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We present the first large-scale methylome-wide association studies (MWAS) for major depressive disorder (MDD) to identify sites of potential importance for MDD etiology. Using a sequencing-based approach that provides near-complete coverage of all 28 million common CpGs in the human genome, we assay methylation in MDD cases and controls from both blood (N = 1132) and postmortem brain tissues (N = 61 samples from Brodmann Area 10, BA10). The MWAS for blood identified several loci with P ranging from 1.91 × 10−8 to 4.39 × 10−8 and a resampling approach showed that the cumulative association was significant (P = 4.03 × 10−10) with the signal coming from the top 25,000 MWAS markers. Furthermore, a permutation-based analysis showed significant overlap (P = 5.4 × 10−3) between the MWAS findings in blood and brain (BA10). This overlap was significantly enriched for a number of features including being in eQTLs in blood and the frontal cortex, CpG islands and shores, and exons. The overlapping sites were also enriched for active chromatin states in brain including genic enhancers and active transcription start sites. Furthermore, three loci located in GABBR2, RUFY3, and in an intergenic region on chromosome 2 replicated with the same direction of effect in the second brain tissue (BA25, N = 60) from the same individuals and in two independent brain collections (BA10, N = 81 and 64). GABBR2 inhibits neuronal activity through G protein-coupled second-messenger systems and RUFY3 is implicated in the establishment of neuronal polarity and axon elongation. In conclusion, we identified and replicated methylated loci associated with MDD that are involved in biological functions of likely importance to MDD etiology.

Background To avoid false-positive findings and detect cell-type specific associations in methylation and transcription investigations with bulk samples, it is critical to know the proportions of the major cell-types. Results We present a novel approach that allows for precise estimation of cell-type proportions using only a few highly informative methylation markers. The most reliable estimates were obtained with 17 amplicons (34 CpGs) using the MuSiC estimator, for which the average correlations between the estimated and the true cell-type proportions were 0.889. Furthermore, the estimates were not significantly different from the true values ( P  = 0.95) indicating that the estimator is unbiased and the standard deviation of the estimates further indicate high precision. Moreover, the overall variability of the estimates as measured by the Root Mean Squared Error (RMSE), which is a function of both bias and precision, was low (mean RMSE = 0.038). Taken together, these results indicate that the approach produced reliable estimates that are both unbiased and highly precise. Conclusion This cost-effective approach for estimating cell-type proportions in bulk samples allows for enhanced targeted analysis, which in turn will minimize the risk of reporting false-positive findings and allowing for detection of cell-type specific associations. The approach is applicable across platforms and can be extended to assess cell-type proportions for various tissues.

Adverse neonatal outcomes are a prevailing risk factor for both short- and long-term mortality and morbidity in infants. Given the importance of these outcomes, refining their assessment is paramount for improving prevention and care. Here we aim to enhance the assessment of these often correlated and multifaceted neonatal outcomes. To achieve this, we employ factor analysis to identify common and unique effects and further confirm these effects using criterion-related validity testing. This validation leverages methylome-wide profiles from neonatal blood. Specifically, we investigate nine neonatal health risk variables, including gestational age, Apgar score, three indicators of body size, jaundice, birth diagnosis, maternal preeclampsia, and maternal age. The methylomic profiles used for this research capture data from nearly all 28 million methylation sites in human blood, derived from the blood spot collected from 333 neonates, within 72 h post-birth. Our factor analysis revealed two common factors, size factor, that captured the shared effects of weight, head size, height, and gestational age and disease factor capturing the orthogonal shared effects of gestational age, combined with jaundice and birth diagnosis. To minimize false positives in the validation studies, validation was limited to variables with significant cumulative association as estimated through an in-sample replication procedure. This screening resulted in that the two common factors and the unique effects for gestational age, jaundice and Apgar were further investigated with full-scale cell-type specific methylome-wide association analyses. Highly significant, cell-type specific, associations were detected for both common effect factors and for Apgar. Gene Ontology analyses revealed multiple significant biologically relevant terms for the five fully investigated neonatal health risk variables. Given the established links between adverse neonatal outcomes and both immediate and long-term health, the distinct factor effects (representing the common and unique effects of the risk variables) and their biological profiles confirmed in our work, suggest their potential role as clinical biomarkers for assessing health risks and enhancing personalized care.

