Serum from Older Adults Increases Apoptosis and Molecular Aging Markers in Human Hippocampal Progenitor Cells

Age-related alteration in neural stem cell function is linked to neurodegenerative conditions and cognitive decline. In rodents, this can be reversed by exposure to a young systemic milieu and conversely, the old milieu can inhibit stem cell function in young rodents. In this study, we investigated the in vitro effect of the human systemic milieu on human hippocampal progenitor cells (HPCs) using human serum from early adulthood, mid-life and older age. We showed that neuroblast number following serum treatment is predictive of larger dentate gyrus, CA3, CA4 and whole hippocampus volumes and that allogeneic human serum from asymptomatic older individuals induced a two-fold increase in apoptotic cell death of HPCs compared with serum from young adults. General linear models revealed that variability in markers of proliferation and differentiation was partly attributable to use of antihypertensive medication and very mild cognitive decline among older subjects. Finally, using an endophenotype approach and whole-genome expression arrays, we showed upregulation of established and novel ageing molecular hallmarks in response to old serum. Serum from older subjects induced a wide range of cellular and molecular phenotypes, likely reflecting a lifetime of environmental exposures. Our findings support a role for the systemic enviroment in neural stem cell maintenance and are in line with others highlighting a distinction between neurobiological and chronological ageing. Finally, the herein described serum assay can be used by future studies to further analyse the effect of environmental exposures as well as to determine the role of the systemic environment in health and disease.

Transcriptomic abnormalities in peripheral blood in bipolar disorder, and discrimination of the major psychoses

We performed a transcriptome-wide meta-analysis and gene co-expression network analysis to identify genes and gene networks dysregulated in the peripheral blood of bipolar disorder (BD) cases relative to unaffected comparison subjects, and determined the specificity of the transcriptomic signatures of BD and schizophrenia (SZ). Nineteen genes and 4 gene modules were significantly differentially expressed in BD cases. Thirteen gene modules were shown to be differentially expressed in a combined case-group of BD and SZ subjects called "major psychosis", including genes biologically linked to apoptosis, reactive oxygen, chromatin remodeling, and immune signaling. No modules were differentially expressed between BD and SZ cases. Machine-learning classifiers trained to separate diagnostic classes based solely on gene expression profiles could distinguish BD cases from unaffected comparison subjects with an area under the curve (AUC) of 0.724, as well as BD cases from SZ cases with AUC = 0.677 in withheld test samples. We introduced a novel and straightforward method called "polytranscript risk scoring" that could distinguish BD cases from unaffected subjects (AUC = 0.672) and SZ cases (AUC = 0.607) significantly better than expected by chance. Taken together, our results highlighted gene expression alterations common to BD and SZ that involve biological processes of inflammation, oxidative stress, apoptosis, and chromatin regulation, and highlight disorder-specific changes in gene expression that discriminate the major psychoses.

Genome-wide Burden of Rare Short Deletions Is Enriched in Major Depressive Disorder in Four Cohorts

BACKGROUND

Major depressive disorder (MDD) is moderately heritable, with a high prevalence and a presumed high heterogeneity. Copy number variants (CNVs) could contribute to the heritable component of risk, but the two previous genome-wide association studies of rare CNVs did not report significant findings.

METHODS

In this meta-analysis of four cohorts (5780 patients and 6626 control subjects), we analyzed the association of MDD to 1) genome-wide burden of rare deletions and duplications, partitioned by length (<100 kb or >100 kb) and other characteristics, and 2) individual rare exonic CNVs and CNV regions.

RESULTS

Patients with MDD carried significantly more short deletions than control subjects (p = .0059) but not long deletions or short or long duplications. The confidence interval for long deletions overlapped with that for short deletions, but long deletions were 70% less frequent genome-wide, reducing the power to detect increased burden. The increased burden of short deletions was primarily in intergenic regions. Short deletions in cases were also modestly enriched for high-confidence enhancer regions. No individual CNV achieved thresholds for suggestive or significant association after genome-wide correction. p values < .01 were observed for 15q11.2 duplications (TUBGCP5, CYFIP1, NIPA1, and NIPA2), deletions in or near PRKN or MSR1, and exonic duplications of ATG5.

