Factor analysis of lifetime psychopathology and its brain morphometric and genetic correlates in a transdiagnostic sample.

There is a lack of knowledge regarding the relationship between proneness to dimensional psychopathological syndromes and the underlying pathogenesis across major psychiatric disorders, i.e., Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizoaffective Disorder (SZA), and Schizophrenia (SZ). Lifetime psychopathology was assessed using the OPerational CRITeria (OPCRIT) system in 1,038 patients meeting DSM-IV-TR criteria for MDD, BD, SZ, or SZA. The cohort was split into two samples for exploratory and confirmatory factor analyses. All patients were scanned with 3-T MRI, and data was analyzed with the CAT-12 toolbox in SPM12. Psychopathological factor scores were correlated with gray matter volume (GMV) and cortical thickness (CT). Finally, factor scores were used for exploratory genetic analyses including genome-wide association studies (GWAS) and polygenic risk score (PRS) association analyses. Three factors (paranoid-hallucinatory syndrome, PHS; mania, MA; depression, DEP) were identified and cross-validated. PHS was negatively correlated with four GMV clusters comprising parts of the hippocampus, amygdala, angular, middle occipital, and middle frontal gyri. PHS was also negatively associated with the bilateral superior temporal, left parietal operculum, and right angular gyrus CT. No significant brain correlates were observed for the two other psychopathological factors. We identified genome-wide significant associations for MA and DEP. PRS for MDD and SZ showed a positive effect on PHS, while PRS for BD showed a positive effect on all three factors. This study investigated the relationship of lifetime psychopathological factors and brain morphometric and genetic markers. Results highlight the need for dimensional approaches, overcoming the limitations of the current psychiatric nosology.

The interplay between polygenic score for tumor necrosis factor-α, brain structural connectivity, and processing speed in major depression.

Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD and specific depressive symptoms. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients. Processing speed performance of n = 284 acutely depressed, n = 177 partially and n = 198 fully remitted patients, and n = 743 healthy controls (HC) was estimated based on five neuropsychological tests. Network-based statistic was used to identify a brain network associated with processing speed. We employed general linear models to examine the association between TNF-α PGS and processing speed. We investigated whether network connectivity mediates the association between TNF-α PGS and processing speed. We identified a structural network positively associated with processing speed in the whole sample. We observed a significant negative association between TNF-α PGS and processing speed in acutely depressed patients, whereas no association was found in remitted patients and HC. The mediation analysis revealed that brain connectivity partially mediated the association between TNF-α PGS and processing speed in acute MDD. The present study provides evidence that TNF-α PGS is associated with decreased processing speed exclusively in patients with acute depression. This association was partially mediated by structural brain connectivity. Using multimodal data, the current findings advance our understanding of cognitive dysfunction in MDD and highlight the involvement of genetic-immunological processes in its pathomechanisms.

Genetic polymorphisms affecting telomere length and their association with cardiovascular disease in the Heinz-Nixdorf-Recall study.

Short telomeres are associated with cardiovascular disease (CVD). We aimed to investigate, if genetically determined telomere-length effects CVD-risk in the Heinz-Nixdorf-Recall study (HNRS) population. We selected 14 single-nucleotide polymorphisms (SNPs) associated with telomere-length (p<10-8) from the literature and after exclusion 9 SNPs were included in the analyses. Additionally, a genetic risk score (GRS) using these 9 SNPs was calculated. Incident CVD was defined as fatal and non-fatal myocardial infarction, stroke, and coronary death. We included 3874 HNRS participants with available genetic data and had no known history of CVD at baseline. Cox proportional-hazards regression was used to test the association between the SNPs/GRS and incident CVD-risk adjusting for common CVD risk-factors. The analyses were further stratified by CVD risk-factors. During follow-up (12.1±4.31 years), 466 participants experienced CVD-events. No association between SNPs/GRS and CVD was observed in the adjusted analyses. However, the GRS, rs10936599, rs2487999 and rs8105767 increase the CVD-risk in current smoker. Few SNPs (rs10936599, rs2487999, and rs7675998) showed an increased CVD-risk, whereas rs10936599, rs677228 and rs4387287 a decreased CVD-risk, in further strata. The results of our study suggest different effects of SNPs/GRS on CVD-risk depending on the CVD risk-factor strata, highlighting the importance of stratified analyses in CVD risk-factors.

