基于大规模遗传和表达数据挖掘与双相情感障碍相关的基因及通路

PSYCH OpenIR > 健康与遗传心理学研究室

	基于大规模遗传和表达数据挖掘与双相情感障碍相关的基因及通路
其他题名	Exploring candidate genes and pathways of bipolar disorder through deep mining of large-scale genetic
	王璟璐
	2016-05
摘要	双相情感障碍（BD）是一种常见的严重精神障碍，目前已被世界卫生组织列为世界上十大致残疾病之一。寻找疾病的遗传易感位点及关键基因，对于研究疾病的发病机制、早期诊断和治疗具有重要的意义。基因组学的进步和分子生物学相关学科的迅猛发展，促进了精神疾病研究的蓬勃发展，产生了大量的遗传关联数据和基因芯片数据。如何从这些丰富的数据中挖掘疾病的致病因子和遗传机制，是目前BD致病机制研究面临的挑战之一。本论文从遗传和表达两个角度出发，对BD已有的全基因组关联数据及表达谱数据进行挖掘，从基因及通路层面探索其致病机制，通过生物信息学分析方法展开了三个方面的研究。本论文的第一个研究是基于GWAS数据挖掘与BD相关的致病通路。本研究通过对申请得到的BD GWAS数据进行分组合并等一系列处理，将其整合为样本含量较大，可信度较强的数据集，并在此基础上使用三种不同的基于通路的分析（PBA）工具分别进行基于通路的分析。最终得到33条疾病可能相关的候选通路，这些通路或是同时出现在不同平台的两组数据集的同一种工具分析结果中，或是同一组数据集中至少由两种PBA工具分析所得。研究共得到33条多证据的通路，其中卵母细胞减数分裂过程（Oocyte meiosis）、泛素介导的蛋白质水解过程（Ubiquitin mediated proteolysis）、视黄醇（维A）的代谢（Retinol metabolism）及细胞色素P450参与的外源性物质代谢过程（Metabolism of xenobiotics by cytochrome P450）具有更高的可靠性。本研究所鉴定出的通路可以为BD的遗传学及机制研究提供更多的新视角。本论文的第二个研究是整合BD基因表达谱数据计算差异表达基因（DEGs）及通路。为从表达层面探索BD致病机制，我们通过文献调研、数据库查询等方法收集BD相关大脑前额叶皮质表达谱数据。经预处理、平台合并等处理，将小样本量的单一研究整合为一个大样本量数据集，再进行基因差异表达的显著性分析。通过建立混合线性模型对合并后的表达矩阵进行计算，共鉴定出198个在病例和对照间差异表达显著的基因。为进一步探索这些基因间的功能关系，本研究又进行了基因功能富集分析、基因-基因间相互作用分析及基因共表达网络分析。由DEGs富集分析所得的通路多与突触传导的调节、细胞死亡和凋亡，以及几个基本的受体及离子结合过程相关，这些结果与已知的BD致病机制假说相一致。由网络分析所得网络图中位于子网络中的中心（hub）基因值得我们更多的关注及更深层次的探索，如最大子网络中的中心基因SUMO1。本论文的第三个研究是遗传与表达分析所得BD相关基因及通路的比较。基因方面，研究二中所得的DEGs中有23个已有遗传学阳性结果支持；有20个与最大规模的BD GWAS得到的最显著的100个SNP所对应的基因一致；在eQTL数据库中，共22个DEGs被发现受到了与BD相关的SNPs的调控。通路方面，研究一的PBA分析结果与研究二中DEGs富集所得通路有一致性结果，如通路细胞外基质受体间的相互作用（ECM-receptor interaction）。这些被验证的基因及通路可能与BD致病机制密切相关，值得在今后的研究中对其进行更加深入的挖掘。综上所述，本论文通过生物信息学分析方法，整合遗传和表达层面的大规模数据，鉴定出与双相情感障碍相关的基因及通路。最后对两个层面的分析结果进行比较，为与疾病相关的候选基因及通路提供了多层面的证据支持，同时也为后续的研究提供了可靠线索。
其他摘要	Bipolar disorder (BD) is an inheritable complex mental disorder. The World Health Organization says BD is one of the top ten leading causes of death and disability in the world. Exploring geneticmechanisms for BD will benefit our understanding about its etiology, as well as the future molecular diagnosis and treatments of it. Nowadays, the rapid advances of genomics have promoted the flourish of genetic studies of psychiatric disorders and a large amount of genetic association datahas been accumulated. While along with the implementation of the human genome project (HGP) and the rapid development of molecular biology, gene chip data are increasing rapidly at an unprecedented rate. However, how to extract useful information from these data becomes a big challenge for us. In this thesis, we explored the pathogenic mechanism of BD from two aspects. One is genetic association data and the other one is gene expression data. Based on these, we conducted researches in three parts by methods of bioinformatics analysis. The first part of this thesis is pathway-based analysis for genome-wide association study data of bipolar disorder. In this study, we conducted pathway-based analysis on BD GWAS data to identify disease-related pathways. To combine the results from different GWAS datasets and pathway-based analysis tools, two groups of GWAS data from Psychiatric Genomics Consortium were analyzed using