CONCLUSIONS

The increased burden of short deletions in patients with MDD suggests that rare CNVs increase the risk of MDD by disrupting regulatory regions. Results for longer deletions were less clear, but no large effects were observed for long multigenic CNVs (as seen in schizophrenia and autism). Further studies with larger sample sizes are warranted.

Liprin alfa 2 gene expression is increased by cannabis use and associated with neuropsychological function

The relation of heavy cannabis use with decreased neuropsychological function has frequently been described but the underlying biological mechanisms are still largely unknown. This study investigates the relation of cannabis use with genome wide gene expression and subsequently examines the relations with neuropsychological function. Genome-wide gene expression in whole blood was compared between heavy cannabis users (N = 90) and cannabis naïve participants (N = 100) that were matched for psychotic like experiences. The results were validated using quantitative real-time PCR. Psychotic like experiences were assessed using the Comprehensive Assessment of Psychotic Experiences (CAPE). Neuropsychological function was estimated using four subtasks of the Wechsler Adult Intelligence Scale (WAIS). Subsequent in vitro studies in monocytes and a neuroblastoma cell line investigated expression changes in response to two major psychotropic components of cannabis; tetrahydrocannabinol (THC) and cannabidiol (CBD). mRNA expression of Protein Tyrosine Phosphatase Receptor Type F Polypeptide-Interacting-Protein Alpha-2 (PPFIA2) was significantly higher in cannabis users (LogFold Change 0.17) and confirmed by qPCR analysis. PPFIA2 expression level was negatively correlated with estimated intelligence (B=-22.9, p = 0.002) also in the 100 non-users (B=-28.5, p = 0.037). In vitro exposure of monocytes to CBD led to significant increase in PPFIA2 expression. However, exposure of monocytes to THC and neuroblastoma cells to THC or CBD did not change PPFIA2 expression. Change in PPFIA2 gene expression in response to cannabinoids is a putative mechanism by which cannabis could influence neuropsychological functions. The findings warrant further exploration of the role of PPFIA2 in cannabis induced changes of neuropsychological function, particularly in relation to CBD.

Genome-wide association study identifies 30 loci associated with bipolar disorder

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.

The Alkaline Phosphatase (ALPL) Locus Is Associated with B6 Vitamer Levels in CSF and Plasma

The active form of vitamin B6, pyridoxal phosphate (PLP), is essential for human metabolism. The brain is dependent on vitamin B6 for its neurotransmitter balance. To obtain insight into the genetic determinants of vitamin B6 homeostasis, we conducted a genome-wide association study (GWAS) of the B6 vitamers pyridoxal (PL), PLP and the degradation product of vitamin B6, pyridoxic acid (PA). We collected a unique sample set of cerebrospinal fluid (CSF) and plasma from the same healthy human subjects of Dutch ancestry (n = 493) and included concentrations and ratios in and between these body fluids in our analysis. Based on a multivariate joint analysis of all B6 vitamers and their ratios, we identified a genome-wide significant association at a locus on chromosome 1 containing the ALPL (alkaline phosphatase) gene (minimal p = 7.89 × 10⁻¹⁰, rs1106357, minor allele frequency (MAF) = 0.46), previously associated with vitamin B6 levels in blood. Subjects homozygous for the minor allele showed a 1.4-times-higher ratio between PLP and PL in plasma, and even a 1.6-times-higher ratio between PLP and PL in CSF than subjects homozygous for the major allele. In addition, we observed a suggestive association with the CSF:plasma ratio of PLP on chromosome 15 (minimal p = 7.93 × 10⁻⁷, and MAF = 0.06 for rs28789220). Even though this finding is not reaching genome-wide significance, it highlights the potential of our experimental setup for studying transport and metabolism across the blood–CSF barrier. This GWAS of B6 vitamers identifies alkaline phosphatase as a key regulator in human vitamin B6 metabolism in CSF as well as plasma. Furthermore, our results demonstrate the potential of genetic studies of metabolites in plasma and CSF to elucidate biological aspects underlying metabolite generation, transport and degradation.

Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition

Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.

Applying polygenic risk scoring for psychiatric disorders to a large family with bipolar disorder and major depressive disorder

Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n ~ 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders.

The genome-wide expression effects of escitalopram and its relationship to neurogenesis, hippocampal volume, and antidepressant response

Antidepressant-induced hippocampal neurogenesis (AHN) is hypothesized to contribute to increases in hippocampal volume among major depressive disorder patients after long-term treatment. Furthermore, rodent studies suggest AHN may be the cellular mechanism mediating the therapeutic benefits of antidepressants. Here, we perform the first investigation of genome-wide expression changes associated with AHN in human cells. We identify gene expression networks significantly activated during AHN, and we perform gene set analyses to probe the molecular relationship between AHN, hippocampal volume, and antidepressant response. The latter were achieved using genome-wide association summary data collected from 30,717 individuals as part of the ENIGMA Consortium (genetic predictors of hippocampal volume dataset), and data collected from 1,222 major depressed patients as part of the NEWMEDS Project (genetic predictors of response to antidepressants dataset). Our results showed that the selective serotonin reuptake inhibitor, escitalopram evoked AHN in human cells; dose-dependently increasing the differentiation of cells into neuroblasts, as well as increasing gliogenesis. Activated genome-wide expression networks relate to axon and microtubule formation, and ribosomal biogenesis. Gene set analysis revealed that gene expression changes associated with AHN were nominally enriched for genes predictive of hippocampal volume, but not for genes predictive of therapeutic response.

Transcriptome-wide mega-analyses reveal joint dysregulation of immunologic genes and transcription regulators in brain and blood in schizophrenia

The application of microarray technology in schizophrenia research was heralded as paradigm-shifting, as it allowed for high-throughput assessment of cell and tissue function. This technology was widely adopted, initially in studies of postmortem brain tissue, and later in studies of peripheral blood. The collective body of schizophrenia microarray literature contains apparent inconsistencies between studies, with failures to replicate top hits, in part due to small sample sizes, cohort-specific effects, differences in array types, and other confounders. In an attempt to summarize existing studies of schizophrenia cases and non-related comparison subjects, we performed two mega-analyses of a combined set of microarray data from postmortem prefrontal cortices (n=315) and from ex-vivo blood tissues (n=578). We adjusted regression models per gene to remove non-significant covariates, providing best-estimates of transcripts dysregulated in schizophrenia. We also examined dysregulation of functionally related gene sets and gene co-expression modules, and assessed enrichment of cell types and genetic risk factors. The identities of the most significantly dysregulated genes were largely distinct for each tissue, but the findings indicated common emergent biological functions (e.g. immunity) and regulatory factors (e.g., predicted targets of transcription factors and miRNA species across tissues). Our network-based analyses converged upon similar patterns of heightened innate immune gene expression in both brain and blood in schizophrenia. We also constructed generalizable machine-learning classifiers using the blood-based microarray data. Our study provides an informative atlas for future pathophysiologic and biomarker studies of schizophrenia.

Immune signatures and disorder-specific patterns in a cross-disorder gene expression analysis

BACKGROUND

Recent studies point to overlap between neuropsychiatric disorders in symptomatology and genetic aetiology.

AIMS

To systematically investigate genomics overlap between childhood and adult attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and major depressive disorder (MDD).

