基于性状-标记回归的QTL区间测验方法

本文提出了两种基于性状-标记回归的QTL区间测验方法,分别称为TMRIT-I和TMRIT-II。前者采用似然比统计量进行显著性测验,与基于最小二乘的简化复合区间定位法（sCIM）等价,但计算上明显简单快捷;后者则采用一种“伪似然比”统计量进行显著性测验,不仅进一步简化计算,而且明显提高统计功效。二者皆可通过排列测验估计显著阈值。给出了一个模拟例子。 Abstract：Two methods of interval test of quantitative trait loci (QTLs) based on trait-marker regression are proposed, named as TMRIT-I and TMRIT-II, respectively. The former uses likelihood ratio statistic for significance test, equivalent to the method of simplified composite interval mapping (sCIM) based on least squares, but much simpler and quicker in calculation. The latter uses a ‘pseudo-likelihood ratio’ statistic for significance test, not only simplifying calculation further, but also significantly increasing statistical power. For both methods, significance threshold can be estimated by permutation tests. A simulated example is given.     

删失数据下非线性半参数回归模型中参数的经验似然推断

考察了响应变量在随机删失情形下的非线性半参数回归模型,构造了未知参数的经验对数似然比统计量和调整经验对数似然比统计量,证明在一定条件下,所构造的经验似然比统计量渐近于X~2分布,并由此构造出未知参数的置信域.此外,又构造了未知参数的最小二乘估计量,证明了它的渐近性质.通过模拟研究表明,经验似然方法在置信域的覆盖概率以及精度方面要优于最小二乘法.     

简明生物统计方法

第二讲计数资料的统计推断方法(下)(二)三个以上样本百分率差异显著性测验方法三个以上样本百分率的差异显著性测验结果,如P<0.05,差异显著时,只说明这些个样本百分率所代表的总体百分率之间有一定的差异;至于说是哪两个之间有明显差异?尚需分别两两加以测验,然后才能     

一种快速鉴定猪肺炎支原体免疫显性蛋白抗原的ELISA方法的建立

旨在建立一种快速鉴定猪肺炎支原体(Mycoplasma hyopneumoniae,Mhp)血清体液免疫显性蛋白抗原的方法。通过构建p GEX-6P-1-mhp366重组表达质粒并转入大肠杆菌BL21(DE3),将GST-Mhp366重组蛋白进行原核表达。将GST-Mhp366重组菌和GST工程菌裂解液加入谷胱甘肽包被板进行抗原包被,分别与17份Mhp阳性血清和13份Mhp阴性血清反应,通过对抗原包被量、封闭液、血清和二抗稀释度的优化,确定间接ELISA的反应条件。最终确定GST-Mhp366重组菌和GST工程菌裂解液原液为抗原的最佳包被量,PBS+10%FBS+2.5%脱脂奶粉为最佳封闭液,分别将血清按照1∶500稀释、酶标二抗按照1∶40 000稀释作为最佳工作浓度,从而建立了一种基于间接ELISA的快速鉴定Mhp血清体液免疫显性蛋白抗原的方法,同时通过已知的血清体液免疫显性蛋白Mhp156和Mhp364对所建立的方法进行了验证。该方法的建立能够用于在基因组水平高通量筛选Mhp血清体液免疫显性蛋白抗原,并为Mhp初乳体液免疫和黏膜免疫显性蛋白抗原鉴定方法的建立奠定基础。     

简单贝叶斯决策的计算神经模型

大脑根据贝叶斯理论处理信息已经得到诸多心理学和神经生理学实验的支持.贝叶斯推理过程往往需要处理复杂的概率计算,最近Shadlen和Gold提出基于对数似然比可以简化基于贝叶斯的两可决策任务.然而,目前并不清楚如何在神经回路中实现基于对数似然比的贝叶斯决策.通过建立具有信息整合与胜者独享特性的决策神经回路,结合奖励调制的突触可塑性学习算法,得到决策行为与突触可塑性之间的对应关系,由此可实现简单贝叶斯决策的计算神经模型.最后,利用该模型模拟出最近Yang和Shadlen关于恒河猴可进行贝叶斯决策的实验结果.     