Although it is widely known that various pharmaceuticals affect the methylome, the knowledge of the effects from anesthesia is limited, and nearly nonexistent regarding the effects of obstetric anesthesia on the newborn child. Using sequencing based-methylation data and a reference-based statistical deconvolution approach we performed methylome-wide association studies (MWAS) of neonatal whole blood, and for each cell-type specifically, to detect methylation variations that are associated with the pain relief administered to the mother during delivery. Significant findings were replicated in a different dataset and followed-up with gene ontology analysis to pinpoint biological functions of potential relevance to these neonatal methylation alterations. The MWAS analyses detected methylome-wide significant (q<0.1) alterations in the newborn for laughing gas in granulocytes (two CpGs, p<5.50x10 -9 , q = 0.067), and for pudendal block in monocytes (five CpGs across three loci, p<1.51 x10 -8 , q = 0.073). Suggestively significant findings (p<1.00x10 -6 ) were detected for both treatments for bulk and all cell-types, and replication analyses showed consistent significant enrichment (odds ratios ranging 3.47–39.02; p<4.00×10 −4 ) for each treatment, suggesting our results are robust. In contrast, we did not observe any overlap across treatments, suggesting that the treatments are associated with different alterations of the neonatal blood methylome. Gene ontology analyses of the replicating suggestively significant results indicated functions related to, for example, cell differentiation, intracellular membrane-bound organelles and calcium transport. In conclusion, for the first time, we investigated and detected effect of obstetric pain-relief on the blood methylome in the newborn child. The observed differences suggest that anesthetic treatment, such as laughing gas or pudendal block, may alter the neonatal methylome in a cell-type specific manner. Some of the observed alterations are part of gene ontology terms that previously have been suggested in relation to anesthetic treatment, supporting its potential role also in obstetric anesthesia.

Anxiety Disorders (ANX) such as panic disorder, generalized anxiety disorder, and phobias, are highly prevalent conditions that are moderately heritable. Evidence suggests that DNA methylation may play a role, as it is involved in critical adaptations to changing environments. Applying an enrichment-based sequencing approach covering nearly 28 million autosomal CpG sites, we conducted a methylome-wide association study (MWAS) of lifetime ANX in 1132 participants (618 cases/514 controls) from the Netherlands Study of Depression and Anxiety. Using epigenomic deconvolution, we performed MWAS for the main cell types in blood: granulocytes, T-cells, B-cells and monocytes. Cell-type specific analyses identified 280 and 82 methylome-wide significant associations ( q -value < 0.1) in monocytes and granulocytes, respectively. Our top finding in monocytes was located in ZNF823 on chromosome 19 ( p  = 1.38 × 10 −10 ) previously associated with schizophrenia. We observed significant overlap ( p  < 1 × 10 −06 ) with the same direction of effect in monocytes (210 sites), T-cells (135 sites), and B-cells (727 sites) between this Discovery MWAS signal and a comparable replication dataset from the Great Smoky Mountains Study ( N  = 433). Overlapping Discovery-Replication MWAS signal was enriched for findings from published GWAS of ANX, major depression, and post-traumatic stress disorder. In monocytes, two specific sites in the FZR1 gene showed significant replication after Bonferroni correction with an additional 15 nominally replicated sites in monocytes and 4 in T-cells. FZR1 regulates neurogenesis in the hippocampus, and its knockout leads to impairments in associative fear memory and long-term potentiation in mice. In the largest and most extensive methylome-wide study of ANX, we identified replicable methylation sites located in genes of potential relevance for brain mechanisms of psychiatric conditions.

Schizophrenia is a disabling disorder involving genetic predisposition in combination with environmental influences that likely act via dynamic alterations of the epigenome and the transcriptome but its detailed pathophysiology is largely unknown. We performed cell-type specific methylome-wide association study of neonatal blood ( N  = 333) from individuals who later in life developed schizophrenia and controls. Suggestively significant associations ( P  < 1.0 × 10 −6 ) were detected in all cell-types and in whole blood with methylome-wide significant associations in monocytes ( P  = 2.85 × 10 −9 –4.87 × 10 −9 ), natural killer cells ( P  = 1.72 × 10 −9 –7.82 × 10 −9 ) and B cells ( P  = 3.8 × 10 −9 ). Validation of methylation findings in post-mortem brains ( N  = 596) from independent schizophrenia cases and controls showed significant enrichment of transcriptional differences (enrichment ratio = 1.98–3.23, P  = 2.3 × 10 −3 –1.0 × 10 −5 ), with specific highly significant differential expression for, for example, BDNF (t = −6.11, P  = 1.90 × 10 −9 ). In addition, expression difference in brain significantly predicted schizophrenia (multiple correlation = 0.15–0.22, P = 3.6 × 10 −4 –4.5 × 10 −8 ). In summary, using a unique design combining pre-disease onset (neonatal) blood methylomic data and post-disease onset (post-mortem) brain transcriptional data, we have identified genes of likely functional relevance that are associated with schizophrenia susceptibility, rather than confounding disease associated artifacts. The identified loci may be of clinical value as a methylation-based biomarker for early detection of increased schizophrenia susceptibility.

