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BackgroundThe psychometric properties of an online test are not necessarily identical to its paper and pencil original. The aim of this study is to test whether the factor structure of the Community Assessment of Psychic Experiences (CAPE) is measurement invariant with respect to online vs. paper and pencil assessment.MethodThe factor structure of CAPE items assessed by paper and pencil (N = 796) was compared with the factor structure of CAPE items assessed by the Internet (N = 21,590) using formal tests for Measurement Invariance (MI). The effect size was calculated by estimating the Signed Item Difference in the Sample (SIDS) index and the Signed Test Difference in the Sample (STDS) for a hypothetical subject who scores 2 standard deviations above average on the latent dimensions.ResultsThe more restricted Metric Invariance model showed a significantly worse fit compared to the less restricted Configural Invariance model (χ(2)(23) = 152.75, pConclusionsOur findings did not support measurement invariance with respect to assessment method. Because of the small effect sizes, the measurement differences between the online assessed CAPE and its paper and pencil original can be neglected without major consequences for research purposes. However, a person with a high vulnerability for psychotic symptoms would score 4.80 points lower on the total scale if the CAPE is assessed online compared to paper and pencil assessment. Therefore, for clinical purposes, one should be cautious with online assessment of the CAPE.

Genetic polymorphisms can shape the global landscape of DNA methylation, by either changing substrates for DNA methyltransferases or altering the DNA binding affinity of cis-regulatory proteins. The interactions between CpG methylation and genetic polymorphisms have been previously investigated by methylation quantitative trait loci (mQTL) and allele-specific methylation (ASM) analysis. However, it remains unclear whether these approaches can effectively and comprehensively identify all genetic variants that contribute to the inter-individual variation of DNA methylation levels. Here we used three independent approaches to systematically investigate the influence of genetic polymorphisms on variability in DNA methylation by characterizing the methylation state of 96 whole blood samples in 52 parent-child trios from 22 nuclear pedigrees. We performed targeted bisulfite sequencing with padlock probes to quantify the absolute DNA methylation levels at a set of 411,800 CpG sites in the human genome. With mid-parent offspring analysis (MPO), we identified 10,593 CpG sites that exhibited heritable methylation patterns, among which 70.1% were SNPs directly present in methylated CpG dinucleotides. We determined the mQTL analysis identified 49.9% of heritable CpG sites for which regulation occurred in a distal cis-regulatory manner, and that ASM analysis was only able to identify 5%. Finally, we identified hundreds of clusters in the human genome for which the degree of variation of CpG methylation, as opposed to whether or not CpG sites were methylated, was associated with genetic polymorphisms, supporting a recent hypothesis on the genetic influence of phenotypic plasticity. These results show that cis-regulatory SNPs identified by mQTL do not comprise the full extent of heritable CpG methylation, and that ASM appears overall unreliable. Overall, the extent of genome-methylome interactions is well beyond what is detectible with the commonly used mQTL and ASM approaches, and is likely to include effects on plasticity.

Despite large-scale genome-wide association studies (GWAS), the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood of schizophrenia patients and controls. We applied a systems biology approach to genome-wide expression data from whole blood of 92 medicated and 29 antipsychotic-free schizophrenia patients and 118 healthy controls. We show that gene expression profiling in whole blood can identify twelve large gene co-expression modules associated with schizophrenia. Several of these disease related modules are likely to reflect expression changes due to antipsychotic medication. However, two of the disease modules could be replicated in an independent second data set involving antipsychotic-free patients and controls. One of these robustly defined disease modules is significantly enriched with brain-expressed genes and with genetic variants that were implicated in a GWAS study, which could imply a causal role in schizophrenia etiology. The most highly connected intramodular hub gene in this module (ABCF1), is located in, and regulated by the major histocompatibility (MHC) complex, which is intriguing in light of the fact that common allelic variants from the MHC region have been implicated in schizophrenia. This suggests that the MHC increases schizophrenia susceptibility via altered gene expression of regulatory genes in this network.

Cytosine-5 methylation within CpG dinucleotides is a potentially important mechanism of epigenetic influence on human traits and disease. In addition to influences of age and gender, genetic control of DNA methylation levels has recently been described. We used whole blood genomic DNA in a twin set (23 MZ twin-pairs and 23 DZ twin-pairs, N = 92) as well as healthy controls (N = 96) to investigate heritability and relationship with age and gender of selected DNA methylation profiles using readily commercially available GoldenGate bead array technology. Despite the inability to detect meaningful methylation differences in the majority of CpG loci due to tissue type and locus selection issues, we found replicable significant associations of DNA methylation with age and gender. We identified associations of genetically heritable single nucleotide polymorphisms with large differences in DNA methylation levels near the polymorphism (cis effects) as well as associations with much smaller differences in DNA methylation levels elsewhere in the human genome (trans effects). Our results demonstrate the feasibility of array-based approaches in studies of DNA methylation and highlight the vast differences between individual loci. The identification of CpG loci of which DNA methylation levels are under genetic control or are related to age or gender will facilitate further studies into the role of DNA methylation and disease.