Hybrid maize breeding with doubled haploids: I. One-stage versus two-stage selection for testcross performance

Optimum allocation of resources is of fundamental importance for the efficiency of breeding programs. The objectives of our study were to (1) determine the optimum allocation for the number of lines and test locations in hybrid maize breeding with doubled haploids (DHs) regarding two optimization criteria, the selection gain ΔG _k and the probability P _k of identifying superior genotypes, (2) compare both optimization criteria including their standard deviations (SDs), and (3) investigate the influence of production costs of DHs on the optimum allocation. For different budgets, number of finally selected lines, ratios of variance components, and production costs of DHs, the optimum allocation of test resources under one- and two-stage selection for testcross performance with a given tester was determined by using Monte Carlo simulations. In one-stage selection, lines are tested in field trials in a single year. In two-stage selection, optimum allocation of resources involves evaluation of (1) a large number of lines in a small number of test locations in the first year and (2) a small number of the selected superior lines in a large number of test locations in the second year, thereby maximizing both optimization criteria. Furthermore, to have a realistic chance of identifying a superior genotype, the probability P _k of identifying superior genotypes should be greater than 75%. For budgets between 200 and 5,000 field plot equivalents, P _k > 75% was reached only for genotypes belonging to the best 5% of the population. As the optimum allocation for P _k (5%) was similar to that for ΔG _k , the choice of the optimization criterion was not crucial. The production costs of DHs had only a minor effect on the optimum number of locations and on values of the optimization criteria. C. Friedrich H. Longin and H. Friedrich Utz contributed equally to this work.     

Modelling the Human Immune System by Combining Bioinformatics and Systems Biology Approaches

Over the past decade a number of bioinformatics tools have been developed that use genomic sequences as input to predict to which parts of a microbe the immune system will react, the so-called epitopes. Many predicted epitopes have later been verified experimentally, demonstrating the usefulness of such predictions. At the same time, simulation models have been developed that describe the dynamics of different immune cell populations and their interactions with microbes. These models have been used to explain experimental findings where timing is of importance, such as the time between administration of a vaccine and infection with the microbe that the vaccine is intended to protect against. In this paper, we outline a framework for integration of these two approaches. As an example, we develop a model in which HIV dynamics are correlated with genomics data. For the first time, the fitness of wild type and mutated virus are assessed by means of a sequence-dependent scoring matrix, derived from a BLOSUM matrix, that links protein sequences to growth rates of the virus in the mathematical model. A combined bioinformatics and systems biology approach can lead to a better understanding of immune system-related diseases where both timing and genomic information are of importance.     

Computational Phlebology: The Simulation of a Vein Valve

We present a three-dimensional computer simulation of the dynamics of a vein valve. In particular, we couple the solid mechanics of the vein wall and valve leaflets with the fluid dynamics of the blood flow in the valve. Our model captures the unidirectional nature of blood flow in vein valves; blood is allowed to flow proximally back to the heart, while retrograde blood flow is prohibited through the occlusion of the vein by the valve cusps. Furthermore, we investigate the dynamics of the valve opening area and the blood flow rate through the valve, gaining new insights into the physics of vein valve operation. It is anticipated that through computer simulations we can help raise our understanding of venous hemodynamics and various forms of venous dysfunction.     

Electrolyte effects on the intervalence transition within discrete binuclear cyano-bridged complexes. An estimation of activation free energy from static, optical and electrochemical data

A study of the metal-to-metal charge-transfer (MMCT) transition within the binuclear cyano-bridged complexes cis-[L¹³Co^III(μ-NC)Fe^II(CN)₅]⁻ (L¹³ = 12-methyl-1,4,7,10-tetraazacyclotridecan-12-amine), trans-[L¹⁴Co^III(μ-NC)Fe^II(CN)₅]⁻ (L¹⁴ = 6-methyl-1,4,8,11-tetraazacyclotetradecan-6-amine) and trans-[L¹⁵Co^III(μ-NC)Fe^II(CN)₅]⁻ (L¹⁵ = 10-methyl-1,4,8,12-tetraazacyclopentadecan-10-amine) has been carried out in electrolyte solutions at varying concentrations. Using these data, as well as the reaction free energies obtained from electrochemical measurements, the reorganisation and activation free energies for the forward and reverse thermal electron-transfer processes have been estimated. The changes of these parameters with the electrolyte concentration, as well as those of the energy of the maximum MMCT band and the reaction free energy, are mainly due to ion-pairing effects.     

