结构方程混合模型在SNP分析中的应用

采用结构方程混合模型(SEMM)对实际SNP数据进行分析,为遗传统计学提供一种新的有效的分析方法。本研究的数据是由GAW17提供的,包含697个个体的22条常染色体的上万个SNP和根据这些SNP所模拟的697个个体的性状特点。随机挑选了1号染色体上的4个SNP和3个定量性状作为研究变量,分别进行潜在类别分析和结构方程混合模型分析。根据4个SNP数据,人群被分为3个潜在类别,概率分别为0.53,0.34,0.13。潜在类别1、2和3中的因子均值Q分别为-4.029、-2.052和0,潜在类别1、2的因子均值均低于3(<0.001)。研究表明:结构方程混合模型(SEMM)综合了结构方程模型和潜在类别模型的思想,形成了自己的优势,可用于处理同时包含分类潜变量和连续潜变量的数据。     

非正态验证性因子分析在基因整体效应中的应用

针对SNPs数据不服从正态分布的情况,拟采用S-B测度调整估计方法拟合验证性因子模型,进行SNPs整体效应和关联性分析。用GAWl7提供的SNPs数据进行实例分析。本研究随机选取2号染色体上,分布在6个基因之中的13个SNPs作为研究对象,对选取的6个基因做潜变量得分,然后对基因和疾病感染做检验。结果显示：X^2／妒最大似然估计方法的卡方自由度比为3．59,S-B测度调整估计方法的卡方自由度比X^2／df为2．89,最大似然估计方法的RMSEA为0．061,S-B测度调整估计方法的RMSEA为0．052。6个基因对该感染都有影响．由此得出结论,在处理SNPs数据时,使用S-B测度调整估计能得到更好的拟合模型。可以推测这6个基因下的13个SNP位点可能是感染的致病位点。     

NBS1基因SNPs与中国汉族人群原发性肝癌的遗传易感性

为分析DNA损伤修复相关基因NBS1单核苷酸多态性(SNPs)与原发性肝癌遗传易感性的关系,并对高分辨率单链构象多态性(SSCP)检测技术在SNPs分型中的适用性进行评估,本研究对来自中国汉族人群的327例原发性肝癌以及295例阴性对照中NBS1基因常见SNPs的稀有等位基因频率进行检测和分析．此外,对NBS1基因6个常见SNPs分别选择部分样本同时进行直接序列测定,以比较2种方法的检测效果．119例原发性肝癌以及95例肝硬化/慢性肝炎组织标本的SSCP分析结果表明,6个常见NBS1基因SNPs位点(102G>A, 320+208G/A, 553G>C, 1197T>C, 2016A>G和2071-30A>T)中,SNP 1197T>C的稀有等位基因频率为68.1%,显著高于肝硬化/慢性肝炎对照的57.9% (P = 0.0298)．对该SNP位点另外采用208份肝细胞癌和200份健康人群血液标本进一步分析, 肝细胞癌SNP 1197T>C的稀有等位基因频率为66.8%,显著高于健康人群对照的58.8% (P = 0.0170)．其他5个SNPs的稀有等位基因频率在原发性肝癌与肝硬化/慢性肝炎之间均无显著性差异．高分辨率SSCP分析法与直接序列测定法对所选样本的SNPs基因分型结果完全一致,而且直接测序法对PCR扩增产物质量的要求相对高分辨率SSCP分析更高．研究表明,中国汉族人群NBS1基因SNP 1197T>C可能与原发性肝癌的发生相关,高分辨率SSCP技术准确度与直接测序法相当,且操作更加简便易行,非常适用于大量样本多个已知SNPs的基因分型．     

混合DNA样品池扩增法及其应用

将个体 DNA提取出来后 ,按一定方式进行混合 ,构成混合 DNA样品池。这种混合 DNA样品可用于病因未明的遗传性及遗传易感性疾病的研究。在研究常染色体隐性遗传性耳聋致病基因时 ,发现与染色体 9q的 D9S92 2和 D9S30 1位点有相关性。此方法比通常的连锁分析法省时省力。在肿瘤相关基因或责任基因的研究、法医学的个体认定、基因突变的检测等方面均显示出实用性 ,值得推广     

冠状动脉粥样硬化性心脏病8号染色体基因扫描

冠状动脉粥样硬化性心脏病(Coronary heart disease,CHD)的全基因组扫描研究在世界各大研究中心展开,关于CHD易感基因位点的报道多集中于1号、3号、9号、11号、16号染色体,对8号染色体的研究报道甚少。在汉族人群中未见关于CHD的8号染色体的基因扫描研究。文章旨在查找汉族人群中CHD易感基因位点,选取8号染色体上间隔10 cM遗传距离的13个微卫星遗传位点,采用DNA混合池的方法对CHD患者组156例和正常对照组1 000例DNA样本进行基因扫描,经卡方检验分析患者组和对照组每个位点的等位基因频率差异。发现在患者组与对照组中D8S264位点(8p23.3-p23.2)及D8S285位点(8q12.1)的等位基因频率差异有统计学意义(P<0.05)。汉族人群中CHD患者8号染色体上8p23.3-p23.2、8q12.1区域可能存在CHD易感基因,需要进行候选基因突变筛查。     

