Editorial: Genetic Mechanisms of Biomarkers in Schizophrenia, Bipolar Disorder and Depression.
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ABSTRACT:
SUBMITTER: Tang Y
PROVIDER: S-EPMC8406636 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVE:Family and twin studies indicate substantial overlap of genetic influences on psychotic and mood disorders. Linkage and candidate gene studies have also suggested overlap across schizophrenia, bipolar disorder, and major depressive disorder. The purpose of this study was to apply genomewide association study (GWAS) analysis to address the specificity of genetic effects on these disorders. METHOD:The authors combined GWAS data from three large effectiveness studies of schizophrenia (CATIE, genotyped: N=741), bipolar disorder (STEP-BD, geno-typed: N=1,575), and major depressive disorder (STAR*D, genotyped: N=1,938) as well as from psychiatrically screened control subjects (NIMH-Genetics Repository: N=1,204). A two-stage analytic procedure involving an omnibus test of allele frequency differences among case and control groups was applied, followed by a model selection step to identify the best-fitting model of allelic effects across disorders. RESULTS:The strongest result was seen for a single nucleotide polymorphism near the adrenomedullin (ADM) gene (rs6484218), with the best-fitting model indicating that the effect was specific to bipolar II disorder. Findings also revealed evidence suggesting that several genes may have effects that transcend clinical diagnostic boundaries, including variants in NPAS3 that showed pleiotropic effects across schizophrenia, bipolar disorder, and major depressive disorder. CONCLUSIONS:This study provides the first genomewide significant evidence implicating variants near the ADM gene on chromosome 11p15 in psychopathology, with effects that appear to be specific to bipolar II disorder. Although genomewide significant evidence of cross-disorder effects was not detected, the results provide evidence that there are both pleiotropic and disorder-specific effects on major mental illness and illustrate an approach to dissecting the genetic basis of mood and psychotic disorders that can inform future large-scale cross-disorder GWAS analyses.
| S-EPMC3880556 | biostudies-literature
Project description:BackgroundResidential mobility during upbringing, and especially adolescence, is associated with multiple negative mental health outcomes. However, whether associations are confounded by unmeasured familial factors, including genetic liability, is unclear.AimsWe used a population-based case-cohort study to assess whether polygenic risk scores (PRSs) for schizophrenia, bipolar disorder and major depression were associated with mobility from ages 10-14 years, and whether PRS and parental history of mental disorder together explained associations between mobility and each disorder.MethodInformation on cases (n = 4207 schizophrenia, n = 1402 bipolar disorder, n = 18 215 major depression) and a random population sample (n = 17 582), born 1981-1997, was linked between Danish civil and psychiatric registries. Genome-wide data were obtained from the Danish Neonatal Screening Biobank and PRSs were calculated based on results of separate, large meta-analyses.ResultsPRSs for schizophrenia and major depression were weakly associated with moving once (odds ratio 1.07, 95% CI 1.00-1.16; and odds ratio 1.10, 95% CI 1.04-1.17, respectively), but not twice or three or more times. Mobility was positively associated with each disorder, with more moves associated with greater risk. Adjustment for PRS produced slight reductions in the magnitude of associations. Adjustment for PRS and parental history of mental disorder together reduced estimates by 5-11%. In fully adjusted models mobility was associated with all three disorders; hazard ratios ranged from 1.33 (95% CI 1.08-1.62; one move and bipolar disorder) to 3.05 (95% CI 1.92-4.86; three or more moves and bipolar disorder).ConclusionsAssociations of mobility with schizophrenia, bipolar disorder and depression do not appear to be attributable to genetic liability as measured here. Potential familial confounding of mobility associations may be predominantly environmental in nature.
