基于关联数据深入挖掘策略研究常见精神疾病的遗传机制
文献类型：学位论文
| 作者 | 张柳燕
|
| 学位类别 | 博士 |
| 答辩日期 | 2013-04 |
| 授予单位 | 中国科学院研究生院 |
| 授予地点 | 北京 |
| 导师 | 王晶 |
| 关键词 | 常见精神疾病 遗传关联数据 深入挖掘 全基因组关联研究（GWAS） |
| 其他题名 | Exploring genetic mechanisms for common psychiatric disorders through deep mining of genetic association data |
| 学位专业 | 心理学 |
| 中文摘要 | 精神疾病严重影响人们的健康和生活， 为家庭和社会带来沉重负担。 据统计，几种常见精神疾病，包括精神分裂症（SZ）、双相情感障碍（BD）、重症抑郁症（MDD）、孤独症（ASD）、注意缺陷多动障碍（ADHD）的发病率均在 1%以上。研究精神疾病的发病机制，寻找遗传易感位点及药物靶点，对于精神疾病的早期诊断和治疗具有重要的意义。另一方面，基因组学的进步促进了精神疾病遗传学研究的蓬勃发展，产生了大量的遗传关联数据。但人们对精神疾病遗传机制的了解依然有限。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，是目前精神疾病遗传机制研究面临的挑战之一。本论文采用三种不同的数据分析策略，对以上五种常见精神疾病的遗传关联数据进行了深入挖掘，以期获得与精神疾病的遗传机制相关的知识。 本文第一部分工作采用多类型遗传关联数据的整合分析策略对 ADHD 的遗传关联数据进行了系统整合和深入挖掘，鉴定了68 个 ADHD核心关联基因，其中包括 24 个热点基因，37 个多证据支持基因和 27 个高优先级基因。对 ADHD关联基因及核心关联基因进行生物通路分析的结果显示，四类生物功能可能在 ADHD 的遗传和生物机制中起关键作用。这四类功能分别是：突触信号传递与离子通道活性、神经递质代谢、钙离子介导的信号传导、突触及细胞质膜组成成分。在这个过程中收集的ADHD遗传关联数据和分析结果也通过ADHDgene数据库的方式进行了更系统的整合。 本文第二部分工作以具有代表性的关联研究类型全基因组关联研究（GWAS）为入手点， 采取基于通路的分析策略 （PBA） 对四种常见精神疾病 （SZ、 BD、 MDD、ADHD）的 GWAS 数据进行了分析。除了鉴定与这几种疾病分别显著关联的生物通路外，我们的分析显示这四种精神疾病的关联通路具有共通性。与糖类和药物代谢相关的生物功能与这四种常见精神疾病之间存在着普遍关联。 此外， SZ与BD都与免疫反应和免疫系统相关的生物过程显著关联。钙离子通道活性同时与SZ和ADHD显著关联。 本论文第三部分工作采用多疾病交叉研究策略探索常见精神疾病的免疫特征。以往的研究显示精神疾病与自身免疫性疾病之间关系密切，但精神疾病的免疫假说尚没有系统的证明和阐释。因此，本文第三部分工作以GWAS 鉴定的高显著性关联数据为基础，通过精神-自免多疾病遗传关联数据的交叉分析，一方面在 SNP和基因层面论证了精神疾病与自免疾病是否具有共享的遗传因子，以及这些共享遗传的显著性；另一方面也在生物通路层面探索这些共享遗传因子可能的作用途径，即与精神疾病相关的免疫特征。结果表明，常见精神疾病中，只有SZ表现出与自免疾病显著的共享遗传特征， 且这种遗传共享性集中表现在与MHC II 相关的免疫反应和免疫过程，以及抗原加工与呈递过程和细胞粘附分子。此外，SZ 与乳糜泄和系统性红斑狼疮的共享基因也在 IgA 参与的肠道免疫网络中显著富集，提示我们肠道免疫反应也可能在 SZ的发病机理中起作用。 综上所述， 本论文主要通过三种遗传关联数据的深入挖掘策略， 从多个层面、不同角度探索精神疾病的遗传机制。这些研究策略涵盖了不同类型的遗传关联数据，涉及单一疾病的信息整合、GWAS 数据的深入挖掘和多疾病交叉研究。通过这些研究，一方面对现有的关联数据进行了有效整合，另一方面为常见精神疾病的遗传机制研究提供重要线索，为后续实验验证打下坚实基础。 |
| 英文摘要 | Psychiatric disorders have seriously impacted on people’s health and normal life, and brought a heavy burden for families and the society. According to statistics, the prevalence for several common psychiatric disorders, such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), autism (ASD), attention deficit hyperactivity disorder (ADHD), are between 1% and 11%. Exploring genetic mechanisms for these disorders will benefit our understanding about their etiology, as well as the future molecular diagnosis and treatments of these mental illnesses. The rapid advances of genomics have promoted the flourish of genetic studies of psychiatric disorders and a large amount of genetic association data has been accumulated. However, the knowledge we obtained from these data is still limited, which stimulates a growing need to extract useful information from these data by in-depth data mining. In this thesis, three different data mining strategies were used to investigate genetic association data for above five common psychiatric disorders in order to obtain knowledge about the genetic mechanisms of these mental illnesses. In the first part of this thesis, we investigated the genetic mechanism of ADHD by using a strategy of integrating various data from multiple types of genetic association studies. Through this work, we identified 68 core-associated genes with ADHD which including 24 hot genes, 37 multiple-evidence supported genes and 27 high-priority genes. The biological pathway analyses for these core-associated genes indicated