European Neuropsychopharmacology最新文献

{"title":"LEVERAGING STATISTICAL GENETICS TOOLS FOR CHARTING TRAJECTORIES OF BRAIN DEVELOPMENT AND ITS RELATION TO PSYCHIATRIC DISORDERS","authors":"Pravesh Parekh , Alison Rigby , Nadine Parker , Diliana Pecheva , Robert Loughnan , Piotr Jahołkowski , Diana M. Smith , Carolina Makowski , Donald J. Hagler Jr. , Oleksandr Frei , Alexey A. Shadrin , Terry L. Jernigan , Thomas E. Nichols , Ole A. Andreassen , Anders M. Dale","doi":"10.1016/j.euroneuro.2025.08.512","DOIUrl":"10.1016/j.euroneuro.2025.08.512","url":null,"abstract":"<div><div>Adolescence is a time of significant structural brain changes, coinciding with the onset of many psychiatric illnesses. It has been hypothesized that deviations in brain maturation during adolescence contribute to the development of psychiatric disorders. Both psychiatric disorders and brain structure are highly heritable; thus, a genetic framework linking these factors may provide additional insights into the etiology of mental health disorders. In this talk, I will present results that apply novel statistical genetics tools to chart the trajectories of brain development and that examine the overlap of associated genetic variants with various psychiatric disorders.</div><div>First, I will introduce the Fast and Efficient Mixed-Effects Algorithm (FEMA, Parekh et al., 2024), a novel statistical method for fitting large-scale mixed-effects models in a computationally efficient manner; and its extension FEMA-Long (Parekh et al., forthcoming), specifically optimized for longitudinal phenotypes. FEMA-Long allows for longitudinal GWAS as well as discovery of SNPs that show time-dependent effects. I will present results from the application of FEMA-Long to cortical thickness, surface area, and sulcal depth derived from T1-weighted structural images from the Adolescent Brain Cognitive Development (ABCD) Study (n = 11,511 subjects, 28,898 observations across four time points, mean age at baseline: 9.96 ± 0.62 years, 11,065 subjects; at first follow-up 12.00 ± 0.65 years, 7,735 subjects; at second follow-up 14.16 ± 0.72 years, 6,116 subjects; and at third follow-up 16.09 ± 0.66 years, 3,982 subjects) and show the patterns of changing heritability over the course of brain development and maturation.</div><div>In the second part of this talk, I will show results from longitudinal GWAS applied to brain structural features. I will specifically highlight results from the non-linear SNP × time interaction, thereby discovering genetic variants that show time-dependent effects on the development and maturation of the brain during adolescence. In addition, I will highlight the use of another statistical genetics method, MOSTest (van der Meer et al., 2020), that can pool distributed genetic signals across brain regions to boost statistical power, maximizing the potential for discovery of genetic variants associated with dynamically changing brain morphometry in adolescence. Finally, I will present results from the application of pleiotropy informed conditional false discovery rate (Andreasen et al., 2013) on the summary statistics from FEMA-GWAS and MOSTest in the ABCD Study to show how these genetic variants, associated with brain structural changes during adolescence, also overlap with the genetic variants for different psychiatric conditions, with particular emphasis on the genetic overlap with schizophrenia and bipolar disorder.</div><div>Overall, this talk will introduce new statistical genetics tools and demonstrate their use in combining longitudina","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 27"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BRINGING ADDICTION GENETICS INTO THE CLINIC: ARE WE THERE YET?","authors":"Sandra Sanchez-Roige","doi":"10.1016/j.euroneuro.2025.08.455","DOIUrl":"10.1016/j.euroneuro.2025.08.455","url":null,"abstract":"<div><div>Precision psychiatry is advancing at a rapid pace, and the coming years are poised to be transformative. Decades of family and twin studies have established that psychiatric disorders have a familial and heritable component. With the advent of genome-wide association studies, our understanding of the genetic factors influencing psychiatric conditions has progressed tremendously; hundreds of locations in the human genome have been implicated in different aspects of disease, and the list is expanding each year. Many of these DNA risk variations are shared across psychiatric and even somatic conditions, and suggest new ways of defining and potentially treating psychiatric conditions. Suddenly, there may be a cause for optimism that genetic research may impact clinical arenas and have important implications for diagnosis and treatment. But this is only the tip of the iceberg. This talk will discuss recent advances in the field of psychiatric genetics, borrowing examples from the field of substance use disorders, and interrogate how far are we from realizing the promise of precision psychiatry.