The use of random controls in genetic association studies

BACKGROUND: The optimal control sample would be ethnically-matched and at minimal risk of developing the disease. Alternatively, one could collect random individuals from the population or select individuals to reduce the number of at-risk individuals in the sample. The effect of randomly selected individuals in a control sample on the statistical power and the odds ratio estimate was investigated. METHODS: Case and control genotype distributions were simulated using standard genetic models with an additional term representing the proportion of unidentified cases in the control sample. Power and odds ratio were calculated from the genotype distributions generated under different sampling scenarios using established methods. RESULTS: Random sampling of controls resulted in a loss in power and a reduction in the odds ratio estimate to a degree that is determined by the proportion of random sampling and the prevalence of the disease. Random sampling resulted in a 19% loss in power for a disease having prevalence of 0.20, compared to a control sample that contained no at-risk individuals. Having random controls results in a decrease in the odds ratio estimate. CONCLUSIONS: Investigators planning case-control genetic association studies should be aware of the statistical costs of different ascertainment approaches.     

The bloodsucking arthropod bite as possible cofactor in the transmission of human herpesvirus-8 infection and in the expression of Kaposi's sarcoma disease

Based on a review of the literature on human herpesvirus-8 (HHV8) and Kaposi's sarcoma (KS) and on the distribution of KS in Italy (Veneto region particularly), we hypothesize that the bite of bloodsucking arthropods is a cofactor in the seroconversion to HHV8 positivity and probably in the pathogenesis of KS. The bloodsucking arthropod releases with saliva powerful antihaemostatics and immunomodulators which may favour the replication and the establishment of the pathogen. Transmission would depend on the close contact of the child with a seropositive mother (or relatives) whose infective saliva is used to relieve itching and scratching at the arthropod bite's sites. During any deregulation of the immune system (e.g. ageing), local immune responses to new insect bites may induce virus activation which could prelude KS insurgence. The pathogen is not directly transmitted by the arthropod which merely prepares the cutaneous microenvironment for the virus. We have therefore introduced a new category of medically important arthropods, "promoter arthropods", besides those already defined as biological or mechanical vectors. Promoter arthropods are species able to induce in the host long-lasting, immediate or delayed-type hypersensitivity responses as well as local immunosuppression due to substances injected with their saliva. The striking variability of ORF-K1 gene of HHV8 could be due to the adaptation of the virus to the specific microenvironments resulting from the immune response to the salivary antigens characteristic of the bloodsucking arthropod species prevalent in each geographical area. It is worth noting that other viruses (especially Hepatitis B Virus) may exploit the same non-sexual transmission route.     

An easy-to-use practical method to measure coincidence in the flow cytometer--the case of platelet-granulocyte complex determination

Cell complexes composed of two different cells labeled with different fluorophores can be detected as double positive events in the flow cytometer. Double positivity can originate not only from real complexes but from non-interacting coinciding cells as well. Coincidence has a high impact on the determination of the amount of platelet–granulocyte complexes since platelet concentration is in the orders of magnitude higher than that of the granulocytes. Mixtures of non-interacting fluorescent beads as well as EDTA anticoagulated or citrated blood samples were analyzed in the flow cytometer in the presence and absence of fluorescent beads at various dilutions. Experimental data were evaluated by mathematical means. The bead or platelet concentration dependence of double positivity was converted into linear functions using Poisson distribution. This linearised form contains information on the detection volume as well as on the presence/absence of dilution independent complexes. The presence of appropriate fluorescent beads in the blood sample makes possible to estimate the fraction of double positivity originating from coincidence if data collection is triggered by the granulocytes or by the fluorescent beads, alternatively. Mixing fluorescent beads into a blood sample is a simple experimental method to distinguish double positivity originating from real cell–cell complexes from the coincidence of cells in a flow cytometer, thus providing a tool for the determination of the real amount of cell–cell complexes.     

