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Although it is well established that experience seeking behavior (ES) is positively related to cognitive functioning, the mechanisms underlying this association are not clearly understood. In a large sample of adult twins and siblings (N = 864, age range 23–75), we studied the causes of covariation between ES and general cognitive ability and we studied whether ES moderates the genetic and environmental causes of variation in general cognitive ability. Results demonstrate a phenotypic correlation of .17 (p

Heritability estimates of general intelligence in adulthood generally range from 75 to 85%, with all heritability due to additive genetic influences, while genetic dominance and shared environmental factors are absent, or too small to be detected. These estimates are derived from studies based on the classical twin design and are based on the assumption of random mating. Yet, considerable positive assortative mating has been reported for general intelligence. Unmodeled assortative mating may lead to biased estimates of the relative magnitude of genetic and environmental factors. To investigate the effects of assortative mating on the estimates of the variance components of intelligence, we employed an extended twin-family design. Psychometric IQ data were available for adult monozygotic and dizygotic twins, their siblings, the partners of the twins and siblings, and either the parents or the adult offspring of the twins and siblings (N = 1314). Two underlying processes of assortment were considered: phenotypic assortment and social homogamy. The phenotypic assortment model was slightly preferred over the social homogamy model, suggesting that assortment for intelligence is mostly due to a selection of mates on similarity in intelligence. Under the preferred phenotypic assortment model, the variance of intelligence in adulthood was not only due to non-shared environmental (18%) and additive genetic factors (44%) but also to non-additive genetic factors (27%) and phenotypic assortment (11%).This non-additive nature of genetic influences on intelligence needs to be accommodated in future GWAS studies for intelligence. Electronic supplementary material The online version of this article (doi:10.1007/s10519-011-9507-9) contains supplementary material, which is available to authorized users.

Realizing that genes often operate together, studies into the molecular biology of cancer shift focus from individual genes to pathways. In order to understand the regulatory mechanisms of a pathway, one must study its genes at all molecular levels. To facilitate such study at the genomic level, we developed exploratory factor analysis for the characterization of the variability of a pathway's copy number data. A latent variable model that describes the call probability data of a pathway is introduced and fitted with an EM algorithm. In two breast cancer data sets, it is shown that the first two latent variables of GO nodes, which inherit a clear interpretation from the call probabilities, are often related to the proportion of aberrations and a contrast of the probabilities of a loss and of a gain. Linking the latent variables to the node's gene expression data suggests that they capture the "global" effect of genomic aberrations on these transcript levels. In all, the proposed method provides an possibly insightful characterization of pathway copy number data, which may be fruitfully exploited to study the interaction between the pathway's DNA copy number aberrations and data from other molecular levels like gene expression. © Copyright 2011, Mary Ann Liebert, Inc. 2011.

Although cognitive ability is a highly heritable complex trait, only a few genes have been identified, explaining relatively low proportions of the observed trait variation. This implies that hundreds of genes of small effect may be of importance for cognitive ability. We applied an innovative method in which we tested for the effect of groups of genes defined according to cellular function (functional gene group analysis). Using an initial sample of 627 subjects, this functional gene group analysis detected that synaptic heterotrimeric guanine nucleotide binding proteins (G proteins) play an important role in cognitive ability (P(EMP) = 1.9 x 10(-4)). The association with heterotrimeric G proteins was validated in an independent population sample of 1507 subjects. Heterotrimeric G proteins are central relay factors between the activation of plasma membrane receptors by extracellular ligands and the cellular responses that these induce, and they can be considered a point of convergence, or a "signaling bottleneck." Although alterations in synaptic signaling processes may not be the exclusive explanation for the association of heterotrimeric G proteins with cognitive ability, such alterations may prominently affect the properties of neuronal networks in the brain in such a manner that impaired cognitive ability and lower intelligence are observed. The reported association of synaptic heterotrimeric G proteins with cognitive ability clearly points to a new direction in the study of the genetic basis of cognitive ability.

Hypomyelination is observed in the context of a growing number of genetic disorders that share clinical characteristics. The aim of this study was to determine the possible role of magnetic resonance imaging pattern recognition in distinguishing different hypomyelinating disorders, which would facilitate the diagnostic process. Only patients with hypomyelination of known cause were included in this retrospective study. A total of 112 patients with Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus-Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis, Salla disease and fucosidosis were included. The brain scans were rated using a standard scoring list; the raters were blinded to the diagnoses. Grouping of the patients was based on cluster analysis. Ten clusters of patients with similar magnetic resonance imaging abnormalities were identified. The most important discriminating items were early cerebellar atrophy, homogeneity of the white matter signal on T2-weighted images, abnormal signal intensity of the basal ganglia, signal abnormalities in the pons and additional T2 lesions in the deep white matter. Eight clusters each represented mainly a single disorder (i.e. Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, infantile GM1 and GM2 gangliosidosis, Pelizaeus-Merzbacher-like disease and fucosidosis); only two clusters contained multiple diseases. Pelizaeus-Merzbacher-like disease was divided between two clusters and Salla disease did not cluster at all. This study shows that it is possible to separate patients with hypomyelination disorders of known cause in clusters based on magnetic resonance imaging abnormalities alone. In most cases of Pelizaeus-Merzbacher disease, hypomyelination with congenital cataract, hypomyelination with hypogonadotropic hypogonadism and hypodontia, Pelizaeus-Merzbacher-like disease, infantile GM1 and GM2 gangliosidosis and fucosidosis, the imaging pattern gives clues for the diagnosis. © 2010 The Author.

