PSYCH OpenIR  > 认知与发展心理学研究室
注意缺陷多动障碍遗传易感性的生物信息学研究
其他题名Bioinformatics Study about Genetic Susceptibility of Attention Deficit Hyperactivity Disorder
常素华
2012-04
摘要注意缺陷多动障碍(ADHD)是儿童/青少年期常见的一种品行障碍,患病率在5%左右。ADHD 是由多种生物、心理和社会因素所致的一种综合征。来自家族、双胞胎及收养实验的研究表明 ADHD 与遗传因素有关,遗传率高达75%~91%。目前,已经有大量针对 ADHD 的遗传学研究,大部分的遗传学研究表明 ADHD 受到多个基因的共同作用,但疾病的遗传机制尚未确立。本文将利用生物信息学方法从多个角度系统地开展 ADHD 的遗传易感性研究,以期识别ADHD的易感基因及可能的作用途径。
本文首先对 ADHD 的遗传学数据进行了多维度整合与挖掘,通过整合遗传学、基因组学及表型相关数据,开发了国际上第一个注意缺陷多动障碍遗传学数据库(ADHDgene)。它一方面整合了通过文献信息挖掘得到的核心数据集,为每个ADHD相关的易感基因/位点提供系统性的综述;另一方面通过连锁不平衡分析、基于通路的分析、基因匹配等策略建立了基于功能分析的扩展数据集,进而为 ADHD 的分子遗传学研究提供更多候选与假设。ADHDgene 提供了功能强大的查询功能及用户友好的图形化基因组浏览功能,以方便用户查询和使用数据及数据之间的联系。ADHDgene 将为全世界的 ADHD 遗传学研究者提供注意缺陷多动障碍遗传机制研究的重要知识库与分析工具。
基于 ADHDgene 所建立的数据集,本文提出了两种基因排序策略,对ADHD候选基因按致病的可能性高低进行优先级排序。首先利用五个基于多数据源的基因排序预测工具对 ADHD候选基因进行优先级排序, 在13个训练基因基础上得到16个具有高优先级的基因。 同时,为了减少训练集对优先级排序结果的影响,我们又建立了基于多维证据的打分系统与随机游走相互作用网络相结合的整合基因优先级排序策略,共得到 44 个高优先级的基因。两种基因排序策略得到的高优先级基因集共享 16个基因,共57个基因。
为进一步探索 ADHD 候选基因可能的作用途径,本文开发了基于通路分析(PBA)的在线分析平台 i-GSEA4GWAS v2.0,首次实现了基于连锁不平衡数据的 SNP 修剪方法以得到相对独立的 SNP 用于 PBA 分析,并提供了分析疾病之间共享基因与共享通路的功能模块。运用此工具,我们首先以 57 个高优先级基因相关的通路作为基因集搜索空间进行 PBA 分析,寻找其可能的作用途径,并结合 PBA 分析结果及基因排序结果中的网络数据对高优先级基因进行综合评估;更进一步,我们运用此工具分析全基因关联学习(GWAS)数据,从全基因组范围内寻找疾病相关的通路,从而为 ADHD机理研究提供新的研究线索与假设。
综上所述, 本文通过生物信息学方法从三个方面系统地研究了注意缺陷多动障碍的遗传易感基因及可能的作用途径。多维度的数据整合与分析为 ADHD 研究者提供了广泛的遗传数据集;基因排序分析鉴定了57个可靠的高优先级基因,这些基因除参与主要的神经递质传递系统外, 参与神经系统发育相关通路的基因(包括 SNAP25、STX1A 和 SYT1)和神经营养因子相关的基因(包括 BDNF 和NTRK2)以及这些基因之间的相互作用可能对疾病的致病过程起着重要的作用,值得进行深入研究;进一步的 PBA 分析揭示了这些基因可能的作用途径,以神经系统发育相关基因 SNAP23、SYT1 和NGF为例,PBA分析表明这些基因可能通过高尔基囊泡运输、分泌通路及细胞凋亡途径影响疾病,基于这些结果的综合评估表明基因STX1、 DRD4、 STX1A、 SLC6A2、 SNAP25 和SYP最有可能与 ADHD相关;更深入的基于 PBA的全基因组范围 GWAS 分析表明钙离子通路活性、糖类代谢相关通路、脂酶活性通路、激素受体等通路相关基因可能共同影响 ADHD的发生与发展。本文结果一方面为 ADHD 的遗传与功能机制研究提供了丰富而可靠的数据资源及可能的理论假设与新的研究视角;另一方面,相关技术和方法的建立可以应用到多种复杂疾病的遗传机制研究中。
其他摘要 With a worldwide prevalence of ~5%, attention deficit hyperactivity disorder (ADHD) has become one of the most common psychiatric disorders. Twin and adoption studies suggest that ADHD has high heritability ranging from 75% to 91%. Up to now, large numbers of genetic studies about ADHD have been conducted and most of the studies showed ADHD is affected by multiple genes, but the genetic mechanism of ADHD has not been established. Unraveling  the genetic  basis of ADHD is of fundamental importance in uncovering disease mechanism and in developing effective methods for ADHD diagnosis, treatment and prevention. In this paper, we conducted  systematic bioinformatic analyses for the exploration of genetic mechanism of ADHD.  