| S-EPMC8130005 | biostudies-literature
Project description:Borderline personality disorder (BOR) is determined by environmental and genetic factors, and characterized by affective instability and impulsivity, diagnostic symptoms also observed in manic phases of bipolar disorder (BIP). Up to 20% of BIP patients show comorbidity with BOR. This report describes the first case-control genome-wide association study (GWAS) of BOR, performed in one of the largest BOR patient samples worldwide. The focus of our analysis was (i) to detect genes and gene sets involved in BOR and (ii) to investigate the genetic overlap with BIP. As there is considerable genetic overlap between BIP, major depression (MDD) and schizophrenia (SCZ) and a high comorbidity of BOR and MDD, we also analyzed the genetic overlap of BOR with SCZ and MDD. GWAS, gene-based tests and gene-set analyses were performed in 998 BOR patients and 1545 controls. Linkage disequilibrium score regression was used to detect the genetic overlap between BOR and these disorders. Single marker analysis revealed no significant association after correction for multiple testing. Gene-based analysis yielded two significant genes: DPYD (P=4.42 × 10-7) and PKP4 (P=8.67 × 10-7); and gene-set analysis yielded a significant finding for exocytosis (GO:0006887, PFDR=0.019; FDR, false discovery rate). Prior studies have implicated DPYD, PKP4 and exocytosis in BIP and SCZ. The most notable finding of the present study was the genetic overlap of BOR with BIP (rg=0.28 [P=2.99 × 10-3]), SCZ (rg=0.34 [P=4.37 × 10-5]) and MDD (rg=0.57 [P=1.04 × 10-3]). We believe our study is the first to demonstrate that BOR overlaps with BIP, MDD and SCZ on the genetic level. Whether this is confined to transdiagnostic clinical symptoms should be examined in future studies.
| S-EPMC5537640 | biostudies-literature
Project description: Not available
| S-EPMC10011613 | biostudies-literature
Project description:OBJECTIVES:Genome-wide association studies (GWAS) in complex phenotypes, including psychiatric disorders, have yielded many replicated findings, yet individual markers account for only a small fraction of the inherited differences in risk. We tested the performance of polygenic models in discriminating between cases and healthy controls and among cases with distinct psychiatric diagnoses. METHODS:GWAS results in bipolar disorder (BD), major depressive disorder (MDD), schizophrenia (SZ), and Parkinson's disease (PD) were used to assign weights to individual alleles, based on odds ratios. These weights were used to calculate allele scores for individual cases and controls in independent samples, summing across many single nucleotide polymorphisms (SNPs). How well allele scores discriminated between cases and controls and between cases with different disorders was tested by logistic regression. RESULTS:Large sets of SNPs were needed to achieve even modest discrimination between cases and controls. The most informative SNPs were overlapping in BD, SZ, and MDD, with correlated effect sizes. Little or no overlap was seen between allele scores for psychiatric disorders and those for PD. CONCLUSIONS:BD, SZ, and MDD all share a similar polygenic component, but the polygenic models tested lack discriminative accuracy and are unlikely to be useful for clinical diagnosis.
| S-EPMC3406228 | biostudies-literature
Project description:Schizophrenia, bipolar disorder, and major depression share several clinical and etiological factors. Coping is a critical mediator of the relationship between stress and psychopathology and a point of clinical intervention for all three disorders. However, little is known about their degree of overlap in coping style, or the influence of unique or shared genetic diathesis. In this study, we examined five factors of coping within and across disorder proband and co-twin groups, modeled heritability, and tested for endophenotypic pattern in a sample of twin pairs recruited from the Swedish Twin Registry (N = 420). Although there was substantial phenotypic overlap across disorders, including low levels of Productive, Problem-Focused coping and high levels of Disengagement, each disorder was associated with a unique profile across other dimensions of coping. We also found evidence of heritability for three of five factors, yet little evidence of genotypic overlap among disorders contributing to similar strategy-use.