This article concerns the power of various data analytic strategies to detect the effect of a single genetic variant (GV) in multivariate data. We simulated exactly fitting monozygotic and dizygotic phenotypic data according to single and two common factor models, and simplex models. We calculated the power to detect the GV in twin 1 data in an ANOVA of phenotypic sum scores, in a MANOVA, and in exploratory factor analysis (EFA), in which the common factors are regressed on the genetic variant. We also report power in the full twin model, and power of the single phenotype ANOVA. The results indicate that (1) if the GV affects all phenotypes, the sum score ANOVA and the EFA are most powerful, while the MANOVA is less powerful. Increasing phenotypic correlations further decreases the power of the MANOVA; and (2) if the GV affects only a subset of the phenotypes, the EFA or the MANOVA are most powerful, while sum score ANOVA is less powerful. In this case, an increase in phenotypic correlations may enhance the power of MANOVA and EFA. If the effect of the GV is modeled directly on the phenotypes in the EFA, the power of the EFA is approximately equal to the power of the MANOVA.

This paper argues that currently available methods for the assessment of the repeatability and reproducibility of ordinal classifications are not satisfactory. The paper aims to study whether we can modify a class of models from Item Response Theory, well established for the study of the reliability of categorical measurements in psychometrics and education, for use in business and industry, and whether the resulting approaches offer a satisfactory solution. The fitted models can be presented graphically, but also allow the calculation of probabilities of correct ordering and consistent classification. In addition, the modelbased approach allows refined diagnostics, giving the user insight into the workings of a classification procedure, which is vital information for a user willing to improve a poor classification procedure. The approach is illustrated from a real-life example, and the proposed analysis is contrasted to two popular alternative analyses, based on Goodman and Kruskal's gamma and Kendall's coefficient of concordance. The datasets and mathematical proofs are available as online supplemental materials. © 2010 American Statistical Association and the American Society for Quality.

Motivation: Many high-resolution array comparative genomic hybridization tumor profiles contain a wave bias, which makes accurate detection of breakpoints in such profiles more difficult. Results: An efficient and highly effective algorithm that largely removes the wave bias from tumor profiles by regressing the tumor profile data on data of profiles from the clinical genetics practice. Results are illustrated on two independent datasets. The algorithm is shown to be robust against the presence of true copy number aberrations. Moreover, the smoothed profiles are able to recapitulate the aberration location and signal for simulated tumor profiles. Availability: Easy-to-use R scripts, user instructions and data are available from http://www.few.vu.nl/~mavdwiel/nowaves.html. Contact: mark.vdwiel@vumc.nl Supplementary information: Supplementary information are available at Bioinformatics online.

The central dogma of molecular biology relates DNA with mRNA. Array CGH measures DNA copy number and gene expression microarrays measure the amount of mRNA. Methods that integrate data from these two platforms may uncover meaningful biological relationships that further our understanding of cancer. We develop nonparametric tests for the detection of copy number induced differential gene expression. The tests incorporate the uncertainty of the calling of genomic aberrations. The test is preceded by a "tuning algorithm" that discards certain genes to improve the overall power of the false discovery rate selection procedure. Moreover, the test statistics are "shrunken" to borrow information across neighboring genes that share the same array CGH signature. For each gene we also estimate its effect, its amount of differential expression due to copy number changes, and calculate the coefficient of determination. The method is illustrated on breast cancer data, in which it confirms previously reported findings, now with a more profound statistical underpinning. © 2008, The International Biometric Society.

Knowledge of transcription of the human genome might greatly enhance our understanding of cancer. In particular, gene expression may be used to predict the survival of cancer patients. Microarray data are characterized by their high-dimensionality: the number of covariates (p~1000) greatly exceeds the number of samples (n~100), which is a considerable challenge in the context of survival prediction. An inventory of methods that have been used to model survival using gene expression is given. These methods are critically reviewed and compared in a qualitative way. Next, these methods are applied to three real-life data sets for a quantitative comparison. The choice of the evaluation measure of predictive performance is crucial for the selection of the best method. Depending on the evaluation measure, either the L"2-penalized Cox regression or the random forest ensemble method yields the best survival time prediction using the considered gene expression data sets. Consensus on the best evaluation measure of predictive performance is needed.

