人类染色体22q11精神分裂症易感基因的筛查研究

目的 在人类22q11区域内筛查与精神分裂症相关的易感基因。方法 由中国汉族精神分裂症患者和他们的健康父母组成的100个核心家系作为研究对象。采用聚合酶链反应-限制性片段长度多态性方法对22q11区域内分别位于腭心面综合征中缺失的犰狳重复基因(ARVCF)上的m165655(A/G碱基改变)、rs165815(C/T碱基改变)和LOC128979(EST序列)位点上的rs756656(A/C碱基改变)3个单核苷酸多态性(SNP)进行检测。用连锁不平衡方法分析基因型数据,连锁不平衡方法包括单体型相对风险分析(HRR),传递不平衡检验(TDT)和单倍型传递分析。结果 3个SNP基因型频率分布都符合Hardy-Weinberg平衡;HRR和TDT分析结果都显示rs165815与精神分裂症有关联(P<0.05),而rs165655和rs756656与精神分裂症无关联;单倍型传递分析结果显示,父母传递给患病子女的rs165655-rs165815单倍体和rs756656-rs165655-rs165815单倍体的频率偏离50％,差异具有统计学意义(P<0.01)。结论 对于中国汉族精神分裂症患者而言,ARVCF基因本身或其附近的基因可能与精神分裂症的易感性有关联。     

24例X等臂染色体核型调查及临床分析

目的 收集并分析X等臂染色体患者的临床资料,探讨该类核型的遗传学特征、临床特点及其所占比例。方法 收集2016年1月—2019年12月间因不孕就诊于山东大学附属生殖医院染色体核型分析报告为X等臂染色体核型患者,共24例,分析X等臂染色体与其临床表现的关系。结果 X染色体等长臂9例,核型为46,X,i(X)(q10),嵌合体10例,核型为45,X/46,X,i(X)(q10),X染色体等短臂5例,核型为46,X,i(X)(p10)。患者雌二醇(E₂)含量相对较低,而卵泡刺激素(FSH)和黄体生成素(LH)浓度相对升高。抗缪勒管激素(AMH)和血清抑制素B(INH B)的含量较低。结论 X等臂染色体有三种表现形式,临床表现和激素水平均不相同,但均有性腺发育不良表现,而等长臂和嵌合体患者表现身材矮小,等短臂患者身高接近正常水平。     

六号染色体短臂MHC区DRB3、DRB1基因多态性与精神分裂症症状的关系探讨

目的 探讨六号染色体短臂MHC区DRB3、DRBl基因多态性与精神分裂症症状的相关性。方法 采用聚合酶链反应(PCR)和限制性内切酶片段长度多态性(RFLP)方法检测两个基因位点上的单核苷酸多态性(SNPs)，并对116例精神分裂症患者家系进行连锁不平衡分析。结果 DRBl的SNP(rs707954：G／T碱基互换)等位基因与关系妄想症状呈显著相关(X^2=5．484，df=l，P=0．019)，GG、GT、TT三种基因型频率在关系妄想症状中呈显著相关(X^2=6．771，df=2，P=0．034)。rs707954的三种基因型频率与情感淡漠症状呈显著相关(X^2=12．110，df=4，P=0．017)。结论 DRBl位点等位基因与关系妄想和情感淡漠症状高度相关，DRBl基因型与关系妄想症状高度相关。     

单纯性马蹄内翻足易感基因位点筛查

目的 将人类单纯性马蹄内翻足(ICTEV)易感基因初步定位,为进一步对其克隆提供基础依据.方法 选取与肢体发育、关节翻转及与软骨发育相关的基因作为候选基因,在基因内或其附近选取微卫星标记,应用PCR-短串联重复序列(STR)方法扩增微卫星片段,对84个ICTEV核心家系的252名成员进行基因型分析,并进行遗传连锁不平衡检验(TDT).结果 IX型胶原COL9A1基因(6q12-13区域)内及其附近的2个位点-D6S348及509-8B2经TDT检验表明,此2个位点与ICTEV有相关性(分别χ²=42.15,χ²=31.18,P<0.05);其他位点经TDT检验,不存在连锁不平衡(均P>0.05).结论 COL9A1基因可能是ICTEV的候选基因,从而将ICTEV易感基因初步定位于COL9A1基因所在的染色体区域6q12-13.     

