Pub Date : 2025-10-01Epub Date: 2025-10-03DOI: 10.1016/j.euroneuro.2025.08.461
John Hettema Chair , Nora Strom Co-chair , Jordan Smoller Discussant
Until recently, research in the genetics of anxiety disorders has suffered from the lack of powerful datasets and significant genetic association findings. Without that valid genetic basis, the application of genetics to fuel anxiety research has been limited compared to other psychiatric disorders. The recently published PGC-ANX meta-analysis has provided a major advancement in anxiety genetics. This symposium will cover a variety of follow-up anxiety genomic research directions. Nora Strom from Humboldt-Universität will present preliminary results from a large GWAS of panic disorder. Dr. Shaunna Clark from Texas A&M University will review the results of the largest meta-analysis of epigenome-wide association studies of anxiety disorders. Dr Mary Mufford from University of Cape Town will discuss the genetic connections between anxiety susceptibility and brain structure. Dr. Brad Verhulst from Texas A&M University will examine genome-wide moderation for anxiety and depression as a function of adverse life events. Dr. Jordan Smoller from Harvard University will serve as the Discussant.
{"title":"NEW DIRECTIONS FOR PGC-ANX GENOMICS RESEARCH: PANIC DISORDER, EPIGENETICS, BRAIN STRUCTURE, AND GENE-ENVIRONMENT MODERATION","authors":"John Hettema Chair , Nora Strom Co-chair , Jordan Smoller Discussant","doi":"10.1016/j.euroneuro.2025.08.461","DOIUrl":"10.1016/j.euroneuro.2025.08.461","url":null,"abstract":"<div><div>Until recently, research in the genetics of anxiety disorders has suffered from the lack of powerful datasets and significant genetic association findings. Without that valid genetic basis, the application of genetics to fuel anxiety research has been limited compared to other psychiatric disorders. The recently published PGC-ANX meta-analysis has provided a major advancement in anxiety genetics. This symposium will cover a variety of follow-up anxiety genomic research directions. Nora Strom from Humboldt-Universität will present preliminary results from a large GWAS of panic disorder. Dr. Shaunna Clark from Texas A&M University will review the results of the largest meta-analysis of epigenome-wide association studies of anxiety disorders. Dr Mary Mufford from University of Cape Town will discuss the genetic connections between anxiety susceptibility and brain structure. Dr. Brad Verhulst from Texas A&M University will examine genome-wide moderation for anxiety and depression as a function of adverse life events. Dr. Jordan Smoller from Harvard University will serve as the Discussant.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 4"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204139","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}
Pub Date : 2025-10-01Epub Date: 2025-10-03DOI: 10.1016/j.euroneuro.2025.08.462
Nora Strom , Brittany Mitchell , John Hettema , Manuel Mattheisen , Iiris Hovatta , Angelika Erhardt
Panic disorder (PD) is a disabling anxiety disorder with a moderate heritability estimated at 30–40%. It often co-occurs with agoraphobia, which is characterized by anxiety in situations perceived as unsafe or difficult to escape, frequently leading to panic attacks. Despite its public health impact, progress in identifying robust genetic risk loci for PD has lagged behind that of other psychiatric conditions. To address this, we will conduct the first large-scale genome-wide association study (GWAS) meta-analysis of PD (with or without agoraphobia) within the Psychiatric Genomics Consortium Anxiety Disorders Working Group (PGC-ANX), leveraging international collaboration to maximize discovery power.
We will aggregate phenotypic and genetic data from over 100K individuals with lifetime PD across multiple international cohorts and consortia. Standardized quality control and imputation protocols will be applied across datasets in accordance with our standard operation procedure. All individual GWASs will consecutively be meta-analyzed. Beyond primary GWAS, secondary analyses will include functional annotation of associated loci, gene-set enrichment, and genetic correlation analyses to explore shared genetic architecture with related psychiatric and somatic phenotypes.
Since panic attacks are also common in other psychiatric disorders - such as anxious depression, OCD, PTSD, and schizophrenia - we will conduct an extended cross-disorder analysis to investigate whether panic attacks represent a transdiagnostic phenotype with distinct genetic features. Additionally, we will examine the genetic relationship between PD and dimensional anxiety phenotypes. Given that genetic liability can aggregate within families, we will include anamnestic information on familial anxiety to test whether individuals with a positive family history show increased genetic risk. Finally, we will explore whether genetic liability is enriched in individuals with an early age of onset.