Sardinia has been used for genetic studies because of its historical isolation, genetic homogeneity and increased prevalence of certain rare diseases. Controversy remains concerning the genetic substructure and the extent of genetic homogeneity, which has implications for the design of genome-wide association studies (GWAS). We revisited this issue by examining the genetic make-up of a sample from North-East Sardinia using a dense set of autosomal, Y chromosome and mitochondrial markers to assess the potential of the sample for GWAS and fine mapping studies. We genotyped individuals for 500K single-nucleotide polymorphisms, Y chromosome markers and sequenced the mitochondrial hypervariable (HVI–HVII) regions. We identified major haplogroups and compared these with other populations. We estimated linkage disequilibrium (LD) and haplotype diversity across autosomal markers, and compared these with other populations. Our results show that within Sardinia there is no major population substructure and thus it can be considered a genetically homogenous population. We did not find substantial differences in the extent of LD in Sardinians compared with other populations. However, we showed that at least 9% of genomic regions in Sardinians differed in LD structure, which is helpful for identifying functional variants using fine mapping. We concluded that Sardinia is a powerful setting for genetic studies including GWAS and other mapping approaches.

Objective To investigate the occurrence of restricted diffusion in vanishing white matter, the affected structures, the time of occurrence in the disease course, and the histopathologic correlate. Design Retrospective observational study. Patients Forty-six patients with vanishing white matter. Setting VU University Medical Center. Main Outcome Measures We evaluated all available diffusion-weighted imaging studies in our database and recorded the areas that displayed restricted diffusion in 1 or more patients. We measured the mean apparent diffusion coefficients of these areas in all patients and used the putamen for internal quality control. We recorded age and disease duration during magnetic resonance imaging, and we obtained a magnetic resonance image of a postmortem vanishing white matter brain slice and subsequently performed histopathologic stainings. Results Areas with decreased apparent diffusion coefficient values were found in the U fibers (n = 21 patients), cerebellar white matter (n = 18), middle cerebellar peduncle (n = 8), pyramids (n = 8), genu (n = 8) or splenium (n = 9) of the corpus callosum, and posterior limb of the internal capsule (n = 10). Overall, patients showing restricted diffusion (n = 32) were younger and had shorter disease duration. Histopathologic analysis of the brain slice revealed that regions with restricted diffusion had a higher cell density. Conclusion In vanishing white matter, restricted diffusion can be found in relatively spared regions with high cellularity particularly in young patients with short disease duration.

Background: An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms.Results: We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes.Conclusions: Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor. © 2012 van Wieringen et al.; licensee BioMed Central Ltd.

Schizophrenia is a highly heritable disorder with a polygenic pattern of inheritance and a population prevalence of 1%. Previous studies have implicated synaptic dysfunction in schizophrenia. We tested the accumulated association of genetic variants in expert-curated synaptic gene groups with schizophrenia in 4673 cases and 4965 healthy controls, using functional gene group analysis. Identifying groups of genes with similar cellular function rather than genes in isolation may have clinical implications for finding additional drug targets. We found that a group of 1026 synaptic genes was significantly associated with the risk of schizophrenia (P7.6 × 10 11) and more strongly associated than 100 randomly drawn, matched control groups of genetic variants (P0.01). Subsequent analysis of synaptic subgroups suggested that the strongest association signals are derived from three synaptic gene groups: intracellular signal transduction (P2.0 × 10 4), excitability (P9.0 × 10 4) and cell adhesion and trans-synaptic signaling (P2.4 × 10 3). These results are consistent with a role of synaptic dysfunction in schizophrenia and imply that impaired intracellular signal transduction in synapses, synaptic excitability and cell adhesion and trans-synaptic signaling play a role in the pathology of schizophrenia. © 2012 Macmillan Publishers Limited All rights reserved.

Background Because of the distinct clinical presentation of early and advanced stage ovarian cancer, we aim to clarify whether these disease entities are solely separated by time of diagnosis or whether they arise from distinct molecular events. Methods Sixteen early and sixteen advanced stage ovarian carcinomas, matched for histological subtype and differentiation grade, were included. Genomic aberrations were compared for each early and advanced stage ovarian cancer by array comparative genomic hybridization. To study how the aberrations correlate to the clinical characteristics of the tumors we clustered tumors based on the genomic aberrations. Results The genomic aberration patterns in advanced stage cancer equalled those in early stage, but were more frequent in advanced stage (p = 0.012). Unsupervised clustering based on genomic aberrations yielded two clusters that significantly discriminated early from advanced stage (p = 0.001), and that did differ significantly in survival (p = 0.002). These clusters however did give a more accurate prognosis than histological subtype or differentiation grade. Conclusion This study indicates that advanced stage ovarian cancer either progresses from early stage or from a common precursor lesion but that they do not arise from distinct carcinogenic molecular events. Furthermore, we show that array comparative genomic hybridization has the potential to identify clinically distinct patients. Electronic supplementary material The online version of this article (doi:10.1007/s13402-012-0077-5) contains supplementary material, which is available to authorized users.