Diffprot - software for non-parametric statistical analysis of differential proteomics data

Mass spectrometry-based global proteomics experiments generate large sets of data that can be converted into useful information only with an appropriate statistical approach. We present Diffprot - a software tool for statistical analysis of MS-derived quantitative data. With implemented resampling-based statistical test and local variance estimate, Diffprot allows to draw significant results from small scale experiments and effectively eliminates false positive results. To demonstrate the advantages of this software, we performed two spike-in tests with complex biological matrices, one label-free and one based on iTRAQ quantification; in addition, we performed an iTRAQ experiment on bacterial samples. In the spike-in tests, protein ratios were estimated and were in good agreement with theoretical values; statistical significance was assigned to spiked proteins and single or no false positive results were obtained with Diffprot. We compared the performance of Diffprot with other statistical tests - widely used t-test and non-parametric Wilcoxon test. In contrast to Diffprot, both generated many false positive hits in the spike-in experiment. This proved the superiority of the resampling-based method in terms of specificity, making Diffprot a rational choice for small scale high-throughput experiments, when the need to control the false positive rate is particularly pressing.     

Distribution of Keratan Sulphate and Chondroitin Sulphate in Wild Type and White Mutant Axolotl Embryos During Neural Crest Cell Migration

In embryos of the white mutant axolotl, prospective pigment cells are unable to migrate from the neural crest (NC) due to a deficiency in the subepidermal extracellular matrix (ECM). This raises the question of the molecular nature of this functional defect. Some PGs can inhibit cell migration on ECM molecules in vitro, and an excess of this class of molecules in the migratory pathways of neural crest cells might cause the restricted migration of prospective pigment cells seen in the white mutant embryo. In the present study, we use several monoclonal antibodies against epitopes on keratan sulphate (KS) and chondroitin sulphate (CS) and LM immunofluorescence to examine the distribution of these glycosaminoglycans at initial (stage 30) and advanced (stage 35) stages of neural crest cell migration. Most KS epitopes are more widely distributed in the white mutant than in the wild type embryo, whereas CS epitopes show very similar distributions in mutant and wild type embryos. This is confirmed quantitatively by immunoblotting: certain KS epitopes are more abundant in the white mutant. TEM immunogold staining reveals that KS as well as CS are present both in the basal lamina and in the interstitial ECM in both types of embryos. It remains to be investigated whether the abundance of certain KS epitopes in the white mutant embryo might contribute to the deficiency in supporting pigment cell migration shown by its ECM.     

The adequacy of aging techniques in vertebrates for rapid estimation of population mortality rates from age distributions

As a key parameter in population dynamics, mortality rates are frequently estimated using mark–recapture data, which requires extensive, long‐term data sets. As a potential rapid alternative, we can measure variables correlated to age, allowing the compilation of population age distributions, from which mortality rates can be derived. However, most studies employing such techniques have ignored their inherent inaccuracy and have thereby failed to provide reliable mortality estimates. In this study, we present a general statistical model linking birth rate, mortality rate, and population age distributions. We next assessed the reliability and data needs (i.e., sample size) for estimating mortality rate of eight different aging techniques. The results revealed that for half of the aging techniques, correlations with age varied considerably, translating into highly variable accuracies when used to estimate mortality rate from age distributions. Telomere length is generally not sufficiently correlated to age to provide reliable mortality rate estimates. DNA methylation, signal‐joint T‐cell recombination excision circle (sjTREC), and racemization are generally more promising techniques to ultimately estimate mortality rate, if a sufficiently high sample size is available. Otolith ring counts, otolithometry, and age‐length keys in fish, and skeletochronology in reptiles, mammals, and amphibians, outperformed all other aging techniques and generated relatively accurate mortality rate estimation with a sample size that can be feasibly obtained. Provided the method chosen is minimizing and estimating the error in age estimation, it is possible to accurately estimate mortality rates from age distributions. The method therewith has the potential to estimate a critical, population dynamic parameter to inform conservation efforts within a limited time frame as opposed to mark–recapture analyses.     