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 1-2"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WHAT CAN DNA METHYLATION TEACH US ABOUT BIPOLAR DISORDER? INSIGHTS FROM A MULTI-CENTER MWAS","authors":"Stephanie Le Hellard ,&nbsp;Anne-Kristin Stavrum ,&nbsp;Markos Tesfaye ,&nbsp;Kira Höffler ,&nbsp;Bipolar MWAS CONSORTIUM","doi":"10.1016/j.euroneuro.2025.08.542","DOIUrl":"10.1016/j.euroneuro.2025.08.542","url":null,"abstract":"<div><div>Methylation-wide association studies (MWAS) are a powerful tool for exploring how epigenetic modifications, particularly DNA methylation, may contribute to the development and manifestation of complex diseases. By scanning the genome for methylation differences associated with specific traits or disorders, MWAS can help identify biologically relevant loci influenced by both genetic and environmental factors. These insights can improve our understanding of disease mechanisms and support the discovery of novel biomarkers.</div><div>We report findings from the largest methylation-wide association study (MWAS) of bipolar disorder to date (N = 3,800), conducted across 9 participating centres. We identified 47 differentially methylated positions and 90 regions associated with bipolar disorder status. Based on these findings, we developed a bipolar disorder-specific methylation score, which also showed overlap with schizophrenia and major depression. We will discuss the potential of these results to inform the biological translation of genetic and environmental risk factors.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 42-43"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CONNECTING GENETIC RISK AND BRAIN STRUCTURE IN ANXIETY DISORDERS","authors":"Maryanne Mufford ,&nbsp;Dennis van der Meer ,&nbsp;Nynke Groenewold ,&nbsp;Manuel Mattheisen ,&nbsp;John Hettema ,&nbsp;Rajendra Morey ,&nbsp;Dan Stein","doi":"10.1016/j.euroneuro.2025.08.464","DOIUrl":"10.1016/j.euroneuro.2025.08.464","url":null,"abstract":"<div><div>Anxiety disorders have been associated with structural differences in various brain regions, including alterations in cortical and subcortical morphology and white matter integrity. Despite growing evidence from neuroimaging studies, the degree to which these neural features are genetically linked to anxiety remains insufficiently understood. Elucidating the shared genetic architecture between anxiety and brain morphology is critical for uncovering the biological mechanisms that contribute to individual differences in vulnerability and expression of these conditions.</div><div>In this study, we investigated the shared genetic architecture between anxiety and brain structure by integrating genome-wide association data from PGC-Anxiety (N = 122,341) and imaging-genetic data from the UK Biobank. The neuroimaging data included 196 global and regional brain phenotypes derived from structural MRI and diffusion tensor imaging, covering cortical thickness, surface area, subcortical volumes, and measures of white matter microstructure such as fractional anisotropy and mean diffusivity.</div><div>To quantify the extent of shared genetic influences, we employed several complementary statistical approaches. Linkage disequilibrium score regression (LDSC) was used to estimate genome-wide genetic correlations between anxiety and brain morphology. MiXeR was applied to characterize the polygenic overlap, estimating the number of shared and trait-specific variants. Additionally, conditional false discovery rate (condFDR) analysis was conducted to identify genomic loci jointly associated with anxiety and brain phenotypes, increasing statistical power by leveraging pleiotropy and utilising mixed effect directions. All analyses were performed at both global and regional levels to identify brain structures most strongly implicated in the shared genetic architecture.</div><div>Our findings highlight significant genetic correlations and overlapping loci between anxiety and multiple brain phenotypes, providing novel insights into the biological pathways that underpin anxiety risk. These results suggest that structural variation in specific brain regions may reflect underlying genetic vulnerability to anxiety and could represent intermediate phenotypes that bridge genetic risk and clinical presentation.