy controls, using publically available gene expression datasets. SETD2 mutations were found in 6 cases, including 2 with nonsense and 4 with missense mutations, and an additional 10 cases had gene deletions spanning 1.8-176 Mb regions commonly affecting the SETD2 locus in chromosome 3p21.31, where SETD2 represented the most frequently deleted gene within the commonly deleted region. SETD2 deletion significantly correlated with reduced SETD2 expression. Moreover, MDS cases showed a significantly higher SETD2 expression than healthy controls. In total, 16 cases had either mutations or deletions of the SETD2 gene, of which 70% (7 out of 10 cases with detailed diagnostic information) were RAEB-1/2 cases. SETD2 -mutated/deleted cases had frequent mutations in TP53 (n=4), SRSF2 (n=3), and ASXL1 (n=3) and showed a significantly poor prognosis compared to those without mutations/deletions (HR=3.82, 95%CI; 1.42-10.32, P=0.004). ASH1L, on the other hand, was mutated and amplified in 7 and 13 cases, respectively, of which a single case carried both mutation and amplification with the mutated allele being selectively amplified. All the mutations were missense variants, of which 3 were clustered between S1201 and S1209. MDS cases showed significantly higher expression of ASH1L compared to healthy controls, suggesting the role of ASH1L overexpression in MDS development. Frequent mutations in TET2 (n=8) and SF3B1 (n=6) were noted among the 19 cases with ASH1L lesions. RAEB-1/2 cases were less frequent (n=11) compared to SETD2-mutated/deleted cases. ASH1L mutations did not significantly affect overall survival compared to ASH1L-intact cases. Gene Set Expression Analysis (Broad Institute) on suppressed SETD2 and accelerated ASH1L demonstrated 2 distinct expression signatures most likely due to the differentially methylated H3K36. We described recurrent mutations and CNVs affecting two histone methyltransferase genes, which are thought to represent novel driver genes in MDS involved in epigenetic regulations. Given that SETD2 overexpression and reduced ASH1L expression are found in as many as 89% of MDS cases, deregulation of both genes might play a more role than expected from the incidence of mutations and CNVs alone. Although commonly involved in histone H3K36 methylation, both methyltransferases have distinct impacts on the pathogenesis and clinical outcome of MDS in terms of the mode of genetic alterations and their functional consequences: SETD2 was frequently affected by truncating mutations and gene deletions, whereas ASH1L underwent gene amplification without no truncating mutations, suggesting different gene targets for both methyltransferases, which should be further clarified through functional studies. Disclosures Alpermann: MLL Munich Leukemia Laboratory: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Shih:Novartis: Research Funding.


2012 ◽  
Vol 51 (7) ◽  
pp. 696-706 ◽  
Author(s):  
Marieke L. Kuijjer ◽  
Halfdan Rydbeck ◽  
Stine H. Kresse ◽  
Emilie P. Buddingh ◽  
Ana B. Lid ◽  
...  

2012 ◽  
Author(s):  
Marieke L. Kuijjer ◽  
Halfdan Rydbeck ◽  
Stine H. Kresse ◽  
Emilie P. Buddingh ◽  
Helene Roelofs ◽  
...  

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 3465-3465
Author(s):  
Daphne R. Friedman ◽  
Joseph R. Nevins

Abstract Abstract 3465 Background: Chronic lymphocytic leukemia (CLL), aggressive B-cell non-Hodgkin lymphomas (NHL), and multiple myeloma (MM) are B-cell malignancies that display biological and clinical heterogeneity. Current investigations into the genetics and biology of these related disorders are using next generation whole genome or exome sequencing. The relatively high cost of these techniques has driven an experimental design in which a small group of samples are initially studied, specific genetic lesions are identified, and then larger cohorts are evaluated for those specific aberrations. Given the biological heterogeneity that is found in each of these disorders, such an approach could skew the direction of research towards results found in a small subset of patients. To determine the extent of genomic heterogeneity within and similarities between CLL, NHL, and MM, and their biologic and clinical relevance, we evaluated publicly available gene expression and single nucleotide polymorphism (SNP) array data from the NCBI Gene Expression Omnibus. Methods: We analyzed 893, 881, and 1744 unique gene expression data files that represent CLL, NHL, and MM, respectively. The gene expression data files represented 15, 11, and 10 distinct data sets, respectively. Prognostic, clinical outcome, and copy number variation data were available for a subset of the samples from each malignancy. Gene expression data were initially normalized using RMA and MAS5 algorithms and batch effect was eliminated using Bayesian Factor Regression Modeling. SNP array data were normalized using Chromosome Copy Number Analysis Tool and amplifications and deletions were identified with circular binary segmentation. Analyses were carried out using Bioconductor packages and the statistical environment R. Results: After elimination of batch effect, we evaluated the data using random subsampling and unsupervised hierarchical clustering to determine the lowest number of samples required to capture genomic heterogeneity. For CLL and NHL, there was no plateau reached for the number of groups defined by hierarchical clustering up through the total number of samples, indicating that a larger number of samples than available in this study are needed to fully document biological and genomic variability. For MM, there was a plateau reached at approximately 1200 samples. We then used unsupervised hierarchical clustering of the entire dataset for each malignancy to define groups of CLL, NHL, and MM based on their raw gene expression data. To evaluate the biological meaning of the groups defined by this process, we used tools such as Gene Set Enrichment Analysis (GSEA) and oncogenic pathway predictions (ScoreSignatures). Groups within each malignancy that were defined using raw gene expression data had differences in biological pathways involving receptor signaling, cell cycle, and stem cell properties. Notably, similarities in biological annotation were seen between groups that represented the different malignancies. Although prognostic data was not available for all the datasets, there appeared to be no differences in clinical prognostic markers between the genomic-defined groups. However, there were statistically significant differences in molecular prognostic data between these groups. In addition, specific regions of DNA copy number variation were enriched within the different genomic-defined groups. Together, these data highlight the biologic distinctions between groups that are defined by raw gene expression data. For datasets in which clinical outcome data were available, we found that genomic-defined groups had different outcomes such as time to first therapy or overall survival. However, the groups did not appear to predict response to chemotherapy or chemo-immunotherapy. Conclusions: CLL, NHL, and MM are heterogeneous malignancies, and very large numbers of patients must be studied to fully capture the genomic and biologic diversity that is present. Despite this limitation, evaluation of existing data reveals subgroups of these disorders are defined by their underlying biology, demonstrate overlap in biological processes, and are clinically relevant. These results have implications on future “omics” related research. Disclosures: No relevant conflicts of interest to declare.


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