Schizophrenia polygenic risk scores, clinical variables and genetic pathways as predictors of phenotypic traits of bipolar I disorder.

AIM: We investigated the predictive value of polygenic risk scores (PRS) derived from the schizophrenia GWAS (Trubetskoy et al., 2022) (SCZ3) for phenotypic traits of bipolar disorder type-I (BP-I) in 1878 BP-I cases and 2751 controls from Romania and UK. METHODS: We used PRSice-v2.3.3 and PRS-CS for computing SCZ3-PRS for testing the predictive power of SCZ3-PRS alone and in combination with clinical variables for several BP-I subphenotypes and for pathway analysis. Non-linear predictive models were also used. RESULTS: SCZ3-PRS significantly predicted psychosis, incongruent and congruent psychosis, general age-of-onset (AO) of BP-I, AO-depression, AO-Mania, rapid cycling in univariate regressions. A negative correlation between the number of depressive episodes and psychosis, mainly incongruent and an inverse relationship between increased SCZ3-SNP loading and BP-I-rapid cycling were observed. In random forest models comparing the predictive power of SCZ3-PRS alone and in combination with nine clinical variables, the best predictions were provided by combinations of SCZ3-PRS-CS and clinical variables closely followed by models containing only clinical variables. SCZ3-PRS performed worst. Twenty-two significant pathways underlying psychosis were identified. LIMITATIONS: The combined RO-UK sample had a certain degree of heterogeneity of the BP-I severity: only the RO sample and partially the UK sample included hospitalized BP-I cases. The hospitalization is an indicator of illness severity. Not all UK subjects had complete subphenotype information. CONCLUSION: Our study shows that the SCZ3-PRS have a modest clinical value for predicting phenotypic traits of BP-I. For clinical use their best performance is in combination with clinical variables.

Genetic associations of cardiovascular risk genes in European patients with coronary artery spasm.

BACKGROUND: Coronary artery spasm (CAS) is a frequent finding in patients presenting with angina pectoris. Although the pathogenesis of CAS is incompletely understood, previous studies suggested a genetic contribution. Our study aimed to elucidate genetic variants in a cohort of European patients with angina and unobstructed coronary arteries who underwent acetylcholine (ACh) provocation testing. METHODS: A candidate association analysis of 208 genes previously associated with cardiovascular conditions was performed using genotyped and imputed variants in patients grouped in epicardial (focal, diffuse) CAS (n = 119) and microvascular CAS (n = 87). Patients with a negative ACh test result (n = 45) served as controls. RESULTS: We found no association below the genome-wide significance threshold of p < 5 × 10-8, thus not confirming variants in ALDH2, NOS3, and ROCK2 previously reported in CAS patients of Asian ancestry. However, the analysis identified suggestive associations (p < 10-05) for the groups of focal epicardial CAS (CDH13) and diffuse epicardial CAS (HDAC9, EDN1). Downstream analysis of the potential EDN1 risk locus showed that CAS patients have significantly increased plasma endothelin-1 levels (ET-1) compared to controls. An EDN1 haplotype comprising rs9349379 and rs2070698 was significantly associated to ET-1 levels (p = 0.01). CONCLUSIONS: In summary, we suggest EDN1 as potential genetic risk loci for patients with diffuse epicardial CAS, and European ancestry. Plasma ET-1 levels may serve as a potential cardiac marker.

Multimodal Associations of FKBP5 Methylation With Emotion-Regulatory Brain Circuits.