6号染色体短臂MHCⅡ类抗原区基因多态性与精神分裂症的关系

目的探讨6号染色体短臂MHCⅡ类抗原区基因多态性与精神分裂症的关系。方法采用聚合酶链反应和限制性内切酶片段长度多态性方法检测位于6号染色体短臂MHCⅡ类抗原区基因上的5个单核苷酸多态性位点,对106个精神分裂症患者核心家系的等位基因和基因型进行传递不平衡和单倍型相对风险分析,同时对多位点构成的单倍型系统进行分析。结果rs1049060位点的C、G等位基因频数分布在病例组和对照组之间差异呈显著性(P<0.05);rs701831、rs707954、rs1047992和rs129190位点的等位基因和基因型频数分布在病例组和对照组之间差异均未见显著性(P>0.05)。rs1049060-rs129190、rs701831-rs707954、rs1047992-rs1049060-rs129190、rs701831-rs707954-rs1047992和rs701831-rs707954-rs1047992-rs1049060单倍型系统与精神分裂症相关联(P<0.05)。结论在人类MHCⅡ类基因区可能存在两个与精神分裂症相关的等位基因。     

人类13q32精神分裂症易感基因的研究

目的　在中国人群中探讨人类 13q32区域内与精神分裂症相关的易感基因位点。 方法　以中国汉族精神分裂症患者和他们的健康父母双亲组成的 91个核心家系为研究对象。采用聚合酶链式反应 -限制性片段长度多态性 (PCR -RFLP)方法对 13q32区域内分别位于STK2 4位点和GPC6位点上的 2个单核苷酸多态性 (SNPs)rs1886 0 89和rs2 892 6 79进行检测。利用拟合优度卡方检验分析基因型分布频率是否符合Hardy -Weinberg平衡定律 ,单体型相对风险分析 (HRR)和传递不平衡检验 (TDT)用于数据基因型分析。结果　 (1)STK2 4 /GPC6基因型频率分布符合Hardy -Weinberg平衡 (P >0 0 5 ) ;(2 )HRR结果显示 ,rs1886 0 89和rs2 892 6 79两个基因多态性与精神分裂症无关联 (P >0 0 5 )。TDT结果表明 ,父母和受累子女之间不存在显著的传递不平衡 (P >0 0 5 ) ,即杂合父母传递给受累子女的等位基因无差异 ;(3)STK2 4rs1886 0 89等位基因与精神分裂症的两种临床症状真性幻听和情感淡漠相关 (χ2 =6 0 0 5df=1P <0 0 5 ;χ2 =6 0 74df=2P <0 0 5 )。GPC6rs2 892 6 79等位基因与精神分裂症的思维贫乏相关(χ2 =6 0 94df=2P <0 0 5 )。结论　STK2 4rs1886 0 89和GPC6rs2 892 6 79基因多态性与精神分裂症的 3种临床症状相关联     

X染色体异常伴原发闭经1例

患者女,19岁,学生,身高138cm,身材矮小,第二性征发育不明显,生理和心理均较同龄人幼稚,因原发闭经,不发育来本院进行检查。经超声检查该患者先天无卵巢,子宫小,性腺不发育;经激素检查泌乳素14.71μg/L(正常值范围3.9～29.5μg/L),睾酮0.007nmol/,L(正常值范     

