Lithium is the first-line treatment for bipolar affective disorder (BPAD) but two-thirds of patients respond only partially or not at all. The reasons for this high variability in lithium response are not well understood. Transcriptome-wide profiling, which tests the interface between genes and the environment, represents a viable means of exploring the molecular mechanisms underlying lithium response variability. Thus, in the present study we performed co-expression network analyses of whole-blood-derived RNA-seq data from n = 50 lithium-treated BPAD patients. Lithium response was assessed using the well-validated ALDA scale, which we used to define both a continuous and a dichotomous measure. We identified a nominally significant correlation between a co-expression module comprising 46 genes and lithium response represented as a continuous (i.e., scale ranging 0-10) phenotype (cor = -0.299, p = 0.035). Forty-three of these 46 genes had reduced mRNA expression levels in better lithium responders relative to poorer responders, and the central regulators of this module were all mitochondrially-encoded (MT-ND1, MT-ATP6, MT-CYB). Accordingly, enrichment analyses indicated that genes involved in mitochondrial functioning were heavily over-represented in this module, specifically highlighting the electron transport chain (ETC) and oxidative phosphorylation (OXPHOS) as affected processes. Disrupted ETC and OXPHOS activity have previously been implicated in the pathophysiology of BPAD. Our data adds to previous evidence suggesting that a normalisation of these processes could be central to lithium's mode of action, and could underlie a favourable therapeutic response.

Major depressive disorder (MDD) is a common but serious psychiatric disorder with significant levels of morbidity and mortality. Recent genome-wide association studies (GWAS) on common variants increase our understanding of MDD; however, the underlying genetic basis remains largely unknown. Many studies have been proposed to explore the genetics of complex diseases from a viewpoint of the "missing heritability" by considering low-frequency and rare variants, copy-number variations, and other types of genetic variants. Here we developed a novel computational and statistical strategy to investigate the "missing heritability" of MDD. We applied Hamming distance on common, low-frequency, and rare single-nucleotide polymorphism (SNP) sets to measure genetic distance between two individuals, and then built the multi-dimensional scaling (MDS) pictures. Whole-exome genotyping data from a Los Angeles Mexican-American cohort (203 MDD and 196 controls) and a European-ancestry cohort (473 MDD and 497 controls) were examined using our proposed methodology. MDS plots showed very significant separations between MDD cases and healthy controls for low-frequency SNP set (P value < 2.2e-16) and rare SNP set (P value = 7.681e-12). Our results suggested that low-frequency and rare variants may play more significant roles in the genetics of MDD.

Major depressive disorder and the anxiety disorders are highly prevalent, disabling and moderately heritable. Depression and anxiety are also highly comorbid and have a strong genetic correlation (rg ≈ 1). Cognitive behavioural therapy is a leading evidence-based treatment but has variable outcomes. Currently, there are no strong predictors of outcome. Therapygenetics research aims to identify genetic predictors of prognosis following therapy. We performed genome-wide association meta-analyses of symptoms following cognitive behavioural therapy in adults with anxiety disorders (n = 972), adults with major depressive disorder (n = 832) and children with anxiety disorders (n = 920; meta-analysis n = 2724). We estimated the variance in therapy outcomes that could be explained by common genetic variants (h2SNP) and polygenic scoring was used to examine genetic associations between therapy outcomes and psychopathology, personality and learning. No single nucleotide polymorphisms were strongly associated with treatment outcomes. No significant estimate of h2SNP could be obtained, suggesting the heritability of therapy outcome is smaller than our analysis was powered to detect. Polygenic scoring failed to detect genetic overlap between therapy outcome and psychopathology, personality or learning. This study is the largest therapygenetics study to date. Results are consistent with previous, similarly powered genome-wide association studies of complex traits.

Machine learning methods show promise to translate univariate biomarker findings into clinically useful multivariate decision support systems. At current, works in major depressive disorder have predominantly focused on neuroimaging and clinical predictor modalities, with genetic, blood-biomarker, and cardiovascular modalities lacking. In addition, the prediction of rehospitalization after an initial inpatient major depressive episode is yet to be explored, despite its clinical importance. To address this gap in the literature, we have used baseline clinical, structural imaging, blood-biomarker, genetic (polygenic risk scores), bioelectrical impedance and electrocardiography predictors to predict rehospitalization within 2 years of an initial inpatient episode of major depression. Three hundred and eighty patients from the ongoing 12-year Bidirect study were included in the analysis (rehospitalized: yes = 102, no = 278). Inclusion criteria was age ≥35 and <66 years, a current or recent hospitalisation for a major depressive episode and complete structural imaging and genetic data. Optimal performance was achieved with a multimodal panel containing structural imaging, blood-biomarker, clinical, medication type, and sleep quality predictors, attaining a test AUC of 67.74 (p = 9.99-05). This multimodal solution outperformed models based on clinical variables alone, combined biomarkers, and individual data modality prognostication for rehospitalization prediction. This finding points to the potential of predictive models that combine multimodal clinical and biomarker data in the development of clinical decision support systems.