METHOD

Analysis of whole-genome blood gene expression and genetic risk scores of 318 individuals. Participants included individuals affected with adult ADHD (n = 93), childhood ADHD (n = 17), MDD (n = 63), ASD (n = 51), childhood dual diagnosis of ADHD-ASD (n = 16) and healthy controls (n = 78).

RESULTS

Weighted gene co-expression analysis results reveal disorder-specific signatures for childhood ADHD and MDD, and also highlight two immune-related gene co-expression modules correlating inversely with MDD and adult ADHD disease status. We find no significant relationship between polygenic risk scores and gene expression signatures.

CONCLUSIONS

Our results reveal disorder overlap and specificity at the genetic and gene expression level. They suggest new pathways contributing to distinct pathophysiology in psychiatric disorders and shed light on potential shared genomic risk factors.

Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis

To elucidate the genetic architecture of amyotrophic lateral sclerosis (ALS) and find associated loci, we assembled a custom imputation reference panel from whole-genome-sequenced patients with ALS and matched controls (n = 1,861). Through imputation and mixed-model association analysis in 12,577 cases and 23,475 controls, combined with 2,579 cases and 2,767 controls in an independent replication cohort, we fine-mapped a new risk locus on chromosome 21 and identified C21orf2 as a gene associated with ALS risk. In addition, we identified MOBP and SCFD1 as new associated risk loci. We established evidence of ALS being a complex genetic trait with a polygenic architecture. Furthermore, we estimated the SNP-based heritability at 8.5%, with a distinct and important role for low-frequency variants (frequency 1-10%). This study motivates the interrogation of larger samples with full genome coverage to identify rare causal variants that underpin ALS risk.

Gene-set analysis based on the pharmacological profiles of drugs to identify repurposing opportunities in schizophrenia

Genome-wide association studies (GWAS) have identified thousands of novel genetic associations for complex genetic disorders, leading to the identification of potential pharmacological targets for novel drug development. In schizophrenia, 108 conservatively defined loci that meet genome-wide significance have been identified and hundreds of additional sub-threshold associations harbour information on the genetic aetiology of the disorder. In the present study, we used gene-set analysis based on the known binding targets of chemical compounds to identify the 'drug pathways' most strongly associated with schizophrenia-associated genes, with the aim of identifying potential drug repositioning opportunities and clues for novel treatment paradigms, especially in multi-target drug development. We compiled 9389 gene sets (2496 with unique gene content) and interrogated gene-based p-values from the PGC2-SCZ analysis. Although no single drug exceeded experiment wide significance (corrected p<0.05), highly ranked gene-sets reaching suggestive significance including the dopamine receptor antagonists metoclopramide and trifluoperazine and the tyrosine kinase inhibitor neratinib. This is a proof of principle analysis showing the potential utility of GWAS data of schizophrenia for the direct identification of candidate drugs and molecules that show polypharmacy.

Gestational Influenza Increases the Risk of Psychosis in Adults

Psychotic disorders are complex and caused by interplay of genetic and environmental factors. Influenza is a common infectious disease in humans, and it has been suggested that maternal influenza is an estimated risk factor for psychotic disorders, especially for schizophrenia. In view of conflicting results of this association in literature, we performed the strict meta-analysis to examine whether maternal influenza is a risk factor for psychosis in the children. Four ecological studies and three birth cohort studies were included in our meta-analysis. It has been observed that the Risk Ratio (RR) of maternal influenza on psychosis is 1.062 (95% Confidence Interval (CI) = 1.004-1.123) for the analysis of ecological studies and the RR is 1.564(95%CI=1.051-2.324) for the analysis of birth cohort studies. Furthermore, a survey of pregnant women and fetus' health in Nanjing of China indicated that only 1.5% of women received the influenza vaccine before pregnancy, 0.4% received it during pregnancy, and 5.1% were willing to receive the influenza vaccine if necessary. These results showed that gestational influenza could increase mental disorders risks in adult offspring besides its established harms for gravidas. Results suggest it might be effective to increase attention to gravidas to protect them from influenza infection through encouragement of vaccinations.