原发性胆汁性胆管炎易感基因多态性的研究现状

原发性胆汁性胆管炎(PBC)是一种慢性进行性肝内胆汁淤积的自身免疫性疾病,临床特点以大量抗线粒体抗体(AMA)的产生和胆管上皮细胞的损伤为特征。PBC的病因和发病机制尚未完全明确,可能与遗传和环境等相互作用所导致的异常自身免疫反应有关。PBC的家族聚集性、女性患病率明显高于男性以及同卵双胞胎患病的高度一致性,均提示了遗传易感性在PBC的发生和发展中起着重要的作用。全基因组关联分析(GWAS)以及病例对照研究已经鉴定出与PBC相关的各种人类白细胞抗原(HLA)和非HLA等位基因,并证实了PBC的种族差异性。本文综述了可能与PBC相关的易感基因多态性的研究现状。     

精神分裂症患者中单胺氧化酶A和B多态性位点的连锁不平衡研究

目的 为研究单胺氧化酶与精神分裂症的关系。方法 采用聚合酶链式反应，对140例无血缘关系的高加索男性精神分裂症患和91例对照的两个X连锁的微卫星序列即单胺氧化酶A位点中的（AC）n重复序列和单胺氧化酶B位点的（TG）n等位基因进行了研究。结果 基因分布频率在病人组和对照组中未发现差异。但（AC）18（TG）23单倍体增多与精神分裂有关，相对危险度是4．05（95％CI1.15-14.26)，精确概率P＝0．011，连锁不平衡系数在精神分裂症组是0．019；在对照组是－0．046。结论 单胺氧化酶A和B多态性位点的连锁不平衡与精神分裂症有关。     

性染色体假常染色体区域DXYS14基因座与精神分裂症的关系

本文依据《中国精神疾病分类方案》(1989西安)及世界卫生组织精神卫生处 ICD—10选取了6对精神分裂症患病同胞和其父母作实验对象,另以7个志愿者作为对照,从位于 DXY S14区域的基因探针 P29 C1进行了限制性片段长度多态的探测,并以6对患病同胞作了基因型配对分析。本项研究的初步结果未证实国外关于精神分裂症基因位于 DXY S14区域附近的假说。     

Genome scanning for complex disease genes using the transmission/disequilibrium test and haplotype-based haplotype relative risk

Two tests for allelic association were applied to a simulated complex disease for the Genetic Analysis Workshop 9. The transmission/disequilibrium test [Spielman et al., 1993] and the haplotype-based haplotype relative risk approach [Terwilliger and Ott, 1992] were used to detect disease-associated alleles in a set of 360 computer-simulated markers located on six chromosomes and genotyped in 200 nuclear families. This analysis emulates a genome-based search for linked markers. Computer simulations were also performed to clarify statistical properties of the TDT. ©1995 Wiley-Liss, Inc.     

Identifying Susceptibility Genes Using Linkage and Linkage Disequilibrium Analysis in Large Pedigrees

Linkage and linkage disequilibrium tests are powerful tools for mapping complex disease genes. We investigated two approaches to identifying markers associated with disease. One method applied linkage analysis and then linkage disequilibrium tests to markers within linked regions. The other method looked for linkage disequilibrium with disease using all markers. Additionally, we investigated using Simes’ test to combine p‐values from linkage disequilibrium tests for nearby markers. We applied both approaches to all replicates of the Genetic Analysis Workshop 12 problem 2 isolated population data set. We reported results from the 25^th replicate as if it were a real problem and assessed the power of our methods using all replicates. Using all replicates, we found that testing all markers for linkage disequilibrium with disease was more powerful than identifying markers that were in linkage with disease and then testing markers within those regions for linkage disequilibrium with the implementations that we chose. Using Simes’ test to combine p‐values for linkage disequilibrium tests on correlated markers seemed to be of marginal value. © 2001 Wiley‐Liss, Inc.     

A general and accurate approach for computing the statistical power of the transmission disequilibrium test for complex disease genes.

