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首页> 外文期刊>Molecular cytogenetics >Laundering CNV data for candidate process prioritization in brain disorders
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Laundering CNV data for candidate process prioritization in brain disorders

机译：洗涤CNV数据用于脑病患者的候选过程优先级

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Background:Prioritization of genomic data has become a useful tool for uncovering the phenotypic effect of genetic variations (e.g. copy number variations or CNV) and disease mechanisms. Due to the complexity, brain disorders represent a major focus of genomic research aimed at revealing pathologic significance of genomic changes leading to brain dysfunction. Here, we propose a "CNV data laundering" algorithm based on filtering and prioritizing of genomic pathways retrieved from available databases for uncovering altered molecular pathways in brain disorders. The algorithm comprises seven consecutive steps of processing individual CNV data sets. First, the data are compared to in-house and web databases to discriminate recurrent non-pathogenic variants. Second, the CNV pool is confined to the genes predominantly expressed in the brain. Third, intergenic interactions are used for filtering causative CNV. Fourth, a network of interconnected elements specific for an individual genome variation set is created. Fifth, ontologic data (pathways/functions) are attributed to clusters of network elements. Sixth, the pathways are prioritized according to the significance of elements affected by CNV. Seventh, prioritized pathways are clustered according to the ontologies.Results:The algorithm was applied to 191 CNV data sets obtained from children with brain disorders (intellectual disability and autism spectrum disorders) by SNP array molecular karyotyping. "CNV data laundering" has identified 13 pathway clusters (39 processes/475 genes) implicated in the phenotypic manifestations.Conclusions:Elucidating altered molecular pathways in brain disorders, the algorithm may be used for uncovering disease mechanisms and genotype-phenotype correlations. These opportunities are strongly required for developing therapeutic strategies in devastating neuropsychiatric diseases.? The Author(s). 2019.
机译：背景技术：基因组数据的优先级已成为揭示遗传变异表型效应（例如拷贝数变异或CNV）和疾病机制的有用工具。由于复杂性，脑疾病代表了基因组研究的主要重点，旨在揭示基因组变化的病理意义导致脑功能障碍。这里，我们提出了一种“CNV数据洗涤”算法，基于从可用数据库检索的基因组途径的过滤和优先化，以揭示脑疾病的改变的分子途径。该算法包括处理各个CNV数据集的七个步骤。首先，将数据与内部和网络数据库进行比较，以区分复发性非致病变体。其次，CNV池仅限于主要在大脑中表达的基因。第三，非基因相互作用用于过滤致病CNV。第四，创建针对单个基因组变化组特异的互连元件网络。第五，本体数据（路径/函数）归因于网络元素的集群。第六，途径根据CNV影响的元素的意义优先考虑。第七，优先途径根据本体群体聚集。结果：将该算法应用于由SNP阵列分子核型拟划分的脑疾病（智力残疾和自闭症谱系统）获得的191个CNV数据集。 “CNV数据洗涤”已鉴定涉及表型表现的13个途径簇（39个方法/ 475个基因）。结论：阐明脑疾病中的改变的分子途径，该算法可用于揭示疾病机制和基因型表型相关性。强烈要求这些机会在毁灭性神经精神疾病中制定治疗策略。作者。 2019年。

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