玉米产量相关性状定位和Meta-QTL整合

以自交系PHB47为轮回亲本,美国GEM种质YUCATAN TOL389 ICA为供体,构建包含115个株系的BC3F4群体为材料,利用玉米56K SNP芯片鉴定基因型,采用基于似然比测验的逐步回归分析法,对产量相关性状进行QTL定位分析。共获得7个穗粒数、穗长、粒宽、株高和穗位高加性QTL,解释3.72%～21.90%的表型变异;5个控制单穗重、百粒重、穗长和株高的显性QTL,表型变异解释率为8.40%～21.90%。同时,利用Meta-QTL分析方法,对2005-2018年已发表的不同环境条件下产量相关性状QTL的定位结果进行了整合分析。在正常田间管理条件和营养胁迫条件分别获得37个和16个MQTL。本研究中穗位高加性位点AX-116871591位于MQTL16区段内,并且与MQTLNP7位置相近,该区段内包含生长素/脱落酸转录因子IAA5。穗粒数加性位点AX-86281411,粒宽加性位点AX-86267702和株高显性位点AX-116875920均位于MQTLNP9区段内,该区段内包含NAC转录因子NACTF36。NAC转录因子、MYB转录因子和SOD基因均存在于两个环境条件MQTL区段内。     

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．     

局部脑缺血—再灌注大鼠行为和运动功能的动态观察

采用定量的方法动态观察局部脑缺血－再灌注大鼠的行为和运动能力,旨在为缺血性脑损伤行为评价提供敏感的指标。用直径０．２ｍｍ的尼龙线经颈内动脉可逆性插入大脑前动脉,建立局部脑缺血－周灌注大鼠模型。术后１、２、４、８、２４、４８ｈ观察神经症状。运动能力的评价采用握－引测验、网屏测验和转棒测验,在术后１、２、３、７、１４ｄ进行。主要结果如下：该模型的偏瘫很快消失,术后４ｈ就难以用肉眼观察出运动异常。而提尾     

小样本情况下基因效应分析中的统计检验

基因效应分析已被广泛地应用于植物遗传学研究和植物育种工作中^[3]。根据中心极限定理,在大样本情况下,可用正态分布对基因效应和尺度测验统计量进行显著性检验。当样本容量较小时,正态分布就不能用于上述显著性检验。一些作者^[1,3,4,7]认为,在小样本情况下应当用t-统计量来进行上述检验,但实际上由于各世代间的总体方差不等,通常的t-统计量不能用于这种检验。     

局部脑缺血－再灌注大鼠行为和运动功能的动态观察

采用定量的方法动态观察局部脑缺血－再灌注大鼠的行为和运动能力,旨在为缺血性脑损伤行为评价提供敏感的指标。用直径０．２ｍｍ的尼龙线经颈内动脉可逆性插入到大脑前动脉,建立局部脑缺血－再灌注大鼠模型。术后１、２、４、８、２４、４８ｈ观察神经症状。运动能力的评价采用握－引测验、网屏测验和转棒测验,在术后１、２、３、７、１４ｄ进行。主要结果如下：该模型的偏瘫很快消失,术后４ｈ就难以用肉眼观察出运动的异常。而提尾悬空试验的阳性体征持续到术后３ｄ,转棒的成绩在术后１周恢复正常。握－引和网屏测验未能显示出肌力的异常,应考虑设计更完善的方法以排除干扰因素。上述结果提示提尾悬空和转棒测验是评价脑缺血大鼠运动缺陷的简便、客观且较敏感方法。     

Detecting differential expression in microarray data: comparison of optimal procedures

Background  

Many procedures for finding differentially expressed genes in microarray data are based on classical or modified t-statistics. Due to multiple testing considerations, the false discovery rate (FDR) is the key tool for assessing the significance of these test statistics. Two recent papers have generalized two aspects: Storey et al. (2005) have introduced a likelihood ratio test statistic for two-sample situations that has desirable theoretical properties (optimal discovery procedure, ODP), but uses standard FDR assessment; Ploner et al. (2006) have introduced a multivariate local FDR that allows incorporation of standard error information, but uses the standard t-statistic (fdr2d). The relationship and relative performance of these methods in two-sample comparisons is currently unknown.     

Sample size determination for establishing equivalence/noninferiority via ratio of two proportions in matched-pair design

In this article, we propose approximate sample size formulas for establishing equivalence or noninferiority of two treatments in match-pairs design. Using the ratio of two proportions as the equivalence measure, we derive sample size formulas based on a score statistic for two types of analyses: hypothesis testing and confidence interval estimation. Depending on the purpose of a study, these formulas can be used to provide a sample size estimate that guarantees a prespecified power of a hypothesis test at a certain significance level or controls the width of a confidence interval with a certain confidence level. Our empirical results confirm that these score methods are reliable in terms of true size, coverage probability, and skewness. A liver scan detection study is used to illustrate the proposed methods.     