An integrative multi-scale analysis of the dynamic DNA methylation landscape in aging

Recent studies have demonstrated that the DNA methylome changes with age. This epigenetic drift may have deep implications for cellular differentiation and disease development. However, it remains unclear how much of this drift is functional or caused by underlying changes in cell subtype composition. Moreover, no study has yet comprehensively explored epigenetic drift at different genomic length scales and in relation to regulatory elements.

Here we conduct an in-depth analysis of epigenetic drift in blood tissue. We demonstrate that most of the age-associated drift is independent of the increase in the granulocyte to lymphocyte ratio that accompanies aging and that enrichment of age-hypermethylated CpG islands increases upon adjustment for cellular composition. We further find that drift has only a minimal impact on in-cis gene expression, acting primarily to stabilize pre-existing baseline expression levels. By studying epigenetic drift at different genomic length scales, we demonstrate the existence of mega-base scale age-associated hypomethylated blocks, covering approximately 14% of the human genome, and which exhibit preferential hypomethylation in age-matched cancer tissue. Importantly, we demonstrate the feasibility of integrating Illumina 450k DNA methylation with ENCODE data to identify transcription factors with key roles in cellular development and aging. Specifically, we identify REST and regulatory factors of the histone methyltransferase MLL complex, whose function may be disrupted in aging.

In summary, most of the epigenetic drift seen in blood is independent of changes in blood cell type composition, and exhibits patterns at different genomic length scales reminiscent of those seen in cancer. Integration of Illumina 450k with appropriate ENCODE data may represent a fruitful approach to identify transcription factors with key roles in aging and disease.

Two well-known features of aging are the gradual decline of the body’s ability to regenerate tissues, as well as an increased incidence of diseases like cancer and Alzheimers. One of the most recent exciting findings which may underlie the aging process is a gradual modification of DNA, called epigenetic drift, which is effected by the covalent addition and removal of methyl groups, which in turn can deregulate the activity of nearby genes. However, this study presents the most convincing evidence to date that epigenetic drift acts to stabilize the activity levels of nearby genes. This study shows that instead, epigenetic drift may act primarly to disrupt DNA binding patterns of proteins which regulate the activity of many genes, and moreover identifies specific regulatory proteins with key roles in cancer and Alzheimers. The study also performs the most comprehensive analysis of epigenetic drift at different spatial scales, demonstrating that epigenetic drift on the largest length scales is highly reminiscent of those seen in cancer. In summary, this work substantially supports the view that epigenetic drift may contribute to the age-associated increased risk of diseases like cancer and Alzheimers, by disrupting master regulators of genomewide gene activity.

Identification of schizophrenia-associated loci by combining DNA methylation and gene expression data from whole blood

Emerging evidence suggests that schizophrenia (SZ) susceptibility involves variation at genetic, epigenetic and transcriptome levels. We describe an integrated approach that leverages DNA methylation and gene expression data to prioritize genetic variation involved in disease. DNA methylation levels were obtained from whole blood of 260 SZ patients and 250 unaffected controls of which a subset with gene expression data was available. By assessing DNA methylation and gene expression in cases and controls, we identified 432 CpG sites with differential methylation levels that are associated with differential gene expression. We hypothesized that genetic factors involved in these methylation levels may be associated with the genetic risk of SZ susceptibility. To test this hypothesis, we used results from the Psychiatric Genomics Consortium SZ genome-wide association study (GWAS). We observe an enrichment of SZ-associated SNPs in the mQTLs of which the associated CpG site is also correlated with differential gene expression in SZ. While this enrichment was already apparent when using nominal significant thresholds, enrichment was even more pronounced when applying more stringent significance levels. One locus, previously identified as susceptibility locus in a SZ GWAS, involves SNP rs11191514:C>T, which regulates DNA methylation of calcium homeostasis modulator 1 that is also associated with differential gene expression in patients. Overall, our results suggest that epigenetic variation plays an important role in SZ susceptibility and that the integration of analyses of genetic, epigenetic and gene expression profiles may be a biologically meaningful approach for identifying disease susceptibility loci, even when using whole blood data in studies of brain-related disorders.

