Genetic Differences in the Immediate Transcriptome Response to Stress Predict Risk-Related Brain Function and Psychiatric Disorders.
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SUBMITTER: Arloth J
PROVIDER: S-EPMC4490780 | biostudies-literature | 2015 Jun
REPOSITORIES: biostudies-literature
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Project description:Depression risk is exacerbated by genetic factors and stress exposure; however, the biological mech- anisms through which these factors interact to confer depression risk are poorly understood. One putative biological mechanism implicates variability in the ability of cortisol, released in response to stress, to trigger a cascade of adaptive genomic and non-genomic processes through glucocorticoid receptor (GR) activation. Here, we demonstrate that common genetic variants in long-range enhancer elements modulate the immediate transcriptional response to GR activation in human blood cells. These functional genetic variants increase risk for depression and co-heritable psychiatric disorders. Moreover, these risk variants are associated with inappropriate amygdala reactivity, a transdiagnostic psychiatric endophenotype and an important stress hormone response trigger. Network modeling and animal experiments suggest that these genetic dif- ferences in GR-induced transcriptional activation may mediate the risk for depression and other psy- chiatric disorders by altering a network of function- ally related stress-sensitive genes in blood and brain.
2013-09-01 | GSE46743 | GEO
Project description:BackgroundPsychiatric disorders are usually caused by the dysfunction of various brain regions. Incorporating the genetic information of brain regions into correlation analysis can provide novel clues for pathogenetic and therapeutic studies of psychiatric disorders.MethodsThe latest genome-wide association study (GWAS) summary data of schizophrenia (SCZ), bipolar disorder (BIP), autism spectrum disorder (AUT), major depression disorder (MDD), and attention-deficit/hyperactivity disorder (ADHD) were obtained from the Psychiatric GWAS Consortium (PGC). The expression quantitative trait loci (eQTLs) datasets of 10 brain regions were driven from the genotype-tissue expression (GTEx) database. The PGC GWAS summaries were first weighted by the GTEx eQTLs summaries for each brain region. Linkage disequilibrium score regression was applied to the weighted GWAS summary data to detect genetic correlation for each pair of 5 disorders.ResultsWithout considering brain region difference, significant genetic correlations were observed for BIP vs SCZ (P = 1.68 × 10-63), MDD vs SCZ (P = 5.08 × 10-45), ADHD vs MDD (P = 1.93 × 10-44), BIP vs MDD (P = 6.39 × 10-9), AUT vs SCZ (P = .0002), and ADHD vs SCZ (P = .0002). Utilizing brain region related eQTLs weighted LD score regression, different strengths of genetic correlations were observed within various brain regions for BIP vs SCZ, MDD vs SCZ, ADHD vs MDD, and SCZ vs ADHD. For example, the most significant genetic correlations were observed at anterior cingulate cortex (P = 1.13 × 10-34) for BIP vs SCZ.ConclusionsThis study provides new clues for elucidating the mechanism of genetic correlations among various psychiatric disorders.
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Project description:Psychiatric disorders affect 29% of the global population at least once in the lifespan, and genetic studies have proved a shared genetic basis among them, although the underlying molecular mechanisms remain largely unknown. DNA methylation plays an important role in complex disorders and, remarkably, enrichment of common genetic variants influencing allele-specific methylation (ASM) has been reported among variants associated with specific psychiatric disorders. In the present study we assessed the contribution of ASM to a set of eight psychiatric disorders by combining genetic, epigenetic and expression data. We interrogated a list of 3896 ASM tagSNPs in the brain in the summary statistics of a cross-disorder GWAS meta-analysis of eight psychiatric disorders from the Psychiatric Genomics Consortium, including more than 162,000 cases and 276,000 controls. We identified 80 SNPs with pleiotropic effects on psychiatric disorders that show an opposite directional effect on methylation and gene expression. These SNPs converge on eight candidate genes: ZSCAN29, ZSCAN31, BTN3A2, DDAH2, HAPLN4, ARTN, FAM109B and NAGA. ZSCAN29 shows the broadest pleiotropic effects, showing associations with five out of eight psychiatric disorders considered, followed by ZSCAN31 and BTN3A2, associated with three disorders. All these genes overlap with CNVs related to cognitive phenotypes and psychiatric traits, they are expressed in the brain, and seven of them have previously been associated with specific psychiatric disorders, supporting our results. To sum up, our integrative functional genomics analysis identified eight psychiatric disease risk genes that impact a broad list of disorders and highlight an etiologic role of SNPs that influence DNA methylation and gene expression in the brain.
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Project description:ObjectiveIn both children and adults, psychiatric illness is associated with structural brain alterations, particularly in the prefrontal cortex (PFC). However, most studies compare gray matter volume (GMV) in healthy volunteers (HVs) to one psychiatric group. We compared GMV among youth with anxiety disorders, bipolar disorder (BD), disruptive mood dysregulation disorder (DMDD), attention-deficit/hyperactivity disorder (ADHD), and HVs.Method3-Tesla T1-weighted magnetic resonance imaging scans were acquired in 184 youths (39 anxious, 20 BD, 52 DMDD, 20 ADHD, and 53 HV). Voxel-based morphometry analyses were conducted. One-way analysis of variance tested GMV differences with whole-brain familywise error (p < .05) correction; secondary, exploratory whole-brain analyses used a threshold of p < .001, ≥200 voxels. Given recent frameworks advocating dimensional approaches in psychopathology research, we also tested GMV associations with continuous anxiety, irritability, and inattention symptoms.ResultsSpecificity emerged in the left dorsolateral PFC (dlPFC), which differed among youth with BD, anxiety, and HVs; GMV was increased in youth with anxiety, but decreased in BD, relative to HVs. Secondary analyses revealed BD-specific GMV decreases in the right lateral PFC, right dlPFC, and dorsomedial PFC, and also anxiety-specific GMV increases in the left dlPFC, right ventrolateral PFC, frontal pole, and right parahippocampal gyrus/lingual gyrus. Both BD and DMDD showed decreased GMV relative to HVs in the right dlPFC/superior frontal gyrus. GMV was not associated with dimensional measures of anxiety, irritability, or ADHD symptoms.ConclusionBoth disorder-specific and shared GMV differences manifest in pediatric psychopathology. Some differences were specific to anxiety disorders, others specific to BD, and others shared between BD and DMDD. Further developmental research might map commonalities and differences of structure and function in diverse pediatric psychopathologies.
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