Hippocampal volume decrease is a structural hallmark of schizophrenia (SCZ), and convergent evidence from postmortem and imaging studies suggests that it may be explained by changes in the cytoarchitecture of the cornu ammonis 4 (CA4) and dentate gyrus (DG) subfields. Increasing evidence indicates that aerobic exercise increases hippocampal volume in CA subfields and improves cognition in SCZ patients. Previous studies showed that the effects of exercise on the hippocampus might be connected to the polygenic burden of SCZ risk variants. However, little is known about cell type-specific genetic contributions to these structural changes. In this secondary analysis, we evaluated the modulatory role of cell type-specific SCZ polygenic risk scores (PRS) on volume changes in the CA1, CA2/3, and CA4/DG subfields over time. We studied 20 multi-episode SCZ patients and 23 healthy controls who performed aerobic exercise, and 21 multi-episode SCZ patients allocated to a control intervention (table soccer) for 3 months. Magnetic resonance imaging-based assessments were performed with FreeSurfer at baseline and after 3 months. The analyses showed that the polygenic burden associated with oligodendrocyte precursor cells (OPC) and radial glia (RG) significantly influenced the volume changes between baseline and 3 months in the CA4/DG subfield in SCZ patients performing aerobic exercise. A higher OPC- or RG-associated genetic risk burden was associated with a less pronounced volume increase or even a decrease in CA4/DG during the exercise intervention. We hypothesize that SCZ cell type-specific polygenic risk modulates the aerobic exercise-induced neuroplastic processes in the hippocampus.

It has been difficult to find robust brain structural correlates of the overall severity of major depressive disorder (MDD). We hypothesized that specific symptoms may better reveal correlates and investigated this for the severity of insomnia, both a key symptom and a modifiable major risk factor of MDD. Cortical thickness, surface area and subcortical volumes were assessed from T1-weighted brain magnetic resonance imaging (MRI) scans of 1053 MDD patients (age range 13-79 years) from 15 cohorts within the ENIGMA MDD Working Group. Insomnia severity was measured by summing the insomnia items of the Hamilton Depression Rating Scale (HDRS). Symptom specificity was evaluated with correlates of overall depression severity. Disease specificity was evaluated in two independent samples comprising 2108 healthy controls, and in 260 clinical controls with bipolar disorder. Results showed that MDD patients with more severe insomnia had a smaller cortical surface area, mostly driven by the right insula, left inferior frontal gyrus pars triangularis, left frontal pole, right superior parietal cortex, right medial orbitofrontal cortex, and right supramarginal gyrus. Associations were specific for insomnia severity, and were not found for overall depression severity. Associations were also specific to MDD; healthy controls and clinical controls showed differential insomnia severity association profiles. The findings indicate that MDD patients with more severe insomnia show smaller surfaces in several frontoparietal cortical areas. While explained variance remains small, symptom-specific associations could bring us closer to clues on underlying biological phenomena of MDD.

Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium. In this study, we replicated the results of our previous study using network propagation methods in a genome-wide association study of an independent sample of 2039 patients from the International Consortium on Lithium Genetics (ConLiGen) study. We identified functional enrichment in focal adhesion and PI3K-Akt pathways, but we did not find an association with the ECM pathway. Our results suggest that deficits in the neuronal growth cone and PI3K-Akt signaling, but not in ECM proteins, may influence response to lithium in BD.

Executive functions are metacognitive capabilities that control and coordinate mental processes. In the transdiagnostic PsyCourse Study, comprising patients of the affective-to-psychotic spectrum and controls, we investigated the genetic basis of the time course of two core executive subfunctions: set-shifting (Trail Making Test, part B (TMT-B)) and updating (Verbal Digit Span backwards) in 1338 genotyped individuals. Time course was assessed with four measurement points, each 6 months apart. Compared to the initial assessment, executive performance improved across diagnostic groups. We performed a genome-wide association study to identify single nucleotide polymorphisms (SNPs) associated with performance change over time by testing for SNP-by-time interactions using linear mixed models. We identified nine genome-wide significant SNPs for TMT-B in strong linkage disequilibrium with each other on chromosome 5. These were associated with decreased performance on the continuous TMT-B score across time. Variant rs150547358 had the lowest P value = 7.2 × 10-10 with effect estimate beta = 1.16 (95% c.i.: 1.11, 1.22). Implementing data of the FOR2107 consortium (1795 individuals), we replicated these findings for the SNP rs150547358 (P value = 0.015), analyzing the difference of the two available measurement points two years apart. In the replication study, rs150547358 exhibited a similar effect estimate beta = 0.85 (95% c.i.: 0.74, 0.97). Our study demonstrates that longitudinally measured phenotypes have the potential to unmask novel associations, adding time as a dimension to the effects of genomics.

