文档介绍：摘要
人类多数(80%)疾病属于复杂疾病,复杂疾病一般是由多个遗传基因及环境因素共同交互作用而发生发展的,并且往往具有家族聚集倾向性、遗传异质性等特征,表型与基因型间没有简单对应关系。现代医学研究认为疾病的发生、易感性及对药物的反应差异性等复杂性状与基因突变或遗传多态性密切相关。因此,利用遗传多态性标记对复杂疾病相关基因进行精确定位是目前研究的热点和难点。在本文的研究中我们提出了利用单核苷酸多态性(SNPs)标记对复杂疾病基因作图(定位)及互作网络构建的新方法,并将该方法应用于GAW14发布的酒精中毒数据相关互作多基因定位问题上,取得了良好的效果。
我们对比了现有的基因定位分析方法,基于连锁分析和关联分析的基因定位方法多是针对单个疾病特征标记来研究,忽略了多个可能疾病特征标记间复杂互作的综合效应。本文中的研究中,我们将基因定位问题看作提取疾病特征标记(比如SNPs)的模式识别问题,提出了SNP协作簇的特征提取算法MPISC,这里我们。这是一种新的全局分析方法,这种全局分析方法能够较好地反映多基因互作、多基因和环境因素共同作用等情况。
复杂疾病是受遗传机制和环境因素共同控制的,因此在我们的复杂疾病互作多基因定位方法的研究中综合考虑到了这两方面的因素。首先将具有家系结构的SNP遗传谱转化为IBD谱,然后基于MPISC算法提取那些IBD分布在两类受累同胞对组中显著差异的SNP协作簇。这些SNP协作簇不仅可以定位复杂疾病相关多基因,而且反应相关基因的互作关系,可以进一步构建SNP虚拟互作网络,进而映射为基因之间的互作关系,最终完成对复杂疾病互作多基因精确定位和基因互作网络构建。
关键词复杂疾病;基因定位;SNP虚拟互作网络;基因互作网络
Abstract
About eighty percent mon human disorders belong plex trait. Complex diseases are often caused by the interaction of many loci and environmental effects, and exhibit a strong ponent and population ic heterogeneity. A simple relationship between the observable phenotypes and the underlying ic effects does not exist. Variants have been deemed to confer susceptibility mon diseases and response to drug therapy in modern medicine study. It advances a challenge for gene mapping using polymorphic markers. Consequently, in our study we propose a new idea for locating interactive multiple genes and constructing their work responsible plex disease utilizing single-nucleotide polymorphisms (SNPs). We demonstrate the properties of this novel approach via an application to the alcoholism data in GAW14.
We have parisons among current methods for gene mapping. Most methods based on linkage analysis and association analysis considers one marker at a time and does not take into account the correlated structure of multiple linked markers. In this paper, we treat the puzzle for gene mapping as a pattern recognition problem and propose a feature selection algorithm(MPISC) to mine bination remarkably associated plex trait.. This method offers us a new way for