Application of the false discovery rate to quantitative trait loci interval mapping with multiple traits

Controlling the false discovery rate (FDR) has been proposed as an alternative to controlling the genome-wise error rate (GWER) for detecting quantitative trait loci (QTL) in genome scans. The objective here was to implement FDR in the context of regression interval mapping for multiple traits. Data on five traits from an F2 swine breed cross were used. FDR was implemented using tests at every 1 cM (FDR1) and using tests with the highest test statistic for each marker interval (FDRm). For the latter, a method was developed to predict comparison-wise error rates. At low error rates, FDR1 behaved erratically; FDRm was more stable but gave similar significance thresholds and number of QTL detected. At the same error rate, methods to control FDR gave less stringent significance thresholds and more QTL detected than methods to control GWER. Although testing across traits had limited impact on FDR, single-trait testing was recommended because there is no theoretical reason to pool tests across traits for FDR. FDR based on FDRm was recommended for QTL detection in interval mapping because it provides significance tests that are meaningful, yet not overly stringent, such that a more complete picture of QTL is revealed.     

The generalized Fisher's combination and accurate p-value calculation under dependence

Combining dependent tests of significance has broad applications but the related p-value calculation is challenging. For Fisher's combination test, current p-value calculation methods (eg, Brown's approximation) tend to inflate the type I error rate when the desired significance level is substantially less than 0.05. The problem could lead to significant false discoveries in big data analyses. This paper provides two main contributions. First, it presents a general family of Fisher type statistics, referred to as the GFisher, which covers many classic statistics, such as Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, and so forth. The GFisher allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and the statistic-defining parameters to a given data. Second, the paper presents several new p-value calculation methods based on two novel ideas: moment-ratio matching and joint-distribution surrogating. Systematic simulations show that the new calculation methods are more accurate under multivariate Gaussian, and more robust under the generalized linear model and the multivariate t-distribution. The applications of the GFisher and the new p-value calculation methods are demonstrated by a gene-based single nucleotide polymorphism (SNP)-set association study. Relevant computation has been implemented to an R package GFisher available on the Comprehensive R Archive Network.     

Confidence Interval for Rate Ratio in a 2 × 2 Table with Structural Zero: An Application in Assessing False-Negative Rate Ratio When Combining Two Diagnostic Tests

Summary . In this article, we consider problems with correlated data that can be summarized in a 2 × 2 table with structural zero in one of the off‐diagonal cells. Data of this kind sometimes appear in infectious disease studies and two‐step procedure studies. Lui (1998, Biometrics 54, 706–711) considered confidence interval estimation of rate ratio based on Fieller‐type, Wald‐type, and logarithmic transformation statistics. We reexamine the same problem under the context of confidence interval construction on false‐negative rate ratio in diagnostic performance when combining two diagnostic tests. We propose a score statistic for testing the null hypothesis of nonunity false‐negative rate ratio. Score test–based confidence interval construction for false‐negative rate ratio will also be discussed. Simulation studies are conducted to compare the performance of the new derived score test statistic and existing statistics for small to moderate sample sizes. In terms of confidence interval construction, our asymptotic score test–based confidence interval estimator possesses significantly shorter expected width with coverage probability being close to the anticipated confidence level. In terms of hypothesis testing, our asymptotic score test procedure has actual type I error rate close to the pre‐assigned nominal level. We illustrate our methodologies with real examples from a clinical laboratory study and a cancer study.     

Precision Mapping of Quantitative Trait Loci

Adequate separation of effects of possible multiple linked quantitative trait loci (QTLs) on mapping QTLs is the key to increasing the precision of QTL mapping. A new method of QTL mapping is proposed and analyzed in this paper by combining interval mapping with multiple regression. The basis of the proposed method is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. This is achieved by fitting other genetic markers in the statistical model as a control when performing interval mapping. Compared with the current QTL mapping method (i.e., the interval mapping method which uses a pair or two pairs of markers for mapping QTLs), this method has several advantages. (1) By confining the test to one region at a time, it reduces a multiple dimensional search problem (for multiple QTLs) to a one dimensional search problem. (2) By conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved. (3) By selectively and simultaneously using other markers in the analysis, the efficiency of QTL mapping can be also improved. The behavior of the test statistic under the null hypothesis and appropriate critical value of the test statistic for an overall test in a genome are discussed and analyzed. A simulation study of QTL mapping is also presented which illustrates the utility, properties, advantages and disadvantages of the method.     