We consider estimation and testing of linkage equilibrium from genotypic data on a random sample of sibs, such as monozygotic and dizygotic twins. We compute the maximum likelihood estimator with an EM-algorithm and a likelihood ratio statistic that takes the family structure into account. As we are interested in applying this to twin data we also allow observations on single children, so that monozygotic twins can be included. We allow non-zero recombination fraction between the loci of interest, so that linkage disequilibrium between both linked and unlinked loci can be tested. The EM-algorithm for computing the maximum likelihood estimator of the haplotype frequencies and the likelihood ratio test-statistic, are described in detail. It is shown that the usual estimators of haplotype frequencies based on ignoring that the sibs are related are inefficient, and the likelihood ratio test for testing that the loci are in linkage disequilibrium. © 2011 The Authors. Statistica Neerlandica © 2011 VVS.

We review methods for assessing the reliability of binary measurements, such as accept/reject inspection in industry. Our framework introduces two factors that are highly relevant in deciding which method to use: (1) whether a reference value (gold standard) can be obtained and (2) whether the underlying measurand is continuous or truly dichotomous. Artificially dichotomizing a continuous measurand, as is commonly done, creates complications that are underappreciated in the literature and in practice. In particular, it introduces an intrinsic reason for the assumption of conditional i.i.d. to be violated. For most methods, this is not crucial provided the samples are random (or at least representative). But, also for most methods, it is, in general, not clear how one can obtain a random sample from the relevant population. The taxonomy presents methods that are generally known in industry, such as nonparametric estimation of false-acceptance and false-rejection probabilities, AIAG’s analytic method (logistic regression), latent class modeling, and latent trait modeling. The methods discussed are applied to an example presented in the measurement-system- analysis manual from the automotive industry.

Leucoencephalopathy with brainstem and spinal cord involvement and elevated lactate is a white matter disorder caused by DARS2 mutations. The pathology is unknown. We observed striking discrepancies between improvement on longitudinal conventional magnetic resonance images and clinical deterioration and between large areas of high signal on diffusion-weighted imaging and small areas with low apparent diffusion coefficient values. These observations prompted a longitudinal and quantitative magnetic resonance imaging study. We investigated eight patients (two males, mean age 27 years). Maps of T2 relaxation times, fractional anisotropy, apparent diffusion coefficients, signal on diffusion-weighted imaging, and axial and radial diffusivities were generated. Brain metabolites, obtained by chemical shift imaging, were quantified. Data analysis focused on: (i) white matter with low apparent diffusion coefficient; (ii) white matter with high T2 values; (iii) white matter with intermediate T2 values; and (iv) normal-appearing white matter. The areas were compared with similarly located areas in eight matched controls. In five patients, T2-weighted images, spectroscopy, apparent diffusion coefficient maps and diffusion-weighted imaging maps were compared with those obtained 5-7 years ago. In white matter with low apparent diffusion coefficient, axial and radial diffusivities were decreased and fractional anisotropy was high. T2 values were intermediate. These areas with truly restricted diffusion were small and often observed at the periphery of areas with high T2 values. In the white matter with high and intermediate T2 values, apparent diffusion coefficients and axial and radial diffusivities were increased and fractional anisotropy decreased. The signal on diffusion-weighted imaging was highest in white matter with high T2 values, an effect of T2 shinethrough. Chemical shift imaging in both white matter types showed increased lactate, increased myo-inositol and decreased N-acetylaspartate, most pronounced in white matter with high T2 values. Normal-appearing white matter was comparable with white matter of control subjects. Over time, mild decreases in T2 signal intensities, signal on diffusion-weighted imaging and in extent of the low apparent diffusion coefficient areas were seen. In conclusion, the disease process in leucoencephalopathy with brainstem and spinal cord involvement and elevated lactate is extremely slow. We hypothesize that diffusion restriction is the first stage of the disease caused by intramyelinic water accumulation, followed by slow shift and then loss of the surplus of water. On conventional T 2 images this leads to improvement. We hypothesize that it is loss of water rather than structural restoration that causes the change in T 2 signal intensity, which would be in better agreement with the slow clinical deterioration. © 2011 The Author.

Although the cognitive and clinical correlates of spontaneous human alpha oscillations as recorded with electroencephalography (EEG) or magnetoencephalography (MEG) are well documented, the dynamics underlying these oscillations is still a matter of debate. This study proposes a data-driven method to reveal the dynamics of these oscillations. It demonstrates that spontaneous human alpha oscillations as recorded with MEG can be viewed as noise-perturbed damped harmonic oscillations. This provides evidence for the hypothesis that these oscillations reflect filtered noise and hence do not possess limit-cycle dynamics. To illustrate the use of the model, we apply it to two data-sets in which a decrease in alpha power can be observed across conditions. The associated differences in the estimated model parameters show that observed decreases in alpha power are associated with different kinds of changes in the dynamics. Thus, the model parameters are useful dynamical biomarkers for spontaneous human alpha oscillations. © 2011 Elsevier Inc.

