亲子典型相关在家蚕育种中的应用

运用典型相关理论分析了家蚕亲代和子代两组数量性状,并对其遗传育种学意义作了探讨。由于亲子典型相关系数在数值上就等于亲子间典型性状的回归系数,所以亲子典型相关系数也代表了亲代典型性状将其遗传特征传递给子代典型性状的能力。典型性状作为多个数量性状的公共因子,在亲子代间具有最大的传递能力,对亲代典型性状进行选择,将具有最大的选择效果和最小的估计误差。     

Testing for familial correlation in age-at-onset

The analysis of family-study data sometimes focuses on whether a dichotomous trait tends to cluster in families. For traits with variable age-at-onset, it may be of interest to investigate whether age-at-onset itself also exhibits familial clustering. A complication in such investigations is that censoring by age-at-ascertainment can induce artifactual familial correlation in the age-at-onset of affected members. A further complication can be that sample inclusion criteria involve the affection status of family members. The purpose here is to present an approach to testing for correlation that is not confounded by censoring by age-at-ascertainment and may be applied with a broad range of inclusion criteria. The approach involves regression statistics in which subjects's covariate terms are chosen to reflect age-at-onset information from the subjects's affected family members. The results of analyses of data from a family-study of panic disorder illustrate the approach.     

Assessing familial aggregation of age at onset, by using estimating equations, with application to breast cancer.

In genetic research of chronic diseases, age-at-onset outcomes within families are often correlated. The nature of correlation of age-at-onset outcomes is indicative of common genetic and/or shared environmental risk factors among family members. Understanding patterns of such correlation may shed light on the disease etiology and, hence, is an important step to take prior to further searching for the responsible genes via segregation and linkage studies. Age-at-onset outcomes are different from those familiar quantitative or qualitative traits for which many statistical methods have been developed. In comparison with the quantitative traits, age-at-onset outcomes are often censored, i.e., instead of actual age-at-onset outcomes, only the current ages or ages at death are observed. They are also different from qualitative traits because of their continuity. Because of the complexity of correlated censored outcomes, few methods have yet been developed. A traditional approach is to impose a parametric joint distribution for the correlated age-at-onset outcomes, which has been criticized for requiring a stringent assumption about the entire distribution of age at onset. The purpose of this paper is to describe a method for assessing familial aggregation of correlated age-at-onset outcomes semiparametrically, by use of estimating equations. This method does not require any parametric assumption for modeling the age at onset. The estimates of parameters, including those quantifying the correlation within families, are consistent and have an asymptotic normal distribution that can be used to make inferences. To illustrate this new method, we analyzed two age-at-onset data sets that were obtained from studies conducted in the States of Washington and Hawaii, with the objective of quantifying the familial aggregations of age at onset of breast cancer.     

A canonical correlation analysis of the association between carcass and ham traits in pigs used to produce dry-cured ham

The association between carcass and ham traits in a pig population used to produce dry-cured ham was studied using canonical correlation analysis. The carcass traits examined were hot carcass weight (HCW), backfat thickness (BT) and loin depth (LD), and the ham traits studied were gross ham weight (GHW), trimmed ham weight (THW), ham inner layer fat thickness (HIFT), ham outer layer fat thickness (HOFT), pH (pH) and the Göfo value. Carcass and ham traits are not independent. The canonical correlations (r) between the carcass and ham traits at 130 kg were 0.77, 0.24 and 0.20 for the first, second and third canonical pair, respectively, and were all significant (p < 0.01) by the Wilks test. The corresponding canonical correlations between the three canonical variate pairs for the carcass and ham traits at 160 kg were 0.88, 0.42 and 0.14, respectively (p < 0.05 for all, except the third). The correlations between the traits and their canonical variate showed an association among HCW, GHW and THW, and between BT and HOFT. These results indicate that carcass traits should be used to cull pigs that are not suitable for dry-cured ham production.     

