中文摘要：

采用贝叶斯（Bayesian）网的潜类模型对GAW17高维SNPs数据进行分析，为复杂性状疾病遗传以及基因定位等方面的研究提供新的方法支持。本研究从GAW17提供的包含697个个体22条常染色体的上万个SNP中，随机挑选出1号染色体上12个基因的29个SNPs作为研究对象。按照累计信息贡献率达到95％的原则，应用贝叶斯网潜变量模型选出CIS11408，CIS3201，C1S1786等15个与X0互信息量大的SNPs位点来对研究人群进行分类与解释。结果表明697个个体总的被分为2个潜在类别，各类别的概率分别为0．68和0．32。对两类人群的疾病分布状况进行分析，结果表明二者不一致，第二个类别人群患病率（38．64％）明显高于第一个类别人群（25．99％）（x2=11．46，P=0．001）。由此可见，两类人群疾病患病率的差别正是由选出的15个SNPs造成的，从而有理由认为这些SNPs为可疑致病位点，为进一步的研究提供明确的思路。

英文摘要：

To analyze high -dimension SNPs data of GAW17 by latent class model based on Bayesian network , and to provide a new method for the study of heredity and gene location of complex diseases. The data provided by GAW17 consists of a collection of 697 individuals and include tens of thousands of SNPs on 22 euchromosome. This research randomly chooses 29 SNPs located 12 gene on chromosome 1 as research object. According to the principle that accumulative contribution rate of information should reach to 95%, the model selects 15 SNPs which contain abundance mutual information with X0, including CIS11408, CIS3201, CIS1786 and so on, classifies the study population, and explains the meaning. The population including 697 individuals is divided into 2 latent classes, the probability of the two classes are 0.68 and 0.32, respectively. To analyze the disease situation of the 2 classes, and the results show that they are not accordance. The prevalence of the second class （38.64%） is higher than the first class （25.99%）, and the difference is statistically significant （X2 = 11.46, P = 0. 001 ）. This difference is caused by the 15 choosed SNPs. So we have reasons to think that these SNPs are suspicious disease locus ,which provide clear idea to the next research.