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 5"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"METHODS FOR UNDERSTANDING GENETIC CONTRIBUTIONS TO LIFECOURSE MENTAL DISORDERS","authors":"Alex Kwong Chair ,&nbsp;Pravesh Parekh Co-chair ,&nbsp;Robyn Wootton Discussant","doi":"10.1016/j.euroneuro.2025.08.481","DOIUrl":"10.1016/j.euroneuro.2025.08.481","url":null,"abstract":"&lt;div&gt;&lt;div&gt;There has been substantial progress into the genetics of mental disorders, leading to the identification of key genomic loci, enhanced understanding of causal pathways and improved scope for drug targets. Much of this is based on cross-sectional work, ignoring the fact that mental disorders are not static and can change across the lifecourse. Emerging evidence now suggests that mental disorders, like depression, can have time specific genetic effects that influence the onset, course and persistence of mental disorders over time. However, the methods available to drive this research forward have rarely been applied to genetic and lifecourse studies.&lt;/div&gt;&lt;div&gt;In this symposium, we will highlight exciting new methods that demonstrate the utility of combining genomic and lifecourse methods for improved understanding of mental disorders. This integration of new methods, genetics and lifecourse data could shed light on when genetic and environmental factors are having their greatest effect, which could in turn lead to better preventions and interventions for mental disorders.&lt;/div&gt;&lt;div&gt;Pravesh Parekh (Oslo) will introduce the Fast and Efficient Mixed Effects Algorithm (FEMA), a novel mixed-modelling framework for performing large-scale longitudinal GWAS that allows discovery of non-linear interaction of SNPs, while allowing for time-varying heritability and random effects. The key methodological concepts of FEMA will be introduced, alongside novel opportunities and applications to mental disorders in the Norwegian Mother, Father and Child Cohort Study (MoBa) and Adolescent Brain Cognitive Development (ABCD) Study.&lt;/div&gt;&lt;div&gt;Esme Elsden (Edinburgh) will present longitudinal phenotyping of depressive symptoms and its applications to PRS prediction and GWAS. Using data from UK Biobank, trajectories of depressive symptoms can be derived which reveal key features that cross-sectional approaches miss. These key features include the cumulative total of exposure to depressive symptoms (Area Under the Curve). Results indicate that polygenic contributions significantly influence the severity and course of depression trajectories across the lifecourse and GWAS of these features also identify novel loci that can be more effectively captured through longitudinal designs.&lt;/div&gt;&lt;div&gt;Chris Rayner (King’s College London) will demonstrate the use of Empirical Bayes Estimates with Simultaneous Correction (SCEBE) for performing repeated measures GWAS of depression symptoms in mothers from MoBa. Repeated measures approaches to GWAS and polygenic scoring will be compared with traditional lifetime history approaches. The impacts of selective participation and attrition on repeated measures analyses will also be discussed.&lt;/div&gt;&lt;div&gt;Christel Middeldorp (Amsterdam) will focus on enduring mental health (EMH), a stable state of mental health over time. Previous research on EMH has primarily focused on the absence of mental health problems, neglecting the equally impor","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 14-15"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"THE SHARED GENETIC ARCHITECTURE OF PTSD AND BRAIN MORPHOLOGY","authors":"Maryanne Mufford ,&nbsp;Dennis van der Meer ,&nbsp;Dan Stein ,&nbsp;Rajendra Morey","doi":"10.1016/j.euroneuro.2025.08.499","DOIUrl":"10.1016/j.euroneuro.2025.08.499","url":null,"abstract":"<div><div>Post-traumatic stress disorder (PTSD) has been associated with alterations in global, subcortical, and cortical brain morphology, as well as regional white matter measures. However, the genetic mechanisms contributing to these brain differences in PTSD remain unknown. Investigating the shared genetic architecture of PTSD and brain morphology may help uncover overlapping biological pathways, clarify aspects of PTSD neurobiology, and improve the power to detect genetic loci for PTSD by leveraging information from genetically correlated brain traits.