数量遗传学中一种新的求综合性状的方法

本文运用申农(Shannon)提供的最大熵原理,提出一种构成单一综合性状的新方法,并以此与数量遗传学中的多元统计法作了比较。在作多元遗传分析吋,常用多元统计法求出多个数量性状的综合性状,再对这些相互关联的基本性状作主成份分析或用典范相关进行遗传分析。本文提出了不同于多元统计学的另一种新的方法——最大熵法求出多个数量性状的单一综合性状值。它具有数学结构简单,过程明晰,结果简明等优点。     

鮸形态性状与体质量的关系

为了研究鮸的形态性状与体质量之间的关系,测量了524尾12月龄左右鮸的体质量(Y, g)和8个形态性状(cm),包括全长(X₁)、体长(X₂)、头长(X₃)、躯干长(X₄)、尾长(X₅)、尾柄长(X₆)、尾柄高(X₇)和体高(X₈).采用相关分析、回归分析、通径分析以及模型拟合等方法对其进行分析.结果表明: 鮸所有性状两两之间存在极显著正相关关系,相关系数在0.834～0.941.经共线性诊断排除了体长这一具有严重共线性的性状后,采用逐步引入-剔除法进行多元回归并进行显著性检验,成功筛选出全长(X₁)、躯干长(X₄)、尾柄高(X₇)和体高(X₈)4个统计检验极显著的形态性状,并建立多元回归方程:Y ＝-444.188 +7.943X₁ +12.861X₄ +38.254X₇ + 42.722X₈.通径分析结果显示,4个形态性状中,体高对体质量的直接作用最大(通径系数=0.351),全长次之(通径系数=0.335).对全长和体高与体质量之间的函数关系进行曲线模型拟合得出,在6个拟合模型中,幂函数模型拟合效果最好,方程为:y=0.013 x^2.891(全长); y=2.028 x^2.751(体高).经适合性检验得知,全长比体高通过幂函数对体质量的预测效果更好.研究结果可为鮸的形态性状在选择育种中的有效利用提供重要参考.     

Data transformation for rank reduction in multi-trait MACE model for international bull comparison

Since many countries use multiple lactation random regression test day models in national evaluations for milk production traits, a random regression multiple across-country evaluation (MACE) model permitting a variable number of correlated traits per country should be used in international dairy evaluations. In order to reduce the number of within country traits for international comparison, three different MACE models were implemented based on German daughter yield deviation data and compared to the random regression MACE. The multiple lactation MACE model analysed daughter yield deviations on a lactation basis reducing the rank from nine random regression coefficients to three lactations. The lactation breeding values were very accurate for old bulls, but not for the youngest bulls with daughters with short lactations. The other two models applied principal component analysis as the dimension reduction technique: one based on eigenvalues of a genetic correlation matrix and the other on eigenvalues of a combined lactation matrix. The first one showed that German data can be transformed from nine traits to five eigenfunctions without losing much accuracy in any of the estimated random regression coefficients. The second one allowed performing rank reductions to three eigenfunctions without having the problem of young bulls with daughters with short lactations.     

Maximum-likelihood estimation of familial correlations from multivariate quantitative data on pedigrees: a general method and examples

A general method for maximum-likelihood estimation of familial correlations from pedigree data is presented. The method is applicable to any type of data structure, including pedigrees in which variable numbers of individuals are present within classes of relatives, data in which multiple phenotypic measures are obtained on each individual, and multiple group analyses in which some correlations are equated across groups. The method is applied to data on high-density lipoprotein cholesterol and total cholesterol levels obtained from participants in the Swedish Twin Family Study. Results indicate that there is strong familial resemblance for both traits but little cross-trait resemblance.     

Sparse canonical correlation analysis from a predictive point of view

Canonical correlation analysis (CCA) describes the associations between two sets of variables by maximizing the correlation between linear combinations of the variables in each dataset. However, in high‐dimensional settings where the number of variables exceeds the sample size or when the variables are highly correlated, traditional CCA is no longer appropriate. This paper proposes a method for sparse CCA. Sparse estimation produces linear combinations of only a subset of variables from each dataset, thereby increasing the interpretability of the canonical variates. We consider the CCA problem from a predictive point of view and recast it into a regression framework. By combining an alternating regression approach together with a lasso penalty, we induce sparsity in the canonical vectors. We compare the performance with other sparse CCA techniques in different simulation settings and illustrate its usefulness on a genomic dataset.     

