Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

Profiling patients on a proposed ‘immunometabolic depression’ (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment.

To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants.

Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses.

Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 0.06, P = 0.049, 95% CI 0.0001–0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, βpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01–0.22, I2 = 23.91%), with a higher – but still small – effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission.

Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

Research of COVID-19-Pandemic mental health impact focus on three groups: the general population, (2) so called vulnerable groups (e.g. individuals with mental disorders) and (3) individuals suffering COVID-19 including Long-COVID syndromes.

We investigate whether individuals with a history of depression in the past, react to the COVID-19 pandemic with increased depressive symptoms.

Longitudinal Data stem from the NAKO-Baseline-Assessment (2014-2019, 18 study centers in Germany, representative sampled individuals from 20 to 74 years) and the subsequent NAKO-COVID-Assessment (5-11/2020). The sample for analysis comprises 115.519 individuals. History of psychiatric disorder was operationalized as lifetime self-report for physician-diagnosed depression. Depressive symptoms were measured with the PHQ 9.

Mean age of the sample at baseline was 49.95 (SD 12.53). It comprised 51.70 women; 14 % of the individuals had a history of physician-diagnosed depression. Considering a PHQ-Score with cut-off 10 as a clinical relevant depression, 3.65 % of the individuals without history of depression and 24.19 % of those with a history of depression were depressed at baseline. The NAKO-COVID-Assessment revealed 6.53 % depressed individuals without any history of depression and a similar rate of 23.29 % in those with history of depression.

In contrast to that what we expected, individuals with a history of a physician-diagnosed depression, did not react with increasing depressiveness during the first phase of the pandemic in Germany. Several reasons could be discussed. Whether there medium and long-term impact remains open.

No significant relationships.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, with its impact on our way of life, is affecting our experiences and mental health. Notably, individuals with mental disorders have been reported to have a higher risk of contracting SARS-CoV-2. Personality traits could represent an important determinant of preventative health behaviour and, therefore, the risk of contracting the virus.

We examined overlapping genetic underpinnings between major psychiatric disorders, personality traits and susceptibility to SARS-CoV-2 infection.

Linkage disequilibrium score regression was used to explore the genetic correlations of coronavirus disease 2019 (COVID-19) susceptibility with psychiatric disorders and personality traits based on data from the largest available respective genome-wide association studies (GWAS). In two cohorts (the PsyCourse (n = 1346) and the HeiDE (n = 3266) study), polygenic risk scores were used to analyse if a genetic association between, psychiatric disorders, personality traits and COVID-19 susceptibility exists in individual-level data.

We observed no significant genetic correlations of COVID-19 susceptibility with psychiatric disorders. For personality traits, there was a significant genetic correlation for COVID-19 susceptibility with extraversion (P = 1.47 × 10−5; genetic correlation 0.284). Yet, this was not reflected in individual-level data from the PsyCourse and HeiDE studies.

We identified no significant correlation between genetic risk factors for severe psychiatric disorders and genetic risk for COVID-19 susceptibility. Among the personality traits, extraversion showed evidence for a positive genetic association with COVID-19 susceptibility, in one but not in another setting. Overall, these findings highlight a complex contribution of genetic and non-genetic components in the interaction between COVID-19 susceptibility and personality traits or mental disorders.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

No significant relationships.

MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood.

We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness.

The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing.

Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome.

Schizotypy is a putative risk phenotype for psychosis liability, but the overlap of its genetic architecture with schizophrenia is poorly understood.

We tested the hypothesis that dimensions of schizotypy (assessed with the SPQ-B) are associated with a polygenic risk score (PRS) for schizophrenia in a sample of 623 psychiatrically healthy, non-clinical subjects from the FOR2107 multi-centre study and a second sample of 1133 blood donors.

We did not find correlations of schizophrenia PRS with either overall SPQ or specific dimension scores, nor with adjusted schizotypy scores derived from the SPQ (addressing inter-scale variance). Also, PRS for affective disorders (bipolar disorder and major depression) were not significantly associated with schizotypy.