Modelling the role of intracolonial genetic diversity on regulation of brood temperature in honey bee (Apis mellifera L.) colonies

In polyandrous social insects such as honey bees, a worker’s affinity for a particular task may be genetically infl uenced and so some patrilines may have lower stimulus thresholds for commencing a task than others. We used simulation models to investigate the effects of intracolonial diversity in the task thresholds that stimulate workers to engage in heating and cooling during nest thermoregulation. First, we simulated colonies comprised of one or 15 patrilines that were engaged in heating the brood nest, and observed that single patriline colonies maintained, on average, less stable brood nest temperatures than multiple patriline colonies. Second we simulated colonies with five patrilines that were engaged in cooling their nest, recording the proportions of bees of different patrilines that engaged in nest cooling in response to changing temperatures. Both of our simulations show remarkably similar qualitative patterns to those that we have previously observed empirically. This provides further support for the hypothesis that geneticallybased variability in task thresholds among patrilines within honey bee colonies is an important contributor to the ability of colonies to precisely thermoregulate their nests, and we suggest that diversity is important for optimal expression of a range of other colony-level phenotypes. Received 17 June 2005; revised 27 October 2005; accepted 23 December 2005.     

Nonparametric estimation of stage occupation probabilities in a multistage model with current status data

Multistage models are used to describe individuals (or experimental units) moving through a succession of "stages" corresponding to distinct states (e.g., healthy, diseased, diseased with complications, dead). The resulting data can be considered to be a form of multivariate survival data containing information about the transition times and the stages occupied. Traditional survival analysis is the simplest example of a multistage model, where individuals begin in an initial stage (say, alive) and move irreversibly to a second stage (death). In this article, we consider general multistage models with a directed tree structure (progressive models) in which individuals traverse through stages in a possibly non-Markovian manner. We construct nonparametric estimators of stage occupation probabilities and marginal cumulative transition hazards. Empirical calculations of these quantities are not possible due to the lack of complete data. We consider current status information which represents a more severe form of censoring than the commonly used right censoring. Asymptotic validity of our estimators can be justified using consistency results for nonparametric regression estimators. Finite-sample behavior of our estimators is studied by simulation, in which we show that our estimators based on these limited data compare well with those based on complete data. We also apply our method to a real-life data set arising from a cardiovascular diseases study in Taiwan.     

A general approach for two-stage analysis of multilevel clustered non-Gaussian data

In this article, we propose a two-stage approach to modeling multilevel clustered non-Gaussian data with sufficiently large numbers of continuous measures per cluster. Such data are common in biological and medical studies utilizing monitoring or image-processing equipment. We consider a general class of hierarchical models that generalizes the model in the global two-stage (GTS) method for nonlinear mixed effects models by using any square-root-n-consistent and asymptotically normal estimators from stage 1 as pseudodata in the stage 2 model, and by extending the stage 2 model to accommodate random effects from multiple levels of clustering. The second-stage model is a standard linear mixed effects model with normal random effects, but the cluster-specific distributions, conditional on random effects, can be non-Gaussian. This methodology provides a flexible framework for modeling not only a location parameter but also other characteristics of conditional distributions that may be of specific interest. For estimation of the population parameters, we propose a conditional restricted maximum likelihood (CREML) approach and establish the asymptotic properties of the CREML estimators. The proposed general approach is illustrated using quartiles as cluster-specific parameters estimated in the first stage, and applied to the data example from a collagen fibril development study. We demonstrate using simulations that in samples with small numbers of independent clusters, the CREML estimators may perform better than conditional maximum likelihood estimators, which are a direct extension of the estimators from the GTS method.     

Optimization of two-stage genetic designs where data are combined using an accurate and efficient approximation for Pearson's statistic

There is an increasing interest in the use of two-stage case-control studies to reduce genotyping costs in the search for genes underlying common disorders. Instead of analyzing the data from the second stage separately, a more powerful test can be performed by combining the data from both stages. However, standard tests cannot be used because only the markers that are significant in the first stage are selected for the second stage and the test statistics at both stages are dependent because they partly involve the same data. Theoretical approximations are not available for commonly used test statistics and in this specific context simulations can be problematic because of the computational burden. We therefore derived a cost-effective, that is, accurate but fast in terms of central processing unit (CPU) time, approximation for the distribution of Pearson's statistic on 2 xm contingency tables in two-stage design with combined data. We included this approximation in an iterative method for designing optimal two-stage studies. Simulations supported the accuracy of our approximation. Numerical results confirmed that the use of two-stage designs reduces the genotyping burden substantially. Compared to not combining data, combining the data decreases the required sample sizes on average by 15% and the genotyping burden by 5%.     

A simple local sensitivity analysis tool for nonignorable coarsening: application to dependent censoring

Right- and interval-censored data are common special cases of coarsened data (Heitjan and Rubin, 1991, Annals of Statistics19, 2244-2253). As with missing data, standard statistical methods that ignore the random nature of the coarsening mechanism may lead to incorrect inferences. We extend a simple sensitivity analysis tool, the index of local sensitivity to nonignorability (Troxel, Ma, and Heitjan, 2004, Statistica Sinica14, 1221-1237), to the evaluation of nonignorability of the coarsening process in the general coarse-data model. By converting this index into a simple graphical display one can easily assess the sensitivity of key inferences to nonignorable coarsening. We illustrate the validity of the method with a simulated example, and apply it to right-censored data from an observational study of cardiac transplantation and to interval-censored data on time to detectable viral load from a clinical trial in HIV disease.