Simone de Jong

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)).

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

BackgroundThe 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.ResultsSystems 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.ConclusionsOur 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.

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.

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.

Stimulated gene expression profiles as a blood marker of major depressive disorder.

BACKGROUND Major depressive disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. METHODS We used a classifier approach on blood gene expression profiles of a unique set of unmedicated subjects (MDD patients and control subjects) to select genes with expression predictive for disease status. To reveal blood gene expression changes related to major depressive disorder-disease, we applied a powerful ex vivo stimulus to the blood: incubation with lipopolysaccharide (LPS; 10 ng/mL blood). RESULTS Based on LPS-stimulated blood gene expression using whole-genome microarrays (primary cohort; 21 MDD patients, 21 healthy control subjects), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated using an independent quantitative polymerase chain reaction method (primary cohort, p = .007). The difference between depressive patients and control subjects was confirmed (p = .019) in a replication cohort of 13 MDD patients and 14 control subjects. The MDD signature score comprised expression levels of seven genes could discriminate depressive patients from control subjects with sensitivity of 76.9% and specificity of 71.8%. CONCLUSIONS We have shown for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of major depressive disorder.

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

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

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.

Expression QTL analysis of top loci from GWAS meta-analysis highlights additional schizophrenia candidate genes

There is genetic evidence that schizophrenia is a polygenic disorder with a large number of loci of small effect on disease susceptibility. Genome-wide association studies (GWASs) of schizophrenia have had limited success, with the best finding at the MHC locus at chromosome 6p. A recent effort of the Psychiatric GWAS consortium (PGC) yielded five novel loci for schizophrenia. In this study, we aim to highlight additional schizophrenia susceptibility loci from the PGC study by combining the top association findings from the discovery stage (9394 schizophrenia cases and 12 462 controls) with expression QTLs (eQTLs) and differential gene expression in whole blood of schizophrenia patients and controls. We examined the 6192 single-nucleotide polymorphisms (SNPs) with significance threshold at P<0.001. eQTLs were calculated for these SNPs in a sample of healthy controls (n=437). The transcripts significantly regulated by the top SNPs from the GWAS meta-analysis were subsequently tested for differential expression in an independent set of schizophrenia cases and controls (n=202). After correction for multiple testing, the eQTL analysis yielded 40 significant cis-acting effects of the SNPs. Seven of these transcripts show differential expression between cases and controls. Of these, the effect of three genes (RNF5, TRIM26 and HLA-DRB3) coincided with the direction expected from meta-analysis findings and were all located within the MHC region. Our results identify new genes of interest and highlight again the involvement of the MHC region in schizophrenia susceptibility.

TCF4 (e2-2; ITF2): a schizophrenia-associated gene with pleiotropic effects on human disease.

Common SNPs in the transcription factor 4 (TCF4; ITF2, E2‐2, SEF‐2) gene, which encodes a basic Helix‐Loop‐Helix (bHLH) transcription factor, are associated with schizophrenia, conferring a small increase in risk. Other common SNPs in the gene are associated with the common eye disorder Fuchs corneal dystrophy, while rare, mostly de novo inactivating mutations cause Pitt‐Hopkins syndrome. In this review, we present a systematic bioinformatics and literature review of the genomics, biological function and interactome of TCF4 in the context of schizophrenia. The TCF4 gene is present in all vertebrates, and although protein length varies, there is high conservation of primary sequence, including the DNA binding domain. Humans have a unique leucine‐rich nuclear export signal. There are two main isoforms (A and B), as well as complex splicing generating many possible N‐terminal amino acid sequences. TCF4 is highly expressed in the brain, where plays a role in neurodevelopment, interacting with class II bHLH transcription factors Math1, HASH1, and neuroD2. The Ca2+ sensor protein calmodulin interacts with the DNA binding domain of TCF4, inhibiting transcriptional activation. It is also the target of microRNAs, including mir137, which is implicated in schizophrenia. The schizophrenia‐associated SNPs are in linkage disequilibrium with common variants within putative DNA regulatory elements, suggesting that regulation of expression may underlie association with schizophrenia. Combined gene co‐expression analyses and curated protein–protein interaction data provide a network involving TCF4 and other putative schizophrenia susceptibility genes. These findings suggest new opportunities for understanding the molecular basis of schizophrenia and other mental disorders.

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.