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Abstract Background The causes of complex diseases are difficult to grasp since many different factors play a role in their onset. To find a common genetic background, many of the existing studies divide their population into controls and cases; a classification that is likely to cause heterogeneity within the two groups. Rather than dividing the study population into cases and controls, it is better to identify the phenotype of a complex disease by a set of intermediate risk factors. But these risk factors often vary over time and are therefore repeatedly measured. Results We introduce a method to associate multiple repeatedly measured intermediate risk factors with a high dimensional set of single nucleotide polymorphisms (SNPs). Via a two-step approach, we summarized the time courses of each individual and, secondly apply these to penalized nonlinear canonical correlation analysis to obtain sparse results. Conclusions Application of this method to two datasets which study the genetic background of cardiovascular diseases, show that compared to progression over time, mainly the constant levels in time are associated with sets of SNPs.

Abstract Background We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the canonical variates, and we applied ridge penalization to the regression of pathway genes on canonical variates of the non-pathway genes, and the elastic net to the regression of non-pathway genes on the canonical variates of the pathway genes. Results We performed a small simulation to illustrate the model's capability to identify new candidate genes to incorporate in the pathway: in our simulations it appeared that a gene was correctly identified if the correlation with the pathway genes was 0.3 or more. We applied the methods to a gene-expression microarray data set of 12, 209 genes measured in 45 patients with glioblastoma, and we considered genes to incorporate in the glioma-pathway: we identified more than 25 genes that correlated > 0.9 with canonical variates of the pathway genes. Conclusion We concluded that penalized canonical correlation analysis is a powerful tool to identify candidate genes in pathway analysis.

Combining different metabolomics platforms can contribute significantly to the discovery of complementary processes expressed under different conditions. However, analysing the fused data might be hampered by the difference in their quality. In metabolomics data, one often observes that measurement errors increase with increasing measurement level and that different platforms have different measurement error variance. In this paper we compare three different approaches to correct for the measurement error heterogeneity, by transformation of the raw data, by weighted filtering before modelling and by a modelling approach using a weighted sum of residuals. For an illustration of these different approaches we analyse data from healthy obese and diabetic obese individuals, obtained from two metabolomics platforms. Concluding, the filtering and modelling approaches that both estimate a model of the measurement error did not outperform the data transformation approaches for this application. This is probably due to the limited difference in measurement error and the fact that estimation of measurement error models is unstable due to the small number of repeats available. A transformation of the data improves the classification of the two groups.

Introduction: To date, there is no universal varicella vaccination in the Netherlands. We studied the seroprevalence of varicella zoster virus (VZV) specific antibodies and determinants for seropositivity among participants of a serosurveillance study, conducted in 2006/2007 among Dutch inhabitants 0-79 years of age., Materials and Methods: Serological testing of 6386 blood samples for VZV was performed with a fluorescent bead-based multiplex immunoassay. Seroprevalence and geometric mean concentration (GMC) were weighted for age, sex, ethnicity, and urbanization rate to the total Dutch population. Determinants for VZV seropositivity were identified among children younger than 6 years of age using a logistic regression model., Results: The overall seroprevalence of VZV specific antibodies in the Dutch population was 94.6% (95% CI: 93.2-96.0%). This seroprevalence increased rapidly with age: at 6 years of age, more than 95% were seropositive. Determinants associated with lower VZV seropositivity were: young age, first-generation non-Dutch ethnicity, and low frequency of attendance at a day care center or nursery school. The GMC increased with age and was lower for women than for men from the age of 20 years onwards., Conclusions: This study confirmed that VZV infection occurs at a younger age in the Netherlands compared to other countries, which might explain the low disease burden due to varicella. Introduction of universal varicella vaccination is not a foregone conclusion in the Netherlands. Changes in migration and day care usage will influence the age-specific risk on varicella and should therefore be monitored. Further research might elucidate the sex differences in VZV specific GMC., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Here we present mumps virus specific antibody levels in a large cross-sectional population-based serosurveillance study performed in the Netherlands in 2006/2007 (n = 7900). Results were compared with a similar study (1995/1996) and discussed in the light of recent outbreaks. Mumps antibodies were tested using a fluorescent bead-based multiplex immunoassay. Overall seroprevalence was 90.9% with higher levels in the naturally infected cohorts compared with vaccinated cohorts. Mumps virus vaccinations at 14 months and 9 years resulted in an increased seroprevalence and antibody concentration. The second vaccination seemed to be important in acquiring stable mumps antibody levels in the long term. In conclusion, the Dutch population is well protected against mumps virus infection. However, we identified specific age- and population groups at increased risk of mumps infection. Indeed, in 2007/2008 an outbreak has occurred in the low vaccination coverage groups emphasizing the predictive value of serosurveillance studies.