Adenosine, its interacting A1 and A2A receptors, and particularly the variant rs5751876 in the A2A gene ADORA2A have been shown to modulate anxiety, arousal, and sleep. In a pilot positron emission tomography (PET) study in healthy male subjects, we suggested an effect of rs5751876 on in vivo brain A1 receptor (A1AR) availability. As female sex and adenosinergic/dopaminergic interaction partners might have an impact on this rs5751876 effect on A1AR availability, we aimed to (1) further investigate the pilot male-based findings in an independent, newly recruited cohort including women and (2) analyze potential modulation of this rs5751876 effect by additional adenosinergic/dopaminergic gene variation. Healthy volunteers (32/11 males/females) underwent phenotypic characterization including self-reported sleep and A1AR-specific quantitative PET. Rs5751876 and 31 gene variants of adenosine A1, A2A, A2B, and A3 receptors, adenosine deaminase, and dopamine D2 receptor were genotyped. Multivariate analysis revealed an rs5751876 effect on A1AR availability (P = 0.047), post hoc confirmed in 30 of 31 brain regions (false discovery rate (FDR) corrected P values < 0.05), but statistically stronger in anxiety-related regions (e.g., amygdala, hippocampus). Additional effects of ADORA1 rs1874142 were identified; under its influence rs5751876 and rs5751876 × sleep had strengthened effects on A1AR availability (Pboth < 0.02; post hoc FDR-corrected Ps < 0.05 for 29/30 regions, respectively). Our results support the relationship between rs5751876 and A1AR availability. Additional impact of rs1874142, together with rs5751876 and sleep, might be involved in regulating arousal and thus the development of mental disorders like anxiety disorders. The interplay of further detected suggestive ADORA2A × DRD2 interaction, however, necessitates larger future samples more comparable to magnetic resonance imaging (MRI)-based samples.

Autoimmune processes are suspected to play a role in the pathophysiology of psychotic disorders. Better understanding of the associations between auto-immunoglobulin G (IgG) repertoires and clinical features of mental illness could yield novel models of the pathophysiology of psychosis, and markers for biological patient stratification. We undertook cross-sectional detection and quantification of auto-IgGs in peripheral blood plasma of 461 people (39% females) with established psychotic disorder diagnoses. Broad screening of 24 individuals was carried out on group level in eight clinically defined groups using planar protein microarrays containing 42,100 human antigens representing 18,914 proteins. Autoantibodies indicated by broad screening and in the previous literature were measured using a 380-plex bead-based array for autoantibody profiling of all 461 individuals. Associations between autoantibody profiles and dichotomized clinical characteristics were assessed using a stepwise selection procedure. Broad screening and follow-up targeted analyses revealed highly individual autoantibody profiles. Females, and people with family histories of obesity or of psychiatric disorders other than schizophrenia had the highest overall autoantibody counts. People who had experienced subjective thought disorder and/or were treated with clozapine (trend) had the lowest overall counts. Furthermore, six autoantibodies were associated with specific psychopathology symptoms: anti-AP3B2 (persecutory delusions), anti-TDO2 (hallucinations), anti-CRYGN (initial insomnia); anti-APMAP (poor appetite), anti-OLFM1 (above-median cognitive function), and anti-WHAMMP3 (anhedonia and dysphoria). Future studies should clarify whether there are causal biological relationships, and whether autoantibodies could be used as clinical markers to inform diagnostic patient stratification and choice of treatment.