As the primary relevant tissue (brain) for psychiatric disorders is commonly not available, we aimed to investigate whether blood can be used as a proxy in methylation studies on the basis of two models. In the 'signature model methylation-disease associations occur because a disease-causing factor affected methylation in the blood. In the 'mirror-site model the methylation status in the blood is correlated with the corresponding disease-causing site in the brain. Methyl-binding domain enrichment and next-generation sequencing of the blood, cortex and hippocampus from four haloperidol-treated and ten untreated C57BL/6 mice revealed high levels of correlation in methylation across tissues. Despite the treatment inducing a large number of methylation changes, this correlation remains high. Our results show that, consistent with the signature model, factors that affect brain processes (i.e., haloperidol) leave biomarker signatures in the blood and, consistent with the mirror-site model, the methylation status of many sites in the blood mirror those in the brain.

We studied the use of methyl-CpG binding domain (MBD) protein-enriched genome sequencing (MBD-seq) as a cost-effective screening tool for methylome-wide association studies (MWAS). Because MBD-seq has not yet been applied on a large scale, we first developed and tested a pipeline for data processing using 1500 schizophrenia cases and controls plus 75 technical replicates with an average of 68 million reads per sample. This involved the use of technical replicates to optimize quality control for multi- and duplicate-reads, an experiment to identify CpGs in loci with alignment problems, CpG coverage calculations based on multiparametric estimates of the fragment size distribution, a two-stage adaptive algorithm to combine data from correlated adjacent CpG sites, principal component analyses to control for confounders and new software tailored to handle the large data set. We replicated MWAS findings in independent samples using a different technology that provided single base resolution. In an MWAS of age-related methylation changes, one of our top findings was a previously reported robust association involving . Our results also suggested that owing to the many confounding effects, a considerable challenge in MWAS is to identify those effects that are informative about disease processes. This study showed the potential of MBD-seq as a cost-effective tool in large-scale disease studies.

DNA methylation is an epigenetic modification that provides stability and diversity to the cellular phenotype. It is influenced by both genetic sequence variation and environmental factors, and can therefore potentially account for variation of heritable phenotypes and disorders. Therefore, methylome-wide association studies (MWAS) are promising complements to genome-wide association studies (GWAS) of genetic variants. Of particular interest are methylation sites (CpGs) that are created or destroyed by the alleles of single-nucleotide polymorphisms (SNPs), as these so-called CpG-SNPs may show variation in methylation levels on top of what can be explained by the sequence variation. Using sequencing-based data from 1132 major depressive disorder (MDD) cases and controls, we performed a MWAS of 970,414 common CpG-SNPs. The analysis identified 27 suggestively significant (P < 1.00 × 10−5) CpG-SNPs associations. Furthermore, the MWAS results were over-represented (odds ratios ranging 1.36–5.00; P ranging 4.9 × 10−3–8.1 × 10−2) among findings from three recent GWAS for MDD-related phenotypes. Overlapping loci included, e.g., ROBO2, ASIC2, and DCC. As the CpG-SNP analysis accounts for the number of alleles that creates CpGs, the methylation differences could not be explained by differences in allele frequencies. Thus, the results show that the MWAS and GWASs provide independent lines of evidence for the involvement of these loci in MDD. In conclusion, our methylation study of MDD contributes novel information about loci of relevance that complements previous findings and generates new hypothesis about MDD etiology, such as that the functional effects of genetic association may be partly mediated and/or enhanced by the methylation status in these loci.

Background Methylation studies are a promising complement to genetic studies of DNA sequence. However, detailed prior biological knowledge is typically lacking, so methylome-wide association studies (MWAS) will be critical to detect disease relevant sites. A cost-effective approach involves the next-generation sequencing (NGS) of single-end libraries created from samples that are enriched for methylated DNA fragments. A limitation of single-end libraries is that the fragment size distribution is not observed. This hampers several aspects of the data analysis such as the calculation of enrichment measures that are based on the number of fragments covering the CpGs. Results We developed a non-parametric method that uses isolated CpGs to estimate sample-specific fragment size distributions from the empirical sequencing data. Through simulations we show that our method is highly accurate. While the traditional (extended) read count methods resulted in severely biased coverage estimates and introduces artificial inter-individual differences, through the use of the estimated fragment size distributions we could remove these biases almost entirely. Furthermore, we found correlations of 0.999 between coverage estimates obtained using fragment size distributions that were estimated with our method versus those that were “observed” in paired-end sequencing data. Conclusions We propose a non-parametric method for estimating fragment size distributions that is highly precise and can improve the analysis of cost-effective MWAS studies that sequence single-end libraries created from samples that are enriched for methylated DNA fragments.