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. 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. 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.

中文翻译：

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清洗 CNV 数据以对脑疾病中的候选过程优先级进行排序

基因组数据的优先排序已成为揭示遗传变异（例如拷贝数变异或 CNV）和疾病机制的表型效应的有用工具。由于复杂性，脑疾病代表了基因组研究的主要焦点，旨在揭示导致脑功能障碍的基因组变化的病理意义。在这里，我们提出了一种“CNV 数据清洗”算法，该算法基于从可用数据库中检索到的基因组通路的过滤和优先级，以揭示大脑疾病中改变的分子通路。该算法包括处理单个 CNV 数据集的七个连续步骤。首先，将数据与内部数据库和网络数据库进行比较，以区分复发的非致病性变异。其次，CNV 库仅限于主要在大脑中表达的基因。第三，基因间相互作用用于过滤致病CNV。第四，创建特定于个体基因组变异集的互连元素网络。第五，本体数据（通路/功能）归属于网络元素集群。第六，根据受 CNV 影响的元素的重要性对途径进行优先排序。第七，根据本体对优先路径进行聚类。该算法应用于通过 SNP 阵列分子核型分析从患有脑部疾病（智力残疾和自闭症谱系障碍）的儿童中获得的 191 个 CNV 数据集。“CNV 数据洗钱”已经确定了与表型表现有关的 13 个通路簇（39 个过程/475 个基因）。阐明脑疾病中改变的分子途径，该算法可用于揭示疾病机制和基因型-表型相关性。这些机会对于制定破坏性神经精神疾病的治疗策略是非常必要的。