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DNA methylation likely plays a role in the regulation of human stress reactivity. Here we show that in a genome-wide analysis of blood DNA methylation in 85 healthy individuals, a locus in the Kit ligand gene ( KITLG ; cg27512205) showed the strongest association with cortisol stress reactivity ( P =5.8 × 10 −6 ). Replication was obtained in two independent samples using either blood ( N =45, P =0.001) or buccal cells ( N =255, P =0.004). KITLG methylation strongly mediates the relationship between childhood trauma and cortisol stress reactivity in the discovery sample (32% mediation). Its genomic location, a CpG island shore within an H3K27ac enhancer mark, and the correlation between methylation in the blood and prefrontal cortex provide further evidence that KITLG methylation is functionally relevant for the programming of stress reactivity in the human brain. Our results extend preclinical evidence for epigenetic regulation of stress reactivity to humans and provide leads to enhance our understanding of the neurobiological pathways underlying stress vulnerability. Exposure to childhood trauma is a major risk factor for the development of almost all psychiatric disorders. By epigenome-wide studies, here, Houtepen et al . show that DNA methylation at a locus in the Kit ligand gene (KITLG) mediates the relationship between childhood trauma and cortisol stress reactivity.

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.

Background The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex. Results Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels. Conclusions Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated.

Hallucinations may arise from an imbalance between sensory and higher cognitive brain regions, reflected by alterations in functional connectivity. It is unknown whether hallucinations across the psychosis continuum exhibit similar alterations in functional connectivity, suggesting a common neural mechanism, or whether different mechanisms link to hallucinations across phenotypes. We acquired resting-state functional MRI scans of 483 participants, including 40 non-clinical individuals with hallucinations, 99 schizophrenia patients with hallucinations, 74 bipolar-I disorder patients with hallucinations, 42 bipolar-I disorder patients without hallucinations, and 228 healthy controls. The weighted connectivity matrices were compared using network-based statistics. Non-clinical individuals with hallucinations and schizophrenia patients with hallucinations exhibited increased connectivity, mainly among fronto-temporal and fronto-insula/cingulate areas compared to controls ( P  < 0.001 adjusted). Differential effects were observed for bipolar-I disorder patients with hallucinations versus controls, mainly characterized by decreased connectivity between fronto-temporal and fronto-striatal areas ( P  = 0.012 adjusted). No connectivity alterations were found between bipolar-I disorder patients without hallucinations and controls. Our results support the notion that hallucinations in non-clinical individuals and schizophrenia patients are related to altered interactions between sensory and higher-order cognitive brain regions. However, a different dysconnectivity pattern was observed for bipolar-I disorder patients with hallucinations, which implies a different neural mechanism across the psychosis continuum.

Background One of the most robust risk factors for developing a mood disorder is having a parent with a mood disorder. Unfortunately, mechanisms explaining the transmission of mood disorders from one generation to the next remain largely elusive. Since timely intervention is associated with a better outcome and prognosis, early detection of intergenerational transmission of mood disorders is of paramount importance. Here, we describe the design of the Mood and Resilience in Offspring (MARIO) cohort study in which we investigate: 1. differences in clinical, biological and environmental (e.g., psychosocial factors, substance use or stressful life events) risk and resilience factors in children of parents with and without mood disorders, and 2. mechanisms of intergenerational transmission of mood disorders via clinical, biological and environmental risk and resilience factors. Methods MARIO is an observational, longitudinal cohort study that aims to include 450 offspring of parents with a mood disorder (uni- or bipolar mood disorders) and 100-150 offspring of parents without a mood disorder aged 10-25 years. Power analyses indicate that this sample size is sufficient to detect small to medium sized effects. Offspring are recruited via existing Dutch studies involving patients with a mood disorder and healthy controls, for which detailed clinical, environmental and biological data of the index-parent (i.e., the initially identified parent with or without a mood disorder) is available. Over a period of three years, four assessments will take place, in which extensive clinical, biological and environmental data and data on risk and resilience are collected through e.g., blood sampling, face-to-face interviews, online questionnaires, actigraphy and Experience Sampling Method assessment. For co-parents, information on demographics, mental disorder status and a DNA-sample are collected. Discussion The MARIO cohort study is a large longitudinal cohort study among offspring of parents with and without mood disorders. A unique aspect is the collection of granular data on clinical, biological and environmental risk and resilience factors in offspring, in addition to available parental data on many similar factors. We aim to investigate the mechanisms underlying intergenerational transmission of mood disorders, which will ultimately lead to better outcomes for offspring at high familial risk.