</div><div>We used GWAS summary statistics from the Psychiatric Genomics Consortium (PGC-PTSD Freeze 3; N = 1,222,882) and conducted both univariate and multivariate GWAS of brain morphology in the UK Biobank (N = 33,735). The brain phenotypes included 196 global and regional measures spanning cortical thickness, surface area, subcortical volume, and white matter microstructure. Multivariate GWAS was performed using MOSTest to identify shared genetic components across brain traits. Genetic correlation between PTSD and brain phenotypes was estimated using linkage disequilibrium score regression (LDSC), and conditional false discovery rate (condFDR) analysis was applied to detect shared loci, including those with mixed effect directions. We further assessed pleiotropy using MiXer, which estimates the number of causal variants shared between traits. To integrate genetic risk across both domains, we applied PleioPGS, a polygenic scoring method that incorporates pleiotropic signals from both PTSD and brain morphology, to estimate the variability explained for PTSD and brain morphology in 22 independent cohorts from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium.</div><div>This study examines the extent to which common genetic variants contribute jointly to PTSD and brain structure, and whether integrating genetic information from both sources enhances our ability to identify novel biological mechanisms. Findings from this work will identify genetically informed imaging biomarkers and deepen insight into neurobiological pathways associated with PTSD.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 22"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GENETICS AND GENOMICS OF ANTIDEPRESSANT ACTION AND RESPONSE","authors":"Naomi Wray Chair ,&nbsp;Graham Murray Co-chair ,&nbsp;Andrew McIntosh Discussant","doi":"10.1016/j.euroneuro.2025.08.501","DOIUrl":"10.1016/j.euroneuro.2025.08.501","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Pharmacological treatment of Major Depressive Disorder (MDD) remains largely empirical with only 27% of patients remitting on first-line medications. These differential responses to antidepressants likely reflect underlying biological heterogeneity in MDD. Here, we investigated MDD heterogeneity through mutually-exclusive groups based on sustained fill of specific antidepressant scripts.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;Using the Australian Genetics of Depression Study (AGDS, 2017-2018) linked to pharmaceutical records (Jul 2013-Dec 2018), we identified mutually exclusive MDD subgroups based on sustained use of a single antidepressant (≥360 cumulative days over 4.5 years) among the 10 most commonly dispensed antidepressants. Among 9,844 participants with self-reported MDD, inferred European ancestry, genotyping, and antidepressant records, we identified 6,106 (62%) with sustained single-antidepressant use without comorbid self-reported bipolar disorder (BIP), 220 (2.2%) with comorbid BIP and ≥4 lithium dispenses (BIP+L group), and 846 (14%) with comorbid BIP but &lt; 4 lithium dispenses (BIP-L group). The reference category for medication class comparisons was the Selective Serotonin Reuptake Inhibitors (SSRIs, N=3,573) group, and for within-class comparisons, it was the SSRI-sertraline (N=1,117) group, as the most common sustained treatments in the AGDS. As a sensitivity analysis, the sustained use threshold was increased to ≥600 cumulative days. To understand innate biological heterogeneity predating depression onset, we investigated associations with 18 polygenic scores (PGS) and reported results passing Bonferroni correction.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;The high self-reported treatment response rates among participants after 360+ days of antidepressant use (&gt;90%) support our dispense threshold as a reasonable proxy for treatment acceptability. Compared to the SSRI group, the tetracyclic antidepressant (TeCA-mirtazapine) group (N=177) had higher self-reported suicidal ideation (OR=1.8, 95% CI=1.3-2.6, p=8.7e-4), while the tricyclic antidepressant (TCA-amitriptyline) group (N=151) showed higher rates of physical comorbidities, specifically chronic pain (OR=4.2, CI=2.9-5.9, p=5.3e-15). Reassuringly, the BIP±L groups were strongly associated with BIP PGS (BIP+L: β=0.280 AGDS standard deviation units, SE=0.069, p=5.6e-5; BIP-L: β=0.323, SE=0.038, p=3.5e-17). Under the more stringent sustained use threshold (≥600 days), the duloxetine group (a serotonin norepinephrine reuptake inhibitor, SNRI; N=500) had higher mean body mass index (BMI) PGS (β=0.18, SE= 0.057, p=1.60e-2) and higher self-reported BMI (β=1.35, SE=0.038, p=3.50e-4) compared to the SSRI-sertraline group. After adjustment for BMI PGS, the higher mean self-reported BMI in the duloxetine group was completely eliminated (β=-0.37, SE=0.85, p=0.66), indicating a potential genetic-cardiometabolic influence on SNRI-duloxetine acceptability.