人肌球蛋白7A基因非同义单核苷酸多态性位点潜在致聋突变的预测分析

目的:人肌球蛋白7A(MYO7A)基因是遗传性耳聋分子筛查的候选基因之一。从已知的MYO7A非同义单核苷酸多态性(ns SNPs)位点数据库中筛选可能与致病表型相关的ns SNPs位点,以提高MYO7A基因耳聋分子诊断的有效性和准确率。方法:首先,从NCBI数据中心的db SNP数据库(db SNP)和Deafness Variation Database数据库获得MYO7A基因的SNPs数据和基因的相关信息;然后,通过SIFT、Poly Phen-2、PANTHER、Ph D-SNP、Mutation Taster、SNPGO和Mut Pred软件进行ns SNPs表型致病性分析,预测潜在致病位点;接着,应用Clustal X2和Gene Doc软件进行同源氨基酸序列比对,分析潜在致病的ns SNPs位点保守性;最后,应用Swiss Model平台选择性地对某些突变蛋白质的三维结构进行建模,并分析结构域的变化。结果:预测出MYO7A的104个高风险致病的ns SNPs位点,包括25个已报道的耳聋相关ns SNPs位点;高风险致病的ns SNPs位点中,有42个位于肌球蛋白马达(myosin motor)结构域,其中12个预测有致病风险的ns SNPs位点与MYO7A基因致聋的突变研究报道一致。肌球蛋白马达结构域中包含30个新预测的潜在致病性ns SNPs位点,其中仅L366P位点在7个预测软件中具有高度一致性。通过对L366P位点位点突变前后的三维模型构建,发现存在蛋白结构的改变,且同源性比对结果显示了该位点的高度保守性。结论:MYO7A的L366P为潜在高风险致病性ns SNP位点,推测该基因突变可能与耳聋表型相关。本研究所采用的分析筛选方法对MYO7A基因突变的临床筛查及其他致病基因的ns SNPs筛选具有重要的参考价值。     

H广义线性模型中的参数估计的比较

本文研究H广义线性模型中未知参数的两种估计方法,一种是边际似然函数法,另一种是Lee和Nelder提出来的L-N法．对于一类具有两个随机效应的典型的Poisson-Gamma类模型,在一些正则性条件之下,我们已经证明了其中固定效应卢的L-N估计的强相合性及渐近正态性,并得到了其收敛于真值的速度．针对这类模型,本文进一步给出了其边际似然函数的解析表达式,并且通过Monte Carlo模拟,对模型中固定效应β的边际似然估计和L—N估计进行了比较,模拟表明L—N估计比边际似然估计在拟Poisson-Gamma模型中有着更加优良的表现,具有更高的精度。     

一个适于最大似然法估计物种分化年代的进化速率平滑近似方法

众所周知 ,物种分化年代的估计对分子钟 (进化速率恒定 )假定很敏感。另一方面 ,在远缘物种 (例如哺乳纲不同目的动物 )的比较中 ,分子钟几乎总是不成立的。这样在估计分化时间时考虑不同进化区系的速率差异至为重要。最大似然法可以很自然地考虑这种速率差异 ,并且可以同时分析多个基因位点的资料以及同时利用多重化石校正数据。以前提出的似然法需要研究者将进化树的树枝按速率分组 ,本文提出一个近似方法以使这个过程自动化。本方法综合了以前的似然法、贝斯法及近似速率平滑法的一些特征。此外 ,还对算法加以改进 ,以适应综合数据分析时某些基因在某些物种中缺乏资料的情形。应用新提出的方法来分析马达加斯加的倭狐猴的分化年代 ,并与以前的似然法及贝斯法的分析进行了比较     

与偏分离位点连锁的QTL作图的统计方法

提出了一种统计方法,可以估计与偏分离位点连锁的QTL的位置和效应。该方法利用回交群体中呈现偏分离的分子标记,首先用最大似然法对偏分离位点与标记位点之间的重组率和配子存活率进行估计,然后用区间作图法估计加性-显性模型下QTL的位置和效应参数。该方法可用于对常规作图研究中表现偏分离的标记进行分析,以帮助我们发现新的偏分离基因（或不育基因）和数量性状位点。     