Analysis of DNA copy number profiles requires methods tailored to the specific nature of these data. The number of available data analysis methods has grown enormously in the last 5 years. We discuss the typical characteristics of DNA copy number data, as measured by microarray technology and review the extensive literature on preprocessing methods such as segmentation and calling. Subsequently, the focus narrows to applications of DNA copy number in cancer, in particular, several downstream analyses of multi-sample data sets such as testing, clustering and classification. Finally, we look ahead: what should we prepare for and which methodology-related topics may deserve attention in the near future? © The Author 2010. Published by Oxford University Press.

Motivation: Quantitative determination of metabolic fluxes in single tissue biopsies is difficult. We report a novel analysis approach and software package for in vivo flux quantification using stable isotope labeling. Results: We developed a protocol based on brief, timed infusion of 13C isotope-enriched substrates for the tricarboxylic acid (TCA) cycle followed by quick freezing of tissue biopsies. NMR measurements of tissue extracts were used for flux estimation based on a computational model of carbon transitions between TCA cycle metabolites and related amino acids. To this end, we developed a computational framework in which metabolic systems can be flexibly assembled, simulated and analyzed. Flux parameters were quantified from NMR multiplets by a partial grid search followed by repeated Nelder–Mead optimizations implemented on a computer grid. We implemented a model of the TCA cycle and showed by extensive simulations that the timed infusion protocol reliably quantitates multiple fluxes. Experimental validation of the method was done in vivo on hearts of anesthetized pigs under two different conditions: basal state (n = 7) and cardiac stress caused by infusion of dobutamine (n = 7). About nine tissue samples (40–200 mg dry-weight) were taken per heart. TCA cycle flux was 6.11 ± 0.28 (SEM) μmol/min · gdw at baseline versus 9.29 ± 1.03 μmol/min · gdw for dobutamine stress. Oxygen consumption calculated from the TCA cycle flux and from ‘gold standard’ blood gas-based measurements were close, correlating with r=0.88 (P < 10−4). Spatial heterogeneity in metabolic fluxes is detectable amongst the small samples. We propose that our novel isotope snapshot methodology is suitable for flux measurements in biopsies in vivo. Availability: Non-profit organizations will, upon request, be granted a non-exclusive license to use the software for internal research and teaching purposes at no charge. A web interface for using the software on our computer grid is available under http://www.ibi.vu.nl/programs/ Contact: hans.van.beek@falw.vu.nl Supplementary information: Supplementary data are available at Bioinformatics online.

http://www.bepress.com/sagmb/; International audience; We propose a new approach for clustering DNA features using array CGH data from multiple tumor samples. We distinguish data-collapsing: joining contiguous DNA clones or probes with extremely similar data into regions, from clustering: joining contiguous, correlated regions based on a maximum likelihood principle. The model-based clustering algorithm accounts for the apparent spatial patterns in the data. We evaluate the randomness of the clustering result by a cluster stability score in combination with cross-validation. Moreover, we argue that the clustering really captures spatial genomic dependency by showing that coincidental clustering of independent regions is very unlikely. Using the region and cluster information, we combine testing of these for association with a clinical variable in an hierarchical multiple testing approach. This allows for interpreting the significance of both regions and clusters while controlling the Family-Wise Error Rate simultaneously. We prove that in the context of permutation tests and permutation-invariant clusters it is allowed to perform clustering and testing on the same data set. Our procedures are illustrated on two cancer data sets.

Background: The variance explained by genetic variants as identified in (genome-wide) genetic association studies is typically small compared to family-based heritability estimates. Explanations of this 'missing heritability' have been mainly genetic, such as genetic heterogeneity and complex (epi-)genetic mechanisms. Methodology:We used comprehensive simulation studies to show that three phenotypic measurement issues also provide viable explanations of the missing heritability: phenotypic complexity, measurement bias, and phenotypic resolution. We identify the circumstances in which the use of phenotypic sum-scores and the presence of measurement bias lower the power to detect genetic variants. In addition, we show how the differential resolution of psychometric instruments (i.e., whether the instrument includes items that resolve individual differences in the normal range or in the clinical range of a phenotype) affects the power to detect genetic variants. Conclusion:We conclude that careful phenotypic data modelling can improve the genetic signal, and thus the statistical power to identify genetic variants by 20-99%. © 2010 van der Sluis et al.

European funding under framework 7 (FP7) for the virtual physiological human (VPH) project has been in place now for nearly 2 years. The VPH network of excellence (NoE) is helping in the development of common standards, open-source software, freely accessible data and model repositories, and various training and dissemination activities for the project. It is also helping to coordinate the many clinically targeted projects that have been funded under the FP7 calls. An initial vision for the VPH was defined by framework 6 strategy for a European physiome (STEP) project in 2006. It is now time to assess the accomplishments of the last 2 years and update the STEP vision for the VPH. We consider the biomedical science, healthcare and information and communications technology challenges facing the project and we propose the VPH Institute as a means of sustaining the vision of VPH beyond the time frame of the NoE. © 2010 The Royal Society.

