Eilers, P.H.

Genetic variants of FOXP3 influence graft survival in kidney transplant patients (Article)

2013-03-25T00:00:00Z

FOXP3+regulatory T cells (Treg) play a role in controlling alloreactivity. It has been shown that short (GT)ndinucleotide repeats (≤(GT)15; S) in the promoter region of the FOXP3 gene enhance the promoter activity when compared to long (GT)nrepeats (≥(GT)16; L). The present study retrospectively investigated the influence of this (GT)nFOXP3 gene polymorphism on renal allograft survival. A total of 599 consecutive first-time kidney transplant patients (median follow-up time 7.7 years) were subdivided according to their FOXP3 genotype into the S-genotype group (SG) and the L-genotype group (LG). The SG was superior to the LG in both general graft survival censored for death (logrank test, p = 0.013) and graft survival following acute rejection (p = 0.021). Multivariate analysis defined the (GT)nFOXP3 dinucleotide repeat polymorphism as an independent factor and confirmed an advantage for the SG in renal allograft survival (HR = 0.67, 95% CI 0.48-0.94, p = 0.02). This gene association study identified a beneficial effect of FOXP3 genetic variants on graft survival in kidney transplant patients.

A phase I/IIa immunotherapy trial of HIV-1-infected patients with Tat, Rev and Nef expressing dendritic cells followed by treatment interruption (Article)

2012-03-01T00:00:00Z

In a phase I/IIa clinical trial, 17 HIV-1 infected patients, stable on cART, received 4 vaccinations with autologous dendritic cells electroporated with mRNA encoding Tat, Rev and Nef, after which cART was interrupted. Vaccination was safe and feasible. During the analytical treatment interruption (ATI), no serious adverse events were observed. Ninety-six weeks following ATI, 6/17 patients remained off therapy. Although induced and/or enhanced CD4+and CD8+T-cell responses specific for the immunogens were observed in most of the patients, we found no correlation with the number of weeks off cART. Moreover, CD4+T-cell counts, plasma viral load and the time remaining off cART following ATI did not differ from historical control data. To conclude, the vaccine was safe, well tolerated and resulted in vaccine-specific immune responses. Since no correlation with clinical parameters could be found, these results warrant further research in order to optimize the efficacy of vaccine-induced T-cell responses.

An R package "VariABEL" for genome-wide searching of potentially interacting loci by testing genotypic variance heterogeneity (Article)

2012-01-24T00:00:00Z

Background: Hundreds of new loci have been discovered by genome-wide association studies of human traits. These studies mostly focused on associations between single locus and a trait. Interactions between genes and between genes and environmental factors are of interest as they can improve our understanding of the genetic background underlying complex traits. Genome-wide testing of complex genetic models is a computationally demanding task. Moreover, testing of such models leads to multiple comparison problems that reduce the probability of new findings. Assuming that the genetic model underlying a complex trait can include hundreds of genes and environmental factors, testing of these models in genome-wide association studies represent substantial difficulties.We and Pare with colleagues (2010) developed a method allowing to overcome such difficulties. The method is based on the fact that loci which are involved in interactions can show genotypic variance heterogeneity of a trait. Genome-wide testing of such heterogeneity can be a fast scanning approach which can point to the interacting genetic variants.Results: In this work we present a new method, SVLM, allowing for variance heterogeneity analysis of imputed genetic variation. Type I error and power of this test are investigated and contracted with these of the Levene's test. We also present an R package, VariABEL, implementing existing and newly developed tests.Conclusions: Variance heterogeneity analysis is a promising method for detection of potentially interacting loci. New method and software package developed in this work will facilitate such analysis in genome-wide context.

