An epigenetic association analysis of childhood trauma in psychosis reveals possible overlap with methylation changes associated with PTSD.

Patients with a severe mental disorder report significantly higher levels of childhood trauma (CT) than healthy individuals. Studies have suggested that CT may affect brain plasticity through epigenetic mechanisms and contribute to developing various psychiatric disorders. We performed a blood-based epigenome-wide association study using the Childhood Trauma Questionnaire-short form in 602 patients with a current severe mental illness, investigating DNA methylation association separately for five trauma subtypes and the total trauma score. The median trauma score was set as the predefined cutoff for determining whether the trauma was present or not. Additionally, we compared our genome-wide results with methylation probes annotated to candidate genes previously associated with CT. Of the patients, 83.2% reported CT above the cutoff in one or more trauma subtypes, and emotional neglect was the trauma subtype most frequently reported. We identified one significant differently methylated position associated with the gene TANGO6 for physical neglect. Seventeen differentially methylated regions (DMRs) were associated with different trauma categories. Several of these DMRs were annotated to genes previously associated with neuropsychiatric disorders such as post-traumatic stress disorder and cognitive impairments. Our results support a biomolecular association between CT and severe mental disorders. Genes that were previously identified as differentially methylated in CT-exposed subjects with and without psychosis did not show methylation differences in our analysis. We discuss this inconsistency, the relevance of our findings, and the limitations of our study.

Identification of pleiotropy at the gene level between psychiatric disorders and related traits.

Major mental disorders are highly prevalent and make a substantial contribution to the global disease burden. It is known that mental disorders share clinical characteristics, and genome-wide association studies (GWASs) have recently provided evidence for shared genetic factors as well. Genetic overlaps are usually identified at the single-marker level. Here, we aimed to identify genetic overlaps at the gene level between 7 mental disorders (schizophrenia, autism spectrum disorder, major depressive disorder, anorexia nervosa, ADHD, bipolar disorder and anxiety), 8 brain morphometric traits, 2 cognitive traits (educational attainment and general cognitive function) and 9 personality traits (subjective well-being, depressive symptoms, neuroticism, extraversion, openness to experience, agreeableness and conscientiousness, children's aggressive behaviour, loneliness) based on publicly available GWASs. We performed systematic conditional regression analyses to identify independent signals and select loci associated with more than one trait. We identified 48 genes containing independent markers associated with several traits (pleiotropy at the gene level). We also report 9 genes with different markers that show independent associations with single traits (allelic heterogeneity). This study demonstrates that mental disorders and related traits do show pleiotropy at the gene level as well as the single-marker level. The identification of these genes might be important for prioritizing further deep genotyping, functional studies, or drug targeting.

Analysis of differentially methylated regions in great apes and extinct hominids provides support for the evolutionary hypothesis of schizophrenia.

INTRODUCTION: The persistence of schizophrenia in human populations separated by geography and time led to the evolutionary hypothesis that proposes schizophrenia as a by-product of the higher cognitive abilities of modern humans. To explore this hypothesis, we used here an evolutionary epigenetics approach building on differentially methylated regions (DMRs) of the genome. METHODS: We implemented a polygenic enrichment testing pipeline using the summary statistics of genome-wide association studies (GWAS) of schizophrenia and 12 other phenotypes. We investigated the enrichment of association of these traits across genomic regions with variable methylation between modern humans and great apes (orangutans, chimpanzees and gorillas; great ape DMRs) and between modern humans and recently extinct hominids (Neanderthals and Denisovans; hominid DMRs). RESULTS: Regions that are hypo-methylated in humans compared to great apes show enrichment of association with schizophrenia only if the major histocompatibility complex (MHC) region is included. With the MHC region removed from the analysis, only a modest enrichment for SNPs of low effect persists. The INRICH pipeline confirms this finding after rigorous permutation and bootstrapping procedures. CONCLUSION: The analyses of regions with differential methylation changes in humans and great apes do not provide compelling evidence of enrichment of association with schizophrenia, in contrast to our previous findings on more recent methylation differences between modern humans, Neanderthals and Denisovans. Our results further support the evolutionary hypothesis of schizophrenia and indicate that the origin of some of the genetic susceptibility factors of schizophrenia may lie in recent human evolution.

Elevated expression of a minor isoform of ANK3 is a risk factor for bipolar disorder.

