Clonal Transgenerational Effects Transmit for Multiple Generations in a Floating Plant

Environmental conditions of a parent plant can influence the performance of their clonal offspring, and such clonal transgenerational effects may help offspring adapt to different environments. However, it is still unclear how many vegetative generations clonal transgenerational effects can transmit for and whether it depends on the environmental conditions of the offspring. We grew the ancestor ramets of the floating clonal plant Spirodela polyrhiza under a high and a low nutrient level and obtained the so-called 1^st-generation offspring ramets of two types (from these two environments). Then we grew the 1^st-generation offspring ramets of each type under the high and the low nutrient level and obtained the so-called 2^nd-generation offspring ramets of four types. We repeated this procedure for another five times and analyzed clonal transgenerational effects on growth, morphology and biomass allocation of the 1^st- to the 6^th-generation offspring ramets. We found positive, negative or neutral (no) transgenerational effects of the ancestor nutrient condition on the offspring of S. polyrhiza, depending on the number of vegetative generations, the nutrient condition of the offspring environment and the traits considered. We observed significant clonal transgenerational effects on the 6^th-generation offspring; such effects occurred for all three types of traits (growth, morphology and allocation), but varied depending on the nutrient condition of the offspring environment and the traits considered. Our results suggest that clonal transgenerational effects can transmit for multiple vegetative generations and such impacts can vary depending on the environmental conditions of offspring.     

Efficiency of Functional Regression Estimators for Combining Multiple Laser Scans of cDNA Microarrays

The first stage in the analysis of cDNA microarray data is estimation of the level of expression of each gene, from laser scans of hybridised microarrays. Typically, data are used from a single scan, although, if multiple scans are available, there is the opportunity to reduce sampling error by using all of them. Combining multiple laser scans can be formulated as multivariate functional regression through the origin. Maximum likelihood estimation fails, but many alternative estimators exist, one of which is to maximise the likelihood of a Gaussian structural regression model. We show by simulation that, surprisingly, this estimator is efficient for our problem, even though the distribution of gene expression values is far from Gaussian. Further, it performs well if errors have a heavier tailed distribution or the model includes intercept terms, but not necessarily in other regions of parameter space. Finally, we show that by combining multiple laser scans we increase the power to detect differential expression of genes. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Multiple Imputation Methods for Estimating Regression Coefficients in the Competing Risks Model with Missing Cause of Failure

We propose a method to estimate the regression coefficients in a competing risks model where the cause-specific hazard for the cause of interest is related to covariates through a proportional hazards relationship and when cause of failure is missing for some individuals. We use multiple imputation procedures to impute missing cause of failure, where the probability that a missing cause is the cause of interest may depend on auxiliary covariates, and combine the maximum partial likelihood estimators computed from several imputed data sets into an estimator that is consistent and asymptotically normal. A consistent estimator for the asymptotic variance is also derived. Simulation results suggest the relevance of the theory in finite samples. Results are also illustrated with data from a breast cancer study.     

Regression Smoothers for Estimating Parameters of Growth Analyses

The objective of regression smoothers is to obtain predictedvalues of a dependent variable and its first derivative fromempirical data without having to assume any particular functionalrelationship between the dependent and independent variables.An early variant of this type of analysis, specifically naturalB-splines, was first applied to growth analyses by Parsons andHunt in 1981 (Annals of Botany 48 : 341–352, 1981). Theobject of this paper is to describe and evaluate two recentadvances in this area (cubic spline smoothers and loess smoothers)in the context of plant growth analysis and compare them tonaturalB -splines. The accuracies of these methods are evaluatedusing simulated data of a type that normally causes difficultieswith other methods. A bootstrap procedure is described thatimproves the estimate of the optimal smoother parameter. Itis shown that these smoothers can capture even subtle changesin relative growth rate. The method is then applied to growthdata ofHolcus lanatus. B -splines; cubic spline smoothers; growth analyses; Holcus lanatus ; loess; relative growth rate; RGR     

Estimating Treatment Effects of Longitudinal Designs using Regression Models on Propensity Scores

Summary We derive regression estimators that can compare longitudinal treatments using only the longitudinal propensity scores as regressors. These estimators, which assume knowledge of the variables used in the treatment assignment, are important for reducing the large dimension of covariates for two reasons. First, if the regression models on the longitudinal propensity scores are correct, then our estimators share advantages of correctly specified model‐based estimators, a benefit not shared by estimators based on weights alone. Second, if the models are incorrect, the misspecification can be more easily limited through model checking than with models based on the full covariates. Thus, our estimators can also be better when used in place of the regression on the full covariates. We use our methods to compare longitudinal treatments for type II diabetes mellitus.     