Testing concordance of clinical characteristics in familial studies with application to inflammatory bowel diseases

The etiology of chronic Inflammatory Bowel Diseases (IBD) remains unknown, with both genetic and environmental risk factors having been implicated. A recent collaborative study of IBD provides clinical data from families with three or more affected first-degree relatives. The scientific question is whether specific clinical characteristics aggregate among affected individuals within families. Gastroenterological researchers have examined the number of concordant familial pairs in familial aggregation studies, but methods and results have been discrepant. This article investigates concepts of concordance and gives a comprehensive statistical treatment for testing concordance of various clinical traits in familial studies. For dichotomous traits, the distribution of this statistic under the null hypothesis of no familial aggregation is obtained by three methods: asymptotic, probability generating function, and permutation. The permutation method is extended to analyze aggregation for non-dichotomous traits and co-aggregations between two traits. We apply the permutation method to analyze the aforementioned multiply-affected IBD family data. Evidence is found for familial clustering of various traits, some of which are not revealed in existing studies. Such analyses provide a basis for investigating the dependence of trait aggregation upon genetic or environmental risk factors.     

Multiple Linear Regression on Canonical Correlation Variables

When the explanatory variables of a linear model are split into two groups, two notions of collinearity are defined: a collinearity between the variables of each group, of which the mean is called residual collinearity, and a collinearity between the two groups called explained collinearity. Canonical correlation analysis provides information about the collinearity: large canonical correlation coefficients correspond to some small eigenvalues and eigenvectors of the correlation matrix and characterise the explained collinearity. Other small eigenvalues of this matrix correspond to the residual collinearity. A selection of predictors can be performed from the canonical correlation variables, according to their partial correlation coefficient with the explained variable. In the proposed application, the results obtained by the selection of canonical variables are better than those given by classical regression and by principal component regression.     

Regressive logistic models for familial disease and other binary traits

The simple Markovian structures of dependence, defined previously for continuous traits, are extended here to familial disease and other binary traits through the use of the logistic function. The regressive models so formulated can incorporate explanatory variables and major gene effects for segregation and linkage analyses. Thus, the goals of epidemiology and genetics in the analysis of familial disease can be accomplished in the same computational scheme.     

湖南栎类天然次生林林分空间结构对灌木物种多样性的影响

林分空间结构会显著影响其林下植被多样性,为明确其影响灌木物种多样性的关键因子,以栎类天然次生林为研究对象,利用Pearson简单相关分析、多元线性逐步回归法与典型相关分析探索乔木层空间结构与林下灌木物种多样性的相关关系,为保护林下植被多样性提供生态经营建议。以混交度、聚集指数和开敞度表达林分空间结构,以Margalef指数、Pielou指数和AuclairGoff指数作为灌木物种多样性评价指标,利用3种统计分析方法分析两者之间的相关关系,研究结果表明:(1)Pearson相关分析结果显示混交度对灌木层Pielou指数和AuclairGoff指数影响显著(P0.05),其余指数均不显著;(2)多元线性逐步回归分析结果显示林下灌木物种多样性受到多个林分空间指标的综合影响,其中Pielou指数受到混交度、聚集指数和开敞度的显著影响,AuclairGoff指数受到混交度与开敞度的显著影响;(3)典型相关分析结果显示,第一对典型相关变量通过显著性检验,典型相关系数为0.5427,林分空间结构与灌木层多样性指标两组变量在整体上相关性较强,其中混交度是影响力最大的变量。因此,欲提高栎类天然次生林下灌木物种多样性,应选择以调整树种结构为主,综合考虑林木空间分布格局的调整方案。     

多元性状同胞对连锁分析方法及其在原发性高血压基因定位数据中的应用

提出新的以广义最小二乘法原理处理同胞对数据间的相关性,以多元响应回归的方法处理多个性状数据间的相关性的多元性状同胞对连锁分析方法,模型的参数估计使用MCMC方法．并把此模型应用于原发性高血压基因定位的实际数据中．结果表明,与把多元性状拆成单一性状进行分析的方法相比,本文的方法可以提高估计的精度和检验的效能．     

An autologistic model for the genetic analysis of familial binary data.

Regressive logistic models specify the probability distribution of familial binary traits by conditioning each individual's phenotype on those of preceding relatives; therefore, the expression of the joint probability of the familial data necessitates ordering the observations. In the present paper, we propose an autologistic model of this familial dependence structure, which does not require specification of a particular ordering of the phenotypic observations. Genetic effects are introduced into the model in order to perform segregation analysis that is aimed at detecting the role of a major locus in the expression of familial phenotypes. In this model, the conditional probabilities have a logistic form, and large patterns of dependence between relatives can be considered with a simple interpretation of the parameters measuring the relationship between two phenotypes. The model is compared with the regressive logistic approach in terms of odds ratios and by using a simulation study.     