Ankyrin-3 (ANK3) is one of the few genes that have been consistently identified as associated with bipolar disorder by multiple genome-wide association studies. However, the exact molecular basis of the association remains unknown. A rare loss-of-function splice-site SNP (rs41283526*G) in a minor isoform of ANK3 (incorporating exon ENSE00001786716) was recently identified as protective of bipolar disorder and schizophrenia. This suggests that an elevated expression of this isoform may be involved in the etiology of the disorders. In this study, we used novel approaches and data sets to test this hypothesis. First, we strengthen the statistical evidence supporting the allelic association by replicating the protective effect of the minor allele of rs41283526 in three additional large independent samples (meta-analysis p-values: 6.8E-05 for bipolar disorder and 8.2E-04 for schizophrenia). Second, we confirm the hypothesis that both bipolar and schizophrenia patients have a significantly higher expression of this isoform than controls (p-values: 3.3E-05 for schizophrenia and 9.8E-04 for bipolar type I). Third, we determine the transcription start site for this minor isoform by Pacific Biosciences sequencing of full-length cDNA and show that it is primarily expressed in the corpus callosum. Finally, we combine genotype and expression data from a large Norwegian sample of psychiatric patients and controls, and show that the risk alleles in ANK3 identified by bipolar disorder GWAS are located near the transcription start site of this isoform and are significantly associated with its elevated expression. Together, these results point to the likely molecular mechanism underlying ANK3´s association with bipolar disorder.

Genetic and transcriptional analysis of inflammatory bowel disease-associated pathways in patients with GUCY2C-linked familial diarrhea.

OBJECTIVE: Activating mutations in the GUCY2C gene, which encodes the epithelial receptor guanylate cyclase C, cause diarrhea due to increased loss of sodium chloride to the intestinal lumen. Patients with familial GUCY2C diarrhea syndrome (FGDS) are predisposed to inflammatory bowel disease (IBD). We investigated whether genes in the guanylate cyclase C pathway are enriched for association with IBD and reversely whether genetic or transcriptional changes associated with IBD are found in FGDS patients. METHODS: (1) A set of 27 genes from the guanylate cyclase C pathway was tested for enrichment of association with IBD by Gene Set Enrichment Analysis, using genome-wide association summary statistics from 12,882 IBD patients and 21,770 controls. (2) We genotyped 163 known IBD risk loci and sequenced NOD2 in 22 patients with FGDS. Eight of them had concomitant Crohn's disease. (3) Global gene expression analysis was performed in ileal tissue from patients with FGDS, Crohn's disease and healthy individuals. RESULTS: The guanylate cyclase C gene set showed a significant enrichment of association in IBD genome-wide association data. Risk variants in NOD2 were found in 7/8 FGDS patients with concomitant Crohn's disease and in 2/14 FDGS patients without Crohn's disease. In ileal tissue, downregulation of metallothioneins characterized FGDS patients compared to healthy controls. CONCLUSIONS: Our results support a role of guanylate cyclase C signaling and disturbed electrolyte homeostasis in development of IBD. Furthermore, downregulation of metallothioneins in the ileal mucosa of FGDS patients may contribute to IBD development, possibly alongside effects from NOD2 risk variants.

Recently evolved human-specific methylated regions are enriched in schizophrenia signals.

BACKGROUND: One explanation for the persistence of schizophrenia despite the reduced fertility of patients is that it is a by-product of recent human evolution. This hypothesis is supported by evidence suggesting that recently-evolved genomic regions in humans are involved in the genetic risk for schizophrenia. Using summary statistics from genome-wide association studies (GWAS) of schizophrenia and 11 other phenotypes, we tested for enrichment of association with GWAS traits in regions that have undergone methylation changes in the human lineage compared to Neanderthals and Denisovans, i.e. human-specific differentially methylated regions (DMRs). We used analytical tools that evaluate polygenic enrichment of a subset of genomic variants against all variants. RESULTS: Schizophrenia was the only trait in which DMR SNPs showed clear enrichment of association that passed the genome-wide significance threshold. The enrichment was not observed for Neanderthal or Denisovan DMRs. The enrichment seen in human DMRs is comparable to that for genomic regions tagged by Neanderthal Selective Sweep markers, and stronger than that for Human Accelerated Regions. The enrichment survives multiple testing performed through permutation (n = 10,000) and bootstrapping (n = 5000) in INRICH (p < 0.01). Some enrichment of association with height was observed at the gene level. CONCLUSIONS: Regions where DNA methylation modifications have changed during recent human evolution show enrichment of association with schizophrenia and possibly with height. Our study further supports the hypothesis that genetic variants conferring risk of schizophrenia co-occur in genomic regions that have changed as the human species evolved. Since methylation is an epigenetic mark, potentially mediated by environmental changes, our results also suggest that interaction with the environment might have contributed to that association.

Genetic variation in 117 myelination-related genes in schizophrenia: Replication of association to lipid biosynthesis genes.

