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Genome-wide association studies (GWAS) have remarkably advanced insight into the genetic basis of schizophrenia (SCZ). Still, most of the functional variance in disease risk remains unexplained. Hence, there is a growing need to map genetic variability-to-genes-to-functions for understanding the pathophysiology of SCZ and the development of better treatments. Genetic variation can regulate various cellular functions including DNA methylation, an epigenetic mark with important roles in transcription and the mediation of environmental influences. Methylation quantitative trait loci (meQTLs) are derived by mapping levels of DNA methylation in genetically different, genotyped individuals and define loci at which DNA methylation is influenced by genetic variation. Recent evidence points to an abundance of meQTLs in brain tissues whose functional contributions to development and mental diseases are still poorly understood. Interestingly, fetal meQTLs reside in regulatory domains affecting methylome reconfiguration during early brain development and are enriched in loci identified by GWAS for SCZ. Moreover, fetal meQTLs are preserved in the adult brain and could trace early epigenomic deregulation during vulnerable periods. Overall, these findings highlight the role of fetal meQTLs in the genetic risk for and in the possible neurodevelopmental origin of SCZ.

The cost of whole-genome bisulfite sequencing (WGBS) remains a bottleneck for many studies and it is therefore imperative to extract as much information as possible from a given dataset. This is particularly important because even at the recommend 30X coverage for reference methylomes, up to 50% of high-resolution features such as differentially methylated positions (DMPs) cannot be called with current methods as determined by saturation analysis. To address this limitation, we have developed a tool that dynamically segments WGBS methylomes into blocks of comethylation (COMETs) from which lost information can be recovered in the form of differentially methylated COMETs (DMCs). Using this tool, we demonstrate recovery of ∼30% of the lost DMP information content as DMCs even at very low (5X) coverage. This constitutes twice the amount that can be recovered using an existing method based on differentially methylated regions (DMRs). In addition, we explored the relationship between COMETs and haplotypes in lymphoblastoid cell lines of African and European origin. Using best fit analysis, we show COMETs to be correlated in a population-specific manner, suggesting that this type of dynamic segmentation may be useful for integrated (epi)genome-wide association studies in the future.

The analysis of the geographical distribution of disease on the scale of geographic areas such as administrative boundaries plays an important role in veterinary epidemiology. Prevalence estimates of wildlife population surveys are often based on regional count data generated by sampling animals shot by hunters. The observed disease rate per spatial unit is not a useful estimate of the underlying disease prevalence due to different sample sizes and spatial dependencies between neighbouring areas. Therefore, it is necessary to account for extra-sample variation and and spatial correlation in the data to produce more accurate maps of disease incidence. For this purpose a hierarchical Bayesian model in which structured and un-structured overdispersion is modelled explicitly in terms of spatial and non-spatial components was implemented by Markov Chain Monte Carlo methods. The model was empirically compared with the results of the non-spatial beta-binomial model using surveillance data of Pseudorabies virus infections of wildboars in the Federal State of Brandenburg, Germany.