Optimal experimental design and sample size for the statistical evaluation of data from somatic mutation and recombination tests (SMART) in Drosophila

In genetic toxicology it is important to know whether chemicals should be regarded as clearly hazardous or whether they can be considered sufficiently safe, which latter would be the case from the genotoxicologist's view if their genotoxic effects are nil or at least significantly below a predefined minimal effect level. A previously presented statistical decision procedure which allows one to make precisely this distinction is now extended to the question of how optimal experimental sample size can be determined in advance for genotoxicity experiments using the somatic mutation and recombination tests (SMART) of Drosophila. Optimally, the statistical tests should have high power to minimise the chance for statistically inconclusive results. Based on the normal test, the statistical principles are explained, and in an application to the wing spot assay, it is shown how the practitioner can proceed to optimise sample size to achieve numerically satisfactory conditions for statistical testing. The somatic genotoxicity assays of Drosophila are in principle based on somatic spots (mutant clones) that are recovered in variable numbers on individual flies. The underlying frequency distributions are expected to be of the Poisson type. However, some care seems indicated with respect to this latter assumption, because pooling of data over individuals, sexes, and experiments, for example, can (but need not) lead to data which are overdispersed, i.e, the data may show more variability than theoretically expected. It is an undesired effect of overdispersion that in comparisons of pooled totals it can lead to statistical testing which is too liberal, because overall it yields too many seemingly significant results. If individual variability considered alone is not contradiction with Poisson expectation, however, experimental planning can help to minimise the undesired effects of overdispersion on statistical testing of pooled totals. The rule for the practice is to avoid disproportionate sampling. It is recalled that for optimal power in statistical testing, it is preferable to use equal total numbers of flies in the control and treated series. Statistical tests which are based on Poisson expectations are too liberal if there is overdispersion in the data due to excess individual variability. In this case we propose to use the U test as a non-parametric two-sample test and to adjust the estimated optimal sample size according to (i) the overdispersion observed in a large historical control and (ii) the relative efficiency of the U test in comparison to the t test and related parametric tests.     

Application of dynamic point process models to cardiovascular control

The development of statistical models that accurately describe the stochastic structure of biological signals is a fast growing area in quantitative research. In developing a novel statistical paradigm based on Bayes' theorem applied to point processes, we are focusing our recent research on characterizing the physiological mechanisms involved in cardiovascular control. Results from a tilt table study point at our statistical framework as a valid model for the heart beat, as generated from complex mechanisms underlying cardiovascular control. The point process analysis provides new quantitative indices that could have important implications for research studies of cardiovascular and autonomic regulation and for monitoring of heart rate and heart rate variability measures in clinical settings.     

A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue

The revival of interest in cancer cell metabolism in recent years has prompted the need for quantitative analytical platforms for studying metabolites from in vivo sources. We implemented a quantitative polar metabolomics profiling platform using selected reaction monitoring with a 5500 QTRAP hybrid triple quadrupole mass spectrometer that covers all major metabolic pathways. The platform uses hydrophilic interaction liquid chromatography with positive/negative ion switching to analyze 258 metabolites (289 Q1/Q3 transitions) from a single 15-min liquid chromatography-mass spectrometry acquisition with a 3-ms dwell time and a 1.55-s duty cycle time. Previous platforms use more than one experiment to profile this number of metabolites from different ionization modes. The platform is compatible with polar metabolites from any biological source, including fresh tissues, cancer cells, bodily fluids and formalin-fixed paraffin-embedded tumor tissue. Relative quantification can be achieved without using internal standards, and integrated peak areas based on total ion current can be used for statistical analyses and pathway analyses across biological sample conditions. The procedure takes ～12 h from metabolite extraction to peak integration for a data set containing 15 total samples (～6 h for a single sample).     

Immunofluorescence study of fimbrial phase variation in Escherichia coli KS71

An immunofluorescence assay was developed to study fimbrial phase variation in a pyelonephritogenic Escherichia coli strain, KS71. By using fluorochrome-labeled antibodies specific for either P, type-1C, or type-1 fimbriae of strain KS71, it was shown that in a broth culture of strain KS71 the fimbrial types mostly occurred on different cells. Only 9% of the cells carried more than one fimbrial type. The KS71 cell population was fractionated into subpopulations expressing only one of the fimbrial types or lacking fimbriae. Immunofluorescence assay of the subpopulations revealed a rapid phase variation in fimbrial synthesis. Kinetic analyses of a nonfimbriated cell population suggested that a change from one fimbrial phase to another was not totally random.     