Preliminary results from Data Freeze 1 will be presented. This meta-analysis aims to significantly advance our understanding of the biological basis of panic disorder and related anxiety dimensions. The findings will highlight the importance of large-scale, collaborative efforts in psychiatric genetics and inform future work toward precision psychiatry in anxiety disorders.
{"title":"DISSECTING THE GENETICS OF PANIC DISORDER, AGORAPHOBIA, AND PANIC ATTACKS: THE FIRST DISORDER-SPECIFIC LARGE-SCALE GENOME-WIDE ASSOCIATION STUDY FROM PGC-ANXIETY","authors":"Nora Strom , Brittany Mitchell , John Hettema , Manuel Mattheisen , Iiris Hovatta , Angelika Erhardt","doi":"10.1016/j.euroneuro.2025.08.462","DOIUrl":"10.1016/j.euroneuro.2025.08.462","url":null,"abstract":"<div><div>Panic disorder (PD) is a disabling anxiety disorder with a moderate heritability estimated at 30–40%. It often co-occurs with agoraphobia, which is characterized by anxiety in situations perceived as unsafe or difficult to escape, frequently leading to panic attacks. Despite its public health impact, progress in identifying robust genetic risk loci for PD has lagged behind that of other psychiatric conditions. To address this, we will conduct the first large-scale genome-wide association study (GWAS) meta-analysis of PD (with or without agoraphobia) within the Psychiatric Genomics Consortium Anxiety Disorders Working Group (PGC-ANX), leveraging international collaboration to maximize discovery power.</div><div>We will aggregate phenotypic and genetic data from over 100K individuals with lifetime PD across multiple international cohorts and consortia. Standardized quality control and imputation protocols will be applied across datasets in accordance with our standard operation procedure. All individual GWASs will consecutively be meta-analyzed. Beyond primary GWAS, secondary analyses will include functional annotation of associated loci, gene-set enrichment, and genetic correlation analyses to explore shared genetic architecture with related psychiatric and somatic phenotypes.</div><div>Since panic attacks are also common in other psychiatric disorders - such as anxious depression, OCD, PTSD, and schizophrenia - we will conduct an extended cross-disorder analysis to investigate whether panic attacks represent a transdiagnostic phenotype with distinct genetic features. Additionally, we will examine the genetic relationship between PD and dimensional anxiety phenotypes. Given that genetic liability can aggregate within families, we will include anamnestic information on familial anxiety to test whether individuals with a positive family history show increased genetic risk. Finally, we will explore whether genetic liability is enriched in individuals with an early age of onset.</div><div>Preliminary results from Data Freeze 1 will be presented. This meta-analysis aims to significantly advance our understanding of the biological basis of panic disorder and related anxiety dimensions. The findings will highlight the importance of large-scale, collaborative efforts in psychiatric genetics and inform future work toward precision psychiatry in anxiety disorders.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 4"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204141","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}
Pub Date : 2025-10-01Epub Date: 2025-10-03DOI: 10.1016/j.euroneuro.2025.08.485
Christel M. Middeldorp , Lianne De Vries , Meike Bartels
Enduring mental health (EMH) is a stable state of mental health over time. More insight into factors associated with EMH can aid prevention of mental health problems. Previous research on EMH has primarily focused on the absence of mental health problems, neglecting the equally important dimension of presence of wellbeing. In this study, EMH is defined by the absence of mental health problems and/or the presence of higher wellbeing over time.
Data on twins and their family members registered in the large Netherlands Twin Register will be examined. Individuals will be included when mental health and/or wellbeing data are available in four or more surveys, to create the EMH phenotype. For more than 17,000 individuals genotypes are also available.
Classical twin analyses will be performed to calculate the heritability of EMH. Furthermore, associations will be presented with polygenic scores (PGS) and educational attainment, self esteem, stressful life events, neuroticism, optimism, loneliness, social support, exercise and self reported health. Besides PGS for mental disorders and traits, PGS for educational attainment, wellbeing, neuroticism, loneliness, childhood maltreatment and risky behavior will be included.