Despite considerable research efforts in specific subpopulations, reliable estimates of the infection attack rates and severity of 2009 influenza A (H1N1) in the general population remain scarce. Such estimates are essential to the tailoring of future control strategies. Therefore, 2 serial population-based serologic surveys were conducted, before and after the 2009 influenza A (H1N1) epidemic, in the Netherlands. Random age-stratified samples were obtained using a 2-stage cluster design. Participants donated blood and completed a questionnaire. Data on sentinel general practitioner-attended influenza-like illness and nationwide hospitalization and mortality were used to assess the severity of infection. The estimated infection attack rates were low in the general population (7.6%, 95% confidence interval: 3.6, 11) but high in children aged 5-19 years (35%, 95% confidence interval: 25, 45). The estimated hospitalization and mortality rates per infection increased significantly with age (5-19 years: 0.042% and 0.00094%, respectively; 20-39 years: 0.12% and 0.0025%; 40-59 years: 0.68% and 0.032%; 60-75 years: >0.81% and >0.068%). The high infection attack rate in children and the very low attack rate in older adults, together with the low severity of illness per infection in children but substantial severity in older adults, produced an epidemic with a low overall impact.

Background: The causes of complex diseases are difficult to grasp since many different factors play a role in their onset. To find a common genetic background, many of the existing studies divide their population into controls and cases; a classification that is likely to cause heterogeneity within the two groups. Rather than dividing the study population into cases and controls, it is better to identify the phenotype of a complex disease by a set of intermediate risk factors. But these risk factors often vary over time and are therefore repeatedly measured., Results: We introduce a method to associate multiple repeatedly measured intermediate risk factors with a high dimensional set of single nucleotide polymorphisms (SNPs). Via a two-step approach, we summarized the time courses of each individual and, secondly apply these to penalized nonlinear canonical correlation analysis to obtain sparse results., Conclusions: Application of this method to two datasets which study the genetic background of cardiovascular diseases, show that compared to progression over time, mainly the constant levels in time are associated with sets of SNPs.

Cardiovascular diseases are associated with combinations of phenotypic traits, which are in turn caused by a combination of environmental and genetic factors. Because of the diversity of pathways that may lead to cardiovascular diseases, we examined the so-called intermediate phenotypes, which are often repeatedly measured. We developed a penalized nonlinear canonical correlation analysis to associate multiple repeatedly measured traits with high-dimensional single-nucleotide polymorphism data.

Motivation: Canonical correlation analysis (CCA) can be used to capture the underlying genetic background of a complex disease, by associating two datasets containing information about a patient's phenotypical and genetic details. Often the genetic information is measured on a qualitative scale, consequently ordinary CCA cannot be applied to such data. Moreover, the size of the data in genetic studies can be enormous, thereby making the results difficult to interpret., Results: We developed a penalized non-linear CCA approach that can deal with qualitative data by transforming each qualitative variable into a continuous variable through optimal scaling. Additionally, sparse results were obtained by adapting soft-thresholding to this non-linear version of the CCA. By means of simulation studies, we show that our method is capable of extracting relevant variables out of high-dimensional sets. We applied our method to a genetic dataset containing 144 patients with glial cancer., Contact: s.waaijenborg@amc.uva.nl.