Multidimensional single-index signal regression (Article)

2011-12-15T00:00:00Z

In general, linearity is assumed to hold in multivariate calibration (MVC), but this may not be true. We approach the MVC problem using multidimensional penalized signal regression, which can be extended with an explicit link function between linear prediction and response and in the spirit of single-index models. As the two-dimensional surface of calibration coefficients is smoothly and generally estimated with tensor product P-splines, the unknown link function is estimated using univariate Psplines. The methods presented are grounded in penalized regression, where difference penalties are placed on the rows and columns of the tensor product coefficients, as well as on the link function coefficients, each having its own tuning parameter. An application to ternary mixture data shows that a non-linearity is present. Performance comparisons are made to standard penalized signal regression, not only demonstrating the nonlinear effect, but also improvements in external prediction.

Mixture models for baseline estimation (Article)

2011-11-25T00:00:00Z

Various instruments produce data consisting of a series of more or less isolated peaks, superimposed on a drifting baseline. The positions and the heights of the peaks are of interest and the baseline is a nuisance. We model a smooth baseline by weighted regression on P-splines, a combination of B-splines and a discrete penalty to tune smoothness. The weights are computed from a mixture model with two component distributions, relative to the baseline, one for noise, the other for the peaks. The algorithm is fast and it shows excellent performance on simulated and experimental data.

Deconvolution of pulse trains with the L0 penalty (Article)

2011-10-31T00:00:00Z

The output of many instruments can be modeled as a convolution of an impulse response and a series of sharp spikes. Deconvolution considers the inverse problem: estimate the input spike train from an observed (noisy) output signal. We approach this task as a linear inverse problem, solved using penalized regression. We propose the use of an L0penalty and compare it with the more common L2and L1penalties. In all cases a simple and iterative weighted regression procedure can be used. The model is extended with a smooth component to handle drifting baselines. Application to three different data sets shows excellent results.

Haplotype estimation from fuzzy genotypes using penalized likelihood (Article)

2011-09-08T00:00:00Z

The Composite Link Model is a generalization of the generalized linear model in which expected values of observed counts are constructed as a sum of generalized linear components. When combined with penalized likelihood, it provides a powerful and elegant way to estimate haplotype probabilities from observed genotypes. Uncertain ("fuzzy") genotypes, like those resulting from AFLP scores, can be handled by adding an extra layer to the model. We describe the model and the estimation algorithm. We apply it to a data set of accurate human single nucleotide polymorphism (SNP) and to a data set of fuzzy tomato AFLP scores.

Parental smoking during pregnancy, early growth, and risk of obesity in preschool children: The Generation R Study (Article)

2011-07-01T00:00:00Z

Background: Maternal smoking during pregnancy seems to be associated with obesity in offspring. Not much is known about the specific critical exposure periods or underlying mechanisms for this association. Objective: We assessed the associations of active maternal and paternal smoking during pregnancy with early growth characteristics and risks of overweight and obesity in preschool children. Design: This study was a population-based, prospective cohort study from early fetal life until the age of 4 y in 5342 mothers and fathers and their children. Growth characteristics [head circumference, length, weight, and body mass index (BMI; in kg/m2)] and overweight and obesity were repeatedly measured at the ages of 1, 2, 3, and 4 y. Results: In comparison with children from nonsmoking mothers, children from mothers who continued smoking during pregnancy had persistently smaller head circumferences and heights until the age of 4 y, whereas their weights were lower only until the age of 3 mo. This smaller length and normal to higher weight led to an increased BMI [SD score difference: 0.11; 95% CI: 0.02, 0.20; P < 0.05)] and an increased risk of obesity (odds ratio: 1.61; 95% CI: 1.03, 2.53; P < 0.05) at the age of 4 y. In nonsmoking mothers, paternal smoking was not associated with postnatal growth characteristics or risk of obesity in offspring. Maternal smoking during pregnancy was associated with a higher BMI at the age of 4 y in children with a normal birth weight and in those who were small for gestational age at birth. Conclusion: Our findings suggest that direct intrauterine exposure to smoke until late pregnancy leads to different height and weight growth adaptations and increased risks of overweight and obesity in preschool children.

Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements (Article)

2011-07-01T00:00:00Z

Time series of vegetation indices like NDVI are used in numerous applications ranging from ecology to climatology and agriculture. Often, these time series have to be filtered before application. The smoothing removes noise introduced by undetected clouds and poor atmospheric conditions. Ground reference measurements are usually difficult to obtain due to the medium/coarse resolution of the imagery. Hence, new filter algorithms are typically only (visually) assessed against the existing smoother. The present work aims to propose a range of quality indicators that could be useful to qualify filter performance in the absence of ground-based reference measurements. The indicators comprise (i) plausibility checks, (ii) distance metrics and (iii) geostatistical measures derived from variogram analysis. The quality measures can be readily derived from any imagery. For illustration, a large SPOT VGT dataset (1999-2008) covering South America at 1km spatial resolution was filtered using the Whittaker smoother.

Three distinct modes of exocytosis revealed by amperometry in neuroendocrine cells (Article)

2011-02-16T00:00:00Z

Neurotransmission requires Ca2+-dependent release of secretory products through fusion pores that open and reclose (partial membrane distention) or open irreversibly (complete membrane distention). It has been challenging to distinguish between these release modes; however, in the work presented here, we were able to deduce different modes of depolarization-evoked exocytosis in neuroendocrine chromaffin and PC12 cells solely by analyzing amperometric recordings. After we determined the quantal size (Q), event half-width (t50), event amplitude (Ipeak), and event decay time constant (τdecay), we fitted scatter plots of log-transformed data with a mixture of one-and two-dimensional Gaussian distributions. Our analysis revealed three distinct and differently shaped clusters of secretory events, likely corresponding to different modes of exocytosis. Complete membrane distention, through fusion pores of widely varying conductances, accounted for 70% of the total amount of released catecholamine. Two different kinds of partial membrane distention (kiss-and-run and kiss-and-stay exocytosis), characterized by mode-specific fusion pores with unitary conductances, accounted for 20% and 10%, respectively. These results show that our novel one-and two-dimensional analysis of amperometric data reveals new release properties and enables one to distinguish at least three different modes of exocytosis solely by analyzing amperometric recordings.

Penalized regression techniques for modeling relationships between metabolites and tomato taste attributes (Article)

2011-02-07T00:00:00Z

The search for models which link tomato taste attributes to their metabolic profiling, is a main challenge within the breeding programs that aim to enhance tomato flavor. In this paper, we compared such models calculated by the traditional statistical approach, stepwise regression, with models obtained by the new generation of regression techniques, known as penalized regression or regularization methods. In addition, for penalized regression, different scenarios and various model selection criteria were discussed to conclude that classical crossvalidation, selects models with many superfluous variables whereas model selection criteria such as Bayesian information criterion, seem to be more suitable, when the goal is to find parsimonious models, to explain tomato taste attributes based on metabolic information. An exhaustive comparison of the discussed methodology was done for six sensory traits, showing that the most important covariates were identified by the stepwise regression as well as by some of the penalized regression methods, despite the general disagreement on the size of the regression coefficients between them. In particular, for stepwise regression the coefficients are inflated due to their high variance which is not the case with penalized regression, showing that this new methodology, can be an alternative to obtain more accurate models.

Reverse engineering of metabolic networks, a critical assessment (Article)

2011-02-01T00:00:00Z

Inferring metabolic networks from metabolite concentration data is a central topic in systems biology. Mathematical techniques to extract information about the network from data have been proposed in the literature. This paper presents a critical assessment of the feasibility of reverse engineering of metabolic networks, illustrated with a selection of methods. Appropriate data are simulated to study the performance of four representative methods. An overview of sampling and measurement methods currently in use for generating time-resolved metabolomics data is given and contrasted with the needs of the discussed reverse engineering methods. The results of this assessment show that if full inference of a real-world metabolic network is the goal there is a large discrepancy between the requirements of reverse engineering of metabolic networks and contemporary measurement practice. Recommendations for improved time-resolved experimental designs are given.