First, univariate regressions will be performed. The significantly associated variables will then be included in multiple logistic regression analyses, using family as a grouping variable to account for clustering. Variables as assessed during adolescents and polygenic scores that are related to EMH can be seen as potential predictors for EMH, whereas variables assessed in the last survey in adulthood can be seen as potential outcomes of EMH.
This project follows up on a project using childhood data on enduring mental health (Alrouh et al, under revision for the Journal of the American Academy of Child and Adolescent Psychiatry). Using measures between age 3 and age 12, 37% of the sample had EMH. This was associated with parental education (OR (low) =0.77 [0.70-0.86]; OR (middle) = 0.88 [0.82-0.95]), child academic achievement (OR=1.07 [1.03,1.12]), and child wellbeing (OR=1.44 [1.35,1.54]), and was weakly associated with ADHD PGS. The heritability was estimated at 54%. These findings are now extended with findings in adults, over a longer timespan and with a more comprehensive analysis of factors that could be associated with EMH.
{"title":"LIVING HAPPILY EVER AFTER: WHAT INFLUENCES ENDURING MENTAL HEALTH?","authors":"Christel M. Middeldorp , Lianne De Vries , Meike Bartels","doi":"10.1016/j.euroneuro.2025.08.485","DOIUrl":"10.1016/j.euroneuro.2025.08.485","url":null,"abstract":"<div><div>Enduring mental health (EMH) is a stable state of mental health over time. More insight into factors associated with EMH can aid prevention of mental health problems. Previous research on EMH has primarily focused on the absence of mental health problems, neglecting the equally important dimension of presence of wellbeing. In this study, EMH is defined by the absence of mental health problems and/or the presence of higher wellbeing over time.</div><div>Data on twins and their family members registered in the large Netherlands Twin Register will be examined. Individuals will be included when mental health and/or wellbeing data are available in four or more surveys, to create the EMH phenotype. For more than 17,000 individuals genotypes are also available.</div><div>Classical twin analyses will be performed to calculate the heritability of EMH. Furthermore, associations will be presented with polygenic scores (PGS) and educational attainment, self esteem, stressful life events, neuroticism, optimism, loneliness, social support, exercise and self reported health. Besides PGS for mental disorders and traits, PGS for educational attainment, wellbeing, neuroticism, loneliness, childhood maltreatment and risky behavior will be included.</div><div>First, univariate regressions will be performed. The significantly associated variables will then be included in multiple logistic regression analyses, using family as a grouping variable to account for clustering. Variables as assessed during adolescents and polygenic scores that are related to EMH can be seen as potential predictors for EMH, whereas variables assessed in the last survey in adulthood can be seen as potential outcomes of EMH.</div><div>This project follows up on a project using childhood data on enduring mental health (Alrouh et al, under revision for the Journal of the American Academy of Child and Adolescent Psychiatry). Using measures between age 3 and age 12, 37% of the sample had EMH. This was associated with parental education (OR (low) =0.77 [0.70-0.86]; OR (middle) = 0.88 [0.82-0.95]), child academic achievement (OR=1.07 [1.03,1.12]), and child wellbeing (OR=1.44 [1.35,1.54]), and was weakly associated with ADHD PGS. The heritability was estimated at 54%. These findings are now extended with findings in adults, over a longer timespan and with a more comprehensive analysis of factors that could be associated with EMH.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 16-17"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204119","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}
Pub Date : 2025-10-01Epub Date: 2025-10-03DOI: 10.1016/j.euroneuro.2025.08.502
Cathryn Lewis
Antidepressants are one of the most widely prescribed medications worldwide, but only one-third of people respond to the first drug prescribed, and there are few predictors of who will respond to which antidepressant. This trial-and-error strategy of finding an effective drug is harmful to patients and families and imposes a burden to health services and the economy.
Pharmacogenetics offers a promising route to personalised prescribing. This symposium introduction will summarise current progress, describe studies underway, and outline the potential for genetic testing for antidepressant prescribing.