Approximate Thresholds of Interval Mapping Tests for Qtl Detection

A general method is proposed for calculating approximate thresholds of interval mapping tests for quantitative trait loci (QTL) detection. Simulation results show that this method, when applied to backcross and F(2) populations, gives good approximations and is useful for any situation. Programs which calculate these thresholds for backcross, recombinant inbreds and F(2) for any given level and any chromosome with any given distribution of codominant markers were written in Fortran 77 and are available under request. The approach presented here could be used to obtain, after suitable calculations, thresholds for most segregating populations used in QTL mapping experiments.     

Monitoring Plasmid Replication in Live Mammalian Cells over Multiple Generations by Fluorescence Microscopy

Few naturally-occurring plasmids are maintained in mammalian cells. Among these are genomes of gamma-herpesviruses, including Epstein-Barr virus (EBV) and Kaposi''s Sarcoma-associated herpesvirus (KSHV), which cause multiple human malignancies ^1-3. These two genomes are replicated in a licensed manner, each using a single viral protein and cellular replication machinery, and are passed to daughter cells during cell division despite their lacking traditional centromeres ^4-8.Much work has been done to characterize the replications of these plasmid genomes using methods such as Southern blotting and fluorescence in situ hybridization (FISH). These methods are limited, though. Quantitative PCR and Southern blots provide information about the average number of plasmids per cell in a population of cells. FISH is a single-cell assay that reveals both the average number and the distribution of plasmids per cell in the population of cells but is static, allowing no information about the parent or progeny of the examined cell.Here, we describe a method for visualizing plasmids in live cells. This method is based on the binding of a fluorescently tagged lactose repressor protein to multiple sites in the plasmid of interest ⁹. The DNA of interest is engineered to include approximately 250 tandem repeats of the lactose operator (LacO) sequence. LacO is specifically bound by the lactose repressor protein (LacI), which can be fused to a fluorescent protein. The fusion protein can either be expressed from the engineered plasmid or introduced by a retroviral vector. In this way, the DNA molecules are fluorescently tagged and therefore become visible via fluorescence microscopy. The fusion protein is blocked from binding the plasmid DNA by culturing cells in the presence of IPTG until the plasmids are ready to be viewed.This system allows the plasmids to be monitored in living cells through several generations, revealing properties of their synthesis and partitioning to daughter cells. Ideal cells are adherent, easily transfected, and have large nuclei. This technique has been used to determine that 84% of EBV-derived plasmids are synthesized each generation and 88% of the newly synthesized plasmids partition faithfully to daughter cells in HeLa cells. Pairs of these EBV plasmids were seen to be tethered to or associated with sister chromatids after their synthesis in S-phase until they were seen to separate as the sister chromatids separated in Anaphase¹⁰. The method is currently being used to study replication of KSHV genomes in HeLa cells and SLK cells. HeLa cells are immortalized human epithelial cells, and SLK cells are immortalized human endothelial cells. Though SLK cells were originally derived from a KSHV lesion, neither the HeLa nor SLK cell line naturally harbors KSHV genomes¹¹. In addition to studying viral replication, this visualization technique can be used to investigate the effects of the addition, removal, or mutation of various DNA sequence elements on synthesis, localization, and partitioning of other recombinant plasmid DNAs.     

