【作者】 袁芳；

【导师】 周艳红；

【作者基本信息】 华中科技大学 ， 生物信息技术， 2008， 博士

【摘要】 疾病的发生是遗传因素与环境因素共同作用的结果。发现疾病的致病基因是理解疾病致病原因、协助临床判断及预防诊治的基础,因此寻找疾病基因是后基因时代的重要目标。如何对连锁分析或基因组关联分析所得定位区域中的众多基因进行疾病基因的凤险评估是寻找疾病基因的关键问题之一。近几年,已经有很多研究者提出了许多基于基因信息的疾病基因的预测软件和在线工具。研究发现,当前用于疾病基因预测的信息资源中最具有预测效力的数据是基因功能相关信息,但是目前已有的基于基因GO功能信息的方法并没有充分的利用GO之间的结构关系,使得基于功能GO的预测方法在性能还有很大的提升空间。本文首先提出一个新的基于已知疾病基因功能注释信息的预测疾病基因的方法。它利用不同GO在DAG中的最短距离来分析待测基因的GO与已知致病基因的GO之间的相似性,对待测定位区间候选致病基因进行评分,按得分高低进行排序。基于OMIM数据库中可用的最大数据集对这个方法进行评估,68.5%的疾病基因排名前10%。但是目前还有很多疾病没有已知致病基因,或者已知致病基因没有注释信息,以上的方法不能对这样的疾病进行疾病基因的预测。对这种缺少基因功能注释的疾病,本文提出另一个新的疾病基因预测方法。它结合文本挖掘技术和上面方法中的GO功能相似性分析方法来实现对这种疾病的候选致病基因的预测。预测的过程是：首先,基于生物医学文献数据库,利用文本挖掘技术获取疾病相关表形信息,并结合人类基因产物蛋白质的功能注释,获取疾病表型与GO注释之间的关联；然后利用以上方法中的GO相似性算法,对所有候选基因进行打分排列。这个方法主要适用于没有已知疾病基因的疾病的致病基因的预测,作为已有的一些通常的利用已知疾病基因信息的预测方法的补充方法,可以更全面的对人类遗传疾病进行致病基因的预测。目前疾病基因预测的方法,包括以上提出的方法,主要集中在对基因信息的分析研究上,很少有人从相似疾病表型的角度进行分析。本文通过挖掘生物医学文献对疾病表型进行标准化,然后计算表型之间相似性,并对相似性进行分析,结果显示这些表型相似性与其功能注释之间存在正向关系,即相似度高的表型的致病基因基因,在功能上的确具有较高的相似性特征。显然,相似度高的疾病表型也可以应用到疾病基因的预测中。在实际预测的时候,可能只知道待测的疾病表型和待测的区间,而不知道该疾病是否已经有已知的致病基因或GO功能注释。为了解决这个问题,我们增加一个判定模块,整合以上两个方法,开发了一个新的疾病候选基因预测工具CDGMiner,既能对已有已知致病基因功能信息的疾病,也能对还没有已知致病基因或其已知致病基因没有具体功能注释的疾病进行候选区间的致病基因预测。更多还原

【Abstract】 Human diseases are commonly associated with both the environmental factors and genetic factors. Identifying genes that are likely to be involved in human genetic disease is of vital importance for both understanding disease pathogenic mechanism and improving clinical practice, and is the main aim in the post-genomic era. Hundreds of candidate genes are located in the chromosome region by linkage analysis or genome-wide association. It is a important problem of disease gene identification to evaluate a candidate gene to be the cause of a disease or not. During recent years, some bioinformatics application softwares or online tools for prioritizing candidate genes have been developed and released to public. Research shows that GO annotation is the most effective data resource among the various of gene information. There is still a big space to improve the performance of GO annotation-based methods, for that the GO structure information is not fully exploited.In this thesis, a new disease gene prediction method based on GO annotation is proposed. It allows users to score and prioritize the candidate genes by using a new functional similarity algorithm, which is based on the shortest distance between GO terms in DAG. And the validation test results show that the target gene was in the top10%with a68.5%chance. The method shows promising performance compared to other existing tools.Like many existing methods for disease gene prediction, the above method suffers from annotation bias as they can not deal with diseases lacking known causative genes. We have developed a new computational method. It prioritizes genes on a chromosomal region according to their possible relation to a genetic disease lacking known causative genes, by using a combination of text mining and gene-function similarity analysis. Firstly, it mines the disease-related GO terms from the MEDLINE/PubMed database and GOA database. Then, it prioritizes the candidate genes by the functional similarity algorithm of the above method. Being a complementary method, the method can handle diseases lacking detailed GO annotation, which are ignored by the above method, as well as many other existing methods.The existing disease genes prediction Strategies, including the above methods we have developed, all focus on the gene information. But, few attempts have been made to systematically analye the relationships at the phenotype level. In this thesis, we formalized1572disease phenotypes’description contained in the OMIM database by mining MeSH C term from medical literature, then evaluated the similarity between phenotypes based on MeSH C tree. We find that these similarities are positively correlated with a number of measures of gene function. Obviously, the highly similar phenotypes may be used to prediction candidate gene for diseases.In many case, we just get the disease phenotype and the region of interest, don’t know if the disease have known causative genes or related GO annotation. For these case, we developed a new web tool, CDGMiner, which is combined with the above two methods, and just is added a judgement module. It can handle both diseases with known related GO annotation and diseases lacking known causative genes and/or diseases with known genes but lacking sufficient function annotations.更多还原