Characterization of genome-methylome interactions in 22 nuclear pedigrees

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.

Common variant at 16p11.2 conferring risk of psychosis

Epidemiological and genetic data support the notion that schizophrenia and bipolar disorder share genetic risk factors. In our previous genome-wide association study, meta-analysis and follow-up (totaling as many as 18 206 cases and 42 536 controls), we identified four loci showing genome-wide significant association with schizophrenia. Here we consider a mixed schizophrenia and bipolar disorder (psychosis) phenotype (addition of 7469 bipolar disorder cases, 1535 schizophrenia cases, 333 other psychosis cases, 808 unaffected family members and 46 160 controls). Combined analysis reveals a novel variant at 16p11.2 showing genome-wide significant association (rs4583255[T]; odds ratio=1.08; P=6.6 × 10(-11)). The new variant is located within a 593-kb region that substantially increases risk of psychosis when duplicated. In line with the association of the duplication with reduced body mass index (BMI), rs4583255[T] is also associated with lower BMI (P=0.0039 in the public GIANT consortium data set; P=0.00047 in 22 651 additional Icelanders).

Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects

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.

Common inversion polymorphism at 17q21.31 affects expression of multiple genes in tissue-specific manner

Background

Chromosome 17q21.31 contains a common inversion polymorphism of approximately 900 kb in populations with European ancestry. Two divergent MAPT haplotypes, H1 and H2 are described with distinct linkage disequilibrium patterns across the region reflecting the inversion status at this locus. The MAPT H1 haplotype has been associated with progressive supranuclear palsy, corticobasal degeneration, Parkinson’s disease and Alzheimer’s disease, while the H2 is linked to recurrent deletion events associated with the 17q21.31 microdeletion syndrome, a disease characterized by developmental delay and learning disability.

Results

In this study, we investigate the effect of the inversion on the expression of genes in the 17q21.31 region. We find the expression of several genes in and at the borders of the inversion to be affected; specific either to whole blood or different regions of the human brain. The H1 haplotype was found to be associated with an increased expression of LRRC37A4, PLEKH1M and MAPT. In contrast, a decreased expression of MGC57346, LRRC37A and CRHR1 was associated with H1.

Conclusions

Studies thus far have focused on the expression of MAPT in the inversion region. However, our results show that the inversion status affects expression of other genes in the 17q21.31 region as well. Given the link between the inversion status and different neurological diseases, these genes may also be involved in disease pathology, possibly in a tissue-specific manner.

A gene co-expression network in whole blood of schizophrenia patients is independent of antipsychotic-use and enriched for brain-expressed genes

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.

Common variants at VRK2 and TCF4 conferring risk of schizophrenia

Common sequence variants have recently joined rare structural polymorphisms as genetic factors with strong evidence for association with schizophrenia. Here we extend our previous genome-wide association study and meta-analysis (totalling 7 946 cases and 19 036 controls) by examining an expanded set of variants using an enlarged follow-up sample (up to 10 260 cases and 23 500 controls). In addition to previously reported alleles in the major histocompatibility complex region, near neurogranin (NRGN) and in an intron of transcription factor 4 (TCF4), we find two novel variants showing genome-wide significant association: rs2312147[C], upstream of vaccinia-related kinase 2 (VRK2) [odds ratio (OR) = 1.09, P = 1.9 × 10(-9)] and rs4309482[A], between coiled-coiled domain containing 68 (CCDC68) and TCF4, about 400 kb from the previously described risk allele, but not accounted for by its association (OR = 1.09, P = 7.8 × 10(-9)).