远缘杂交中不育基因的位置和效应的最大似然估计

提出了一种统计方法,利用标记位点的异常分离,来估计远缘杂交中不育基因位点的位置和效应,在回交群体中,用最大似然法对不育基因与标记位点之间的生组值和配子存活率进行估计。将表现连续分布的育性指标转化为百连续变异的遗传标记的分离,可以避免对育性直接观测所带来的重组值估计结果的不稳定,还可以同时估计雌雄配子的存活率。     

两个位点主基因控制的质量—数量性状的遗传分析

应用极大似然法和EM算法提出了关于两个位点主基因控制的质量．数量性状的遗传分析方法,参照质量性状两位点互作在F_2代的分离比率建立了7种遗传假设及其似然比测验的程序,讨论了应用这一方法时应注意的几个问题．     

突变积累实验中极大似然法和距法的比较

最近,人们突变积累实验（MA）中测定有害基因突变（DGM）的兴趣大增。在MA实验中有两种常见的DGM估计方法（极大似然法ML和距法MM）,依靠计算机模拟和处理真实数据的应用软件来比较这两种方法。结论是：ML法难于得到最大似然估计（MLEs),所以ML法不如MM法估计有效;即使MLEs可得,也因其具严重的微样误差（据偏差和抽样差异）而产生估计偏差;似然函数曲线较平坦而难于区分高峰态和低峰态的分布。     

双向等位基因特异性PCR与限制性片段长度多态性基因分型方法比较

目的:比较双向等位基因特异性PCR(Bi-PASA)法与聚合酶链式反应-限制性片段长度多态性(RFLP)法对EZH2基因单核苷酸多态性(SNPs)位点rs887569基因分型结果有无差异,并用Bi-PASA法对EZH2基因rs17171119位点基因分型后分析与结直肠癌(CRC)易感性的相关性。方法:提取96名CRC患者与100名体检健康者的外周血DNA,分别用Bi-PASA法与聚合PCR-RFLP法检测EZH2基因单核苷酸多态性(SNPs)位点rs887569基因型,对两种分型结果进行比较;使用Bi-PASA法对EZH2基因rs17171119位点进行基因分型后用病例-对照方法分析该SNPs在中国人群中的分布。结果:Bi-PASA与PCR-RFLP对EZH2基因rs887569位点基因分型的准确率分别为99.5%和100%;EZH2基因的rs17171119 SNPs位点多态性与结直肠癌易感性无显著相关性(P=0.938,OR=0.846,95%CI:0.586-1.221)。结论:Bi-PASA是一种简单有效检测SNPs的方法,分型结果较为可靠;rs17171119 SNPs位点多态性与结直肠癌易感性无关,但本结论还有待更大样本量基因分型的验证。     

吉富罗非鱼两种生长激素受体基因的分离及与增重相关的SNPs位点

实验分离了吉富罗非鱼(Oreochromis niloticus)两种生长激素受体基因GHR1和GHR2的全长cDNA以及GHR1阅读框中外显子2～9、GHR2阅读框外显子2～8的DNA序列.在GHR1和GHR2分别找到6个和16个SNPs位点,其中只有2个位点在外显子部分.使用PCR-RFLP方法检测了5个家系共12...     