BACKGROUND: Understanding the biological processes that underlie individual differences in emotion regulation and stress responsivity is a key challenge for translational neuroscience. The gene FKBP5 is a core regulator in molecular stress signaling that is implicated in the development of psychiatric disorders. However, it remains unclear how FKBP5 DNA methylation in peripheral blood is related to individual differences in measures of neural structure and function and their relevance to daily-life stress responsivity. METHODS: Here, we characterized multimodal correlates of FKBP5 DNA methylation by combining epigenetic data with neuroimaging and ambulatory assessment in a sample of 395 healthy individuals. RESULTS: First, we showed that FKBP5 demethylation as a psychiatric risk factor was related to an anxiety-associated reduction of gray matter volume in the ventromedial prefrontal cortex, a brain area that is involved in emotion regulation and mental health risk and resilience. This effect of epigenetic upregulation of FKBP5 on neuronal structure is more pronounced where FKBP5 is epigenetically downregulated at baseline. Leveraging 208 functional magnetic resonance imaging scans during a well-established emotion-processing task, we found that FKBP5 DNA methylation in peripheral blood was associated with functional differences in prefrontal-limbic circuits that modulate affective responsivity to daily stressors, which we measured using ecological momentary assessment in daily life. CONCLUSIONS: Overall, we demonstrated how FKBP5 contributes to interindividual differences in neural and real-life affect regulation via structural and functional changes in prefrontal-limbic brain circuits.

Cohort profile: BioMD-Y (biopsychosocial factors of major depression in youth) - a biobank study on the molecular genetics and environmental factors of depression in children and adolescents in Munich.

PURPOSE: BioMD-Y is a comprehensive biobank study of children and adolescents with major depression (MD) and their healthy peers in Germany, collecting a host of both biological and psychosocial information from the participants and their parents with the aim of exploring genetic and environmental risk and protective factors for MD in children and adolescents. PARTICIPANTS: Children and adolescents aged 8-18 years are recruited to either the clinical case group (MD, diagnosis of MD disorder) or the typically developing control group (absence of any psychiatric condition). FINDINGS TO DATE: To date, four publications on both genetic and environmental risk and resilience factors (including FKBP5, glucocorticoid receptor activation, polygenic risk scores, psychosocial and sociodemographic risk and resilience factors) have been published based on the BioMD-Y sample. FUTURE PLANS: Data collection is currently scheduled to continue into 2026. Research questions will be further addressed using available measures.

A New Intervention for Implementation of Pharmacogenetics in Psychiatry: A Description of the PSY-PGx Clinical Study.

(1) Background Pharmacological treatment for psychiatric disorders has shown to only be effective in about one-third of patients, as it is associated with frequent treatment failure, often because of side effects, and a long process of trial-and-error pharmacotherapy until an effective and tolerable treatment is found. This notion emphasizes the urgency for a personalized medicine approach in psychiatry. (2) Methods This prospective patient- and rater-blinded, randomized, controlled study will investigate the effect of dose-adjustment of antidepressants escitalopram and sertraline or antipsychotics risperidone and aripiprazole according to the latest state-of-the-art international dosing recommendations for CYP2C19 and CYP2D6 metabolizer status in patients with mood, anxiety, and psychotic disorders. A total sample of N = 2500 will be recruited at nine sites in seven countries (expected drop-out rate of 30%). Patients will be randomized to a pharmacogenetic group or a dosing-as-usual group and treated over a 24-week period with four study visits. The primary outcome is personal recovery using the Recovery Assessment Scale as assessed by the patient (RAS-DS), with secondary outcomes including clinical effects (response or symptomatic remission), side effects, general well-being, digital phenotyping, and psychosocial functioning. (3) Conclusions This is, to our knowledge, the first international, multi-center, non-industry-sponsored randomized controlled trial (RCT) that may provide insights into the effectiveness and utility of implementing pharmacogenetic-guided treatment of psychiatric disorders, and as such, results will be incorporated in already available dosing guidelines.

Meta-analysis of ACE inhibitor-induced angioedema identifies novel risk locus.