Background The offspring of consanguineous relations have an increased risk of congenital/genetic disorders and early mortality. Consanguineous couples and their offspring account for approximately 10% of the global population. The increased risk for congenital/genetic disorders is most marked for autosomal recessive disorders and depends on the degree of relatedness of the parents. For children of first cousins the increased risk is 2-4%. For individual couples, however, the extra risk can vary from zero to 25% or higher, with only a minority of these couples having an increased risk of at least 25%. It is currently not possible to differentiate between high-and low-risk couples. The quantity of DNA identical-by-descent between couples with the same degree of relatedness shows a remarkable variation. Here we hypothesize that consanguineous partners with children affected by an autosomal recessive disease have more DNA identical-by-descent than similarly-related partners who have only healthy children. The aim of the study is thus to establish whether the amount of DNA identical-by-descent in consanguineous parents of children with an autosomal recessive disease is indeed different from its proportion in consanguineous parents who have healthy children only. Methods/Design This project is designed as a case-control study. Cases are defined as consanguineous couples with one or more children with an autosomal recessive disorder and controls as consanguineous couples with at least three healthy children and no affected child. We aim to include 100 case couples and 100 control couples. Control couples are matched by restricting the search to the same family, clan or ethnic origin as the case couple. Genome-wide SNP arrays will be used to test our hypothesis. Discussion This study contains a new approach to risk assessment in consanguineous couples. There is no previous study on the amount of DNA identical-by-descent in consanguineous parents of affected children compared to the consanguineous parents of healthy children. If our hypothesis proves to be correct, further studies are needed to obtain different risk figure estimates for the different proportions of DNA identical-by-descent. With more precise information about their risk status, empowerment of couples can be improved when making reproductive decisions.

Background Ratio-based analysis is the current standard for the analysis of dual-color microarray data. Indeed, this method provides a powerful means to account for potential technical variations such as differences in background signal, spot size and spot concentration. However, current high density dual-color array platforms are of very high quality, and inter-array variance has become much less pronounced. We therefore raised the question whether it is feasible to use an intensity-based analysis rather than ratio-based analysis of dual-color microarray datasets. Furthermore, we compared performance of both ratio- and intensity-based analyses in terms of reproducibility and sensitivity for differential gene expression. Results By analyzing three distinct and technically replicated datasets with either ratio- or intensity-based models, we determined that, when applied to the same dataset, intensity-based analysis of dual-color gene expression experiments yields 1) more reproducible results, and 2) is more sensitive in the detection of differentially expressed genes. These effects were most pronounced in experiments with large biological variation and complex hybridization designs. Furthermore, a power analysis revealed that for direct two-group comparisons above a certain sample size, ratio-based models have higher power, although the difference with intensity-based models is very small. Conclusions Intensity-based analysis of dual-color datasets results in more reproducible results and increased sensitivity in the detection of differential gene expression than the analysis of the same dataset with ratio-based analysis. Complex dual-color setups such as interwoven loop designs benefit most from ignoring the array factor. The applicability of our approach to array platforms other than dual-color needs to be further investigated.

Background Determining a suitable sample size is an important step in the planning of microarray experiments. Increasing the number of arrays gives more statistical power, but adds to the total cost of the experiment. Several approaches for sample size determination have been developed for expression array studies, but so far none has been proposed for array comparative genomic hybridization (aCGH). Results Here we explore power calculations for aCGH experiments comparing two groups. In a pilot experiment CGHpower estimates the biological diversity between groups and provides a statistical framework for estimating average power as a function of sample size. As the method requires pilot data, it can be used either in the planning stage of larger studies or in estimating the power achieved in past experiments. Conclusions The proposed method relies on certain assumptions. According to our evaluation with public and simulated data sets, they do not always hold true. Violation of the assumptions typically leads to unreliable sample size estimates. Despite its limitations, this method is, at least to our knowledge, the only one currently available for performing sample size calculations in the context of aCGH. Moreover, the implementation of the method provides diagnostic plots that allow critical assessment of the assumptions on which it is based and hence on the feasibility and reliability of the sample size calculations in each case. The CGHpower web application and the program outputs from evaluation data sets can be freely accessed at http://www.cangem.org/cghpower/