多重二元响应Probit模型的渐近有效估计

针对多重二元响应Probit模型提出了两步估计方法,第一步由边际似然得到参数√n相合的估计,第二步通过一步迭代得到渐近有效估计,由于只需一步迭代,因此在利用模拟方法计算信息阵时,可以增加模拟的次数,从而减少模拟所产生的扰动对估计的影响．     

Usefulness of single nucleotide polymorphism data for estimating population parameters

Single nucleotide polymorphism (SNP) data can be used for parameter estimation via maximum likelihood methods as long as the way in which the SNPs were determined is known, so that an appropriate likelihood formula can be constructed. We present such likelihoods for several sampling methods. As a test of these approaches, we consider use of SNPs to estimate the parameter Theta = 4N(e)micro (the scaled product of effective population size and per-site mutation rate), which is related to the branch lengths of the reconstructed genealogy. With infinite amounts of data, ML models using SNP data are expected to produce consistent estimates of Theta. With finite amounts of data the estimates are accurate when Theta is high, but tend to be biased upward when Theta is low. If recombination is present and not allowed for in the analysis, the results are additionally biased upward, but this effect can be removed by incorporating recombination into the analysis. SNPs defined as sites that are polymorphic in the actual sample under consideration (sample SNPs) are somewhat more accurate for estimation of Theta than SNPs defined by their polymorphism in a panel chosen from the same population (panel SNPs). Misrepresenting panel SNPs as sample SNPs leads to large errors in the maximum likelihood estimate of Theta. Researchers collecting SNPs should collect and preserve information about the method of ascertainment so that the data can be accurately analyzed.     

Estimation of population parameters and recombination rates from single nucleotide polymorphisms

Some general likelihood and Bayesian methods for analyzing single nucleotide polymorphisms (SNPs) are presented. First, an efficient method for estimating demographic parameters from SNPs in linkage equilibrium is derived. The method is applied in the estimation of growth rates of a human population based on 37 SNP loci. It is demonstrated how ascertainment biases, due to biased sampling of loci, can be avoided, at least in some cases, by appropriate conditioning when calculating the likelihood function. Second, a Markov chain Monte Carlo (MCMC) method for analyzing linked SNPs is developed. This method can be used for Bayesian and likelihood inference on linked SNPs. The utility of the method is illustrated by estimating recombination rates in a human data set containing 17 SNPs and 60 individuals. Both methods are based on assumptions of low mutation rates.     

Determination and correction of aberrations in full field optical coherence tomography using phase gradient autofocus by maximizing the likelihood function

A method for numerical estimation and correction of aberrations of the eye in fundus imaging with optical coherence tomography (OCT) is presented. Aberrations are determined statistically by using the estimate based on likelihood function maximization. The method can be considered as an extension of the phase gradient autofocusing algorithm in synthetic aperture radar imaging to 2D optical aberration correction. The efficacy of the proposed method has been demonstrated in OCT fundus imaging with 6λ aberrations. After correction, single photoreceptors were resolved. It is also shown that wave front distortions with high spatial frequencies can be determined and corrected.     

Accounting for linkage disequilibrium among markers in linkage analysis: impact of haplotype frequency estimation and molecular haplotypes for a gene in a candidate region for Alzheimer's disease

OBJECTIVES: Linkage disequilibrium (LD) between closely spaced SNPs can be accommodated in linkage analysis by specifying the multi-SNP haplotype frequencies, if known. Phased haplotypes in candidate regions can provide gold standard haplotype frequency estimates, and may be of inherent interest as markers. We evaluated the effects of different methods of haplotype frequency estimation, and the use of marker phase information, on linkage analysis of a multi-SNP cluster in a candidate region for Alzheimer's disease (AD). METHODS: We performed parametric linkage analysis of a five-SNP cluster in extended pedigrees to compare the use of: (1) haplotype frequencies estimated by molecular phase determination, maximum likelihood estimation, or by assuming linkage equilibrium (LE); (2) AD families or controls as the frequency source; and (3) unphased or molecularly phased SNP data. RESULTS: There was moderate to strong pairwise LD among the five SNPs. Falsely assuming LE substantially inflated the LOD score, but the method of haplotype frequency estimation and particular sample used made little difference provided that LD was accommodated. Use of phased haplotypes produced a modest increase in the LOD score over unphased SNPs. CONCLUSIONS: Ignoring LD between markers can lead to substantially inflated evidence for linkage in LOD score analysis of extended pedigrees with missing data. Use of marker phase information in linkage analysis may be important in disease studies where the costs of family recruitment and phenotyping greatly exceed the costs of phase determination.     