BACKGROUND: Angioedema is a rare but potentially life-threatening adverse drug reaction in patients receiving angiotensin-converting enzyme inhibitors (ACEis). Research suggests that susceptibility to ACEi-induced angioedema (ACEi-AE) involves both genetic and nongenetic risk factors. Genome- and exome-wide studies of ACEi-AE have identified the first genetic risk loci. However, understanding of the underlying pathophysiology remains limited. OBJECTIVE: We sought to identify further genetic factors of ACEi-AE to eventually gain a deeper understanding of its pathophysiology. METHODS: By combining data from 8 cohorts, a genome-wide association study meta-analysis was performed in more than 1000 European patients with ACEi-AE. Secondary bioinformatic analyses were conducted to fine-map associated loci, identify relevant genes and pathways, and assess the genetic overlap between ACEi-AE and other traits. Finally, an exploratory cross-ancestry analysis was performed to assess shared genetic factors in European and African-American patients with ACEi-AE. RESULTS: Three genome-wide significant risk loci were identified. One of these, located on chromosome 20q11.22, has not been implicated previously in ACEi-AE. Integrative secondary analyses highlighted previously reported genes (BDKRB2 [bradykinin receptor B2] and F5 [coagulation factor 5]) as well as biologically plausible novel candidate genes (PROCR [protein C receptor] and EDEM2 [endoplasmic reticulum degradation enhancing alpha-mannosidase like protein 2]). Lead variants at the risk loci were found with similar effect sizes and directions in an African-American cohort. CONCLUSIONS: The present results contributed to a deeper understanding of the pathophysiology of ACEi-AE by (1) providing further evidence for the involvement of bradykinin signaling and coagulation pathways and (2) suggesting, for the first time, the involvement of the fibrinolysis pathway in this adverse drug reaction. An exploratory cross-ancestry comparison implicated the relevance of the associated risk loci across diverse ancestries.

A Systematic Evaluation of Machine Learning-Based Biomarkers for Major Depressive Disorder.

Importance: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative biomarkers have been identified. Objective: To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD. Design, Setting, and Participants: This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023. Exposure: Patients with MDD and healthy controls. Main Outcome and Measure: Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression. Results: Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups. Conclusion and Relevance: Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified.

Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias.

BACKGROUND: Skeletal dysplasias collectively affect a large number of patients worldwide. Most of these disorders cause growth anomalies. Hence, evaluating skeletal maturity via the determination of bone age (BA) is a useful tool. Moreover, consecutive BA measurements are crucial for monitoring the growth of patients with such disorders, especially for timing hormonal treatment or orthopedic interventions. However, manual BA assessment is time-consuming and suffers from high intra- and inter-rater variability. This is further exacerbated by genetic disorders causing severe skeletal malformations. While numerous approaches to automate BA assessment have been proposed, few are validated for BA assessment on children with skeletal dysplasias. OBJECTIVE: We present Deeplasia, an open-source prior-free deep-learning approach designed for BA assessment specifically validated on patients with skeletal dysplasias. MATERIALS AND METHODS: We trained multiple convolutional neural network models under various conditions and selected three to build a precise model ensemble. We utilized the public BA dataset from the Radiological Society of North America (RSNA) consisting of training, validation, and test subsets containing 12,611, 1,425, and 200 hand and wrist radiographs, respectively. For testing the performance of our model ensemble on dysplastic hands, we retrospectively collected 568 radiographs from 189 patients with molecularly confirmed diagnoses of seven different genetic bone disorders including achondroplasia and hypochondroplasia. A subset of the dysplastic cohort (149 images) was used to estimate the test-retest precision of our model ensemble on longitudinal data. RESULTS: The mean absolute difference of Deeplasia for the RSNA test set (based on the average of six different reference ratings) and dysplastic set (based on the average of two different reference ratings) were 3.87 and 5.84 months, respectively. The test-retest precision of Deeplasia on longitudinal data (2.74 months) is estimated to be similar to a human expert. CONCLUSION: We demonstrated that Deeplasia is competent in assessing the age and monitoring the development of both normal and dysplastic bones.

Abelson Helper Integration Site 1 haplotypes and peripheral blood expression associates with lithium response and immunomodulation in bipolar patients.