three PBA tools with different input data types to yield unified result.As a result, we obtained 33 pathways overlapped between two groups of GWAS datasets or validated by at least two PBA tools. Among these pathways, the representative pathways are ‘Oocyte meiosis’, ‘Ubiquitin mediated proteolysis’, ‘Retinol metabolism’ and ‘Metabolism of xenobiotics by cytochrome P450’. These new discoveries may provide different perspective for the biological mechanism of BD and deserve further verification. The second part of this thesis is a combined analysis of genome-wide expression profiling of bipolar disorder in human prefrontal cortex. We searched the public databases of genome-wide gene expression data sets, and obtained six prefrontal cortex (PFC) data sets of bipolar disorder to conduct a combined analysis. We obtained raw data from each study and usedstandardized procedures to process and analyze the data. A standard linear mixed effects model was used to calculate the differentially expressed genes (DEGs). Finally, 198 unique differentially expressed genes in the PFC of bipolar disorder and control were identified. Then multiple levels of validation and confirmation were conducted. Functional analysis and network analysis were also carried out on basis of the obtained DEGs. In this study, the results of gene functional enrichment analysis were most related to synaptic transmission, cell death and the process of some basic ion bing and receptors. Besides, the genes in the hub of a sub-network deserve to explore in depth. For example, SUMO1, the hub gene of the biggest sub-network from gene-gene interaction network, may have a relationship with the pathogenic mechanism of BD. The third part of this thesis is the comparison of the results obtained from genetic studies and gene expression profilings. In the view of gene, 23 genes of 198 DEGs were reported by BD genetic studies in BDgene as BD candidate genes; 20 DEGs were the genes mapped by the top 100 susceptible SNPs of BD GWAS in PGC. In addition, 22 DEGs were identified to be regulated by some susceptible SNPs of BD in eQTL database. In the terms of pathways, some coherent results were found in the PBA analysis of BD and the functional enrichment analysis of 198 DEGs, such as ‘ECM-receptor interaction’. These validated genes and pathways may have high possibility of the pathogenic mechanism of BD, and need further study. In summary, in this thesis, to identify the BD associated genes and pathways, we collected large-scale BD GWAS data and gene expression data and carried out researches in three parts by methods of bioinformatics analysis. Our study provided multi-level evidence to support the BD candidate genes and pathways, which might facilitate further research on pathogenesis of BD.
学科领域	应用心理学
关键词	双相情感障碍双相情感障碍基于通路的分析基因表达差异表达基因生物信生物信息
学位类型	硕士
语种	中文
学位专业	心理学
学位授予单位	中国科学院研究生院
学位授予地点	北京
文献类型	学位论文
条目标识符	http://ir.psych.ac.cn/handle/311026/19939
专题	健康与遗传心理学研究室
作者单位	中国科学院心理研究所
推荐引用方式 GB/T 7714	王璟璐. 基于大规模遗传和表达数据挖掘与双相情感障碍相关的基因及通路[D]. 北京. 中国科学院研究生院,2016.

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