Growth charts for children with Ellis-van Creveld syndrome (Article)

2011-02-01T00:00:00Z

Ellis-van Creveld (EvC) syndrome is a congenital malformation syndrome with marked growth retardation. In this study, specific growth charts for EvC patients were derived to allow better follow-up of growth and earlier detection of growth patterns unusual for EvC. With the use of 235 observations of 101 EvC patients (49 males, 52 females), growth charts for males and females from 0 to 20 years of age were derived. Longitudinal and cross-sectional data were collected from an earlier review of growth data in EvC, a database of EvC patients, and from recent literature. To model the growth charts, the GAMLSS package for the R statistical program was used. Height of EvC patients was compared to healthy children using Dutch growth charts. Data are presented both on a scale for age and on a scale for the square root of age. Compared to healthy Dutch children, mean height standard deviation score values for male and female EvC patients were -3.1 and -3.0, respectively. The present growth charts should be useful in the follow-up of EvC patients. Most importantly, early detection of growth hormone deficiency, known to occur in EvC, will be facilitated.

Improved parametric time warping for proteomics (Article)

2010-11-01T00:00:00Z

We present an improved version of parametric time warping, which enables the method to be used in LC-MS measurements in proteomics. The new features include a new similarity measure for comparing warped chromatograms, an insurance against peaks at the extremes of the chromatograms disappearing because of the warping, and the possibility to select and use multiple traces in searching the optimal alignment. Moreover, we present an alignment strategy combining global and individual alignments for aligning LC-MS data that exhibit different shifts within the same sample, at the same retention time. Using an LC-MS data set consisting of E. coli homogenates that were measured in several batches over a period of six months, we show the benefits of the improved algorithm and the merits of the new strategy. The algorithm is publicly available as the R package ptw.

Splines, knots, and penalties (Article)

2010-11-01T00:00:00Z

Penalized splines have gained much popularity as a flexible tool for smoothing and semi-parametric models. Two approaches have been advocated: (1) use a B-spline basis, equally spaced knots, and difference penalties [Eilers PHC, Marx BD. Flexible smoothing using B-splines and penalized likelihood (with Comments and Rejoinder). Stat Sci 1996, 11:89-121.] and (2) use truncated power functions, knots based on quantiles of the independent variable and a ridge penalty [Ruppert D, Wand MP, Carroll RJ. Semiparametric Regression. New York: Cambridge University Press; 2003]. We compare the two approaches on many aspects: numerical stability, quality of the fit, interpolation/extrapolation, derivative estimation, visual presentation and extension to multidimensional smoothing. We discuss mixed model and Bayesian parallels to penalized regression. We conclude that B-splines with difference penalties are clearly to be preferred.

Bilinear modulation models for seasonal tables of counts (Article)

2010-04-01T00:00:00Z

We propose generalized linear models for time or age-time tables of seasonal counts, with the goal of better understanding seasonal patterns in the data. The linear predictor contains a smooth component for the trend and the product of a smooth component (the modulation) and a periodic time series of arbitrary shape (the carrier wave). To model rates, a population offset is added. Two-dimensional trends and modulation are estimated using a tensor product B-spline basis of moderate dimension. Further smoothness is ensured using difference penalties on the rows and columns of the tensor product coefficients. The optimal penalty tuning parameters are chosen based on minimization of a quasi-information criterion. Computationally efficient estimation is achieved using array regression techniques, avoiding excessively large matrices. The model is applied to female death rate in the US due to cerebrovascular diseases and respiratory diseases.