Objective: Vanishing white matter (VWM) is an autosomal recessive leukoencephalopathy characterized by slowly progressive ataxia and spasticity with additional stress-provoked episodes of rapid and major deterioration. The disease is caused by mutations in the genes encoding the subunits of eukaryotic initiation factor 2B, which is pivotal in translation of mRNAs into proteins. The disease onset, clinical severity, and disease course of VWM vary greatly. The influence of genotype and gender on the phenotype is unclear. Methods: From our database of 184 patients with VWM, we selected those with the following mutations in the gene EIF2B5: p.Arg113His in the homozygous state (n = 23), p.Arg113His in the compound-heterozygous state (n = 49), p.Thr91Ala in the homozygous state (n = 8), p.Arg113His/p.Arg339any (n = 9), and p.Thr91Ala/p.Arg339any (n = 7). We performed a cross-sectional observational study. Evaluated clinical characteristics were gender, age at onset, age at loss of walking without support, and age at death. Means, male/female ratios, and Kaplan-Meier curves were compared. Results: Patients homozygous for p.Arg113His had a milder disease than patients compound heterozygous for p.Arg113His and patients homozygous for p.Thr91Ala. Patients with p.Arg113His/p.Arg339any had a milder phenotype than patients with p.Thr91Ala/p.Arg339any. Overall, females tended to have a milder disease than males. Conclusions: The clinical phenotype in VWM is influenced by the combination of both mutations. Females tend to do better than males. Copyright © 2010 by AAN Enterprises, Inc.

Regional co-expression refers to the phenomenon of contiguous genes exhibiting similar expression patterns. Among others, DNA copy number aberrations may be causally involved in regional co-expression. We propose a random coefficients model to explain regional co-expression from DNA copy number information, while modeling residual co-expression due to other causes by a correlated error structure. We show how the model parameters may be estimated (computationally efficient and consistently) from high-dimensional data, and suggest several robustifications of the estimation procedure. From the model we are able to assess whether there is a shared effect on expression levels due to the DNA copy number aberrations, but also whether this effect is homogeneous across genes. In two examples we use the proposed methodology to investigate the association between DNA copy number aberrations and regional co-expression. Copyright © 2010 The Berkeley Electronic Press. All rights reserved.

We develop an inference framework for the difference in errors between 2 prediction procedures. The 2 procedures may differ in any aspect and possibly utilize different sets of covariates. We apply training and testing on the same data set, which is accommodated by sample splitting. For each split, both procedures predict the response of the same samples, which results in paired residuals to which a signed-rank test is applied. Multiple splits result in multiple p-values. The median p-value and the mean inverse normal transformed p-value are proposed as summary (test) statistics, for which bounds on the overall type I error rate under a variety of assumptions are proven. A simulation study is performed to check type I error control of the least conservative bound. Moreover, it confirms superior power of our method with respect to a one-split approach. Our inference framework is applied to genomic survival data sets to study 2 issues: compare lasso and ridge regression and decide upon use of both methylation and gene expression markers or the latter only. The framework easily accommodates any prediction paradigm and allows comparing any 2, possibly nonmodel-based, prediction procedures.

The inverse problem of multi-channel MEG/EEG data is considered as a parameter estimation problem. The stability of the solution of the inverse problem, which decreases with the number of included dipoles, can be improved by either adding constraints to the model parameters, or by adding more data of related data sets. The latter approach was taken by Bijma et al. [Bijma F, de Munck JC, Bocker KBE, Huizenga HM, Heethaar RM. The coupled dipole model: an integrated model for multiple MEG/EEG data sets. NeuroImage 2004;23(3):890–904; Bijma F, de Munck JC, Huizenga HM, Heethaar RM, Nehorai A. Simultaneous estimation and testing in multiple MEG data sets. IEEE Trans SP 2005;53(9):3449–60] by introducing coupling matrices that link dipole parameters and source time functions of different data sets. Here, the theoretical foundations of the coupled dipole model are explored and the MUSIC algorithm is generalised to the analysis of multiple related data sets. Similar to the MUSIC algorithm, the number of sources and the number of constraints are derived from the data by considering the minimum possible residual error as a function of the number of sources and constraints. However, contrary to the MUSIC algorithm, where the minimum residual error can be obtained from an SVD analysis of a two-way data matrix, here we deal with multiple data sets and therefore three-way matrix analysis is used. From a simulation study it appears that the number of sources and constraints can be clearly determined from a generalised SVD analysis. The generalisation of the MUSIC algorithm to three-way data gives reasonable estimates of the dipole parameters. These results can be used in the simultaneous analysis of MEG/EEG data of multiple subjects, multiples data sets of the same subject or models where subsequent trials of data show habituation effects.

