摘要
人类基因组计划是人类认识自我的伟大科学工程，计划的目标是测定人类基因组的全部序列，从而通过解读全部遗传信息，阐明人类基因组及所有基因的结构和功能，揭开人类生命的奥秘。
人类基因组是一个十分稳定却又存在各种变异的体系。不同种族、群体和个体都有46条染色体，有相同数目的基因和基因分布，也有基本相同的核苷酸序列。这种基因组结构的稳定性保证了人类作为一个物种的共同性和稳定性。在长期进化的过程中，基因组DNA序列又不断发生变异，其中一些变异被保存下来，导致了不同种族、群体和个体间基因组的差异和多态性。人类基因组计划的最伟大成就之一就是发现数以百万计的多态性遗传标记，众多的多态性遗传标记不仅有助于基因的鉴别和定位，在整个基因组水平上探讨基因与基因、基因与环境的相互作用机制，而且对于揭示与遗传因素密切相关的人类重大疾病（如肿瘤、心脑血管疾病、神经与精神性疾病）的遗传本质具有重要意义，为人类最终在基因水平上预防、诊断和治疗疾病奠定了重要基础。
人类基因组中，最常见的DNA序列变异形式是单核苷酸多态性（SNP），约占90%左右。至今已发现超过15000个SNPs并保存到公共数据库。SNPs以其丰富、稳定和容易判定等特点逐步取代微卫星成为研究基因组多样性和识别、定位疾病相关基因的新一代遗传标记。它不但有助于解释个体的表型差异、不同群体和个体对疾病，特别是对复杂疾病易感性的差异，对各种药物反应性、耐受性的差异及对环境因子反应性的差异，而且通过建立序列变异与表型、序列变异与疾病风险之间的关系，将复杂疾病的预防、诊断和治疗置于坚实的遗传学基础上，从而使HGP给人类健康带来实际的利益。
精神分裂症是一种以基本个性改变，思维、情感和行为的分裂，精神活动与环境不协调为主要特征的精神疾患，是人类面临的最为严重的疾病之一。在全世界范围内终生患病率接近1%（种族、地域、文化等差异不显著）。精神分裂症的严重之处在于：不仅给患者本人造成终身的痛苦，这种痛苦除来自疾病本身的折磨外，还来自于社会经济地位的下降、社会的歧视及治疗药物的副作用等；而且给家庭和社
会造成长期沉重的负担，精神分裂症约占全球疾病总负担的2.8%，占用大量的医疗卫生资源。因此，确定精神分裂症的病因并在基因水平上预防和治疗疾病是当前医学研究中的重大课题。
遗传流行病学研究证实，精神分裂症与遗传因素关系密切，但并非经典的孟德尔单基因遗传疾病，不是由一对显性或隐性基因所导致，而是多对微效基因所产生的协同作用，属于多基因遗传的人类复杂疾病。全基因组扫描连锁分析及连锁不平衡研究为筛检精神分裂症易感基因提供了强有力的研究手段，人类基因组计划已构建和正在构建的遗传图、物理图、基因图、DNA序列图、SNP图和单倍型图为定位精神分裂症易感基因提供了强有力的研究工具。目前，精神分裂症易感基因研究已经取得了较大进展，许多研究报道证实1q21-22、5q31-33、6p21-24，6q25-26、8p21-22、10p11-15、13q14-33及22q11-13等染色体区域可能含有精神分裂症易感基因。
本研究工作选择人类第13号染色体长臂三区二带（13q32）为精神分裂症易感基因候选区域，应用基于家系的连锁不平衡分析方法和基于群体的case-control相关分析方法在13q32染色体区域内筛检与精神分裂症相关联的基因位点。
以229个中国汉族精神分裂症患者及其健康父母双亲组成的核心家系为研究对象，利用生物信息学工具，通过http://www.ncbi.nlm.nih.gov/、http://www.ncbi.nlm.nih.gov/SNP及http://snp.cshl.org/等数据库，确定13q32染色体区域内的48个已知基因，浏览每个基因上的SNPs。在每个基因DNA序列上分析1～2个含有限制性内切酶酶切位点，杂合度大于10%的SNPs。选定13q32染色体区域内3个候选基因和2个EST上的7个SNPs，包括结合糖基磷脂酰肌醇的硫酸乙酰肝素蛋白多糖6基因（GPC6）的rs2892679、丝氨酸/苏氨酸激酶24基因（STK24）的rs1886089和亲核素β3基因（KPNB3）的rs626716、rs624066和rs2761072及LOC160889（EST位点）的rs2282135和LOC122330（EST位点）的rs942358。长度横跨13139.81kb，在GPC6和LOC122330两个基因之间构建SNPs连锁不平衡图。用PCR-RFLP方法检测SNPs基因型，采用SPSS统计学软件管理和分析基因分型数据。
用拟合优度卡方检验验证每个SNP基因型在抽样群体中的分布是否符合Hardy-Weinberg平衡。用EH plus和UNPHASED软件分析两个SNPs之间的连锁不平衡程度。对于单位点的SNP数据，除进行单倍型相对风险（HRR）和传递不平衡（TDT）方法分析外，还要进行临床亚组分析。这是因为由于SNPs的突变时间不同，每个SNP有其自己的遗传祖先，它们的等位基因频数分布可能互相干扰，只有进行临床亚组分析才能发现与疾病相关联的SNPs，排除假阴性结果，并证实临床异质性与遗传异质性的存在。对于两个以上SNPs构成的单倍型数据，用Transmit软件进
行分析。
针对精神分裂症的某一特定临床症状将患者分为有或无该症状两组，分析每个SNP位点等位基因及基因型在两组之间的分布是否有显著性差异，从而推测SNPs位点与精神分裂症临床症状的关系
The Human Genome Project (HGP), a marvelous science approach to human biology, is attempting to elucidate the structure and constituent of human genome, and to decipher the sequence of DNA. These achievements facilitate the human being to understand themselves well and unravel the mystery of human life.
The human genome is the whole set of DNA arrayed in 24 distinct chromosomes, which consists of 3.2 billions of base pairs (bp). The genomic DNA is 99.9% identical to that of other humans and 0.1% is variable. One of the fruits of the Human Genome Project is the discovery of millions of DNA sequence variants in the human genome. The use of these genetic markers has been playing an increasing part in genetics studies. Most variation of the human genome is attributable to single nucleotide polymorphisms (SNPs), which contributes to the differences between individuals in physical appearances, susceptibility to a disease