A significant proportion of the personal and economic burden of schizophrenia can be attributed to the late diagnosis or misdiagnosis of the disorder. A novel, objective diagnostic approaches could facilitate the early detection and treatment of schizophrenia and improve patient outcomes. In the present study, we aimed to identify robust schizophrenia-specific blood biomarkers, with the goal of developing an accurate diagnostic model. The levels of selected serum and peripheral blood mononuclear cell (PBMC) markers relevant to metabolic and immune function were measured in healthy controls (n = 26) and recent-onset schizophrenia patients (n = 36) using multiplexed immunoassays and flow cytometry. Analysis of covariance revealed significant upregulation of insulin receptor (IR) and fatty acid translocase (CD36) levels in T helper cells (F = 10.75, P = 0.002, Q = 0.024 and F = 21.58, P = 2.8 × 10-5, Q = 0.0004, respectively), as well as downregulation of glucose transporter 1 (GLUT1) expression in monocytes (F = 21.46, P = 2.9 × 10-5, Q = 0.0004). The most robust predictors, monocyte GLUT1 and T helper cell CD36, were used to develop a diagnostic model, which showed a leave-one-out cross-validated area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI: 0.66-0.92). The diagnostic model was validated in two independent datasets. The model was able to distinguish first-onset, drug-naïve schizophrenia patients (n = 34) from healthy controls (n = 39) with an AUC of 0.75 (95% CI: 0.64-0.86), and also differentiated schizophrenia patients (n = 22) from patients with other neuropsychiatric conditions, including bipolar disorder, major depressive disorder and autism spectrum disorder (n = 68), with an AUC of 0.83 (95% CI: 0.75-0.92). These findings indicate that PBMC-derived biomarkers have the potential to support an accurate and objective differential diagnosis of schizophrenia.

Cognitive deficits are a core feature of psychiatric disorders like schizophrenia and bipolar disorder. Evidence supports a genome-wide polygenic score (GPS) for educational attainment (GPSEDU) can be used to explain variability in cognitive performance. We aimed to identify different cognitive domains associated with GPSEDU in a transdiagnostic clinical cohort of chronic psychiatric patients with known cognitive deficits. Bipolar and schizophrenia patients from the PsyCourse cohort (N = 730; 43% female) were used. Likewise, we tested whether GPSs for schizophrenia (GPSSZ) and bipolar disorder (GPSBD) were associated with cognitive outcomes. GPSEDU explained 1.5% of variance in the backward verbal digit span, 1.9% in the number of correctly recalled words of the Verbal Learning and Memory Test, and 1.1% in crystallized intelligence. These effects were robust to the influences of treatment and diagnosis. No significant associations between GPSSZ or GPSBD with cognitive outcomes were found. Furthermore, these risk scores did not confound the effect of GPSEDU on cognitive outcomes. GPSEDU explains a small fraction of cognitive performance in adults with psychiatric disorders, specifically for domains related to linguistic learning and working memory. Investigating such a proxy-phenotype longitudinally, could give intriguing insight into the disease course, highlighting at what time genes play a more influential role on cognitive performance. Better understanding the origin of these deficits might help identify those patients at risk for lower levels of functioning and poor social outcomes. Polygenic estimates may in the future be part of predictive models for more personalized interventions.

The identification of biomarkers to support the diagnosis and prediction of treatment response for attention-deficit/hyperactivity disorder (ADHD) is still a challenge. Our previous works highlighted the DRD4 (dopamine receptor D4) as the best potential genetic marker for childhood diagnosis and methylphenidate (MPH) response. Here, we aimed to provide additional evidence on biomarkers for ADHD diagnosis and treatment response, by using more specific approaches such as meta-analytic and bioinformatics tools. Via meta-analytic approaches including over 3000 cases and 16,000 controls, we demonstrated that, among the different variants studied in DRD4 gene, the 48-base pair, Variable Tandem Repeat Polymorphism, VNTR in exon 3 showed an age/population-specificity and an allelic heterogeneity. In particular, the 7R/"long" allele was identified as an ADHD risk factor in European-Caucasian populations (d = 1.31, 95%CI: 1.17-1.47, Z = 4.70/d = 1.36, 95%CI: 1.20-1.55, Z = 4.78, respectively), also, from the results of last meta-analysis, linked to the poor MPH efficacy. The 4R/"short" allele was a protective factor in European-Caucasian and South American populations (d = 0.83, 95%CI: 0.75-0.92, Z = 3.58), and was also associated to positive MPH response. These results refer to children with ADHD. No evidence of such associations was detected for adults with persistent ADHD (data from the last meta-analysis). Moreover, we found evidence that the 4R allele leads to higher receptor expression and increased sensitivity to dopamine, as compared with the 7R allele (d = 1.20, 95%CI: 0.71-1.69, Z = 4.81), and this is consistent with the ADHD protection/susceptibility effects of the respective alleles. Using bioinformatics tools, based on the latest genome-wide association (GWAS) meta-analysis of the Psychiatry Genomic Consortium (PGC), we demonstrated that the 48 bp VNTR is not in Linkage Disequilibrium with the DRD4 SNPs (Single Nucleotide Polymorphisms), which were not found to be associated with ADHD. Moreover, a DRD4 expression downregulation was found in ADHD specific brain regions (Putamen, Z score = -3.02, P = 0.00252). Overall, our results suggest that DRD4 48 bp VNTR variants should be considered as biomarkers to support the diagnosis of ADHD and to predict MPH response, although the accuracy of such a biomarker remains to be further elucidated.