Most antidepressants are metabolised by CYP2C19, CYP2D6 and CYP2C9, but genetic variation in these genes accounts for only a small proportion of response to antidepressants, suggesting broader genetic contributions. The largest genome-wide association study for antidepressant response, conducted by the Psychiatric Genomics Consortium, assessed remission and percentage change in depressive symptoms in 5,000 patients from clinical trials and research studies. The study reported a SNP-based heritability of 13%, with modest polygenic prediction between studies. Notably, antidepressant response was only weakly associated with polygenic scores for depression, indicating a largely distinct genetic architecture between susceptibility to depression and treatment response. New results from a targeted GWAS of selective serotonin reuptake inhibitors (SSRIs) will be reported, providing response predictors by drug class.
As the polygenic basis of antidepressant response becomes clearer, the primary barrier to expanding our understanding of the genetic predictors becomes sample size. Few studies collect the longitudinal information necessary to robustly evaluate response to treatment, and although clinical trials are the gold standard for rigorous assessment of drug response, their scale is limited.
Harnessing real world data from electronic health records, or assessing self-reported response from patients, will allow us to define proxy phenotypes of antidepressant response, and perform sufficiently powerful genetic studies. In this talk, I will assess the potential of these novel sources of treatment response phenotypes, which we are investigating in the AMBER project. I will outline a plan for expanding genetic studies of antidepressant response to build pharmacopolygenic predictors that might be powerful enough to test and implement clinically, with the potential of personalised prescribing for depression.
{"title":"PHARMACOPOLYGENIC PREDICTORS FOR ANTIDEPRESSANT RESPONSE: CURRENT STATUS, FUTURE DIRECTIONS","authors":"Cathryn Lewis","doi":"10.1016/j.euroneuro.2025.08.502","DOIUrl":"10.1016/j.euroneuro.2025.08.502","url":null,"abstract":"<div><div>Antidepressants are one of the most widely prescribed medications worldwide, but only one-third of people respond to the first drug prescribed, and there are few predictors of who will respond to which antidepressant. This trial-and-error strategy of finding an effective drug is harmful to patients and families and imposes a burden to health services and the economy.</div><div>Pharmacogenetics offers a promising route to personalised prescribing. This symposium introduction will summarise current progress, describe studies underway, and outline the potential for genetic testing for antidepressant prescribing.</div><div>Most antidepressants are metabolised by CYP2C19, CYP2D6 and CYP2C9, but genetic variation in these genes accounts for only a small proportion of response to antidepressants, suggesting broader genetic contributions. The largest genome-wide association study for antidepressant response, conducted by the Psychiatric Genomics Consortium, assessed remission and percentage change in depressive symptoms in 5,000 patients from clinical trials and research studies. The study reported a SNP-based heritability of 13%, with modest polygenic prediction between studies. Notably, antidepressant response was only weakly associated with polygenic scores for depression, indicating a largely distinct genetic architecture between susceptibility to depression and treatment response. New results from a targeted GWAS of selective serotonin reuptake inhibitors (SSRIs) will be reported, providing response predictors by drug class.</div><div>As the polygenic basis of antidepressant response becomes clearer, the primary barrier to expanding our understanding of the genetic predictors becomes sample size. Few studies collect the longitudinal information necessary to robustly evaluate response to treatment, and although clinical trials are the gold standard for rigorous assessment of drug response, their scale is limited.</div><div>Harnessing real world data from electronic health records, or assessing self-reported response from patients, will allow us to define proxy phenotypes of antidepressant response, and perform sufficiently powerful genetic studies. In this talk, I will assess the potential of these novel sources of treatment response phenotypes, which we are investigating in the AMBER project. I will outline a plan for expanding genetic studies of antidepressant response to build pharmacopolygenic predictors that might be powerful enough to test and implement clinically, with the potential of personalised prescribing for depression.</div><div><strong>Disclosure:</strong> Myriad Genetics, Advisory Board</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 23"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204253","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}
