Early-childhood temperament is associated with later mental health. Temperament continues to develop throughout the first years of life, and a single assessment cannot capture its trajectory. Whether departures from an individual's developmental trajectory carry psychiatric risk remains unknown. Using data from more than 50,000 children in the Norwegian Mother, Father and Child Cohort Study, we modeled longitudinal temperament at 1.5, 3, and 5 years of age with the FEMA-Long mixed-effects framework. We then quantified each child's departures from their predicted trajectories. Multivariate analysis revealed two transdiagnostic dimensions linking trajectory departures to psychiatric diagnoses across childhood and adolescence. Higher scores on the first dimension were associated with an increased hazard of subsequent ADHD diagnosis (hazard ratio = 1.54), and higher scores on the second with an increased hazard of Asperger syndrome (hazard ratio = 1.64). To examine the genetic basis of these associations, we performed longitudinal GWAS of temperament and conjunctional FDR analysis to detect loci shared with the associated diagnoses. The effects of these loci changed across early childhood, with some strengthening and others attenuating with age. These findings show that departures from predicted temperament trajectories reflect transdiagnostic psychiatric risk with a shared genetic basis. Longitudinal, trajectory-based monitoring could help identify children at elevated psychiatric risk.
J. Kopal, N. Bakken, P. Parekh et al.· Molecular Psychiatry· 0 citations
Abstract Background Alcohol use disorder (AUD) and alcohol consumption (AC) are highly heritable, globally burdensome, and frequently comorbid with severe psychiatric disorders like schizophrenia (SCZ) and bipolar disorder (BD). While these comorbidities are often linked to greater illness severity, it remains unclear whether they arise solely as complications of substance use or from a shared underlying genetic architecture. Aims & Objectives The overall aim was to leverage massive, diverse datasets and novel statistical frameworks to: i) Identify novel genetic loci associated with a narrow AUD phenotype across multiple ancestries. ii) Characterize the shared genomic loci and polygenic overlap between alcohol traits (AUD/AC) and psychiatric phenotypes (SCZ/BD). iii) Map identified variants to biological pathways and brain regions to uncover potential drug targets. Method A multi-ancestry GWAS was conducted on 1,041,450 individuals (including European, African, Hispanic, and Asian ancestries) using novel statistical tools and cross-ancestry functional analyses. We also used European-ancestry summary statistics (AUD: 34,658 cases; AC: n=200,680; SCZ: 31,013 cases; BD: 20,352 cases), and applied conjunctional False Discovery Rate (conjFDR) analysis to increase the power to detect shared genomic loci. The identified loci were mapped to gene expression data in the brain and examined for enrichment in specific neuronal pathways (GABAergic, dopaminergic, serotonergic) and immune-related gene sets. Results The multi-ancestry analysis identified 37 genome-wide significant loci, including seven novel for AUD. The conjFDR analysis further identified 28 loci shared between SCZ and AUD, and 2 loci shared between BD and AUD, many of which were previously unknown for these phenotypes. Loci were mapped to genes with altered expression in the striatum, hypothalamus, and prefrontal cortex. While European and African samples showed distinct immune-related patterns, shared loci between AUD and psychiatric disorders exhibited a complex mixture of both same and opposite effect directions. Extensive positive genetic correlations and polygenic overlap were found between AUD and both mental and general medical phenotypes, confirming that AUD shares a significant genetic liability with these conditions. Discussion & Conclusions These findings underscore the value of multi-ancestry and cross-disorder genetic studies in SUD. By identifying shared and novel genomic loci, we demonstrates that the relationship between alcohol use and psychiatric disorders is driven by a complex, shared genetic architecture rather than environmental complications alone. This advances our understanding of AUD risk and highlights potential neuronal and immune pathways for future clinical intervention.