Accounting for uncertainty in model-based prevalence estimation: paratuberculosis control in dairy herds

ABSTRACT: BACKGROUND: A common approach to the application of epidemiological models is to determine a single (point estimate) parameterisation using the information available in the literature. However, in many cases there is considerable uncertainty about parameter values, reflecting both the incomplete nature of current knowledge and natural variation, for example between farms. Furthermore model outcomes may be highly sensitive to different parameter values. Paratuberculosis is an infection for which many of the key parameter values are poorly understood and highly variable, and for such infections there is a need to develop and apply statistical techniques which make maximal use of available data. RESULTS: A technique based on Latin hypercube sampling combined with a novel reweighting method was developed which enables parameter uncertainty and variability to be incorporated into a model-based framework for estimation of prevalence. The method was evaluated by applying it to a simulation of paratuberculosis in dairy herds which combines a continuous time stochastic algorithm with model features such as within herd variability in disease development and shedding, which have not been previously explored in paratuberculosis models. Generated sample parameter combinations were assigned a weight, determined by quantifying the model's resultant ability to reproduce prevalence data. Once these weights are generated the model can be used to evaluate other scenarios such as control options. To illustrate the utility of this approach these reweighted model outputs were used to compare standard test and cull control strategies both individually and in combination with simple husbandry practices that aim to reduce infection rates. CONCLUSIONS: The technique developed has been shown to be applicable to a complex model incorporating realistic control options. For models where parameters are not well known or subject to significant variability, the reweighting scheme allowed estimated distributions of parameter values to be combined with additional sources of information, such as that available from prevalence distributions, resulting in outputs which implicitly handle variation and uncertainty. This methodology allows for more robust predictions from modelling approaches by allowing for parameter uncertainty and combining different sources of information, and is thus expected to be useful in application to a large number of disease systems.     

Post-transplant Kaposi sarcoma originates from the seeding of donor-derived progenitors

Kaposi sarcoma (KS) is a vascular tumor that can develop in recipients of solid tissue transplants as a result of either primary infection or reactivation of a gammaherpesvirus, the KS- associated herpesvirus, also known as human herpesvirus-8 (HHV-8). We studied whether HHV-8 and the elusive KS progenitor cells could be transmitted from the donor through the grafts. We used a variety of molecular, cytogenetic, immunohistochemical and immunofluorescence methods to show that the HHV-8-infected neoplastic cells in post-transplant KS from five of eight renal transplant patients harbored either genetic or antigenic markers of their matched donors. These data suggest the use of donor-derived HHV-8-specific T cells for the control of post-transplant KS.     

Analytical method for keratan sulfates by high-performance liquid chromatography/turbo-ionspray tandem mass spectrometry

We established a highly sensitive LC/MS/MS method for the analysis of the disaccharides produced from keratan sulfates (KS). It was revealed that the disaccharides produced by keratanase II enzymatic digestion of KS could be determined with high sensitivity by the negative-ion mode of multiple reaction monitoring. Furthermore, monosulfated and disulfated disaccharides can be separated using a short column of Capcell Pak NH2 UG80 (35 mm x 2 mm i.d.). The complete analysis of one sample can be performed within 5 min. The assay method was validated and showed satisfactory sensitivity, precision, and accuracy, which enabled quantitation at subpicomole levels. From the results of analyses of KS obtained from cornea, nasal cartilage, and brain, it was found that the degree of sulfation at the C-6 position of the galactose residues differed among those samples in the following order: nasal cartilage > cornea > brain. Our analytical method is very useful for the analyses of KS in various biological materials and for comparison of the degree of sulfation of KS from various biological samples.     