玉米穗部性状的多世代联合遗传分析

以玉米自交系095和L26为亲本,通过对P1、P2、F1、F2、B1、B26个基本世代联合分析,研究了秃尖长、穗行数、穗粗、千粒重、穗重、单株产量等穗粒性状的遗传模型。结果表明:穗行数、穗重、单株产量的最适模型为D-2模型,即1对加性主基因+加性-显性多基因混合遗传模型;穗粗、千粒重的最佳模型为B-1模型,符合两对基因加性-显性-上位性模型;秃尖长的最佳模型为E-3,符合两对加性主基因+加性-显性多基因模型。本研究利用主基因与多基因混合遗传模型分析方法对玉米穗部性状进行遗传分析,有助于阐明玉米穗部性状的遗传规律。     

High Efficiency Gene Transfer by Multiple Transfection Protocol

A high efficiency transfection protocol employing a common polycationic lipid is described. Using LipofectAMINE, a widely used transfection reagent, we transfected 293T cells with a plasmid harboring the -galactosidase (-gal) gene. The transfection efficiency was determined by direct staining for X-gal. The conventional transfection protocol achieved an efficiency of <40% while our protocol, which employs the repetition of transfection a few times, achieved an efficiency of approximately 80%. Thus, a dramatic increase in transfection efficiency can be obtained by simply repeating transfection with the use of a common polycationic lipid. This method will be useful in many molecular biological experiments.     

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits

To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI’s Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.     

Efficiency of Batteries of Tests for Estimating Potential Mutagenicity of Chemicals

A new method for assessing the efficiency of batteries of arbitrary numbers of tests is proposed. The posterior probability of the mutagenicity of the substances studied has been estimated using discriminant analysis. The results of tests in each test system has been presented as the probability to obtain a positive result in the given test system. This has made it possible to decrease the sample size as the number of tests in the battery increased. As a result, prognostic power may be assessed even if the matrix of results is incomplete. This approach has been used to estimate the weights of evidence for mutagenic activities of 105 chemical compounds studied by means of a battery of four tests: Ames's test, the test for chromosome aberrations in vitro, the test for cytogenetic defects in vivo, and the test for dominant lethal mutations in rodents.     

生长曲线参数估计的一种新方法-优化回归组合法

在现有文献研究的基础上,对生长曲线参数估计问题又作了进一步研究,给出了生长曲线参数估计的一种新方法优化回归组合法,该方法创造性地将最优化方法与回归方法结合在一起,利用最优化理论中的区间搜索和一维搜索,可以得到一系列c^＊值,利用回归方法可求得与其相对应的一系列a和b的值．当c取最优值c时,a和b便得到最优值a^＊和b^＊经示例计算表明,这种参数估计法具有较高的精度,     

Estimating Haplotype Effects for Survival Data

Summary Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness‐of‐fit procedure for Cox's regression model including haplotype effects. Finally, we apply the procedures presented in this article to investigate possible haplotype effects of the PAF‐receptor on cardiovascular events in patients with coronary artery disease, and compare our results to those based on the EM algorithm.     

利用各世代的小区平均数估计遗传参数的加权最小二乘法

本文提出了在随机区组设计下利用六个世代的小区平均数估计加性-显性-二基因互作模型的各参数、检验该模型的加权最小二乘法的基本步骤。     

Linear Predictivity: An Alternative for MANOVA and Multivariate Multiple Regression

The maximal linear predictable combination of a set of dependent variables is defined as that linear combination maximizing the multiple correlation coefficient with the predictor set. It allows the relative importance of a number of factors to be evaluated for the joint response, rather than for the response of each dependent variable in turn. The procedure is illustrated by an example. AMS subject classification: major 62J10, 62H20; minor 62H25.     

A Dynamic Logistic Regression Model for Multiple Spell Failure Time Data

The paper deals with discrete-time regression models to analyze multistate-multiepisode failure time data. The covariate process may include fixed and external as well as internal time dependent covariates. The effects of the covariates may differ among different kinds of failures and among successive episodes. A dynamic form of the logistic regression model is investigated and maximum likelihood estimation of the regression coefficients is discussed. In the last section we give an application of the model to the analysis of survival time after breast cancer operation.     

