An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder.

An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder.
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评估双相情感障碍遗传标记的综合计算方法

DOI:
10.1038/s41598-017-05846-4
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发表时间:
2017-07-27
期刊:
影响因子:
4.6
通讯作者:
Zhang F
Zhang F
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Xu Y;Wang J;Rao S;Ritter M;Manor LC;Backer R;Cao H;Cheng Z;Liu S;Liu Y;Tian L;Dong K;Yao Shugart Y;Wang G;Zhang F

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迄今为止的研究已经报道了数百个与双相情感障碍(BP)有关的基因。然而,许多确定候选基因的研究缺乏重复性,而且他们的结果有时相互不一致。因此,本文提供了一个计算工作流程,可以管理和评估BP相关的遗传数据。我们的方法整合了大规模的文献数据和基因表达数据,这些数据是从死后人脑区域(BP病例/对照:45/50)和外周血单核细胞(BP病例/对照:193/593)中获得的。为了评估候选基因的致病谱,我们进行了路径富集、子网络富集和基因-基因相互作用分析,为每个基因提出并验证了4个指标。我们的方法开发了一个可扩展的BP遗传数据库(BP_GD),包括BP相关基因,药物,途径,疾病和支持参考文献。这4个指标成功鉴定了经常研究的BP基因(例如GRIN 2A、DRD 1、DRD 2、HTR 2A、CACNA 1C、TH、BDNF、SLC 6A 3、P2 RX 7、DRD 3和DRD 4),并突出了最近报道的几个BP基因(例如GRIK 5、GRM 1和CACNA 1A)。计算生物学方法和BP数据库的开发,在这项研究中可以有助于更好地了解BP遗传研究的当前阶段,并协助在该领域的进一步研究。
Studies to date have reported hundreds of genes connected to bipolar disorder (BP). However, many studies identifying candidate genes have lacked replication, and their results have, at times, been inconsistent with one another. This paper, therefore, offers a computational workflow that can curate and evaluate BP-related genetic data. Our method integrated large-scale literature data and gene expression data that were acquired from both postmortem human brain regions (BP case/control: 45/50) and peripheral blood mononuclear cells (BP case/control: 193/593). To assess the pathogenic profiles of candidate genes, we conducted Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analyses, with 4 metrics proposed and validated for each gene. Our approach developed a scalable BP genetic database (BP_GD), including BP related genes, drugs, pathways, diseases and supporting references. The 4 metrics successfully identified frequently-studied BP genes (e.g. GRIN2A, DRD1, DRD2, HTR2A, CACNA1C, TH, BDNF, SLC6A3, P2RX7, DRD3, and DRD4) and also highlighted several recently reported BP genes (e.g. GRIK5, GRM1 and CACNA1A). The computational biology approach and the BP database developed in this study could contribute to a better understanding of the current stage of BP genetic research and assist further studies in the field.
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