Schizophrenia is a serious psychotic disorder with high heritability. Several common genetic variants, rare copy number variants and ultra-rare gene-disrupting mutations have been linked to disease susceptibility, but there is still a large gap between the estimated and explained heritability. Since several studies have indicated brain myelination abnormalities in schizophrenia, we aimed to examine whether variants in myelination-related genes could be associated with risk for schizophrenia. We established a set of 117 myelination genes by database searches and manual curation. We used a combination of GWAS (SCZ_N = 35,476; CTRL_N = 46,839), exome chip (SCZ_N = 269; CTRL_N = 336) and exome sequencing data (SCZ_N = 2,527; CTRL_N = 2,536) from schizophrenia cases and healthy controls to examine common and rare variants. We found that a subset of lipid-related genes was nominally associated with schizophrenia (p = 0.037), but this signal did not survive multiple testing correction (FWER = 0.16) and was mainly driven by the SREBF1 and SREBF2 genes that have already been linked to schizophrenia. Further analysis demonstrated that the lowest nominal p-values were p = 0.0018 for a single common variant (rs8539) and p = 0.012 for burden of rare variants (LRP1 gene), but none of them survived multiple testing correction. Our findings suggest that variation in myelination-related genes is not a major risk factor for schizophrenia.

Analysis of the joint effect of SNPs to identify independent loci and allelic heterogeneity in schizophrenia GWAS data.

We have tested published methods for capturing allelic heterogeneity and identifying loci of joint effects to uncover more of the "hidden heritability" of schizophrenia (SCZ). We used two tools, cojo-GCTA and multi-SNP, to analyze meta-statistics from the latest genome-wide association study (GWAS) on SCZ by the Psychiatric Genomics Consortium (PGC). Stepwise regression on markers with p values <10-7 in cojo-GCTA identified 96 independent signals. Eighty-five passed the genome-wide significance threshold. Cross-validation of cojo-GCTA by CLUMP was 76%, i.e., 26 of the loci identified by the PGC using CLUMP were found to be dependent on another locus by cojo-GCTA. The overlap between cojo-GCTA and multi-SNP was better (up to 92%). Three markers reached genome-wide significance (5 × 10-8) in a joint effect model. In addition, two loci showed possible allelic heterogeneity within 1-Mb genomic regions, while CLUMP analysis had identified 16 such regions. Cojo-GCTA identified fewer independent loci than CLUMP and seems to be more conservative, probably because it accounts for long-range LD and interaction effects between markers. These findings also explain why fewer loci with possible allelic heterogeneity remained significant after cojo-GCTA analysis. With multi-SNP, 86 markers were selected at the threshold 10-7. Multi-SNP identifies fewer independent signals, due to splitting of the data and use of smaller samples. We recommend that cojo-GCTA and multi-SNP are used for post-GWAS analysis of all traits to call independent loci. We conclude that only a few loci in SCZ show joint effects or allelic heterogeneity, but this could be due to lack of power for that data set.

Genetic evidence for a role of the SREBP transcription system and lipid biosynthesis in schizophrenia and antipsychotic treatment.

Schizophrenia is a serious psychotic disorder, with disabling symptoms and markedly reduced life expectancy. The onset is usually in late adolescence or early adulthood, which in time overlaps with the maturation of the brain including the myelination process. Interestingly, there seems to be a link between myelin abnormalities and schizophrenia. The oligodendrocyte-derived myelin membranes in the CNS are highly enriched for lipids (cholesterol, phospholipids and glycosphingolipids), thereby pointing at lipid homeostasis as a relevant target for studying the genetics and pathophysiology of schizophrenia. The biosynthesis of fatty acids and cholesterol is regulated by the sterol regulatory element binding protein (SREBP) transcription factors SREBP1 and SREBP2, which are encoded by the SREBF1 and SREBF2 genes on chromosome 17p11.2 and 22q13.2, respectively. Here we review the evidence for the involvement of SREBF1 and SREBF2 as genetic risk factors in schizophrenia and discuss the role of myelination and SREBP-mediated lipid biosynthesis in the etiology, pathophysiology and drug treatment of schizophrenia.

Hybrid algorithms for multiple change-point detection in biological sequences.

Array comparative genomic hybridization (aCGH) is one of the techniques that can be used to detect copy number variations in DNA sequences in high resolution. It has been identified that abrupt changes in the human genome play a vital role in the progression and development of many complex diseases. In this study we propose two distinct hybrid algorithms that combine efficient sequential change-point detection procedures (the Shiryaev-Roberts procedure and the cumulative sum control chart (CUSUM) procedure) with the Cross-Entropy method, which is an evolutionary stochastic optimization technique to estimate both the number of change-points and their corresponding locations in aCGH data. The proposed hybrid algorithms are applied to both artificially generated data and real aCGH experimental data to illustrate their usefulness. Our results show that the proposed methodologies are effective in detecting multiple change-points in biological sequences of continuous measurements.