Transmission disequilibrium test (TDT) is a nuclear family-based analysis that can test linkage in the presence of association. It has gained extensive attention in theoretical investigation and in practical application; in both cases, the accuracy and generality of the power computation of the TDT are crucial. Despite extensive investigations, previous approaches for computing the statistical power of the TDT are neither accurate nor general. In this paper, we develop a general and highly accurate approach to analytically compute the power of the TDT. We compare the results from our approach with those from several other recent papers, all against the results obtained from computer simulations. We show that the results computed from our approach are more accurate than or at least the same as those from other approaches. More importantly, our approach can handle various situations, which include (1) families that consist of one or more children and that have any configuration of affected and nonaffected sibs; (2) families ascertained through the affection status of parent(s); (3) any mixed sample with different types of families in (1) and (2); (4) the marker locus is not a disease susceptibility locus; and (5) existence of allelic heterogeneity. We implement this approach in a user-friendly computer program: TDT Power Calculator. Its applications are demonstrated. The approach and the program developed here should be significant for theoreticians to accurately investigate the statistical power of the TDT in various situations, and for empirical geneticists to plan efficient studies using the TDT.     

A unified partial likelihood approach for X‐chromosome association on time‐to‐event outcomes

The expression of X‐chromosome undergoes three possible biological processes: X‐chromosome inactivation (XCI), escape of the X‐chromosome inactivation (XCI‐E), and skewed X‐chromosome inactivation (XCI‐S). Although these expressions are included in various predesigned genetic variation chip platforms, the X‐chromosome has generally been excluded from the majority of genome‐wide association studies analyses; this is most likely due to the lack of a standardized method in handling X‐chromosomal genotype data. To analyze the X‐linked genetic association for time‐to‐event outcomes with the actual process unknown, we propose a unified approach of maximizing the partial likelihood over all of the potential biological processes. The proposed method can be used to infer the true biological process and derive unbiased estimates of the genetic association parameters. A partial likelihood ratio test statistic that has been proved asymptotically chi‐square distributed can be used to assess the X‐chromosome genetic association. Furthermore, if the X‐chromosome expression pertains to the XCI‐S process, we can infer the correct skewed direction and magnitude of inactivation, which can elucidate significant findings regarding the genetic mechanism. A population‐level model and a more general subject‐level model have been developed to model the XCI‐S process. Finite sample performance of this novel method is examined via extensive simulation studies. An application is illustrated with implementation of the method on a cancer genetic study with survival outcome.     

X‐Chromosome Genetic Association Test Accounting for X‐Inactivation,Skewed X‐Inactivation,and Escape from X‐Inactivation

X‐chromosome inactivation (XCI) is the process in which one of the two copies of the X‐chromosome in females is randomly inactivated to achieve the dosage compensation of X‐linked genes between males and females. That is, 50% of the cells have one allele inactive and the other 50% of the cells have the other allele inactive. However, studies have shown that skewed or nonrandom XCI is a biological plausibility wherein more than 75% of cells have the same allele inactive. Also, some of the X‐chromosome genes escape XCI, i.e., both alleles are active in all cells. Current statistical tests for X‐chromosome association studies can either account for random XCI (e.g., Clayton's approach) or escape from XCI (e.g., PLINK software). Because the true XCI process is unknown and differs across different regions on the X‐chromosome, we proposed a unified approach of maximizing likelihood ratio over all biological possibilities: random XCI, skewed XCI, and escape from XCI. A permutation‐based procedure was developed to assess the significance of the approach. We conducted simulation studies to compare the performance of the proposed approach with Clayton's approach and PLINK regression. The results showed that the proposed approach has higher powers in the scenarios where XCI is skewed while losing some power in scenarios where XCI is random or XCI is escaped, with well‐controlled type I errors. We also applied the approach to the X‐chromosomal genetic association study of head and neck cancer.     

Tests for genetic association using family data.