| S-EPMC4808056 | biostudies-literature
Project description:Brain-Derived Neurotrophic Factor (BDNF) is crucial for various aspects of neuronal development and function, including synaptic plasticity, neurotransmitter release, and supporting neuronal differentiation, growth, and survival. It is involved in the formation and preservation of dopaminergic, serotonergic, GABAergic, and cholinergic neurons, facilitating efficient stimulus transmission within the synaptic system and contributing to learning, memory, and overall cognition. Furthermore, BDNF demonstrates involvement in neuroinflammation and showcases neuroprotective effects. In contrast, BDNF antisense RNA (BDNF-AS) is linked to the regulation and control of BDNF, facilitating its suppression and contributing to neurotoxicity, apoptosis, and decreased cell viability. This review article aims to comprehensively overview the significance of single nucleotide polymorphisms (SNPs) in BDNF/BDNF-AS genes within psychiatric conditions, with a specific focus on their associations with depression, schizophrenia, and bipolar disorder. The independent influence of each BDNF/BDNF-AS gene variation, as well as the interplay between SNPs and their linkage disequilibrium, environmental factors, including early-life experiences, and interactions with other genes, lead to alterations in brain architecture and function, shaping vulnerability to mental health disorders. The potential translational applications of BDNF/BDNF-AS polymorphism knowledge can revolutionize personalized medicine, predict disease susceptibility, treatment outcomes, and guide the selection of interventions tailored to individual patients.
| S-EPMC10533016 | biostudies-literature
Project description:Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium's therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org ). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD.
| S-EPMC8630000 | biostudies-literature
Project description:This study developed potential blood-based biomarker tests for diagnosing and differentiating schizophrenia (SZ), bipolar disorder type I (BD), and normal control (NC) subjects using mRNA gene expression signatures. A total of 90 subjects (n = 30 each for the three groups of subjects) provided blood samples at two visits. The Affymetrix exon microarray was used to profile the expression of over 1.4 million probesets. We selected potential biomarker panels using the temporal stability of the probesets and also back-tested them at two different visits for each subject. The 18-gene biomarker panels, using logistic regression modeling, correctly differentiated the three groups of subjects with high accuracy across the two different clinical visits (83-88% accuracy). The results are also consistent with the actual data and the "leave-one-out" analyses, indicating that the models should be predictive when applied to independent data cohorts. Many of the SZ and BD subjects were taking antipsychotic and mood stabilizer medications at the time of blood draw, raising the possibility that these drugs could have affected some of the differential transcription signatures. Using an independent Illumina data set of gene expression data from antipsychotic medication-free SZ subjects, the 18-gene biomarker panels produced a receiver operating characteristic curve accuracy greater than 0.866 in patients that were less than 30 years of age and medication free. We confirmed select transcripts by quantitative PCR and the nCounter® System. The episodic nature of psychiatric disorders might lead to highly variable results depending on when blood is collected in relation to the severity of the disease/symptoms. We have found stable trait gene panel markers for lifelong psychiatric disorders that may have diagnostic utility in younger undiagnosed subjects where there is a critical unmet need. The study requires replication in subjects for ultimate proof of the utility of the differential diagnosis.
| S-EPMC5981774 | biostudies-literature
Project description:Although a genetic basis of depression has been well established in twin studies, identification of genome-wide significant loci has been difficult. We hypothesized that bivariate analyses of findings from a meta-analysis of genome-wide association studies (meta-GWASs) of the broad depression phenotype with those from meta-GWASs of self-reported and recurrent major depressive disorder (MDD), bipolar disorder and schizophrenia would enhance statistical power to identify novel genetic loci for depression. LD score regression analyses were first used to estimate the genetic correlations of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia. Then, we performed four bivariate GWAS analyses. The genetic correlations (rg ± SE) of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia were 0.79 ± 0.07, 0.24 ± 0.08, 0.53 ± 0.09 and 0.57 ± 0.05, respectively. From a total of 20 independent genome-wide significant loci, 13 loci replicated of which 8 were novel for depression. These were MUC21 for the broad depression phenotype with self-reported MDD and ZNF804A, MIR3143, PSORS1C2, STK19, SPATA31D1, RTN1 and TCF4 for the broad depression phenotype with schizophrenia. Post-GWAS functional analyses of these loci revealed their potential biological involvement in psychiatric disorders. Our results emphasize the genetic similarities among different psychiatric disorders and indicate that cross-disorder analyses may be the best way forward to accelerate gene finding for depression, or psychiatric disorders in general.
| S-EPMC7303007 | biostudies-literature