scholarly journals CDKL5 variants

2017 ◽  
Vol 3 (6) ◽  
pp. e200 ◽  
Author(s):  
Ralph D. Hector ◽  
Vera M. Kalscheuer ◽  
Friederike Hennig ◽  
Helen Leonard ◽  
Jenny Downs ◽  
...  
Keyword(s):  
Large Scale ◽  
Catalytic Domain ◽  
Genetic Diagnosis ◽  
Missense Variant ◽  
Functional Domain ◽  
Sequencing Data ◽  
Missense Variants ◽  
Combining Data ◽  
Sequencing Studies ◽  
Scale Population

Objective:To provide new insights into the interpretation of genetic variants in a rare neurologic disorder, CDKL5 deficiency, in the contexts of population sequencing data and an updated characterization of the CDKL5 gene.Methods:We analyzed all known potentially pathogenic CDKL5 variants by combining data from large-scale population sequencing studies with CDKL5 variants from new and all available clinical cohorts and combined this with computational methods to predict pathogenicity.Results:The study has identified several variants that can be reclassified as benign or likely benign. With the addition of novel CDKL5 variants, we confirm that pathogenic missense variants cluster in the catalytic domain of CDKL5 and reclassify a purported missense variant as having a splicing consequence. We provide further evidence that missense variants in the final 3 exons are likely to be benign and not important to disease pathology. We also describe benign splicing and nonsense variants within these exons, suggesting that isoform hCDKL5_5 is likely to have little or no neurologic significance. We also use the available data to make a preliminary estimate of minimum incidence of CDKL5 deficiency.Conclusions:These findings have implications for genetic diagnosis, providing evidence for the reclassification of specific variants previously thought to result in CDKL5 deficiency. Together, these analyses support the view that the predominant brain isoform in humans (hCDKL5_1) is crucial for normal neurodevelopment and that the catalytic domain is the primary functional domain.

scholarly journals Predicting functional effect of missense variants using graph attention neural networks

2021 ◽  
Author(s):  
Haicang Zhang ◽  
Michelle S. Xu ◽  
Wendy K. Chung ◽  
Yufeng Shen
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
De Novo ◽  
Neurodevelopmental Disorder ◽  
Loss Of Function ◽  
Sequencing Data ◽  
Missense Variants ◽  
Scale Population ◽  
Scan Data ◽  
Main Component

AbstractAccurate prediction of damaging missense variants is critically important for interpretating genome sequence. While many methods have been developed, their performance has been limited. Recent progress in machine learning and availability of large-scale population genomic sequencing data provide new opportunities to significantly improve computational predictions. Here we describe gMVP, a new method based on graph attention neural networks. Its main component is a graph with nodes capturing predictive features of amino acids and edges weighted by coevolution strength, which enables effective pooling of information from local protein sequence context and functionally correlated distal positions. Evaluated by deep mutational scan data, gMVP outperforms published methods in identifying damaging variants in TP53, PTEN, BRCA1, and MSH2. Additionally, it achieves the best separation of de novo missense variants in neurodevelopmental disorder cases from the ones in controls. Finally, the model supports transfer learning to optimize gain- and loss-of-function predictions in sodium and calcium channels. In summary, we demonstrate that gMVP can improve interpretation of missense variants in clinical testing and genetic studies.

scholarly journals The single-cell eQTLGen consortium

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
MGP van der Wijst ◽  
DH de Vries ◽  
HE Groot ◽  
G Trynka ◽  
CC Hon ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Sequencing Data ◽  
Scale Population ◽  
Trait Locus ◽  
Different Cell Types ◽  
Affect Gene Expression

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

scholarly journals Detecting cancer vulnerabilities through gene networks under purifying selection in 4,700 cancer genomes

2017 ◽  
Author(s):  
Anika Gupta ◽  
Heiko Horn ◽  
Parisa Razaz ◽  
April Kim ◽  
Michael Lawrence ◽  
...  
Keyword(s):  
Gene Networks ◽  
Large Scale ◽  
Significant Proportion ◽  
Low Frequency ◽  
Interaction Network ◽  
Purifying Selection ◽  
Sequencing Data ◽  
Gene Sets ◽  
Sequencing Studies ◽  
Significant Enrichment

ABSTRACTLarge-scale cancer sequencing studies have uncovered dozens of mutations critical to cancer initiation and progression. However, a significant proportion of genes linked to tumor propagation remain hidden, often due to noise in sequencing data confounding low frequency alterations. Further, genes in networks under purifying selection (NPS), or those that are mutated in cancers less frequently than would be expected by chance, may play crucial roles in sustaining cancers but have largely been overlooked. We describe here a statistical framework that identifies genes that have a first order protein interaction network significantly depleted for mutations, to elucidate key genetic contributors to cancers. Not reliant on and thus, unbiased by, the gene of interest’s mutation rate, our approach has identified 685 putative genes linked to cancer development. Comparative analysis indicates statistically significant enrichment of NPS genes in previously validated cancer vulnerability gene sets, while further identifying novel cancer-specific candidate gene targets. As more tumor genomes are sequenced, integrating systems level mutation data through this network approach should become increasingly useful in pinpointing gene targets for cancer diagnosis and treatment.

