Distinct genetic patterns of shared and unique genes across four neurodevelopmental disorders

Zhang, Yijia; Wang, Ruochen; Liu, Zhenwei; Jiang, Shan; Du, Lifeng; Qiu, Kairui; Li, Fengxia; Wang, Qiongdan; Jin, Jing; Chen, Xiaomin; Li, Zhongshan; Wu, Jinyu; Zhang, Na

doi:10.1002/ajmg.b.32821

Cited by 6 publications

(7 citation statements)

References 80 publications

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“…To show the biological significance of DGH-GO, it was also applied to genes known for multiple disorders such as ASD, ID, EE, and MD. The analysis of genes known for multiple disorders clustered in to same groups, this was inline with previous studies showing that genes involved in multiple disorders are aggregated in the same clusters (24).…”

Section: Discussionsupporting

confidence: 86%

“…For the study of shared etiology of complex diseases, a list of genes known for, ID, DD and EE was obtained from Zhang et al (24). ASD genes were obtained from SFARI gene databases (https://gene.sfari.org/).…”

Section: Methodsmentioning

confidence: 99%

“…To show another application of DGH-GO in dissecting the shared etiology of complex disorders, genes known for ASD, Developmental Disorders (DD), Intellectual Disability (ID), and Epileptic Encephalopathy (EE) were obtained from Zhang et al (24). Functional similarity matrix was of 427 (ASD=74, ASD+DD=10, ASD+DD+EE=1, ASD+DD+ID=19, ASD+DD+ID+EE=2, ASD+ID=2, DD=204, DD+EE=4, DD+ID=25, DD+ID+EE=3, ID=63, EE=19, ID+EE=1).…”

Section: ) Understanding the Shared Etiology Of Complex Diseasesmentioning

confidence: 99%

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DGH-GO: Dissecting the Genetic Heterogeneity of complex diseases using Gene Ontology

Asif

Martiniano

Lamurias

et al. 2022

Preprint

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Complex diseases such as neurodevelopmental disorders (NDDs) lack biological markers for their diagnosis and are phenotypically heterogeneous, which makes them difficult to diagnose at early-age. The genetic heterogeneity corresponds to their clinical phenotype variability and, because of this, complex diseases exhibit multiple etiologies. The multi-etiological aspects of complex-diseases emerge from distinct but functionally similar group of genes. Different diseases sharing genes of such groups show related clinical outcomes that further restrict our understanding of disease mechanisms, thus, limiting the applications of personalized medicine or systems biomedicine approaches to complex genetic disorders.Here, we present an interactive and user-friendly application, DGH-GO that allows biologists to dissect the genetic heterogeneity of complex diseases by stratifying the putative disease-causing genes into clusters that may lead to or contribute to a specific disease traits development. The application can also be used to study the shared etiology of complex-diseases.DGH-GO creates a semantic similarity matrix of putative disease-causing genes or known-disease genes for multiple disorders using Gene Ontology (GO). The resultant matrix can be visualized in a 2D space using different dimension reduction methods (T-SNE, Principal component analysis and Principal coordinate analysis). Functional similarities assessed through GO and semantic similarity measure can be used to identify clusters of functionally similar genes that may generate a disease specific traits. This can be achieved by employing four different clustering methods (K-means, Hierarchical, Fuzzy and PAM). The user may change the clustering parameters and see their effect on stratification results immediately.DGH-GO was applied to genes disrupted by rare genetic variants in Autism Spectrum Disorder (ASD) patients. The analysis confirmed the multi-etiological nature of ASD by identifying the four clusters that were enriched for distinct biological mechanisms and phenotypic terms. In the second case study, the analysis of genes shared by different NDDs showed that genes involving in multiple disorders tend to aggregate in similar clusters, indicating a possible shared etiology. In summary, functional similarities, dimension reduction and clustering methods, coupled with interactive visualization and control over analysis allows biologists to explore and analyze their datasets without requiring expert knowledge on these methods.The source code of proposed application is available athttps://github.com/Muh-Asif/DGH-GOGraphical abstract

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Section: Discussionsupporting

confidence: 86%

Section: Methodsmentioning

confidence: 99%

Section: ) Understanding the Shared Etiology Of Complex Diseasesmentioning

confidence: 99%

See 1 more Smart Citation

DGH-GO: Dissecting the Genetic Heterogeneity of complex diseases using Gene Ontology