Pub Date : 2025-10-01Epub Date: 2025-10-03DOI: 10.1016/j.euroneuro.2025.08.541
Tracey van der Veen , Markos Tesfaye , Andrew McQuillin , Arianna Di Florio , Bipolar Disorder Working Group of the Psychiatric Genomics Consortium Bipolar Disorder Working Group of the Psychiatric Genomics Consortium
<div><div>Bipolar disorder, characterized by variability in its manifestation across individuals, poses challenges to the identification of the underlying genetic factors. The objective was guided by the a priori hypothesis that distinct clinical presentations (subphenotypes) exhibit unique contributions from common genetic variants. We initiated a series of subphenotype-specific genome-wide association studies (GWAS). These initial analyses were subsequently expanded to incorporate additional BD cases for whom detailed subphenotype data were unavailable, thereby increasing the overall statistical power. To further enhance the sample size and leverage existing knowledge, a comprehensive meta-analysis was performed, integrating the results of these subphenotype-specific GWAS with the most recent findings from a large-scale schizophrenia GWAS, utilizing the powerful Multi-Trait-Analysis-of-GWAS (MTAG) methodology. This integrated analysis allowed for a more robust examination of prioritized genetic variants, implicated genes, and the underlying biological processes that are potentially linked to the distinct pathophysiologies of the various BD subphenotypes. The study involved data collected through (semi-)structured clinical interviews for 25,543 cases and 312,788 control individuals across 58 different research cohorts. The main outcomes and measures of the study revealed that the presence of psychosis and the occurrence of comorbid psychiatric conditions were the primary factors explaining much of the variance observed in the clinical subphenotype data. The subphenotypes were differentiated by several key genetic characteristics, including their SNP-based heritability (h²SNP), patterns of global and local genetic correlations, signatures of negative selection in the genome, specific genomic loci, prioritized sets of genes, enrichment in particular cell types within the brain, and patterns of gene expression across different brain tissues. Fifty novel loci not previously associated with the subphenotypes, BD, or schizophrenia were identified. A substantial proportion (85%) of the up to 609 independent single nucleotide polymorphisms (SNPs) located within these genomic loci were found to be shared across the various subphenotypes examined. However, the study also revealed differential gene enrichment across 46 distinct gene sets that are known to be implicated in critical neuronal processes such as synaptic neuroplasticity and signalling. Furthermore, enrichment was observed in 53 specific cell types within the brain, with a notable prevalence of GABAergic interneurons, excitatory pyramidal neurons, and dopamine neurons. Divergent transcriptome-wide associations (TWAS) were detected across 15 human fetal and adult brain tissues, suggesting that the genetic risk for different subphenotypes may exert its effects through distinct patterns of gene expression in specific brain regions and developmental stages. In conclusion, this research has significa
{"title":"GENETIC LANDSCAPE OF BIPOLAR DISORDER HETEROGENEITY","authors":"Tracey van der Veen , Markos Tesfaye , Andrew McQuillin , Arianna Di Florio , Bipolar Disorder Working Group of the Psychiatric Genomics Consortium Bipolar Disorder Working Group of the Psychiatric Genomics Consortium","doi":"10.1016/j.euroneuro.2025.08.541","DOIUrl":"10.1016/j.euroneuro.2025.08.541","url":null,"abstract":"<div><div>Bipolar disorder, characterized by variability in its manifestation across individuals, poses challenges to the identification of the underlying genetic factors. The objective was guided by the a priori hypothesis that distinct clinical presentations (subphenotypes) exhibit unique contributions from common genetic variants. We initiated a series of subphenotype-specific genome-wide association studies (GWAS). These initial analyses were subsequently expanded to incorporate additional BD cases for whom detailed subphenotype data were unavailable, thereby increasing the overall statistical power. To further enhance the sample size and leverage existing knowledge, a comprehensive meta-analysis was performed, integrating the results of these subphenotype-specific GWAS with the most recent findings from a large-scale schizophrenia GWAS, utilizing the powerful Multi-Trait-Analysis-of-GWAS (MTAG) methodology. This integrated analysis allowed for a more robust examination of prioritized genetic variants, implicated genes, and the underlying biological processes that are potentially linked to the distinct pathophysiologies of the various BD subphenotypes. The study involved data collected through (semi-)structured clinical interviews for 