This important negative finding demonstrates that despite the hypothesised continuum of schizotypy and schizophrenia, schizotypy might share less genetic risk with schizophrenia than previously assumed (and possibly less compared to psychotic-like experiences).

Age-of-onset (AO) seems to be a phenotypic variable with a strong genetic component and therefore useful in molecular analysis of bipolar disorder (BP). A debate about the cut-off point for defining early AO has developed over the last few years. Using an Expectation-Maximization algorithm Bellivier et al. (2001) found the best fit for a model with three onset-groups, proposing the age 20-21 as cut-off for early onset, while using the same algorithm Kennedy et al. (2005) found the best fit for a two onset-group model with age 40 as cut-off with an incidence peak for mania in the age-band 21-25. Based on segregation analysis, we proposed a two AO-group model with cut-off age 25 for early onset (Grigoroiu-Serbanescu et al. 2001). The present study aimed at investigating the best AO-model in 500 Romanian BPI and 1458 German BPI patients using commingling analysis (SAGEv6.01-software) (Elston et al, 2009). The best model was selected according to Akaike's Information Criterion (AIC).

The two AO-group and three AO-group models provided similar AIC-values both in the Romanian and the German sample. The Romanian early-onset group (40% cases) had means around 18 years, SDs=6-7, while in the German early-onset group the mean AO was around 20 years (SDs=9-11) (50% cases). Thus the cut-off for early-onset (X +1SD) was different.

Our results overlapped with the findings of Kennedy et al (2005) showing that two-curve and three-curve AO mixtures similarly fit the AO-distribution in BPI disorder and the cut-offs for early-onset differ by sample.

In two recent studies the SNP rs2230912 (Gln460Arg) located in exon 13 of P2RX7-gene (chromosome 12q24) provided the strongest evidence of association with bipolar disorder (BP) (Barden et al,2006; McQuillin et al, 2008) and in one study with the unipolar major depression (Mdd-UP) ( Lucae et al, 2006).

In the present study we investigated the involvement of the SNP rs2230912 in BPI in four European samples from Germany, Poland, Romania and Russia and in the combined sample (N=1445) in comparison with a combined sample of 2006 normal controls. Additionally, a Mdd-UP sample (N=640) from Germany was studied.

All patients were diagnosed according to DSM-IV-R. The BPI sample consisted of 802 females (55.5%) and 643 males (44.5%); the mean AO was 26.9 (SD=10.6); the mean age-at-interview was 42.7 (SD=12.5). The control sample had a mean age of 39.74 (SD=11.22) [1113 females (55.5%); 893 males (44.5%)]. Genotyping of all national samples was performed at Bonn University using the Mass ARRAY system on a Sequenom Compact MALDI-TOF-device. The single marker analysis was performed with FAMHAP software.

There was no allelic association between the G-allele of the SNP rs2230912 and either BPI or Mdd-UP both in the national samples and in the combined sample. The genotypic analysis also indicated no significant results. Our samples of patients and controls had genotypic distributions similar to those of the previously published studies and even in these studies there were no significant differences in genotype frequencies between BPI patients and controls.

Differences in personality traits have long been acknowledged as potential risk factors in developing psychiatric disorders. Lately, several susceptibility genes of different psychiatric disorders have been linked to personality traits. This has not been done for schizophrenia yet. Neuregulin1 has been repeatedly shown to be associated with schizophrenia and is involved in numerous neurodevelopmental functions such as neuronal migration and myelination. The impact of this gene might also modulate personality traits in healthy subjects.

The NRG1 status of 523 healthy subjects was determined with a single nucleotide polymorphism (SNP8NRG221533) which has been described as a tagging marker being part of the core at-risk haplotype for schizophrenia. Genotype was correlated with personality traits using the NEO-FFI questionnaire.

Subjects with the NRG1 risk allele scored higher on neuroticism (p <.05) and lower on conscientiousness (p <.05). Further, interactions of genotype by gender for extraversion (p <.05), openness (p <.05) and conscientiousness (p <.05) were found with men carrying the risk allele scoring the lowest.

The data indicate that the NRG1 gene which has found to be associated with schizophrenia may also influence personality differences in healthy subjects.