&lt;/div&gt;&lt;/","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 22-23"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MULTI-ANCESTRY FINE-MAPPING REVEALS BIPOLAR DISORDER RISK GENES AND HIGHLIGHTS DRUG REPURPOSING OPPORTUNITIES","authors":"Maria Koromina ,&nbsp;Kai Yuan ,&nbsp;Kevin O'Connell ,&nbsp;Friederike S. David ,&nbsp;Bipolar Disorder Working Group ,&nbsp;Biao Zeng ,&nbsp;Gabriel Hoffman ,&nbsp;Panos Roussos ,&nbsp;Niamh Mullins","doi":"10.1016/j.euroneuro.2025.08.540","DOIUrl":"10.1016/j.euroneuro.2025.08.540","url":null,"abstract":"<div><div>Genome wide association studies (GWAS) have identified hundreds of loci contributing to bipolar disorder (BD) risk, yet identifying the causal variants and their functional roles remains a challenge owing to linkage disequilibrium (LD) between risk variants, and incomplete understanding of the non-coding regulatory mechanisms in the brain. The latest multi-ancestry GWAS meta-analysis—which integrated data from European, East Asian, African American, and Latino cohorts (comprising 158,036 cases and 2,796,499 controls)— identified 298 genome-wide significant loci for BD.</div><div>To narrow down these associated regions, we applied SuSiEx, a statistical fine-mapping method that leverages the varied LD architecture across populations, allowing us to prioritize 113 likely causal single-nucleotide polymorphisms (SNPs) within these 298 loci. These SNPs were then mapped to their corresponding genes, and we explored their functional roles for BD by integrating several lines of evidence. First, we employed Summary data-based Mendelian Randomization (SMR) to interpret these SNPs within the context of brain bulk tissue quantitative trait loci (QTLs)—covering expression, splicing, and methylation QTLs. Next, we refined the cell-type specificity of these effects using SMR analysis on a newly published resource of brain single nuclei eQTLs.</div><div>Our integrative analysis highlighted several candidate genes, including TRANK1, CACNA1B, BCL11B, RGPD8, SP4 and POU6F2, with prioritized SNPs showing regulatory effects that are specific to inhibitory and excitatory neurons as well as oligodendrocytes and astrocytes. These observations hint at potential targets for future functional studies aiming to improve our understanding of the biological mechanisms underlying BD. Future directions include drug repurposing analyses using resources such as Drugbank and the Drug Gene Interaction Database (DGIdb), to assess whether existing drugs might modulate these candidate genes to offer innovative treatments for BD.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 41-42"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ENHANCED MECHANISTIC INSIGHTS AND PREDICTIONS THROUGH INTEGRATING GENETICS AND NEUROIMAGING IN PSYCHIATRY","authors":"Ole Andreassen Chair ,&nbsp;Carolina Makowski Co-chair ,&nbsp;Jakub Kopal Discussant","doi":"10.1016/j.euroneuro.2025.08.511","DOIUrl":"10.1016/j.euroneuro.2025.08.511","url":null,"abstract":"<div><div>To accomplish a comprehensive understanding of complex psychiatric disorders, a multimodal approach is required. Alterations in brain structure and function sit along the causal continuum from genetic variants to behavioural symptoms defining psychiatric disorders. Combining neuroimaging and genetics can expand our understanding of the underlying mechanisms and pathophysiology of psychiatric disorders. The knowledge gained from such multimodal investigations has the potential to inform clinical translation of psychiatric genomics findings.</div><div>This symposium will include four presentations of frontline research which incorporate common or rare genetic variants in conjunction with brain imaging. To understand and leverage the underlying biology of psychiatric disorders, the speakers use multimodal approaches and novel analytical tools. Each presentation will illustrate how integrating data from psychiatry, genetics, and neuroimaging can enhance genomic discoveries, advance mechanistic insights, and improve prediction and/or patient stratification.</div><div>Dr. Pravesh Parekh will introduce the FEMA-GWAS tool and show results from its application on charting the genetic variants associated with longitudinal changes in brain development, using data from the Adolescent Brain Cognitive Development Study. Patterns of genetic overlap with psychiatric disorders will be discussed.