RATIONALE: In bipolar disorder (BD), immunological factors play a role in the pathogenesis and treatment of the illness. Studies showed the potential link between Abelson Helper Integration Site 1 (AHI1) protein, behavioural changes and innate immunity regulation. An immunomodulatory effect was suggested for lithium, a mood stabilizer used in BD treatment. OBJECTIVES: We hypothesized that AHI1 may be an important mediator of lithium treatment response. Our study aimed to investigate whether the AHI1 haplotypes and expression associates with lithium treatment response in BD patients. We also examined whether AHI1 expression and lithium treatment correlate with innate inflammatory response genes. RESULTS: We genotyped seven AHI1 single nucleotide polymorphisms in 97 euthymic BD patients and found that TG haplotype (rs7739635, rs9494332) was significantly associated with lithium response. We also showed significantly increased AHI1 expression in the blood of lithium responders compared to non-responders and BD patients compared to healthy controls (HC). We analyzed the expression of genes involved in the innate immune response and inflammatory response regulation (TLR4, CASP4, CASP5, NLRP3, IL1A, IL1B, IL6, IL10, IL18) in 21 lithium-treated BD patients, 20 BD patients treated with other mood stabilizer and 19 HC. We found significantly altered expression between BD patients and HC, but not between BD patients treated with different mood stabilizers. CONCLUSIONS: Our study suggests the involvement of AHI1 in the lithium mode of action. Moreover, mood-stabilizing treatment associated with the innate immunity-related gene expression in BD patients and only the lithium-treated BD patients showed significantly elevated expression of anti-inflammatory IL10, suggesting lithium's immunomodulatory potential.

Impulsivity, decision-making, and risk behavior in bipolar disorder and major depression from bipolar multiplex families.

OBJECTIVES: Bipolar disorder (BD) and major depressive disorder (MDD) are characterized by specific alterations of mood. In both disorders, alterations in cognitive domains such as impulsivity, decision-making, and risk-taking have been reported. Identification of similarities and differences of these domains in BD and MDD could give further insight into their etiology. The present study assessed impulsivity, decision-making, and risk-taking behavior in BD and MDD patients from bipolar multiplex families. METHODS: Eighty-two participants (BD type I, n = 25; MDD, n = 26; healthy relatives (HR), n = 17; and healthy controls (HC), n = 14) underwent diagnostic interviews and selected tests of a cognitive battery assessing neurocognitive performance across multiple subdomains including impulsivity (response inhibition and delay aversion), decision-making, and risk behavior. Generalized estimating equations (GEEs) were used to analyze whether the groups differed in the respective cognitive domains. RESULTS: Participants with BD and MDD showed higher impulsivity levels compared to HC; this difference was more pronounced in BD participants. BD participants also showed lower inhibitory control than MDD participants. Overall, suboptimal decision-making was associated with both mood disorders (BD and MDD). In risk-taking behavior, no significant impairment was found in any group. LIMITATIONS: As sample size was limited, it is possible that differences between BD and MDD may have escaped detection due to lack of statistical power. CONCLUSIONS: Our findings show that alterations of cognitive domains-while present in both disorders-are differently associated with BD and MDD. This underscores the importance of assessing such domains in addition to mere diagnosis of mood disorders.

Evidence for a shared genetic contribution to loneliness and borderline personality disorder.

Loneliness, influenced by genetic and environmental factors such as childhood maltreatment, is one aspect of interpersonal dysfunction in Borderline Personality Disorder (BPD). Numerous studies link loneliness and BPD and twin studies indicate a genetic contribution to this association. The aim of our study was to investigate whether genetic predisposition for loneliness and BPD risk overlap and whether genetic risk for loneliness contributes to higher loneliness reported by BPD patients, using genome-wide genotype data. We assessed the genetic correlation of genome-wide association studies (GWAS) of loneliness and BPD using linkage disequilibrium score regression and tested whether a polygenic score for loneliness (loneliness-PGS) was associated with case-control status in two independent genotyped samples of BPD patients and healthy controls (HC; Witt2017-sample: 998 BPD, 1545 HC; KFO-sample: 187 BPD, 261 HC). In the KFO-sample, we examined associations of loneliness-PGS with reported loneliness, and whether the loneliness-PGS influenced the association between childhood maltreatment and loneliness. We found a genetic correlation between the GWAS of loneliness and BPD in the Witt2017-sample (rg = 0.23, p = 0.015), a positive association of loneliness-PGS with BPD case-control status (Witt2017-sample: NkR² = 2.3%, p = 2.7*10-12; KFO-sample: NkR² = 6.6%, p = 4.4*10-6), and a positive association between loneliness-PGS and loneliness across patient and control groups in the KFO-sample (ß = 0.186, p = 0.002). The loneliness-PGS did not moderate the association between childhood maltreatment and loneliness in BPD. Our study is the first to use genome-wide genotype data to show that the genetic factors underlying variation in loneliness in the general population and the risk for BPD overlap. The loneliness-PGS was associated with reported loneliness. Further research is needed to investigate which genetic mechanisms and pathways are involved in this association and whether a genetic predisposition for loneliness contributes to BPD risk.