25,543 cases and 312,788 control individuals across 58 different research cohorts. The main outcomes and measures of the study revealed that the presence of psychosis and the occurrence of comorbid psychiatric conditions were the primary factors explaining much of the variance observed in the clinical subphenotype data. The subphenotypes were differentiated by several key genetic characteristics, including their SNP-based heritability (h²SNP), patterns of global and local genetic correlations, signatures of negative selection in the genome, specific genomic loci, prioritized sets of genes, enrichment in particular cell types within the brain, and patterns of gene expression across different brain tissues. Fifty novel loci not previously associated with the subphenotypes, BD, or schizophrenia were identified. A substantial proportion (85%) of the up to 609 independent single nucleotide polymorphisms (SNPs) located within these genomic loci were found to be shared across the various subphenotypes examined. However, the study also revealed differential gene enrichment across 46 distinct gene sets that are known to be implicated in critical neuronal processes such as synaptic neuroplasticity and signalling. Furthermore, enrichment was observed in 53 specific cell types within the brain, with a notable prevalence of GABAergic interneurons, excitatory pyramidal neurons, and dopamine neurons. Divergent transcriptome-wide associations (TWAS) were detected across 15 human fetal and adult brain tissues, suggesting that the genetic risk for different subphenotypes may exert its effects through distinct patterns of gene expression in specific brain regions and developmental stages. In conclusion, this research has significa","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 42"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204266","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}
<div><h3>Background</h3><div>Autism spectrum disorder is a neurodevelopmental condition that is being identified in increasing numbers. As of 2022 data released this year, 3.2% of U.S. 8-year-olds at 16 study sites have been identified as having ASD. The causes of autism are complex. While genes are clearly a factor, increasing evidence suggests that nongenetic variables – termed “environmental exposures” – are also contributors to causes of autism and co-occurring conditions that can significantly impact the lives of people with autism and their families. Environmental exposures may be modifiable, holding promise that identifying and communicating them to the public could improve lives. Research priorities in this gene-by-environment (GxE) area have been largely shaped by researchers. However, it is crucially important to involve stakeholders to understand their needs, their priorities, and their views on the best ways to communicate GxE concepts and sensitively share research findings back to the community. As part of the GEARs ACE Network, one aim was to conduct this stakeholder research. In this talk, the authors present qualitative findings from focus groups conducted in four types of stakeholder groups.</div></div><div><h3>Methods</h3><div>After developing an interview guide with help from a community advisory board, researchers conducted interviews with four key stakeholder groups: adults with autism; caregivers; medical providers; and adult siblings of people with autism. The guide asked multiple open-ended questions about 1) co-occurring conditions; 2) genetics; 3) environmental factors; and 4) information sources. The research team included a member with autism. Purposeful online sampling was used to recruit participants in the United States. Data were drawn from four 60-to-90-minute sessions that each involved a maximum of six participants. Data were analyzed using a content analysis deductive approach.</div></div><div><h3>Results</h3><div>Across the four groups, a key theme that emerged was the need for immediate support to mitigate the impacts of multiple co-occurring conditions. Many autistic participants expressed a preference to focus on improving specific challenging symptoms/co-occurring conditions, as opposed to fully preventing autism. Participants had a desire for more genetic and environmental research related to current quality of life. Participants expressed interest in genetics but also shared concern about the fraught history of genetic research. Groups varied in their preferred sources of information, as well as what they found trustworthy. Groups noted the importance of disseminating information in a way that does not feed negative perceptions of an autism diagnosis or convey blame on parents regarding modifiable risk factors.</div></div><div><h3>Conclusion</h3><div>Findings inform what questions researchers should address to meet the needs of stakeholders and how those results should be translated back to the communit