four categories of biological function which may play a key role in the mechanism of ADHD. They are synaptic transmission and channel activity, metabolism of neurotransmitters, calcium-mediated neuronal conduction, and plasma membrane part for synapse. On the other hand, based on the collection of ADHD genetic association data, ADHDgene, a genetic database for ADHD, was also built to make an in-depth integration and extended analyses of ADHD genetic data. In the second part of this thesis, we used the strategy of pathway-based analysis (PBA) to explore the GWAS data sets for four common psychiatric disorders, SZ, BD, MDD and ADHD. This work aims to bridging association signals with biological functions to identify the etiology of mental disorders. In this work, we identified significantly associated pathways for each psychiatric disorder. Moreover, the common associated pathways among these disorders were also identified. Through this work, we found that the four psychiatric disorders under study do share some biological mechanisms. All of them were found associated with biological functions about carbohydrates and drug metabolism. In addition, SZ and BD are both associated with biological processes relating to immune response and immune system. And calcium channel activity was significantly associated with both SZ and ADHD. Previous studies have shown that mental illness is associated with autoimmune diseases. However, the immune hypothesis for mental illness is still lack of systematic interpretation and strong proofs. Therefore, in the third part of this thesis, we investigated the common genetics between psychiatric disorders and autoimmune diseases with significant and suggestive association data from GWAS. By analyzing common SNPs and common genes between traits following in two different classes, we found that among five psychiatric disorders under study, only SZ showed significant share genetic characteristics with the autoimmune diseases. And the common genetic mechanisms between SZ and autoimmune disease mainly focus on biological processes involved by MHC Class II genes; antigen processing and presentation; and cell adhesion molecules. In addition, their shared associated genes were also enriched in the biological pathway of intestinal immune network for IgA production, which indicating a role of intestinal immune system in the pathogenesis of SZ. In summary, in this thesis, we explored the genetic mechanisms for five common psychiatric disorders through three strategies for deep mining of genetic association data. The three strategies cover different types of genetic association data and perform data mining in different levels and aspects, such as integration of multiple-type information for specific traits, in-depth exploration for GWAS data sets, and cross analyses for multiple traits. Results from these works provide useful information for the understanding of etiology of psychiatry disorders, as well as candidates and clues for further investigation. |
| 学科主题 | 行为遗传学 |
| 语种 | 中文 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/19761]  |
| 专题 | 心理研究所_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 | 张柳燕. 基于关联数据深入挖掘策略研究常见精神疾病的遗传机制[D]. 北京. 中国科学院研究生院. 2013. |
入库方式： OAI收割
来源：心理研究所
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