and response to medication with drugs. SNPs have been widely used. A dense set of SNP markers opens up the possibility of studying the genetic basis of complex disease. In recent years, SNPs have been used as new DNA markers to replace microsatellites in mapping of disease-related genes in humans. There is no doubt that SNPs will play a key role in identifying and cloning of disease-related genes, investigating the mechanism of the genome-environment interaction and gene-gene interaction.
Schizophrenia is a serious mental disorder with a lifetime prevalence rate of 1% in the general population worldwide. It was characterized by the abnormal mental functions and disturbed behaviors, which characteristically appear as a series of clinical features, such as positive and negative symptoms, and disturbances in basic cognitive functions. Because the illness causes heavy economical and social burdens to families and societies, it is very important to establish a procedure of treating and preventing schizophrenia. While the cause for schizophrenia remains unknown, several lines of evidence from
family, twin and adoption suggest that genetic factors are likely to play an essential role in the developing of schizophrenia and influence susceptibility to schizophrenia. Epidemiological data have demonstrated that schizophrenia is not a simple Mendelian disease but looks like a complex disease involving several genes with each susceptiblity gene having only a modest individual effect. The completion of the HGP has provided a good opportunity for mapping all the genes involved in human diseases, including schizophrenia. Basically, there are two steps for mapping a disease-related gene in the human genome, the linkage-based genome-wide scan and the regional mapping with linkage disequilibrium (LD) analysis. Genome-wide scan has suggested that the following chromosomal regions may contain several genes contributing to schizophrenia, including 1q21-22, 5q31-33, 6p21-24, 6q25-26, 8p21-22, 10p11-15, 13q14-33 and 22q11-13. To validate these initial findings, the present study has been focused on identifying the candidate susceptibility genes on the 13q32 region using a family-based LD and case-control analysis and was designed to construct a SNP-based LD map.
The family trio consists of healthy fathers, healthy mothers and affected offspring with schizophrenia. The SNP-based LD map was constructed between the GPC6 gene and the LOC122330 by looking through the known genes and SNPs of each gene on the 13q32 region using bioinformatics methods. 7 SNPs were chosen in this region spanning 13139.8kb including rs2892679 present in the GPC6 locus, rs2282135 in the EST LOC160889, rs2761072、rs626716 and rs624066 in the KPNB3 locus, rs1886089 in the STK24 locus, as well as rs942358 in the EST LOC122330.
SNPs were genotyped using PCR-based RFLP analysis. Genotyping data were put into the SPSS database. The Hardy-Weinberg (H-W) equilibrium was tested for genotype frequency distributions of SNPs using the goodness of fit test. The LD between paired SNPs was estimated with the EH plus (version 1.11) and UNPHASED programs. The
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