First, we made multi-dimension  data integration  analyses  for the genetic  data, genomic data and phenotype data for ADHD and developed  the  first ADHD genetic database (ADHDgene)  by integrating  ADHD-related genetic factors by profound literature  reading and extended functional analyses, including linkage disequilibrium (LD) analysis, pathway-based analysis (PBA) and gene mapping. Moreover, powerful search tools and a graphical browser were developed to facilitate the navigation of the data and data connections. ADHDgene aims to provide researchers  with a central genetic resource and analysis platform for ADHD.  
Based on the genetic data provided in ADHDgene, we proposed two strategies for candidate gene prioritization to get promising ADHD candidate genes. First, we combined five multiple data sources based tools to prioritize ADHD candidate genes in ADHDgene. 16 prioritized candidate genes were detected  based on 13 training genes. Meanwhile, to reduce the influence of training genes on the final ranking result, we established a gene prioritization strategy which integrates multiple evidences to build score system and then uses  random walk interactome method to optimize parameters  of the score system. 44 prioritized genes were  identified  by the second strategy, of which 16 genes were overlapped with the result of the first strategy, and finally, combined 57 high prioritization genes were obtained.  
To further investigate the functional pathways involved by prioritized genes, we first upgraded  the web server we developed for GWAS data mining based  on PBA i-GSEA4GWAS v2.0, which first implemented LD-based SNP pruning method to get independent SNPs to reduce PBA bias and provided the pathway comparison function for detection of shared genes and pathways among traits. Based on the web server, we first made PBA analysis by using 57 prioritized genes related pathways as  gene set search space to explore possible contribution of these genes to ADHD. Combined the PBA result and network analyses from gene prioritization, systematic evaluation of the prioritized genes was conducted. Furthermore, we used  i-GSEA4GWAS v2.0  to analyze ADHD GWAS data to detect novel pathways related with ADHD, which will provide new clues and hypotheses for ADHD mechanism research.  
To sum up, in this paper, we systematically investigated the genetic susceptibility of ADHD  and their possible functional pathways from three aspects  by using bioinformatic methods. Multi-dimension data integration provided a comprehensive data set for ADHD genetic study. Gene prioritization analyses identified 57 candidate genes with high prioritization, in which, besides the neurotransmitter  system related genes, genes  involved in nervous system development (such as SNAP25, STX1A and SYT1) and neurotrophic factor (such as BDNF and NTRK2) deserve more attention in future study.  Further PBA analysis showed  several  possible functional pathways of prioritized genes. For example, nervous system development related genes SNAP23, SYT1 and NGF may contribute to ADHD by involvement in golgi vesicle transport, secretory pathway and apoptosis. Systematic evaluation of 57 prioritized genes based on  these results  showed  STX1, DRD4,  STX1A,  SLC6A2,  SNAP25  and  SYP  are the most promising ADHD related genes. Finally, PBA analysis for GWAS provided several novel pathways related with ADHD, including calcium channel activity, starch and sucrose metabolism,  lipase activity,  hormone receptor binding, and the related genes may contribute to ADHD in combination pattern together. The results  in this paper will not only provide plentiful and reliable data resources for the genetic study of ADHD, but also new hypotheses and clues for the etiology mechanism research of ADHD. Meanwhile, related methods and algorithms established here can be applied easily to the genetic research of other complex disease.
学科领域行为遗传学
关键词注意缺陷多动障碍 遗传易感性 数据库 基因优先级排序 基于通路
学位类型博士
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/311026/20185
专题认知与发展心理学研究室
作者单位中国科学院心理研究所
推荐引用方式
GB/T 7714
常素华. 注意缺陷多动障碍遗传易感性的生物信息学研究[D]. 北京. 中国科学院研究生院,2012.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
注意缺陷多动障碍遗传易感性的生物信息学研(2858KB)学位论文 限制开放CC BY-NC-SA请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[常素华]的文章
百度学术
百度学术中相似的文章
[常素华]的文章
必应学术
必应学术中相似的文章
[常素华]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。