癫痫相关基因SCN1A启动子区多态性位点的生物信息学分析

SCN1A是多种神经系统疾病的致病基因,该基因的精确表达对于维持神经系统正常功能非常重要.为了认识SCN1A启动子区多态性位点(single nucleotide polymorphisms,SNPs)的保守性及其功能意义,应用生物信息学方法分析了目前已发表位于SCN1A启动子区的11个SNPs,分别命名S1～S11.分析结果表明,S3、S5和S7等位基因频率没有人种差异性,其余SNP等位基因频率均有人种差异性;接近核心启动子的SNPs要比远离核心启动子SNPs的保守程度高,提示靠近核心启动子的SNPs影响SCN1A基因表达的概率可能越大;大部分SNPs祖传等位基因在哺乳动物中是保守的(S3除外),暗示这些SNPs新生等位基因有可能在人类进化过程起到一定的作用:启动子分析软件预测发现,含S2、S4、S8及S9等4个SNPs不同等位基因的同一序列分别存在不同转录因子结合元件,而含S1和S11不同等位基因的同一序列都只能预测到含其中一个等位基因的序列存在转录因子结合元件,这些差异可能是SNPs影响SCN1A表达的重要原因之一.这些分析将为进一步研究SCN1A启动子区SNPs与神经系统疾病的相互关系打下基础.     

30个祖先信息位点的筛选及应用

摘要：目的 筛选一组祖先信息SNPs位点（AIMs,Ancestry Informative Markers）,构建复合检测体系,用于东亚、欧洲和非洲人群遗传成分描述及个体种族来源推断。方法 以HapMap数据库9个人群的658份样本的分型数据为基础,从30个表型相关基因总共282个SNPs位点中筛选出30个AIMs位点,基于微测序-通用芯片技术构建复合检测体系,并建立人群等位基因频率数据库。使用这组位点分析HapMap数据库中658份人群样本,初步验证位点的区分效能;然后,使用研究构建的体系检验收集的5个人群194份无关个体的DNA样本。最后,通过Structure软件分析获取人群的成分构成以及个体的遗传成分,对个体样本进行种族来源推断。 结果 筛选的30个AIMs位点符合哈迪温伯格平衡（p>0.01）,位点之间没有连锁（r2<0.1）, 658份HapMap数据库样本和194份实验样本的祖先成分分析结果与已知结果完全一致。 结论 本文筛选并建立的30个AIMs位点复合检测体系,能够有效实现东亚、欧洲、非洲人群及混合人群的成分构成和个体遗传成分的分析,有效控制遗传连锁分析中由于人群分层现象带来的误差,也可以用于法医DNA检验中个体祖先来源推断。     

吉林人群强直性脊柱炎6号染色体短臂上的HLA区域遗传易感基因定位研究

为了寻找中国人群中与强直性脊柱炎相关的新的易感基因及其所在位置, 在与强直性脊柱炎强连锁的6 号染色体短臂上的HLA基因区域内选取11个SNPs多态位点, 通过对中国吉林地区79名AS患者和132名正常对照者进行case-control分析, 发现TNF-a -850处TT突变基因型在AS组中的分布高于正常对照组（P=0.027）, 突变型T等位基因在AS组和正常对照组中的分布差异更为显著（P=0.002）。通过多位点之间的连锁不平衡分析发现, LTA基因、TNF-a基因、LST1基因和NCR3基因中的 5个SNPs多态位点之间存在连锁不平衡, 范围是15 kb, 在这5个SNPs多态位点组成的单体型中, TCTTC单体型在AS组和正常对照组中的分布有显著差异（c2=7.406, P=0.0065）,并且该单体型中含有具有统计学意义的TNF-a –850的突变型等位基因T。提示在LTA、TNF-a、NCR3和LST1 这4个基因构成的15 kb范围内可能存在增加AS患病易感性的位点, 可能是TNF-a –850 C→T突变, 也可能是在TNF-a –850附近的其他位点。     

