基于高血压遗传变异数据的荟萃分析模型和算法的研究

发布时间：2018-06-26 20:56

  本文选题：荟萃分析 + M-H算法　； 参考：《哈尔滨工业大学》2017年硕士论文

【摘要】：荟萃分析是将相同的多个研究的统计量进行合并,概括主要研究结果,得出简单而重要的结论。本质上是利用统计方法来增强主要研究效应的方法。本文介绍了荟萃分析方法的概念以及两种荟萃分析模型,即固定效应模型与随机效应模型,着重考虑了相应模型的算法:M-H算法和DSL算法,并对两种算法进行了分析。在原算法的基础上考虑权重因子的大小以及对数对原算法的影响,改进了两种算法。具体的,在DSL校正因子的启发下,将M-H和DSL算法中权重设置为调和平均数,运用统计学上的Z检验方法进行检验,对改进前后的算法结果进行了比较。在应用方面,用改进后的荟萃分析模型算法分析高血压遗传变异和SNP的关系。在Pub Med和Web of Science文献搜索引擎上搜索与高血压和rs1799983相关的文章,收集整理数据,进行数据预处理,用改进的荟萃分析模型算法对高血压遗传变异与e NOS基因上的rs1799983位点的关系进行实验与分析。通过发表偏倚分析、异质性分析得出隐性模型虽然存在了发表性偏倚但不存在异质性,其他模型不存在发表性偏倚但存在异质性,同时通过Z检验对改进前后的算法结果进行比较,得出改进后结果更明显。
[Abstract]:Meta-analysis is to combine the statistics of the same multiple studies, summarize the main results and draw simple and important conclusions. In essence, statistical methods are used to enhance the main research effects. This paper introduces the concept of meta-analysis method and two meta-analysis models, that is, fixed effect model and stochastic effect model. The algorithms of the corresponding model, namely, the algorithm of: M-H and the algorithm of DSL, are emphatically considered, and the two algorithms are analyzed. On the basis of the original algorithm, the influence of the weight factor and the logarithm on the original algorithm is considered, and two algorithms are improved. Specifically, under the inspiration of DSL correction factor, the weights of M-H and DSL algorithms are set to harmonic average, and the results of the improved algorithm are compared by using the Z test method in statistics. In application, the improved meta-analysis model algorithm is used to analyze the relationship between hypertension genetic variation and SNP. Search for articles related to hypertension and rs1799983 on the search engine of Pub Med and web of science literature, collect and organize data, carry out data preprocessing, The relationship between the genetic variation of hypertension and the rs1799983 locus on the Enos gene was studied by using the improved meta-analysis model algorithm. Through the analysis of publication bias, heterogeneity analysis shows that although there is publication bias, there is no heterogeneity in the recessive model, and there is no publication bias but heterogeneity in other models. At the same time, the results of the improved algorithm are compared by Z test, and the improved results are more obvious.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R544.1
，

本文编号：2071381