Predicting ADHD in alcohol dependence using polygenic risk scores for ADHD.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with a high degree of comorbidity, including substance misuse. We aimed to assess whether ADHD polygenic risk scores (PRS) could predict ADHD diagnosis in alcohol dependence (AD). ADHD PRS were generated for 1223 AD subjects with ADHD diagnosis information and 1818 healthy controls. ADHD PRS distributions were compared to evaluate the differences between healthy controls and AD cases with and without ADHD. We found increased ADHD PRS means in the AD cohort with ADHD (mean 0.30, standard deviation (SD) 0.92; p = 3.9 × 10-6 ); and without ADHD (mean - 0.00, SD 1.00; p = 5.2 × 10-5 ) compared to the healthy control subjects (mean - 0.17, SD 0.99). The ADHD PRS means differed within the AD group with a higher ADHD PRS mean in those with ADHD, odds ratio (OR) 1.34, confidence interval (CI) 1.10 to 1.65; p = 0.002. This study showed a positive relationship between ADHD PRS and risk of ADHD in individuals with co-occurring AD indicating that ADHD PRS may have utility in identifying individuals that are at a higher or lower risk of ADHD. Further larger studies need to be conducted to confirm the reliability of the results before ADHD PRS can be considered as a robust biomarker for diagnosis.

GWAS meta-analysis of psoriasis identifies new susceptibility alleles impacting disease mechanisms and therapeutic targets.

Psoriasis is a common, debilitating immune-mediated skin disease. Genetic studies have identified biological mechanisms of psoriasis risk, including those targeted by effective therapies. However, the genetic liability to psoriasis is not fully explained by variation at robustly identified risk loci. To move towards a saturation map of psoriasis susceptibility we meta-analysed 18 GWAS comprising 36,466 cases and 458,078 controls and identified 109 distinct psoriasis susceptibility loci, including 45 that have not been previously reported. These include susceptibility variants at loci in which the therapeutic targets IL17RA and AHR are encoded, and deleterious coding variants supporting potential new drug targets (including in STAP2, CPVL and POU2F3). We conducted a transcriptome-wide association study to identify regulatory effects of psoriasis susceptibility variants and cross-referenced these against single cell expression profiles in psoriasis-affected skin, highlighting roles for the transcriptional regulation of haematopoietic cell development and epigenetic modulation of interferon signalling in psoriasis pathobiology.

Analysis of 72,469 UK Biobank exomes links rare variants to male-pattern hair loss.

Male-pattern hair loss (MPHL) is common and highly heritable. While genome-wide association studies (GWAS) have generated insights into the contribution of common variants to MPHL etiology, the relevance of rare variants remains unclear. To determine the contribution of rare variants to MPHL etiology, we perform gene-based and single-variant analyses in exome-sequencing data from 72,469 male UK Biobank participants. While our population-level risk prediction suggests that rare variants make only a minor contribution to general MPHL risk, our rare variant collapsing tests identified a total of five significant gene associations. These findings provide additional evidence for previously implicated genes (EDA2R, WNT10A) and highlight novel risk genes at and beyond GWAS loci (HEPH, CEPT1, EIF3F). Furthermore, MPHL-associated genes are enriched for genes considered causal for monogenic trichoses. Together, our findings broaden the MPHL-associated allelic spectrum and provide insights into MPHL pathobiology and a shared basis with monogenic hair loss disorders.

Genome-wide association study of antisocial personality disorder diagnostic criteria provides evidence for shared risk factors across disorders.