节肢动物群落稳定性分析灰典型相关模型及其应用

稳定性是生态系统最重要的特征之一。根据累加生成可增强两单调增序列线性相关性原理,应用典型相关分析方法,以比值mn/mp(mp为害虫个体数,mn为天敌个体数)为测度群落稳定性的指数,构建群落稳定性分析灰典型相关模型。具体做法是:(1)将各种群的数量序列按害虫各次总数量的递增顺序进行重排,用各种群数量序列的极差去除该序列,将其无量纲化,然后对各序列进行累加生成;(2)以害虫各种群为一组变量,天敌各种群为另一组变量,应用典型相关分析的方法,求出各对典型变量。对达到线性拟合要求的典型变量对,以害虫为自变量,天敌为因变量建立回归方程,并对这些方程中的变量进行累减还原;(3)对这些方程进行线性组合,合并成一个方程,组合系数是使这些害虫典型变量的线性组合与害虫总数量序列的线性相关值达到最大;(4)引进转换系数的概念,建立天敌、害虫总量相互转换模型,该模型称之为灰典型相关模型,通过模型可分析各种群在群落稳定性中的作用。将模型应用于福州金山茶园节肢动物群落的稳定性分析,所得结果与实际基本相符,表明建立的灰典型相关模型是可行的。     

植被生态信息的典范相关分析技术理论

植被生态信息分析中对两（多）组取样集间的相关分析的需求导致了典范相关分析技术的应用和发展。本文对典范相关分析技术的原理、典范参数的生态学意义进行了提示,比较了典范相关分析与ＰＣＡ之间的差异,产在典范相关分析技术的应用过程中,原始数据的预处理提出了建议。     

A bivariate cure-mixture approach for modeling familial association in diseases

For modeling correlation in familial diseases with variable ages at onset, we propose a bivariate model that incorporates two types of pairwise association, one between the lifetime risk or the overall susceptibility of two individuals and one between the ages at onset between two susceptible individuals. For estimation, we consider a two-stage estimation procedure similar to that of Shih (1998, Biometrics 54, 1115-1128). We evaluate the properties of the estimators through simulations and compare the performance with that from a bivariate survival model that allows correlation between ages at onset only. We apply the methodology to breast cancer using the kinship data from the Washington Ashkenazi Study. We also discuss potential applications of the proposed method in the area of cure modeling.     

广东省福寿螺形态性状变异及其与体质量的关系

【背景】福寿螺是危害极其严重的入侵我国的水生生物,目前利用相关分析、通径分析对福寿螺形态性状变异的研究较少。【方法】随机采集7个地区的福寿螺(雌雄比例差别很大),测量其壳高、壳宽、口宽、层高4个形态性状和体质量,采用相关分析、通径分析方法计算福寿螺的各形态指标变异系数、相关系数和通径系数,剔除对体质量影响不显著的指标,确定每个地区与体质量最相关的指标。【结果】除惠州之外,其余地区福寿螺的壳高和壳宽与体质量的相关性较高,且口宽与壳高共同作用对体质量的直接影响很大。【结论与意义】此结果为研究不同生境、不同温度下福寿螺的形态性状变异程度奠定了基础,同时为福寿螺形态鉴定、分类及其灾害预测预报提供了基础数据。     

Selection criterion of work matrix as a function of limiting estimates of the covariance matrix of correlated data in GEE

The modeling of generalized estimating equations used in the analysis of longitudinal data whether in continuous or discrete variables, necessarily requires the prior specification of a correlation matrix in its iterative process in order to obtain the estimates of the regression parameters. Such a matrix is called working correlation matrix and its incorrect specification produces less efficient estimates for the model parameters. Due to this fact, this study aims to propose a selection criterion of working correlation matrix based on the covariance matrix estimates of correlated responses resulting from the limiting values of the association parameter estimates. For validation of the criterion, we used simulation studies considering normal and binary correlated responses. Compared to some criteria in the literature, it was concluded that the proposed criterion resulted in a better performance when the correlation structure for exchangeable working correlation matrix was considered as true structure in the simulated samples and for large samples, the proposed criterion showed similar behavior to the other criteria, resulting in higher success rates.