Dose-response model for Burkholderia pseudomallei (melioidosis)

Aims: The objective of this study was development of a dose–response model for exposure to Burkholderia pseudomallei in different animal hosts and analysis of the results. The data sets with which the model was developed were taken from the open literature. Methods and Results: All data sets were initially tested for a trend between dose and outcome using the Cochran–Armitage test. Only data showing a statistically significant trend were subjected to further analysis (fitting with parametric dose–response relationships). Dose–response relationships (exponential, beta-Poisson and log-probit) were fit to data using the method of maximum likelihood estimation. Conclusions: Dose–response analysis of BALB/c mice, C57BL/6 mice, guinea pigs and diabetic rats showed that BALB/c mice exposed intranasally (i.n.) and guinea pigs exposed intraperitoneally (i.p.) are significantly more sensitive to B. pseudomallei than C57BL/6 mice exposed i.n. and diabetic rats exposed i.p. Significance and Impact of the Study: The results confirmed the findings of a study of outbreak data that the diabetic population is more susceptible to infection with B. pseudomallei than the general population. The low dose prediction from best fit dose–response models can be used to draw guidelines for public health decision making processes, including consideration of sensitive subpopulations.     

质量—数量性状的遗传参数估计Ⅲ：利用杂种F2或回交世代

采用混合分布模型和极大似然法,提出了杂种早期世代Ｆ_２与回交群体中两个位点之 基因控制的质量－数量性状的遗传分析方法,据此可以进行主基因及其作用方式的测验、主基因 和微基因效应的估计等．探讨了利用回交群体进行质量－数量性状遗传分析与检测的适用范围和 有效范围等．     

Robust Gaussian Graphical Modeling Via l1 Penalization

Summary Gaussian graphical models have been widely used as an effective method for studying the conditional independency structure among genes and for constructing genetic networks. However, gene expression data typically have heavier tails or more outlying observations than the standard Gaussian distribution. Such outliers in gene expression data can lead to wrong inference on the dependency structure among the genes. We propose a l₁ penalized estimation procedure for the sparse Gaussian graphical models that is robustified against possible outliers. The likelihood function is weighted according to how the observation is deviated, where the deviation of the observation is measured based on its own likelihood. An efficient computational algorithm based on the coordinate gradient descent method is developed to obtain the minimizer of the negative penalized robustified‐likelihood, where nonzero elements of the concentration matrix represents the graphical links among the genes. After the graphical structure is obtained, we re‐estimate the positive definite concentration matrix using an iterative proportional fitting algorithm. Through simulations, we demonstrate that the proposed robust method performs much better than the graphical Lasso for the Gaussian graphical models in terms of both graph structure selection and estimation when outliers are present. We apply the robust estimation procedure to an analysis of yeast gene expression data and show that the resulting graph has better biological interpretation than that obtained from the graphical Lasso.     

Validating genome‐wide associated signals for clinical mastitis in German Holstein cattle

A validation study for six genomic regions previously identified by a genome‐wide association study for somatic cell score was conducted with data of clinical mastitis in German Holstein cattle. Out of 10 tested SNPs, five on chromosomes 6, 13 and 19 were significantly associated with clinical mastitis (P < 0.05). Three SNPs on chromosomes 6 and 19 had the same direction of effect as those previously reported in the initial genome‐wide association study for somatic cell score. The other two SNPs on chromosome 13 had opposite effects. As well as validating associations within known QTL from previous studies, e.g. chromosomes 6 and 19, novel loci on chromosome 13 were confirmed. Promising candidate genes are, for example: deoxycytidine kinase, immunoglobulin J chain, vitamin D binding protein, forkhead box K2, sodium/hydrogen exchanger 8 and cytoplasmic nuclear factor of activated T‐cells 2. Our confirmation study provides additional evidence for the functional role of the linked genomic regions to immune response. This information can be used as a basis for further functional studies for those potential genes.