Background: We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the canonical variates, and we applied ridge penalization to the regression of pathway genes on canonical variates of the non-pathway genes, and the elastic net to the regression of non-pathway genes on the canonical variates of the pathway genes., Results: We performed a small simulation to illustrate the model's capability to identify new candidate genes to incorporate in the pathway: in our simulations it appeared that a gene was correctly identified if the correlation with the pathway genes was 0.3 or more. We applied the methods to a gene-expression microarray data set of 12, 209 genes measured in 45 patients with glioblastoma, and we considered genes to incorporate in the glioma-pathway: we identified more than 25 genes that correlated > 0.9 with canonical variates of the pathway genes., Conclusion: We concluded that penalized canonical correlation analysis is a powerful tool to identify candidate genes in pathway analysis.

Multiple changes at the DNA level are at the basis of complex diseases. Identifying the genetic networks that are influenced by these changes might help in understanding the development of these diseases. Canonical correlation analysis is used to associate gene expressions with DNA-markers and thus reveals sets of co-expressed and co-regulated genes and their associating DNA-markers. However, when the number of variables gets high, e.g. in the case of microarray studies, interpretation of these results can be difficult. By adapting the elastic net to canonical correlation analysis the number of variables reduces, and interpretation becomes easier, moreover, due to the grouping effect of the elastic net co-regulated and co-expressed genes cluster. Additionally, our adaptation works well in situations where the number of variables exceeds by far the number of subjects.

This paper summarizes contributions to group 12 of the 15th Genetic Analysis Workshop. The papers in this group focused on multivariate methods and applications for the analysis of molecular data including genotypic data as well as gene expression microarray measurements and clinical phenotypes. A range of multivariate techniques have been employed to extract signals from the multi-feature data sets that were provided by the workshop organizers. The methods included data reduction techniques such as principal component analysis and cluster analysis; latent variable models including structural equations and item response modeling; joint multivariate modeling techniques as well as multivariate visualization tools. This summary paper categorizes and discusses individual contributions with regard to multiple classifications of multivariate methods. Given the wide variety in the data considered, the objectives of the analysis and the methods applied, direct comparison of the results of the various papers is difficult. However, the group was able to make many interesting comparisons and parallels between the various approaches. In summary, there was a consensus among authors in group 12 that the genetic research community should continue to draw experiences from other fields such as statistics, econometrics, chemometrics, computer science and linear systems theory., ((c) 2007 Wiley-Liss, Inc.)

Inter-individual variation in gene expression levels can arise as an effect of variation in DNA markers. When associating multiple gene expression variables with multiple DNA marker variables, multivariate techniques, such as canonical correlation analysis, should be used to deal with the effect of co-regulating genes. We adapted the elastic net, a penalized approach proposed for variable selection in regression context, to canonical correlation analysis. The number of variables within each canonical component could be greatly reduced without too much loss of information, so the canonical components become easier to interpret. Another advantage is that it groups co-regulating genes, so that they end up in the same canonical components. Furthermore, our adaptation works well in situations where the number of variables greatly exceeds the number of subjects.

Localization and coordinate phosphorylation/dephosphorylation of endothelial nitric oxide synthase (eNOS) are critical determinants for the basal and stimulated production of nitric oxide. Several phosphorylation sites in eNOS have been identified as targets of the cAMP-dependent protein kinase A (PKA). Basal eNOS activity is also regulated by interaction with caveolin-1, the major coat protein of caveolae. In the present study we have examined in rat aorta endothelium the subcellular steady-state distribution of eNOS, the catalytic subunit of PKA (PKA-c), and caveolin-1. Basal eNOS expression was found in two distinct locations, the endothelial cell surface and the Golgi complex. Cell surface eNOS was equally distributed over caveolar and non-caveolar membranes but was 2.5-fold enriched on luminal lamellipodia located at endothelial cell contacts. PKA-c colocalized with eNOS in the lamellipodia, whereas caveolin-1 was absent from these membrane domains. PKA-c was also found associated with cell surface caveolae and with tubulovesicular membranes of Golgi complex and endosomes. The topological proximity of eNOS with the catalytic subunit of PKA in restricted intracellular locations may provide mechanisms for differential PKA-mediated eNOS regulation., (Copyright The Histochemical Society, Inc.)