No Longer Confidential: Estimating the Confidence of Individual Regression Predictions

Quantitative predictions in computational life sciences are often based on regression models. The advent of machine learning has led to highly accurate regression models that have gained widespread acceptance. While there are statistical methods available to estimate the global performance of regression models on a test or training dataset, it is often not clear how well this performance transfers to other datasets or how reliable an individual prediction is–a fact that often reduces a user’s trust into a computational method. In analogy to the concept of an experimental error, we sketch how estimators for individual prediction errors can be used to provide confidence intervals for individual predictions. Two novel statistical methods, named CONFINE and CONFIVE, can estimate the reliability of an individual prediction based on the local properties of nearby training data. The methods can be applied equally to linear and non-linear regression methods with very little computational overhead. We compare our confidence estimators with other existing confidence and applicability domain estimators on two biologically relevant problems (MHC–peptide binding prediction and quantitative structure-activity relationship (QSAR)). Our results suggest that the proposed confidence estimators perform comparable to or better than previously proposed estimation methods. Given a sufficient amount of training data, the estimators exhibit error estimates of high quality. In addition, we observed that the quality of estimated confidence intervals is predictable. We discuss how confidence estimation is influenced by noise, the number of features, and the dataset size. Estimating the confidence in individual prediction in terms of error intervals represents an important step from plain, non-informative predictions towards transparent and interpretable predictions that will help to improve the acceptance of computational methods in the biological community.     

Evaluation of Soy Sauce Flavor by Stepwise Multiple Regression Analysis of Gas Chromatographic Profiles

The linear correlation was found by the stepwise multiple regression analysis between the sensory test of soy sauce flavor and the gas Chromatographic (GLC) data transformed with arc-sine and logarithm. GLC data will possibly be used for objective evaluation of soy sauce flavor. A multiple correlation coefficient or of a determination coefficient of more than 0.9 was respectively obtained at the 5 th or 10th of 47 steps. The fact that the minimum standard error of an estimate was found at the 24th step suggests the importance of selecting proper peaks from the whole gas chromatogram. High estimated accuracy was acquired by application of GLC data to the calculated multiple regression model.     

Estimating the Efficacy and Efficiency of Cascade Genetic Screening

Screening for genetic variants that predispose individuals or their offspring to disease may be performed at the general population level or may instead be targeted at the relatives of previously identified carriers. The latter strategy has come to be known as "cascade genetic screening." Since the carrier risk of close relatives of known carriers is generally higher than the population risk, cascade screening is more efficient than population screening, in the sense that fewer individuals have to be genotyped per detected carrier. The efficacy of cascade screening, as measured by the overall proportion of carriers detected in a given population, is, however, lower than that of population-wide screening, and the respective inclusion rates vary according to the population frequency and mode of inheritance of the predisposing variants. For dominant mutations, we have developed equations that allow the inclusion rates of cascade screening to be calculated in an iterative fashion, depending upon screening depth and penetrance. For recessive mutations, we derived only equations for the screening of siblings and the children of patients. Owing to their mathematical complexity, it was necessary to study more extended screening strategies by simulation. Cascade screening turned out to result in low inclusion rates (<1%) when aimed at the identification of heterozygous carriers of rare recessive variants. Considerably higher rates are achievable, however, when screening is performed to detect covert homozygotes for frequent recessive mutations with reduced penetrance. This situation is exemplified by hereditary hemochromatosis, for which up to 40% of at-risk individuals may be identifiable through screening of first- to third-degree relatives of overt carriers (i.e., patients); the efficiency of this screening strategy was found to be approximately 50 times higher than that of population-wide screening. For dominant mutations, inclusion rates of cascade screening were estimated to be higher than for recessive variants. Thus, some 80% of all carriers of the factor V Leiden mutation would be detected if screening were to be targeted specifically at first- to third-degree relatives of patients with venous thrombosis. The relative cost efficiency of cascade as compared with population-wide screening (i.e., the overall savings in the extra managerial cost of the condition) is also likely to be higher for dominant than for recessive mutations. This notwithstanding, once screening has become cost-effective at the population level, it can be expected that cascade screening would only transiently represent an economically viable option.