Confidence Intervals of the Simple Difference between the Proportions of a Primary Infection and a Secondary Infection,Given the Primary Infection

This paper discusses interval estimation of the simple difference (SD) between the proportions of the primary infection and the secondary infection, given the primary infection, by developing three asymptotic interval estimators using Wald's test statistic, the likelihood‐ratio test, and the basic principle of Fieller's theorem. This paper further evaluates and compares the performance of these interval estimators with respect to the coverage probability and the expected length of the resulting confidence intervals. This paper finds that the asymptotic confidence interval using the likelihood ratio test consistently performs well in all situations considered here. When the underlying SD is within 0.10 and the total number of subjects is not large (say, 50), this paper further finds that the interval estimators using Fieller's theorem would be preferable to the estimator using the Wald's test statistic if the primary infection probability were moderate (say, 0.30), but the latter is preferable to the former if this probability were large (say, 0.80). When the total number of subjects is large (say, ≥200), all the three interval estimators perform well in almost all situations considered in this paper. In these cases, for simplicity, we may apply either of the two interval estimators using Wald's test statistic or Fieller's theorem without losing much accuracy and efficiency as compared with the interval estimator using the asymptotic likelihood ratio test.     

Exact unconditional inference for risk ratio in a correlated 2 x 2 table with structural zero

In this article, we consider small-sample statistical inference for rate ratio (RR) in a correlated 2 x 2 table with a structural zero in one of the off-diagonal cells. Existing Wald's test statistic and logarithmic transformation test statistic will be adopted for this purpose. Hypothesis testing and confidence interval construction based on large-sample theory will be reviewed first. We then propose reliable small-sample exact unconditional procedures for hypothesis testing and confidence interval construction. We present empirical results to evince the better confidence interval performance of our proposed exact unconditional procedures over the traditional large-sample procedures in small-sample designs. Unlike the findings given in Lui (1998, Biometrics 54, 706-711), our empirical studies show that the existing asymptotic procedures may not attain a prespecified confidence level even in moderate sample-size designs (e.g., n = 50). Our exact unconditional procedures on the other hand do not suffer from this problem. Hence, the asymptotic procedures should be applied with caution. We propose two approximate unconditional confidence interval construction methods that outperform the existing asymptotic ones in terms of coverage probability and expected interval width. Also, we empirically demonstrate that the approximate unconditional tests are more powerful than their associated exact unconditional tests. A real data set from a two-step tuberculosis testing study is used to illustrate the methodologies.     

Local multiplicity adjustment for the spatial scan statistic using the Gumbel distribution

The spatial scan statistic is an important and widely used tool for cluster detection. It is based on the simultaneous evaluation of the statistical significance of the maximum likelihood ratio test statistic over a large collection of potential clusters. In most cluster detection problems, there is variation in the extent of local multiplicity across the study region. For example, using a fixed maximum geographic radius for clusters, urban areas typically have many overlapping potential clusters, whereas rural areas have relatively few. The spatial scan statistic does not account for local multiplicity variation. We describe a previously proposed local multiplicity adjustment based on a nested Bonferroni correction and propose a novel adjustment based on a Gumbel distribution approximation to the distribution of a local scan statistic. We compare the performance of all three statistics in terms of power and a novel unbiased cluster detection criterion. These methods are then applied to the well-known New York leukemia dataset and a Wisconsin breast cancer incidence dataset.     

A Nonparametric Approach for Mapping Quantitative Trait Loci

Genetic mapping of quantitative trait loci (QTLs) is performed typically by using a parametric approach, based on the assumption that the phenotype follows a normal distribution. Many traits of interest, however, are not normally distributed. In this paper, we present a nonparametric approach to QTL mapping applicable to any phenotypic distribution. The method is based on a statistic Z(w), which generalizes the nonparametric Wilcoxon rank-sum test to the situation of whole-genome search by interval mapping. We determine the appropriate significance level for the statistic Z(w), by showing that its asymptotic null distribution follows an Ornstein-Uhlenbeck process. These results provide a robust, distribution-free method for mapping QTLs.