Objective: Synchronization between distributed rhythms in the brain is commonly assessed by estimating the synchronization strength from simultaneous measurements. This approach, however, does not elucidate the phase dynamics that underlies synchronization. For this, an explicit dynamical model is required. Based on the assumption that the recorded rhythms can be described as weakly coupled oscillators, we propose a method for characterizing their phase-interaction dynamics. Methods: We propose to model ongoing magnetoencephalographic (MEG) oscillations as weakly coupled oscillators. Based on this model, the phase interactions between simultaneously recorded signals are characterized by estimating the modulation in instantaneous frequency as a function of their phase difference. Furthermore, we mathematically derive the effect of volume conduction on the model and show how indices for strength and direction of coupling can be derived. Results: The methodology is tested using simulations and is applied to ongoing occipital–frontal MEG oscillations of healthy subjects in the alpha and beta bands during rest. The simulations show that the model is robust against the presence of noise, short observation times, and model violations. The application to MEG data shows that the model can reconstruct the observed occipital–frontal phase difference distributions. Furthermore, it suggests that phase locking in the alpha and beta band is established by qualitatively different mechanisms. Conclusion: When the recorded rhythms are assumed to be weakly coupled oscillators, a dynamical model for the phase interactions can be fitted to data. The model is able to reconstruct the observed phase difference distribution, and hence, provides a dynamical explanation for observed phase locking. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc.

In this paper, we investigate the origin of the large inter-subject-variability of EEG-fMRI correlation patterns. For that purpose, a simplified representation of the fMRI signal is obtained by using a hierarchical clustering algorithm detecting spatial patterns of mutually correlated voxels. The general-linear model is subsequently used to determine which of the identified patterns correlates significantly to the spontaneous variations of the alpha rhythm. This strategy provides insight in the nature of resting state fMRI and reduces the number of statistical tests in the GLM correlation analysis. For all 16 subjects except one, the clustering of BOLD signal yielded very consistent regions which included areas belonging to the "default mode"network as well as the neuronal networks involved in the generation of the alpha and mu rhythms. These BOLD clusters showed much less inter-subject variability than the Alpha-BOLD statistical parametric maps obtained on a voxel-by-voxel basis. It is shown that hierarchical clustering is applicable to whole head fMRI and that it is very appropriate to obtain data reduction thereby facilitating the comparison of the results of individual subjects. The very consistent results of BOLD clustering over subjects suggests that the large inter-subject variability observed in the Alpha-BOLD statistical parametric maps is related to the individual variations in the EEG. © 2008 IEEE.

In MEG source localization the estimated source parameters will be more reliable when the spatiotemporal covariance of the noise and background activity is taken into account. Since this covariance is in general too large to estimate based on the data and to invert efficiently, different parametrizations have been proposed in the literature. These models can be seen as special cases of the general decomposition of the covariance into a sum of Kronecker products of spatial matrices Xn and temporal matrices Tn (Van Loan, 2000). In this study we investigate the assumption of the matrices Tn being Toeplitz. If so, the covariance matrix in the space–frequency domain will have an approximate block-diagonal structure, facilitating inversion, which is a prerequisite for source localization. In this study we address the question whether the Toeplitz approximation is valid for data sets obtained in visual evoked field, auditory evoked field, somatosensory evoked field experiments and data sets containing spontaneous activity. It turns out that on average 87% is in the block-diagonal of the sample covariance, which is close to the values obtained for real Toeplitz matrices Tn. This implies that the space–frequency domain is very interesting for source localization since the major part of the entire covariance can be incorporated in that domain straightforwardly. Finally, the two major processes in the background activity are characterized in terms of their spatial and frequency patterns, yielding a focal and a non-focal pattern in 8 of 10 data sets analyzed in this study. The focal pattern represents the alpha frequency at parieto-occipital areas, whereas the non-focal pattern is more widespread both in space and in frequency.

Consistent but indirect evidence has implicated genetic factors in smoking behavior. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], Β = 1.03, standard error (s.e.) = 0.053, P = 2.8 × 10 73). Two 10q25 SNPs (rs1329650[G], Β = 0.367, s.e. = 0.059, P = 5.7 × 10 10; and rs1028936[A], Β = 0.446, s.e. = 0.074, P = 1.3 × 10 9) and one 9q13 SNP in EGLN2 (rs3733829[G], Β = 0.333, s.e. = 0.058, P = 1.0 × 10 8) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95% confidence interval (Cl) 1.04-1.08, P = 1.8 × 10 8). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95% Cl 1.08-1.18, P = 3.6 × 10 8) was significantly associated with smoking cessation. © 2010 Nature America, Inc. All rights reserved.

The variance components models for gene-environment interaction proposed by Purcell in 2002 are widely used. In both the bivariate and the univariate parameterization of these models, the variance decomposition of trait T is a function of moderator M. We show that if M and T are correlated, and moderator M is correlated between twins as well, the univariate parameterization produces a considerable increase in false positive moderation effects. A simple extension of this univariate moderation model prevents this elevation of the false positive rate provided the covariance between M and T is itself not also subject to moderation. If the covariance between M and T varies as a function of M, then moderation effects observed in the univariate setting should be interpreted with care as these can have their origin in either moderation of the covariance between M and T or in moderation of the unique paths of T. We conclude that researchers should use the full bivariate moderation model to study the presence of moderation on the covariance between M and T. If such moderation can be ruled out, subsequent use of the extended univariate moderation model, as proposed in this paper, is recommended as this model is more powerful than the full bivariate moderation model. © 2011 The Author(s).