{"title":"TRANSLATING AUTISM STAKEHOLDERS’ PRIORITIES TO SHAPE RESEARCH AND DISSEMINATE SCIENTISTS’ FINDINGS TO THE AUTISM COMMUNITY AND PUBLIC","authors":"Jessica Walton , Michelle Trice , Mirian Ofonedu , Robin Baumeister , Aidan Hunter , Heather Volk , Christine Ladd-Acosta","doi":"10.1016/j.euroneuro.2025.08.467","DOIUrl":"10.1016/j.euroneuro.2025.08.467","url":null,"abstract":"<div><h3>Background</h3><div>Autism spectrum disorder is a neurodevelopmental condition that is being identified in increasing numbers. As of 2022 data released this year, 3.2% of U.S. 8-year-olds at 16 study sites have been identified as having ASD. The causes of autism are complex. While genes are clearly a factor, increasing evidence suggests that nongenetic variables – termed “environmental exposures” – are also contributors to causes of autism and co-occurring conditions that can significantly impact the lives of people with autism and their families. Environmental exposures may be modifiable, holding promise that identifying and communicating them to the public could improve lives. Research priorities in this gene-by-environment (GxE) area have been largely shaped by researchers. However, it is crucially important to involve stakeholders to understand their needs, their priorities, and their views on the best ways to communicate GxE concepts and sensitively share research findings back to the community. As part of the GEARs ACE Network, one aim was to conduct this stakeholder research. In this talk, the authors present qualitative findings from focus groups conducted in four types of stakeholder groups.</div></div><div><h3>Methods</h3><div>After developing an interview guide with help from a community advisory board, researchers conducted interviews with four key stakeholder groups: adults with autism; caregivers; medical providers; and adult siblings of people with autism. The guide asked multiple open-ended questions about 1) co-occurring conditions; 2) genetics; 3) environmental factors; and 4) information sources. The research team included a member with autism. Purposeful online sampling was used to recruit participants in the United States. Data were drawn from four 60-to-90-minute sessions that each involved a maximum of six participants. Data were analyzed using a content analysis deductive approach.</div></div><div><h3>Results</h3><div>Across the four groups, a key theme that emerged was the need for immediate support to mitigate the impacts of multiple co-occurring conditions. Many autistic participants expressed a preference to focus on improving specific challenging symptoms/co-occurring conditions, as opposed to fully preventing autism. Participants had a desire for more genetic and environmental research related to current quality of life. Participants expressed interest in genetics but also shared concern about the fraught history of genetic research. Groups varied in their preferred sources of information, as well as what they found trustworthy. Groups noted the importance of disseminating information in a way that does not feed negative perceptions of an autism diagnosis or convey blame on parents regarding modifiable risk factors.</div></div><div><h3>Conclusion</h3><div>Findings inform what questions researchers should address to meet the needs of stakeholders and how those results should be translated back to the communit","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 7"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204420","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}
Pub Date : 2025-10-01Epub Date: 2025-10-03DOI: 10.1016/j.euroneuro.2025.08.515
Carolina Makowski , Chun Chieh Fan , Alexey Shadrin , Anders Dale , Ole Andreassen , Dennis van der Meer
<div><h3>Background</h3><div>Anorexia Nervosa (AN) is a severe psychiatric condition marked by extreme food restriction and body image concerns, with limited treatments and poor prognosis. AN has a strong genetic component, with heritability estimates ranging between 48-74%. Its genetic architecture suggests metabo-psychiatric origins, highlighting the contribution of metabolic factors to AN’s clinical presentation. Given the strong neurobiological underpinnings of AN and the important role of metabolic processes in shaping brain structure, genetic influences on brain tissue microstructure may provide a mechanistic link between AN and metabolic health.</div></div><div><h3>Methods</h3><div>We leveraged three sets of Genome-Wide Association Studies including i) 207,836 individuals from the UK Biobank across 249 circulating plasma metabolites; ii) 23,543 individuals from the UK Biobank with two restricted diffusion (RD) neuroimaging-derived phenotypes capturing genetic architecture of multivariate patterns of whole-brain tissue microstructure; and iii) 72,517 individuals (16,992 cases with AN) from the Psychiatric Genomics Consortium. First, we assessed genetic correlations between AN and 249 metabolites, and compared estimates with other well-characterized cardiometabolic traits (body mass index (BMI), type II diabetes (T2D)) and highly comorbid psychiatric traits (anxiety). We then assessed genetic overlap between pairwise traits (e.g., AN-metabolites; AN-RD) through conjunctional false discovery rate (cFDR). Finally, we mapped overlapping variants to genes and analyzed gene ontologies of shared biological pathways between AN, RD, and metabolites.