Asif

Martiniano

Lamurias

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To show another application of DGH-GO in dissecting the shared etiology of complex disorders, genes known for ASD, Developmental Disorders (DD), Intellectual Disability (ID), and Epileptic Encephalopathy (EE) were obtained from Zhang et al [ 34 ]. The aim of this case study was to test the multi-etiological nature of ASD, instead of discussing the extent of shared etiology of different NDDs.…”

Section: Applicationsmentioning

confidence: 99%

DGH-GO: dissecting the genetic heterogeneity of complex diseases using gene ontology

et al. 2023

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Background Complex diseases such as neurodevelopmental disorders (NDDs) exhibit multiple etiologies. The multi-etiological nature of complex-diseases emerges from distinct but functionally similar group of genes. Different diseases sharing genes of such groups show related clinical outcomes that further restrict our understanding of disease mechanisms, thus, limiting the applications of personalized medicine approaches to complex genetic disorders. Results Here, we present an interactive and user-friendly application, called DGH-GO. DGH-GO allows biologists to dissect the genetic heterogeneity of complex diseases by stratifying the putative disease-causing genes into clusters that may contribute to distinct disease outcome development. It can also be used to study the shared etiology of complex-diseases. DGH-GO creates a semantic similarity matrix for the input genes by using Gene Ontology (GO). The resultant matrix can be visualized in 2D plots using different dimension reduction methods (T-SNE, Principal component analysis, umap and Principal coordinate analysis). In the next step, clusters of functionally similar genes are identified from genes functional similarities assessed through GO. This is achieved by employing four different clustering methods (K-means, Hierarchical, Fuzzy and PAM). The user may change the clustering parameters and explore their effect on stratification immediately. DGH-GO was applied to genes disrupted by rare genetic variants in Autism Spectrum Disorder (ASD) patients. The analysis confirmed the multi-etiological nature of ASD by identifying four clusters of genes that were enriched for distinct biological mechanisms and clinical outcome. In the second case study, the analysis of genes shared by different NDDs showed that genes causing multiple disorders tend to aggregate in similar clusters, indicating a possible shared etiology. Conclusion DGH-GO is a user-friendly application that allows biologists to study the multi-etiological nature of complex diseases by dissecting their genetic heterogeneity. In summary, functional similarities, dimension reduction and clustering methods, coupled with interactive visualization and control over analysis allows biologists to explore and analyze their datasets without requiring expert knowledge on these methods. The source code of proposed application is available at https://github.com/Muh-Asif/DGH-GO

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“…More than 400 cases have been reported so far ( 19 , 44 – 103 ). However, for many individuals, detailed clinical information was not available.…”

Section: Snare Complex Assembly Factorsmentioning

confidence: 99%

Epileptic Phenotypes Associated With SNAREs and Related Synaptic Vesicle Exocytosis Machinery

et al. 2022

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SNAREs (soluble N-ethylmaleimide sensitive factor attachment protein receptor) are an heterogeneous family of proteins that, together with their key regulators, are implicated in synaptic vesicle exocytosis and synaptic transmission. SNAREs represent the core component of this protein complex. Although the specific mechanisms of the SNARE machinery is still not completely uncovered, studies in recent years have provided a clearer understanding of the interactions regulating the essential fusion machinery for neurotransmitter release. Mutations in genes encoding SNARE proteins or SNARE complex associated proteins have been associated with a variable spectrum of neurological conditions that have been recently defined as “SNAREopathies.” These include neurodevelopmental disorder, autism spectrum disorder (ASD), movement disorders, seizures and epileptiform abnormalities. The SNARE phenotypic spectrum associated with seizures ranges from simple febrile seizures and infantile spasms, to severe early-onset epileptic encephalopathies. Our study aims to review and delineate the epileptic phenotypes associated with dysregulation of synaptic vesicle exocytosis and transmission, focusing on the main proteins of the SNARE core complex (STX1B, VAMP2, SNAP25), tethering complex (STXBP1), and related downstream regulators.

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Distinct genetic patterns of shared and unique genes across four neurodevelopmental disorders

Cited by 6 publications

References 80 publications

DGH-GO: Dissecting the Genetic Heterogeneity of complex diseases using Gene Ontology

DGH-GO: Dissecting the Genetic Heterogeneity of complex diseases using Gene Ontology

DGH-GO: dissecting the genetic heterogeneity of complex diseases using gene ontology

Epileptic Phenotypes Associated With SNAREs and Related Synaptic Vesicle Exocytosis Machinery

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