O. Andreassen, R. Icick, E. Wistrom et al.· International Journal of Neu...· 0 citations
Background: Early diagnosis and etiological classification of dementia remain challenging, as clinicians typically lack tools to integrate cognitive, neuroimaging, and genetic data quantitatively. We developed and validated multimodal risk models to support early diagnosis of dementia and differential diagnosis of Alzheimer's disease (AD) versus non-AD dementias in real-world clinical settings and translated model outputs into individualized risk reports. Methods: Utilizing real-world clinical cohorts (n = 1,100 for early diagnosis of dementia, using clinical diagnoses up to three years after clinical assessment; n = 788 for AD differential diagnosis) from Norwegian Memory Clinics, we trained and validated the Multimodal Hazard Score for Real-World Data (MHS-RWD) model integrating demographics (age, sex), cognitive assessments (MMSE-NR3 or CERAD 10-word delayed recall), the MRI-derived Imaging Hazard Score, and the Polygenic Hazard Score. Discrimination performance was examined using the area under the receiver operating characteristic curve (AUC). Results: In real-world clinical data, the MHS-RWD consistently outperformed any single predictor used alone. For early diagnosis of dementia, the full model achieved an AUC of 0.89 in females and 0.84 in males. For the differential diagnosis of AD from other dementias, the multimodal model yielded an AUC of 0.91 in females and 0.83 in males. A patient-level risk report was designed to present individualized risk estimates. Conclusions: Multimodal integration of cognitive, neuroimaging, and polygenic data in the MHS-RWD tool yields strong discrimination for both early diagnosis of dementia and AD differential diagnosis. The tool relies on data obtainable in clinical care, and genetic information that is becoming increasingly available in routine practice. Delivered through intuitive patient-level risk reports, it could support etiologically informed dementia decisions in real-world settings, with potential utility in primary care.
T. T. Filiz, V. Fominykh, K. Persson et al.· medRxiv· 0 citations
Recent large-scale studies have enabled new knowledge about genetic underpinnings of morphological and electrophysiological alterations of the retina. Variation in retinal traits, often of neurodevelopmental origin, have been linked to major psychiatric disorders (MPDs). Here, we investigate the genetic overlap between MPDs and key retinal traits to identify underlying molecular mechanisms. We obtained genome-wide associations studies data for bipolar disorder (BD), major depression (MD), schizophrenia (SCZ), and the retinal traits retinal nerve fibre layer thickness (RNFL), ganglion cell inner plexiform layer thickness (GCIPL), and vertical cup-disc ratio (VCDR). We estimated the number of trait-influencing variants shared between traits with MiXeR and identified shared genetic loci with condFDR. Subsequently, we examined the biological pathways of the genes mapped to shared loci. This revealed that GCIPL shared the most genetic variants with MPDs (~60%), followed by RNFL (~40%), and VCDR (~20%). The genetic variants shared between retinal traits and MPDs showed disorder-specific patterns with more pronounced overlaps of SCZ and BD with RNFL, and MD negatively correlated with GCIPL. Gene-pathway analysis highlighted the importance of GABAergic neurotransmission and a two-stage neurodevelopmental process in SCZ, whereas the role of mitochondria and a weaker developmental component were observed in BD. The results also implicated synaptic functioning and gene-expression processes in MD. Furthermore, polygenic analysis suggested that the genetic architecture of retinal traits can distinguish between MPDs. Our findings indicate shared genetic underpinnings between retinal traits and SCZ, BD, and MD, implicating altered neurodevelopment and neurotransmission underlying the retinal link to major psychiatric disorders.
P. Jahołkowski, N. Parker, I. Sveen et al.· medRxiv· 0 citations
Antisocial behaviour in youth has serious consequences at individual and societal level. Adolescence is also a critical period for the emergence of prodromal psychosis symptoms. Their co-occurrence may aggravate aggression and lead to severe adverse outcomes. Common genetic risk for psychosis, antisocial behaviour, and substance use has been suggested, but the putatively shared genetic architecture is unknown, and evidence in youth is limited. Here we examined whether common genetic liability indexed by polygenic risk scores (PRSs) relates to these complex traits in the Norwegian Mother, Father and Child Cohort Study (MoBa; n = 18960). Antisocial psychopathology was operationalised as three complementary dimensions: conduct-disorder (CD) traits, oppositional-defiant disorder (ODD) traits, and psychopathy traits. We first quantified phenotypic correlations between psychotic-like experiences (PLEs) and the antisocial dimensions. We then evaluated PRS-phenotype associations (single-PRS and joint multivariable models) using PRSs for antisocial behaviour (PRSASB) and schizophrenia (PRSSCZ), in addition to PRS for alcohol use disorder (PRSAUD). We found phenotypic correlations between antisocial traits and PLEs (rs=0.13-0.34, p < 0.0001). This overlap was reflected at the genetic level, as we found associations between PRSASB and PLEs (p = 0.0004). The associations between PRSSCZ and antisocial traits were present for CD (p = 0.005) and ODD (p = 0.003) traits. Both PLEs and antisocial traits were associated with PRSAUD. Patterns persisted in mutually adjusted models. These findings indicate cross-trait associations between polygenic liability for SCZ, ASB, AUD and adolescent PLEs and antisocial dimensions in the general youth population, consistent with partially shared common genetic influences. Future studies should further delineate underlying biological mechanisms.
N. Tesli, P. Jahołkowski, J. Rokicki et al.· European Child and Adolescen...· 0 citations
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