scholarly journals Reveel: large-scale population genotyping using low-coverage sequencing data

2015 ◽  
Vol 32 (11) ◽  
pp. 1686-1696 ◽  
Author(s):  
Lin Huang ◽  
Bo Wang ◽  
Ruitang Chen ◽  
Sivan Bercovici ◽  
Serafim Batzoglou
Keyword(s):  
Large Scale ◽  
Sequencing Data ◽  
Scale Population ◽  
Low Coverage

scholarly journals Improving the diagnostic yield of exome-sequencing, by predicting gene-phenotype associations using large-scale gene expression analysis

2018 ◽  
Author(s):  
Patrick Deelen ◽  
Sipko van Dam ◽  
Johanna C. Herkert ◽  
Juha M. Karjalainen ◽  
Harm Brugge ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Gene Expression Analysis ◽  
Diagnostic Yield ◽  
Genetic Diagnosis ◽  
Added Value ◽  
Disease Genes ◽  
Rna Seq ◽  
Sequencing Data ◽  
Causative Gene

AbstractClinical interpretation of exome and genome sequencing data remains challenging and time consuming, with many variants with unknown effects found in genes with unknown functions. Automated prioritization of these variants can improve the speed of current diagnostics and identify previously unknown disease genes. Here, we used 31,499 RNA-seq samples to predict the phenotypic consequences of variants in genes. We developed GeneNetwork Assisted Diagnostic Optimization (GADO), a tool that uses these predictions in combination with a patient’s phenotype, denoted using HPO terms, to prioritize identified variants and ease interpretation. GADO is unique because it does not rely on existing knowledge of a gene and can therefore prioritize variants missed by tools that rely on existing annotations or pathway membership. In a validation trial on patients with a known genetic diagnosis, GADO prioritized the causative gene within the top 3 for 41% of the cases. Applying GADO to a cohort of 38 patients without genetic diagnosis, yielded new candidate genes for seven cases. Our results highlight the added value of GADO (www.genenetwork.nl) for increasing diagnostic yield and for implicating previously unknown disease-causing genes.

scholarly journals A framework for annotation of antigen specificities in high-throughput T-cell repertoire sequencing studies

2019 ◽  
Author(s):  
Mikhail V Pogorelyy ◽  
Mikhail Shugay
Keyword(s):  
T Cell ◽  
Large Scale ◽  
Cell Receptor ◽  
Specific Response ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Antigen Specificity ◽  
Cell Repertoire ◽  
Sequencing Studies ◽  
Repertoire Sequencing

AbstractRecently developed molecular methods allow large-scale profiling of T-cell receptor (TCR) sequences that encode for antigen specificity and immunological memory of these cells. However, it is well known, that the even unperturbed TCR repertoire structure is extremely complex due to the high diversity of TCR rearrangements and multiple biases imprinted by VDJ rearrangement process. The latter gives rise to the phenomenon of “public” TCR clonotypes that can be shared across multiple individuals and non-trivial structure of the TCR similarity network. Here we outline a framework for TCR sequencing data analysis that can control for these biases in order to infer TCRs that are involved in response to antigens of interest. Using an example dataset of donors with known HLA haplotype and CMV status we demonstrate that by applying HLA restriction rules and matching against a database of TCRs with known antigen specificity it is possible to robustly detect motifs of an epitope-specific responses in individual repertoires. We also highlight potential shortcomings of TCR clustering methods and demonstrate that highly expanded TCRs should be individually assessed to get the full picture of antigen-specific response.

scholarly journals Multi-platform discovery of haplotype-resolved structural variation in human genomes

2017 ◽  
Author(s):  
Mark J.P. Chaisson ◽  
Ashley D. Sanders ◽  
Xuefang Zhao ◽  
Ankit Malhotra ◽  
David Porubsky ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Large Scale ◽  
Structural Variation ◽  
High Throughput Sequencing ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Full Spectrum ◽  
Variant Discovery ◽  
Sequencing Technologies ◽  
Sequencing Studies

ABSTRACTThe incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, and strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three human parent–child trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs (≥50 bp) per human genome. We also discover 156 inversions per genome—most of which previously escaped detection. Fifty-eight of the inversions we discovered intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The method and the dataset serve as a gold standard for the scientific community and we make specific recommendations for maximizing structural variation sensitivity for future large-scale genome sequencing studies.