Estimating Invasion Probabilities: A Case Study of fire Blight Disease and the Importation of Apple Fruits

A statistical method is proposed for the estimation of the probability of invasion of fire blight disease via the importation of apple fruits, by considering that the proportion of infected fruits varies depending on the production area and the year. The following three assumptions, which might be applicable to biological invasions of several diseases of plants and animals, are used: (1) A beta distribution approximately describes the probability distribution of the proportion of infected fruits in the production area of a given consignment, (2) every consignment contains fruits that were drawn at random from the infinite population of the production area, and (3) each infected fruit causes infection of fire blight in the importing country by an independent constant probability. The estimate of the expected time required for invasion is 1707 years if we ignore the variability of infection, whereas the estimate is 334 years if we consider the variability. Thus, it is suggested that the estimation of the risk of invasion might be quite biased if we ignore the variability of infection.     

A quantitative study of command elements for abdominal positioning behavior in the crayfish Procambarus clarkii

We develop a statistical method to estimate the total number of command elements devoted to abdominal positioning behavior in crayfish. We assumed that all command elements can be identified, that each identified cell is equivalent to a tagged individual in a population, and that the cells were sampled randomly. Samples of 29, 30, 20, and 35 cells from abdominal ganglia A3, A4, A5, and A6, respectively, were taken from our catalog. We characterized each cell using several morphological and physiological criteria, determined how many times each identified cell was present in the sample, and estimated the total number of command elements using both a maximum likelihood method and a modification of the Lincoln index. The larger the proportion of identified cells seen only once in the sample, the more identified cells there were that were unrepresented in the sample. We estimate there are approximately 34, 60, 86, and 98 command elements in ganglia A3, A4, A5, and A6, respectively. Using a slightly different data set we show that the motor output of unipolar cells is more often stronger in the direction of the cell's axonal projection. In bipolar command elements, the output strength was uncorrelated with the relative sizes of the two projecting axons. No two cells in our sample were completely identical, and this morphological variability sometimes made it difficult to determine whether or not two cells obtained from different individuals were the same identified cell. We discuss why caution should be exercised in studies requiring precision in cell identification.     

Using public surveys to reliably and rapidly estimate the distributions of multiple invasive species on the Andaman archipelago

To effectively manage multiple biological invasions, information on their distributions must be generated rapidly and over large spatial scales. Using public surveys in a false‐positive occupancy framework, we reliably estimate the distributions of three synanthropic invasive species on the Andaman Islands.     

Characterization and Simulation of Uncertain Frequency Distributions: Effects of Distribution Choice,Variability, Uncertainty,and Parameter Dependence

We use bootstrap simulation to characterize uncertainty in parametric distributions, including Normal, Lognormal, Gamma, Weibull, and Beta, commonly used to represent variability in probabilistic assessments. Bootstrap simulation enables one to estimate sampling distributions for sample statistics, such as distribution parameters, even when analytical solutions are not available. Using a two-dimensional framework for both uncertainty and variability, uncertainties in cumulative distribution functions were simulated. The mathematical properties of uncertain frequency distributions were evaluated in a series of case studies during which the parameters of each type of distribution were varied for sample sizes of 5, 10, and 20. For positively skewed distributions such as Lognormal, Weibull, and Gamma, the range of uncertainty is widest at the upper tail of the distribution. For symmetric unbounded distributions, such as Normal, the uncertainties are widest at both tails of the distribution. For bounded distributions, such as Beta, the uncertainties are typically widest in the central portions of the distribution. Bootstrap simulation enables complex dependencies between sampling distributions to be captured. The effects of uncertainty, variability, and parameter dependencies were studied for several generic functional forms of models, including models in which two-dimensional random variables are added, multiplied, and divided, to show the sensitivity of model results to different assumptions regarding model input distributions, ranges of variability, and ranges of uncertainty and to show the types of errors that may be obtained from mis-specification of parameter dependence. A total of 1,098 case studies were simulated. In some cases, counter-intuitive results were obtained. For example, the point value of the 95th percentile of uncertainty for the 95th percentile of variability of the product of four Gamma or Weibull distributions decreases as the coefficient of variation of each model input increases and, therefore, may not provide a conservative estimate. Failure to properly characterize parameter uncertainties and their dependencies can lead to orders-of-magnitude mis-estimates of both variability and uncertainty. In many cases, the numerical stability of two-dimensional simulation results was found to decrease as the coefficient of variation of the inputs increases. We discuss the strengths and limitations of bootstrap simulation as a method for quantifying uncertainty due to random sampling error.