</div><div>Dr. Nadine Parker will present novel studies that integrate polygenic and neuroimaging risk scores for the improved prediction of psychiatric disorders. This work combines data derived from the Psychiatric Genomics Consortium (PGC) and the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium.</div><div>Dr. Rune Bøen will present new findings from the multi-site analysis of the mechanistic relationships between rare recurrent copy number variants and brain structure. Leveraging data from rare genetic variant carriers diagnosed with autism spectrum disorder and psychosis, his work informs neurobiological pathways of psychiatric disorders.</div><div>Dr. Carolina Makowski will present new work improving the mechanistic understanding of eating disorders through integrating neuroimaging and metabolic genetics. She will highlight genetically-based insights from metabolic psychiatry and their intersection with the genetic architecture of brain tissue microstructure, with a discussion on how these findings may help inform the treatment of anorexia nervosa.</div><div>Dr. Jakub Kopal, the symposium discussant, will present a comprehensive overview of integrative approaches in psychiatric and neuroimaging genetics. Particular emphasis will be placed on the translation of research findings into clinical practice.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 26-27"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NEXT-GENERATION IMPUTATION FOR PSYCHIATRIC GENETICS: A NOVEL 515K DIVERSE REFERENCE PANEL AND SERVICE","authors":"Franjo Ivankovic ,&nbsp;Arthur Ko ,&nbsp;Jose Soto ,&nbsp;Morgan Aster ,&nbsp;Ricky Magner ,&nbsp;Kate Balaconis ,&nbsp;Beth Sheets ,&nbsp;Lee Lichtenstein ,&nbsp;Benjamin Neale ,&nbsp;Chris Kachulis ,&nbsp;Brian L. Browning","doi":"10.1016/j.euroneuro.2025.08.490","DOIUrl":"10.1016/j.euroneuro.2025.08.490","url":null,"abstract":"<div><div>Accurate genotype imputation and cross-dataset harmonization of genomic data are essential for identifying the complex genetic underpinnings of mental health disorders. One of the most significant obstacles to the expanding diversity in genetic studies today is the lack of adequate reference data for individuals of non-European ancestries. While the recruitment of participants from diverse populations has improved, the availability of genomic tools to analyze their data is still lacking. Large electronic health records and biobanks, such as the NIH initiatives All of Us (AoU) and Analysis, Visualization, and Informatics Lab-space (AnVIL) have aggregated hundreds thousands of individuals from diverse ancestries.</div><div>Here, we present a novel reference panel built from over 515,579 individuals from AoU and AnVIL data. In addition to being the largest reference panel to date, this panel prioritizes ancestral variability, encompassing 261,163 samples from non-European ancestries–a representation nearly twice the size of the entire TOPMed reference panel. Specifically, this panel includes 101,982 individuals with African, 90,553 admixed/Latin American, 13,226 East Asian, 9,710 South Asian, 1,065 Middle Eastern/North African, and 44,627 other non-European ancestries.</div><div>The high coverage (30x) whole genome sequencing data in 414,830 AoU and 100,749 AnVIL samples were harmonized and QCd using Hail. A total of 665,398,839 high-quality variants from autosomes were exported as VCF files, removing the variants with: number alternate alleles greater than 31, average sum of allele depths (AD) less than 12 (proxy for depth; DP), singletons, variants with excessive heterozygosity, mean genotype quality (GQ) under 30, and call rate under 0.9. Exported VCFs were subsequently phased using Beagle 5.5 and shuffled with RESHAPE for increased security of the sensitive data.</div><div>This resource will significantly improve the accuracy of genotype imputation, particularly for rare variants and underrepresented populations, empowering novel discoveries in psychiatric genetics. The reference panel will be available for imputing genotype array (using Beagle) and low-pass sequencing (using Glimpse) data through Broad Institute of MIT and Harvard. The service is set to become available mid-2025, and will be first accessible as a command-line tool with a forthcoming web-based user interface.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 18-19"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}