Risk factors and outcomes associated with first-trimester fetal growth restriction (Article)

2010-02-10T00:00:00Z

Context: Adverse environmental exposures lead to developmental adaptations in fetal life. The influences of maternal physical characteristics and lifestyle habits on first-trimester fetal adaptations and the postnatal consequences are not known. Objective: To determine the risk factors and outcomes associated with firsttrimester growth restriction. Design, Setting, and Participants: Prospective evaluation of the associations of maternal physical characteristics and lifestyle habits with first-trimester fetal crown to rump length in 1631 mothers with a known and reliable first day of their last menstrual period and a regular menstrual cycle. Subsequently, we assessed the associations of first-trimester fetal growth restriction with the risks of adverse birth outcomes and postnatal growth acceleration until the age of 2 years. The study was based in Rotterdam, the Netherlands. Mothers were enrolled between 2001 and 2005. Main Outcome Measures: First-trimester fetal growth was measured as fetal crown to rump length by ultrasound between the gestational age of 10 weeks 0 days and 13 weeks 6 days. Main birth outcomes were preterm birth (gestational age <37 weeks), low birth weight (<2500 g), and small size for gestational age (lowest fifth birth centile). Postnatal growth was measured until the age of 2 years. Results In the multivariate analysis, maternal age was positively associated with firsttrimester fetal crown to rump length (difference per maternal year of age, 0.79 mm; 95% confidence interval [CI], 0.41 to 1.18 per standard deviation score increase). Higher diastolic blood pressure and higher hematocrit levels were associated with a shorter crown to rump length (differences, -0.40 mm; 95% CI, -0.74 to -0.06 and -0.52 mm; 95% CI, -0.90 to -0.14 per standard deviation increase, respectively). Compared with mothers who were nonsmokers and optimal users of folic acid supplements, those who both smoked and did not use folic acid supplements had shorter fetal crown to rump lengths (difference, -3.84 mm; 95% CI, -5.71 to -1.98). Compared with normal first-trimester fetal growth, first-trimester growth restriction was associated with increased risks of preterm birth (4.0% vs 7.2%; adjusted odds ratio [OR], 2.12; 95% CI, 1.24 to 3.61), low birth weight (3.5% vs 7.5%; adjusted OR, 2.42; 95% CI, 1.41 to 4.16), and small size for gestational age at birth (4.0% vs 10.6%; adjusted OR, 2.64; 95% CI, 1.64 to 4.25). Each standard deviation decrease in firsttrimester fetal crown to rump length was associated with a postnatal growth acceleration until the age of 2 years (standard deviation score increase, 0.139 per 2 years; 95% CI, 0.097 to 0.181). Conclusions Maternal physical characteristics and lifestyle habits were independently associated with early fetal growth. First-trimester fetal growth restriction was associated with an increased risk of adverse birth outcomes and growth acceleration in early childhood.

MLPAinter for MLPA interpretation: An integrated approach for the analysis, visualisation and data management of Multiplex Ligation-dependent Probe Amplification (Article)

2010-01-29T00:00:00Z

Background: Multiplex Ligation-Dependent Probe Amplification (MLPA) is an application that can be used for the detection of multiple chromosomal aberrations in a single experiment. In one reaction, up to 50 different genomic sequences can be analysed. For a reliable work-flow, tools are needed for administrative support, data management, normalisation, visualisation, reporting and interpretation.Results: Here, we developed a data management system, MLPAInter for MLPA interpretation, that is windows executable and has a stand-alone database for monitoring and interpreting the MLPA data stream that is generated from the experimental setup to analysis, quality control and visualisation. A statistical approach is applied for the normalisation and analysis of large series of MLPA traces, making use of multiple control samples and internal controls.Conclusions: MLPAinter visualises MLPA data in plots with information about sample replicates, normalisation settings, and sample characteristics. This integrated approach helps in the automated handling of large series of MLPA data and guarantees a quick and streamlined dataflow from the beginning of an experiment to an authorised report.

Penalized regression with individual deviance effects (Article)

2009-12-03T00:00:00Z

The present work addresses the problem of model estimation and computations for discrete data when some covariates are modeled smoothly using splines. We propose to introduce and explicitly estimate individual deviance effects (one for each observation), constrained by a ridge penalty. This turns out to be an effective way to absorb model excess variation and detect systematic patterns. Large but very sparse systems of penalized likelihood equations have to be solved. We present fast and compact algorithms for fitting, estimation and computation of the effective dimension. Applications to counts, binomial, and survival data illustrate practical use of this model.

Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology (Article)

2009-12-01T00:00:00Z

Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival, >4.7 years), two with intermediate prognosis (median survival, 1-4 years), two with poor prognosis (median survival, <1 year), and one control group. The intrinsic molecular subtypes of glioma are different from histologic subgroups and correlate better to patient survival. The prognostic value of molecular subgroups was validated on five independent sample cohorts (The Cancer Genome Atlas, Repository for Molecular Brain Neoplasia Data, GSE12907, GSE4271, and Li and colleagues). The power of intrinsic subtyping is shown by its ability to identify a subset of prognostically favorable tumors within an external data set that contains only histologically confirmed glioblastomas (GBM). Specific genetic changes (epidermal growth factor receptor amplification, IDH1 mutation, and 1p/19q loss of heterozygosity) segregate in distinct molecular subgroups. We identified a subgroup with molecular features associated with secondary GBM, suggesting that different genetic changes drive gene expression profiles. Finally, we assessed response to treatment in molecular subgroups. Our data provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histologic classification. Molecular classification therefore may aid diagnosis and can guide clinical decision making.

Optimal expectile smoothing (Article)

2009-10-01T00:00:00Z

Quantiles are computed by optimizing an asymmetrically weighted L1norm, i.e. the sum of absolute values of residuals. Expectiles are obtained in a similar way when using an L2norm, i.e. the sum of squares. Computation is extremely simple: weighted regression leads to the global minimum in a handful of iterations. Least asymmetrically weighted squares are combined with P-splines to compute smooth expectile curves. Asymmetric cross-validation and the Schall algorithm for mixed models allow efficient optimization of the smoothing parameter. Performance is illustrated on simulated and empirical data.

An analysis of life expectancy and economic production using expectile frontier zones (Article)

2009-08-07T00:00:00Z

The wealth of a country is assumed to have a strong non-linear influence on the life expectancy of its inhabitants. We follow up on research by Preston and study the relationship with gross domestic product. Smooth curves for the average but also for upper frontiers are constructed by a combination of least asymmetrically weighted squares and P-splines. Guidelines are given for optimizing the amount of smoothing and the definition of frontiers. The model is applied to a large set of countries in different years. It is also used to estimate life expectancy performance for individual countries and to show how it changed over time.

Progression and tumor heterogeneity analysis in early rectal cancer (Article)

2008-02-01T00:00:00Z

Purpose: Adequate preoperative staging of large sessile rectal tumors requires identifying adenomas that already contain an invasive focus, specifically those that are growing in or beyond the submucosa. We systematically compared chromosomal instability patterns in adenoma and carcinoma fractions of the same lesion to assess specific steps in rectal tumor progression. Experimental Design: We analyzed 36 formalin-fixed, paraffin-embedded tumors. Both the adenoma and carcinoma fractions were typed with single nucleotide polymorphism arrays and compared with 21 previously described pure adenomas. Eighteen cases were included in an intratumor heterogeneity analysis. Results: Five specific "malignant" events (gain of 8q, 13q, and 20q and loss of 17p and 18q) and aberrant staining for p53 and SMAD4 were all increased in the adenoma fractions of carcinoma cases compared with pure adenomas. Paired analysis revealed that 31% of the samples had an equal amount of malignant aberrations in their adenoma and carcinoma fractions, whereas 25% had one and 33% had two or more extra malignant events in the carcinoma fraction. Analysis of three core biopsies per patient showed a large degree of intratumor heterogeneity. However, the number of malignant aberrations in the biopsy with the most aberrations per tumor correlated with the corresponding adenoma or carcinoma fraction (r = 0.807; P < 0.001). Conclusion: Five specific chromosomal aberrations, combined with immunohistochemistry for p53 and SMAD4, can predict possible progression of sessile rectal adenomas to early rectal cancer and can, after validation studies, be added to preoperative staging. Preferably, three biopsies should be taken from each tumor to address intratumor heterogeneity.