We use likelihood-based score statistics to test for association between a disease and a diallelic polymorphism, based on data from arbitrary types of nuclear families. The Nonfounder statistic extends the transmission disequilibrium test (TDT) to accommodate affected and unaffected offspring, missing parental genotypes, phenotypes more general than qualitative traits, such as censored survival data and quantitative traits, and residual correlation of phenotypes within families. The Founder statistic compares observed or inferred parental genotypes to those expected in the general population. Here the genotypes of affected parents and those with many affected offspring are weighted more heavily than unaffected parents and those with few affected offspring. We illustrate the tests by applying them to data on a polymorphism of the SRD5A2 gene in nuclear families with multiple cases of prostate cancer. We also use simulations to compare the power of these family-based statistics to that of the score statistic based on Cox's partial likelihood for censored survival data, and find that the family-based statistics have considerably more power when there are many untyped parents. The software program FGAP for computing test statistics is available at http://www.stanford.edu/dept/HRP/epidemiology/FGAP.     

Parametric Linkage Analysis and Disequilibrium Methods to Identify Loci for Complex Disease

A two‐step process was used to find loci contributing to the qualitative disease phenotype in the Genetic Analysis Workshop (GAW) 12 simulated data. The first step used parametric linkage analysis with a limited number of dominant and recessive models to detect linkage to chromosomal regions. Subsequently, a subset of the simulated biallelic sequence polymorphisms was used for transmission/disequilibrium tests and to build haplotypes to fine map the disease‐predisposing polymorphism(s). A haplotype, strongly associated with the disease phenotype whose proximal end was within 39 base pairs of the functional allele for simulated major gene 6, was identified in the isolated population. © 2001 Wiley‐Liss, Inc.     

Computational Methods for Single‐Point and Multipoint Analysis of Genetic Variants Associated with a Simulated Complex Disorder in a General Population

Several techniques for association analysis have been applied to simulated genetic data for a general population. We describe and compare the performance of three single‐point methods and two multipoint approaches rooted in machine learning and data mining. © 2001 Wiley‐Liss, Inc.     

Multipoint linkage disequilibrium mapping using case-control designs

Case-control study has been and continues to be one of the most popular designs in epidemiology. More recently, this design has been adopted to test for candidate genes when searching for disease genetic etiology. In this report, we present a multipoint linkage disequilibrium (LD) mapping approach with the focus on estimating the location of the target trait locus. It builds upon a representation, which shows that the difference between a case and a control in probabilities of carrying the target allele of a marker is proportional to that of the trait locus and that the proportionality factor is simply a measure of LD between the trait locus and the marker. Our method has the desired properties that (1) there is no need to specify phases of genotypic data with multiple markers, (2) it provides an estimate of location of the disease locus along with sampling uncertainty to help investigators to narrow chromosomal regions, and (3) a single test statistic is provided to test for LD in the framed region rather than testing the hypothesis one marker at a time. Our simulation work suggests that the proposed method performs well in terms of bias and coverage probability. Extension of the proposed method to account for confounding and genetic heterogeneity is discussed. We apply the proposed method to a published case-control data set for cystic fibrosis.     

Statistics for X‐chromosome associations

In a genome‐wide association study (GWAS), association between genotype and phenotype at autosomal loci is generally tested by regression models. However, X‐chromosome data are often excluded from published analyses of autosomes because of the difference between males and females in number of X chromosomes. Failure to analyze X‐chromosome data at all is obviously less than ideal, and can lead to missed discoveries. Even when X‐chromosome data are included, they are often analyzed with suboptimal statistics. Several mathematically sensible statistics for X‐chromosome association have been proposed. The optimality of these statistics, however, is based on very specific simple genetic models. In addition, while previous simulation studies of these statistics have been informative, they have focused on single‐marker tests and have not considered the types of error that occur even under the null hypothesis when the entire X chromosome is scanned. In this study, we comprehensively tested several X‐chromosome association statistics using simulation studies that include the entire chromosome. We also considered a wide range of trait models for sex differences and phenotypic effects of X inactivation. We found that models that do not incorporate a sex effect can have large type I error in some cases. We also found that many of the best statistics perform well even when there are modest deviations, such as trait variance differences between the sexes or small sex differences in allele frequencies, from assumptions.