INTRODUCTION: While progress has been made in determining the genetic basis of antisocial behaviour, little progress has been made for antisocial personality disorder (ASPD), a condition that often co-occurs with other psychiatric conditions including substance use disorders, attention deficit hyperactivity disorder (ADHD), and anxiety disorders. This study aims to improve the understanding of the genetic risk for ASPD and its relationship with other disorders and traits. METHODS: We conducted a genome-wide association study (GWAS) of the number of ASPD diagnostic criteria data from 3217 alcohol-dependent participants recruited in the UK (UCL, N = 644) and the USA (Yale-Penn, N = 2573). RESULTS: We identified rs9806493, a chromosome 15 variant, that showed a genome-wide significant association ( Z -score = -5.501, P = 3.77 × 10 -8 ) with ASPD criteria. rs9806493 is an eQTL for SLCO3A1 (Solute Carrier Organic Anion Transporter Family Member 3A1), a ubiquitously expressed gene with strong expression in brain regions that include the anterior cingulate and frontal cortices. Polygenic risk score analysis identified positive correlations between ASPD and smoking, ADHD, depression traits, and posttraumatic stress disorder. Negative correlations were observed between ASPD PRS and alcohol intake frequency, reproductive traits, and level of educational attainment. CONCLUSION: This study provides evidence for an association between ASPD risk and SLCO3A1 and provides insight into the genetic architecture and pleiotropic associations of ASPD.

Short anagen hair syndrome: association with mono- and biallelic variants in WNT10A and a genetic overlap with male pattern hair loss.

BACKGROUND: Short anagen hair (SAH) is a rare paediatric hair disorder characterized by a short anagen phase, an inability to grow long scalp hair and a negative psychological impact. The genetic basis of SAH is currently unknown. OBJECTIVES: To perform molecular genetic investigations in 48 individuals with a clinical phenotype suggestive of SAH to identify, if any, the genetic basis of this condition. METHODS: Exome sequencing was performed in 27 patients diagnosed with SAH or with a complaint of short, nongrowing hair. The cohort was screened for variants with a minor allele frequency (MAF) < 5% in the general population and a Combined Annotation Dependent Depletion (CADD) score > 15, to identify genes whose variants were enriched in this cohort. Sanger sequencing was used for variant validation and screening of 21 additional individuals with the same clinical diagnosis and their relatives. Genetic association testing of SAH-related variants for male pattern hair loss (MPHL) was performed using UK Biobank data. RESULTS: Analyses revealed that 20 individuals (42%) carried mono- or biallelic pathogenic variants in WNT10A. Rare WNT10A variants are associated with a phenotypic spectrum ranging from no clinical signs to severe ectodermal dysplasia. A significant association was found between WNT10A and SAH, and this was mostly observed in individuals with light-coloured hair and regression of the frontoparietal hairline. Notably, the most frequent variant in the cohort [c.682T>A;p.(Phe228Ile)] was in linkage disequilibrium with four common WNT10A variants, all of which have a known association with MPHL. Using UK Biobank data, our analyses showed that c.682T>A;p.(Phe228Ile) and one other variant identified in the SAH cohort are also associated with MPHL, and partially explain the known associations between WNT10A and MPHL. CONCLUSIONS: Our results suggest that WNT10A is associated with SAH and that SAH has a genetic overlap with the common phenotype MPHL. The presumed shared biologic effect of WNT10A variants in SAH and MPHL is a shortening of the anagen phase. Other factors, such as modifier genes and sex, may also play a role in the clinical manifestation of hair phenotypes associated with the WNT10A locus.

Addressing Global Environmental Challenges to Mental Health Using Population Neuroscience: A Review.

Importance: Climate change, pollution, urbanization, socioeconomic inequality, and psychosocial effects of the COVID-19 pandemic have caused massive changes in environmental conditions that affect brain health during the life span, both on a population level as well as on the level of the individual. How these environmental factors influence the brain, behavior, and mental illness is not well known. Observations: A research strategy enabling population neuroscience to contribute to identify brain mechanisms underlying environment-related mental illness by leveraging innovative enrichment tools for data federation, geospatial observation, climate and pollution measures, digital health, and novel data integration techniques is described. This strategy can inform innovative treatments that target causal cognitive and molecular mechanisms of mental illness related to the environment. An example is presented of the environMENTAL Project that is leveraging federated cohort data of over 1.5 million European citizens and patients enriched with deep phenotyping data from large-scale behavioral neuroimaging cohorts to identify brain mechanisms related to environmental adversity underlying symptoms of depression, anxiety, stress, and substance misuse. Conclusions and Relevance: This research will lead to the development of objective biomarkers and evidence-based interventions that will significantly improve outcomes of environment-related mental illness.