Hippocampal gene expression analysis highlights Ly6a/Sca-1 as candidate gene for previously mapped novelty induced behaviors in mice

In this study, we show that the covariance between behavior and gene expression in the brain can help further unravel the determinants of neurobehavioral traits. Previously, a QTL for novelty induced motor activity levels was identified on murine chromosome 15 using consomic strains. With the goal of narrowing down the linked region and possibly identifying the gene underlying the quantitative trait, gene expression data from this F₂-population was collected and used for expression QTL analysis. While genetic variation in these mice was limited to chromosome 15, eQTL analysis of gene expression showed strong cis-effects as well as trans-effects elsewhere in the genome. Using weighted gene co-expression network analysis, we were able to identify modules of co-expressed genes related to novelty induced motor activity levels. In eQTL analyses, the expression of Ly6a (a.k.a. Sca-1) was found to be cis-regulated by chromosome 15. Ly6a also surfaced in a group of genes resulting from the network analysis that was correlated with behavior. Behavioral analysis of Ly6a knock-out mice revealed reduced novelty induced motor activity levels when compared to wild type controls, confirming functional importance of Ly6a in this behavior, possibly through regulating other genes in a pathway. This study shows that gene expression profiling can be used to narrow down a previously identified behavioral QTL in mice, providing support for Ly6a as a candidate gene for functional involvement in novelty responsiveness.

Gene networks associated with conditional fear in mice identified using a systems genetics approach

Background

Our understanding of the genetic basis of learning and memory remains shrouded in mystery. To explore the genetic networks governing the biology of conditional fear, we used a systems genetics approach to analyze a hybrid mouse diversity panel (HMDP) with high mapping resolution.

Results

A total of 27 behavioral quantitative trait loci were mapped with a false discovery rate of 5%. By integrating fear phenotypes, transcript profiling data from hippocampus and striatum and also genotype information, two gene co-expression networks correlated with context-dependent immobility were identified. We prioritized the key markers and genes in these pathways using intramodular connectivity measures and structural equation modeling. Highly connected genes in the context fear modules included Psmd6, Ube2a and Usp33, suggesting an important role for ubiquitination in learning and memory. In addition, we surveyed the architecture of brain transcript regulation and demonstrated preservation of gene co-expression modules in hippocampus and striatum, while also highlighting important differences. Rps15a, Kif3a, Stard7, 6330503K22RIK, and Plvap were among the individual genes whose transcript abundance were strongly associated with fear phenotypes.

Conclusion

Application of our multi-faceted mapping strategy permits an increasingly detailed characterization of the genetic networks underlying behavior.

Gene expression profiling in C57BL/6J and A/J mouse inbred strains reveals gene networks specific for brain regions independent of genetic background

BACKGROUND

We performed gene expression profiling of the amygdala and hippocampus taken from inbred mouse strains C57BL/6J and A/J. The selected brain areas are implicated in neurobehavioral traits while these mouse strains are known to differ widely in behavior. Consequently, we hypothesized that comparing gene expression profiles for specific brain regions in these strains might provide insight into the molecular mechanisms of human neuropsychiatric traits. We performed a whole-genome gene expression experiment and applied a systems biology approach using weighted gene co-expression network analysis.

RESULTS

We were able to identify modules of co-expressed genes that distinguish a strain or brain region. Analysis of the networks that are most informative for hippocampus and amygdala revealed enrichment in neurologically, genetically and psychologically related pathways. Close examination of the strain-specific gene expression profiles, however, revealed no functional relevance but a significant enrichment of single nucleotide polymorphisms in the probe sequences used for array hybridization. This artifact was not observed for the modules of co-expressed genes that distinguish amygdala and hippocampus.

CONCLUSIONS

The brain-region specific modules were found to be independent of genetic background and are therefore likely to represent biologically relevant molecular networks that can be studied to complement our knowledge about pathways in neuropsychiatric disease.