数据库辅助筛选自闭症谱系障碍相关基因组。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-08-09 DOI:10.1186/s13041-024-01127-0
Éva Kereszturi
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引用次数: 0

摘要

自闭症谱系障碍(ASD)是一种以社交和沟通障碍以及重复行为为特征的神经发育疾病。虽然遗传因素在自闭症谱系障碍中起着重要作用,但确切的遗传情况仍然复杂,尚未完全清楚,尤其是在非综合征病例中。该研究对三个基因数据库进行了硅学比较。研究利用 ClinVar、SFARI Gene 和 AutDB 来识别与非综合征 ASD 相关的基因子集和遗传变异。研究人员进行了基因组富集分析(GSEA)和蛋白质-蛋白质相互作用(PPI)网络分析,以阐明所发现基因的生物学意义。对 ASD 相关基因子集的完整性及其变异分布进行了统计评估。结果发现了20个可能特异于非综合征ASD的重叠基因子集。GSEA显示了与神经元发育和分化、突触功能和社交技能相关的生物过程的富集,突出了它们在ASD发病机制中的重要性。PPI网络分析显示了已鉴定基因之间的功能关系。遗传变异分析表明罕见变异占主导地位,并显示了特定数据库的分布模式。研究结果为了解 ASD 的遗传情况提供了宝贵的见解,并概述了该病症所涉及的基因和生物过程,同时考虑到该研究完全依赖于硅学分析,而硅学分析可能会受到数据库方法固有偏差的影响。我们有必要结合多组学数据和实验验证开展进一步研究,以加深我们对非综合症 ASD 遗传学的了解,促进有针对性的研究、干预和治疗的发展。
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Database-assisted screening of autism spectrum disorder related gene set.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social and communication difficulties, along with repetitive behaviors. While genetic factors play a significant role in ASD, the precise genetic landscape remains complex and not fully understood, particularly in non-syndromic cases. The study performed an in silico comparison of three genetic databases. ClinVar, SFARI Gene, and AutDB were utilized to identify relevant gene subset and genetic variations associated with non-syndromic ASD. Gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analysis were conducted to elucidate the biological significance of the identified genes. The integrity of ASD-related gene subset and the distribution of their variations were statistically assessed. A subset of twenty overlapping genes potentially specific for non-syndromic ASD was identified. GSEA revealed enrichment of biological processes related to neuronal development and differentiation, synaptic function, and social skills, highlighting their importance in ASD pathogenesis. PPI network analysis demonstrated functional relationships among the identified genes. Analysis of genetic variations showed predominance of rare variants and database-specific distribution patterns. The results provide valuable insights into the genetic landscape of ASD and outline the genes and biological processes involved in the condition, while taking into account that the study relied exclusively on in silico analyses, which may be subject to biases inherent to database methodologies. Further research incorporating multi-omics data and experimental validation is warranted to enhance our understanding of non-syndromic ASD genetics and facilitate the development of targeted research, interventions and therapies.

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CiteScore
7.20
自引率
4.30%
发文量
567
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