We report results from the Bipolar Exome (BipEx) collaboration analysis of whole-exome sequencing of 13,933 patients with bipolar disorder (BD) matched with 14,422 controls. We find an excess of ultra-rare protein-truncating variants (PTVs) in patients with BD among genes under strong evolutionary constraint in both major BD subtypes. We find enrichment of ultra-rare PTVs within genes implicated from a recent schizophrenia exome meta-analysis (SCHEMA; 24,248 cases and 97,322 controls) and among binding targets of CHD8. Genes implicated from genome-wide association studies (GWASs) of BD, however, are not significantly enriched for ultra-rare PTVs. Combining gene-level results with SCHEMA, AKAP11 emerges as a definitive risk gene (odds ratio (OR) = 7.06, P  = 2.83 × 10 −9 ). At the protein level, AKAP-11 interacts with GSK3B, the hypothesized target of lithium, a primary treatment for BD. Our results lend support to BD’s polygenicity, demonstrating a role for rare coding variation as a significant risk factor in BD etiology. Exome sequencing analysis of 13,933 individuals with bipolar disorder finds enrichment of ultra-rare protein-truncating variants in constrained genes. Combined analysis with schizophrenia exome data identifies AKAP11 as a risk gene for both disorders.

Background Little attention has been paid to the generalizability of cohort studies in bipolar disorder (BD) to patient with BD in everyday clinical practice. Methods A sample of patients with bipolar I disorder (BD-I) treated at a Dutch outpatient clinic for BD were compared with Dutch participants with BD-I of four clinical cohort studies, and participants with BD-I in a general population study in the Netherlands, on sociodemographic and clinical characteristics. Results On many variables participants from the outpatient sample matched with those of the included studies. However, compared with participants of several of the clinical cohort studies, these outpatients were significantly younger, had an earlier age of onset of mood symptoms, and had a shorter duration of illness. Compared with participants in the general population study, outpatients had significant higher levels of education and less often lived together or were married. One cohort study reported much lower comorbidity rates of alcohol use disorders, drug use disorders, and anxiety disorders than in the outpatient sample. In contrast, comorbidity rates were higher in the population study. Limitations Due to methodological differences between studies, comparisons between several variables was limited, and for some variables data was lacking. Conclusions Our findings suggest that many findings from cohort studies and general population study in BD-I are generalizable to everyday clinical practice, especially mood disorder outpatient centers. However, differences between samples indicate some selection and referral bias.

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In view of the potential effects of psychiatric drugs on DNA methylation, we investigated whether medication use in bipolar disorder is associated with DNA methylation signatures. Blood-based DNA methylation patterns of six frequently used psychotropic drugs (lithium, quetiapine, olanzapine, lamotrigine, carbamazepine, and valproic acid) were examined in 172 bipolar disorder patients. After adjustment for cell type composition, we investigated gene networks, principal components, hypothesis-driven genes and epigenome-wide individual loci. Valproic acid and quetiapine were significantly associated with altered methylation signatures after adjustment for drug-related changes on celltype composition. Psychiatric drugs influence DNA methylation patterns over and above cell type composition in bipolar disorder. Drug-related changes in DNA methylation are therefore not only an important confounder in psychiatric epigenetics but may also inform on the biological mechanisms underlying drug efficacy.

Purpose Both increased as well as decreased cancer mortality among psychiatric patients has been reported, but competing death causes were not included in the analyses. This study aims to investigate whether observed cancer mortality in patients with psychiatric disorders might be biased by competing death causes. Method In this retrospective cohort study on data from the Psychiatric Case Register Middle Netherlands linked to the death register of Statistics Netherlands, the risk of cancer death among patients with schizophrenia ( N  = 4,590), bipolar disorder ( N  = 2,077), depression ( N  = 15,130) and their matched controls ( N  = 87,405) was analyzed using a competing risk model. Results Compared to controls, higher hazards of cancer death were found in patients with schizophrenia (HR = 1.61, 95 % CI 1.26–2.06), bipolar disorder (HR = 1.20, 95 % CI 0.81–1.79) and depression (HR = 1.26, 95 % CI 1.10–1.44). However, the HRs of death due to suicide and other death causes were more elevated. Consequently, among those who died, the 12-year cumulative risk of cancer death was significantly lower. Conclusions Our analysis shows that, compared to the general population, psychiatric patients are at higher risk of dying from cancer, provided that they survive the much more elevated risks of suicide and other death causes.