日本沼虾ITS1序列分析及SNPs位点的筛选

研究采用直接测序法,分析日本沼虾(Macrobrachium nipponense)rDNA基因内转录间隔区ITS1的DNA序列,以筛选日本沼虾SNPs位点。共分析了32个太湖水域野生日本沼虾样本,结果表明,日本沼虾ITS1序列平均长度为1749.8bp,是迄今已报道的最长的ITS1序列,A、G、T和C的平均含量分别为29.9%、28.3%、27.7%、14.0%,G+C的含量平均为42.3%。通过序列比对,共筛选出22个SNPs位点,SNPs位点出现频率为0.0126,其中9个为C/T转换(占40.91%),4个为A/G转换(占18.18%),2个为A/T颠换(占9.09%),5个为T/G颠换(占22.73%),1个为A/C颠换(占4.55%),1个A/T或C颠换(占4.55%)。日本沼虾ITS1序列的22个SNP位点中,21个位点为2个等位基因,1个位点出现了3个等位基因,为复等位基因位点。日本沼虾ITS1序列中还发现3个具有多态性的微卫星位点、1个高度变异区以及大量的缺失、插入。研究首次对日本沼虾ITS1序列进行了分析,并发现了大量的SNP位点,为日本沼虾遗传育种研究提供了新的分子标记。       

贵州白山羊GOLA-DQA1基因多态性及其与血液免疫指标的关联性

为了解贵州白山羊GOLA-DQA1基因SNPs与血液免疫指标的相关性,本研究采用PCR直接测序技术对122只贵州白山羊周岁羊群体GOLA-DQA1基因Exon1及部分内含子进行多态性的检测,进一步将各突变位点与血液免疫指标进行关联性分析。结果:在试验群体中共发现7个SNPs位点,其中C+36G、C+48T、A+49G、T+65G位于外显子上;A+122G、T+125C、A+129G位点位于内含子上,属于中度多态性(0.5PIC0.25)。存在连锁不平衡现象且各位点均偏离了Hardy-Weinberg平衡,分析发现C+48T、A+49G位点与中间细胞比率具有显著的相关性,其中CT基因型个体的中间细胞比率显著高于CC基因型(p0.05);AG基因型个体中间细胞比率值显著高于AA基因型。由此推断GOLA-DQA1基因C+48T、A+49G位点与贵州白山羊血液免疫指标具有一定的相关性,为山羊抗病育种的辅助选择标记的选择提供一定的参考。     

中华绒螯蟹MSTN基因SNPs多态性及与生长性状的关联分析

为研究中华绒螯蟹(Eriocheir sinensis)肌肉生长抑制素基因(myostatin, MSTN)的多态性及其与生长性状的相关性, 对中华绒螯蟹3个群体(育种群体、大赛群体、野生群体)共321个个体MSTN基因的多态性进行筛选, 发现该基因的第1外显子存在3个多态性SNP位点(S1: C714T; S2:G729A; S3:G753T), 均为处于Hardy-Weinberg平衡(P>0.05)的中、高度多态性位点。利用一般线性模型分析3个位点及其基因型组合与生长性状的相关性, 发现S1位点对中华绒螯蟹的体重和壳长等生长性状有显著影响(P≤0.05), 而其余2个位点与生长性状无显著关联性。结果表明S1位点的TT基因型对中华绒螯蟹的生长最为有利, 可作为分子标记辅助育种的候选标记。     

Evidence of Associations between Cytokine Genes and Subjective Reports of Sleep Disturbance in Oncology Patients and Their Family Caregivers

The purposes of this study were to identify distinct latent classes of individuals based on subjective reports of sleep disturbance; to examine differences in demographic, clinical, and symptom characteristics between the latent classes; and to evaluate for variations in pro- and anti-inflammatory cytokine genes between the latent classes. Among 167 oncology outpatients with breast, prostate, lung, or brain cancer and 85 of their FCs, growth mixture modeling (GMM) was used to identify latent classes of individuals based on General Sleep Disturbance Scale (GSDS) obtained prior to, during, and for four months following completion of radiation therapy. Single nucleotide polymorphisms (SNPs) and haplotypes in candidate cytokine genes were interrogated for differences between the two latent classes. Multiple logistic regression was used to assess the effect of phenotypic and genotypic characteristics on GSDS group membership. Two latent classes were identified: lower sleep disturbance (88.5%) and higher sleep disturbance (11.5%). Participants who were younger and had a lower Karnofsky Performance status score were more likely to be in the higher sleep disturbance class. Variation in two cytokine genes (i.e., IL6, NFKB) predicted latent class membership. Evidence was found for latent classes with distinct sleep disturbance trajectories. Unique genetic markers in cytokine genes may partially explain the interindividual heterogeneity characterizing these trajectories.     

Finite mixture modeling with mixture outcomes using the EM algorithm

This paper discusses the analysis of an extended finite mixture model where the latent classes corresponding to the mixture components for one set of observed variables influence a second set of observed variables. The research is motivated by a repeated measurement study using a random coefficient model to assess the influence of latent growth trajectory class membership on the probability of a binary disease outcome. More generally, this model can be seen as a combination of latent class modeling and conventional mixture modeling. The EM algorithm is used for estimation. As an illustration, a random-coefficient growth model for the prediction of alcohol dependence from three latent classes of heavy alcohol use trajectories among young adults is analyzed.     

Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure

Single-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for population genetic studies and for identifying genetic variations underlying complex traits. However, the usefulness and efficiency of SNPs in comparison to microsatellites in different scientific contexts, e.g., population structure inference or association analysis, still must be systematically evaluated through large empirical studies. In this article, we use the Collaborative Studies on Genetics of Alcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to compare the performance of microsatellites and SNPs in the whole human genome in the context of population structure inference. A total of 328 microsatellites and 15,840 SNPs are used to infer population structure in 236 unrelated individuals. We find that, on average, the informativeness of random microsatellites is four to twelve times that of random SNPs for various population comparisons, which is consistent with previous studies. Our results also indicate that for the combined set of microsatellites and SNPs, SNPs constitute the majority among the most informative markers and the use of these SNPs leads to better inference of population structure than the use of microsatellites. We also find that the inclusion of less informative markers may add noise and worsen the results.     

Latent class model diagnosis

In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the intention of hierarchical modeling. Problems arise when it is not clear how many disease classes are appropriate, creating a need for model selection and diagnostic techniques. Previous work has shown that the Pearson chi 2 statistic and the log-likelihood ratio G2 statistic are not valid test statistics for evaluating latent class models. Other methods, such as information criteria, provide decision rules without providing explicit information about where discrepancies occur between a model and the data. Identifiability issues further complicate these problems. This paper develops procedures for assessing Markov chain Monte Carlo convergence and model diagnosis and for selecting the number of categories for the latent variable based on evidence in the data using Markov chain Monte Carlo techniques. Simulations and a psychiatric example are presented to demonstrate the effective use of these methods.     

Demographic analysis from summaries of an age-structured population

Demographic analyses of age-structured populations typically rely on life history data for individuals, or when individual animals are not identified, on information about the numbers of individuals in each age class through time. While it is usually difficult to determine the age class of a randomly encountered individual, it is often the case that the individual can be readily and reliably assigned to one of a set of age classes. For example, it is often possible to distinguish first-year from older birds. In such cases, the population age structure can be regarded as a latent variable governed by a process prior, and the data as summaries of this latent structure. In this article, we consider the problem of uncovering the latent structure and estimating process parameters from summaries of age class information. We present a demographic analysis for the critically endangered migratory population of whooping cranes (Grus americana), based only on counts of first-year birds and of older birds. We estimate age and year-specific survival rates. We address the controversial issue of whether management action on the breeding grounds has influenced recruitment, relating recruitment rates to the number of seventh-year and older birds, and examining the pattern of variation through time in this rate.     

Score Test for Conditional Independence Between Longitudinal Outcome and Time to Event Given the Classes in the Joint Latent Class Model

Summary .  Latent class models have been recently developed for the joint analysis of a longitudinal quantitative outcome and a time to event. These models assume that the population is divided in  G  latent classes characterized by different risk functions for the event, and different profiles of evolution for the markers that are described by a mixed model for each class. However, the key assumption of conditional independence between the marker and the event given the latent classes is difficult to evaluate because the latent classes are not observed. Using a joint model with latent classes and shared random effects, we propose a score test for the null hypothesis of independence between the marker and the outcome given the latent classes versus the alternative hypothesis that the risk of event depends on one or several random effects from the mixed model in addition to the latent classes. A simulation study was performed to compare the behavior of the score test to other previously proposed tests, including situations where the alternative hypothesis or the baseline risk function are misspecified. In all the investigated situations, the score test was the most powerful. The methodology was applied to develop a prognostic model for recurrence of prostate cancer given the evolution of prostate-specific antigen in a cohort of patients treated by radiation therapy.     

A Bayesian Latent Variable Mixture Model for Longitudinal Fetal Growth

Summary Fetal growth restriction is a leading cause of perinatal morbidity and mortality that could be reduced if high‐risk infants are identified early in pregnancy. We propose a Bayesian model for aggregating 18 longitudinal ultrasound measurements of fetal size and blood flow into three underlying, continuous latent factors. Our procedure is more flexible than typical latent variable methods in that we relax the normality assumptions by allowing the latent factors to follow finite mixture distributions. Using mixture distributions also permits us to cluster individuals with similar observed characteristics and identify latent classes of subjects who are more likely to be growth or blood flow restricted during pregnancy. We also use our latent variable mixture distribution model to identify a clinically meaningful latent class of subjects with low birth weight and early gestational age. We then examine the association of latent classes of intrauterine growth restriction with latent classes of birth outcomes as well as observed maternal covariates including fetal gender and maternal race, parity, body mass index, and height. Our methods identified a latent class of subjects who have increased blood flow restriction and below average intrauterine size during pregnancy. These subjects were more likely to be growth restricted at birth than a class of individuals with typical size and blood flow.