A methylome-wide mQTL analysis reveals associations of methylation sites with GAD1 and HDAC3 SNPs and a general psychiatric risk score

Abstract

Genome-wide association studies have identified a number of single-nucleotide polymorphisms (SNPs) that are associated with psychiatric diseases. Increasing body of evidence suggests a complex connection of SNPs and the transcriptional and epigenetic regulation of gene expression, which is poorly understood. In the current study, we investigated the interplay between genetic risk variants, shifts in methylation and mRNA levels in whole blood from 223 adolescents distinguished by a risk for developing psychiatric disorders. We analyzed 37 SNPs previously associated with psychiatric diseases in relation to genome-wide DNA methylation levels using linear models, with Bonferroni correction and adjusting for cell-type composition. Associations between DNA methylation, mRNA levels and psychiatric disease risk evaluated by the Development and Well-Being Assessment (DAWBA) score were identified by robust linear models, Pearson’s correlations and binary regression models. We detected five SNPs (in HCRTR1, GAD1, HADC3 and FKBP5) that were associated with eight CpG sites, validating five of these SNP–CpG pairs. Three of these CpG sites, that is, cg01089319 (GAD1), cg01089249 (GAD1) and cg24137543 (DIAPH1), manifest in significant gene expression changes and overlap with active regulatory regions in chromatin states of brain tissues. Importantly, methylation levels at cg01089319 were associated with the DAWBA score in the discovery group. These results show how distinct SNPs linked with psychiatric diseases are associated with epigenetic shifts with relevance for gene expression. Our findings give a novel insight on how genetic variants may modulate risks for the development of psychiatric diseases.

Epigenetic Modifications in Schizophrenia and Related Disorders: Molecular Scars of Environmental Exposures and Source of Phenotypic Variability

“…There is also an increasing number of genome-wide DNAm profiling studies of non-central nervous system (CNS) tissues such as blood (12,13,(48)(49)(50)(51)(52)(53) and saliva (54) ( Table 2). These can be further subdivided into longitudinal studies (48)(49)(50)(51)54), which assessed markers of conversion to psychosis in twins discordant for schizophrenia or high-risk individuals, and biomarker studies (12,13,52,53), which aim at uncovering biomarkers for schizophrenia in large cohorts. One advantage…”

“…While certain epigenetic modifications in schizophrenia may have a genetic origin (12,13,53,97), converging evidence suggests that a substantial portion of epigenetic alterations may be acquired through environmental factors (98)(99)(100)(101). Hence, epigenetic modifications in schizophrenia may represent molecular scars of exposures to certain environmental adversities, especially when encountered during sensitive developmental periods (102)(103)(104)(105)(106).…”

Beyond the looking glass: recent advances in understanding the impact of environmental exposures on neuropsychiatric disease

“…Rare variant genetic studies of autism spectrum and bipolar disorders have shown chromatin remodeling genes are more frequently dysregulated among affected individuals [57][58][59][60]. Integration of genetic and epigenetic data has shown that genetic risk variants for ASD, schizophrenia, ADHD, and bipolar disorder control DNA methylation levels more often than non-psychiatric genetic variants [61][62][63][64][65][66][67][68]. Epigenetic changes have also been directly observed in postmortem brain [69][70][71], blood [72][73][74], semen [75,76], saliva [77][78][79], placenta [80], and buccal [81][82][83] tissues in individuals with a psychiatric disorder.…”

Insights into the multifactorial causation of obesity by integrated genetic and epigenetic analysis

“…The epigenetic profile is not only influenced by environmental exposure, but likewise seems to be modulated by genetic variants 120 . Evidence is available that SNPs can affect methylation in nearby ( cis ) or distant ( trans ) CpGs, thus supporting the genotype‐epigenotype effect on complex traits 113, 115, 121–124 . Such variants are defined as methylation quantitative trait loci (meQTLs) and could offer some clarification for the unsolved previously identified GWAS polymorphisms.…”