Abstract 19585: Overestimation of Functional Cardiomyopathy Disease Variants in the Human Gene Mutation Database

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Kian Huat Lim ◽  
Richard Aplenc ◽  
Joseph Rossano
Keyword(s):  
Gene Mutation ◽  
Large Scale ◽  
Human Gene ◽  
Missense Variant ◽  
Human Gene Mutation Database ◽  
Substantial Fraction ◽  
Functional Impact ◽  
Missense Variants ◽  
Mutation Database ◽  
Disease Specific

Introduction: Dilated cardiomyopathy (DM), hypertrophic cardiomyopathy (HCM), and arryhmogenic right ventricular dysplasia (ARVD) have been associated with germline genetic variants. However, uncertainty exists regarding the functional impact of specific variants described in the literature. Hypothesis: We hypothesized that a substantial fraction of presumed casual variants would be identified as non-disease causing in a normal patient population. Methods: Missense variants associated with three main subtypes of cardiomyopathy were extracted from Human Gene Mutation Database Professional (HGMD). To assess the prevalence and pathogenicity of these potential disease-causing alleles, the population frequency of each variant was screened in a recently published large-scale exome database of over 60,000 whole exomes (ExAC). In addition, PolyPhen-2 was used to predict the functional impact of amino acid substitution for each missense variant. Results: In the HGMD, 1,405 missense variants were associated with cardiomyopathy, of which 25% were found in 2 or more samples in the ExAC database. Specifically 121 of 422 (28.7%) DM associated variants, 201 variants of 900 (22.3%) HCM associated variant, and 40 of 83 ARVD (48.2%) associated variants were observed in the ExAC database. Polyphen predicted 331 (23.9%) of variants as benign, 290 (20.6%) as possibly damaging, and 784 (55.8%) as probably damaging. Table 1 presents disease specific Polyphen results. Conclusions: We observed that a large proportion (25-45%) of cardiomyopathy-associated missense variants predicted as being damaging were indistinguishable from the background in ExAC. Using stringent cutoffs derived from these observations, we estimated more than 50% of previously associated cardiomyopathy variants may be non-functional or non-monogenic causes of cardiomyopathy.

scholarly journals Genome-wide identification of directed gene networks using large-scale population genomics data

2017 ◽  
Author(s):  
René Luijk ◽  
Koen F. Dekkers ◽  
Maarten van Iterson ◽  
Wibowo Arindrarto ◽  
Annique Claringbould ◽  
...  
Keyword(s):  
Gene Networks ◽  
Large Scale ◽  
Target Genes ◽  
Population Genomics ◽  
Regulatory Gene ◽  
Sequencing Data ◽  
Scale Population ◽  
Transcriptional Changes ◽  
New Gene ◽  
Novel Target

ABSTRACTIdentification of causal drivers behind regulatory gene networks is crucial in understanding gene function. We developed a method for the large-scale inference of gene-gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). The analysis of genotype and whole-blood RNA-sequencing data from 3,072 individuals identified 49 genes as drivers of downstream transcriptional changes (P < 7 × 10−10), among which transcription factors were overrepresented (P = 3.3 × 10−7). Our analysis suggests new gene functions and targets including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (novel target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6,600 genes with a genetic instrument can be explored individually using a web-based browser.

scholarly journals The Role of Microtubule Associated Serine/Threonine Kinase 3 Variants in Neurodevelopmental Diseases: Genotype-Phenotype Association

2022 ◽  
Vol 14 ◽  
Author(s):  
Li Shu ◽  
Neng Xiao ◽  
Jiong Qin ◽  
Qi Tian ◽  
Yanghui Zhang ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Large Scale ◽  
De Novo ◽  
Sequencing Data ◽  
Phenotype Correlation ◽  
Serine Threonine Kinase ◽  
Zebrafish Model ◽  
Threonine Kinase ◽  
Missense Variants ◽  
Genotype Phenotype Correlation

Objective: To prove microtubule associated serine/threonine kinase 3 (MAST3) gene is associated with neurodevelopmental diseases (NDD) and the genotype-phenotype correlation.Methods: Trio exome sequencing (trio ES) was performed on four NDD trios. Bioinformatic analysis was conducted based on large-scale genome sequencing data and human brain transcriptomic data. Further in vivo zebrafish studies were performed.Results: In our study, we identified four de novo MAST3 variants (NM_015016.1: c.302C &gt; T:p.Ser101Phe; c.311C &gt; T:p.Ser104Leu; c.1543G &gt; A:p.Gly515Ser; and c.1547T &gt; C:p.Leu516Pro) in four patients with developmental and epileptic encephalopathy (DEE) separately. Clinical heterogeneities were observed in patients carrying variants in domain of unknown function (DUF) and serine-threonine kinase (STK) domain separately. Using the published large-scale exome sequencing data, higher CADD scores of missense variants in DUF domain were found in NDD cohort compared with gnomAD database. In addition, we obtained an excess of missense variants in DUF domain when compared autistic spectrum disorder (ASD) cohort with gnomAD database, similarly an excess of missense variants in STK domain when compared DEE cohort with gnomAD database. Based on Brainspan datasets, we showed that MAST3 expression was significantly upregulated in ASD and DEE-related brain regions and was functionally linked with DEE genes. In zebrafish model, abnormal morphology of central nervous system was observed in mast3a/b crispants.Conclusion: Our results support the possibility that MAST3 is a novel gene associated with NDD which could expand the genetic spectrum for NDD. The genotype-phenotype correlation may contribute to future genetic counseling.

Sign in / Sign up

Export Citation Format

Share Document