</div></div><div><h3>Results</h3><div>Strong but opposite directions of genetic correlations were found between AN with 249 circulating metabolites, compared to those seen in metabolic traits such as BMI and T2D (rs∼-0.94). Anxiety, a commonly comorbid trait with AN, had weaker correlations with the metabolites, emphasizing the unique metabolic component of AN. CFDR revealed shared genetic architecture between AN-metabolites and AN-RD. More pronounced overlap was found between AN and lipid-based metabolites, with 57.1% of shared variants having opposite effects on AN and metabolites. There were 23 and 20 overlapping SNPs between directional and isotropic RD measures, respectively, and AN. Both AN-RD, particularly isotropic RD, and AN-metabolite overlapping SNPs were mapped to genes involved in developmental growth and cell homeostatic processes (q < 0.05).</div></div><div><h3>Discussion</h3><div>The shared biological pathways between AN, brain tissue microstructure and metabolites offer a multimodal perspective on the complex etiology of AN and provide new research directions for treatment targets. Links between AN and metabolites, which can be easily obtained from plasma markers, could guide metabolism-informed treatments. Neuroimaging also provides an important mechanistic layer to inform how metabol
{"title":"INSIGHTS INTO THE METABOLIC ORIGINS OF ANOREXIA NERVOSA THROUGH GENOMICS AND NEUROIMAGING","authors":"Carolina Makowski , Chun Chieh Fan , Alexey Shadrin , Anders Dale , Ole Andreassen , Dennis van der Meer","doi":"10.1016/j.euroneuro.2025.08.515","DOIUrl":"10.1016/j.euroneuro.2025.08.515","url":null,"abstract":"<div><h3>Background</h3><div>Anorexia Nervosa (AN) is a severe psychiatric condition marked by extreme food restriction and body image concerns, with limited treatments and poor prognosis. AN has a strong genetic component, with heritability estimates ranging between 48-74%. Its genetic architecture suggests metabo-psychiatric origins, highlighting the contribution of metabolic factors to AN’s clinical presentation. Given the strong neurobiological underpinnings of AN and the important role of metabolic processes in shaping brain structure, genetic influences on brain tissue microstructure may provide a mechanistic link between AN and metabolic health.</div></div><div><h3>Methods</h3><div>We leveraged three sets of Genome-Wide Association Studies including i) 207,836 individuals from the UK Biobank across 249 circulating plasma metabolites; ii) 23,543 individuals from the UK Biobank with two restricted diffusion (RD) neuroimaging-derived phenotypes capturing genetic architecture of multivariate patterns of whole-brain tissue microstructure; and iii) 72,517 individuals (16,992 cases with AN) from the Psychiatric Genomics Consortium. First, we assessed genetic correlations between AN and 249 metabolites, and compared estimates with other well-characterized cardiometabolic traits (body mass index (BMI), type II diabetes (T2D)) and highly comorbid psychiatric traits (anxiety). We then assessed genetic overlap between pairwise traits (e.g., AN-metabolites; AN-RD) through conjunctional false discovery rate (cFDR). Finally, we mapped overlapping variants to genes and analyzed gene ontologies of shared biological pathways between AN, RD, and metabolites.</div></div><div><h3>Results</h3><div>Strong but opposite directions of genetic correlations were found between AN with 249 circulating metabolites, compared to those seen in metabolic traits such as BMI and T2D (rs∼-0.94). Anxiety, a commonly comorbid trait with AN, had weaker correlations with the metabolites, emphasizing the unique metabolic component of AN. CFDR revealed shared genetic architecture between AN-metabolites and AN-RD. More pronounced overlap was found between AN and lipid-based metabolites, with 57.1% of shared variants having opposite effects on AN and metabolites. There were 23 and 20 overlapping SNPs between directional and isotropic RD measures, respectively, and AN. Both AN-RD, particularly isotropic RD, and AN-metabolite overlapping SNPs were mapped to genes involved in developmental growth and cell homeostatic processes (q < 0.05).</div></div><div><h3>Discussion</h3><div>The shared biological pathways between AN, brain tissue microstructure and metabolites offer a multimodal perspective on the complex etiology of AN and provide new research directions for treatment targets. Links between AN and metabolites, which can be easily obtained from plasma markers, could guide metabolism-informed treatments. Neuroimaging also provides an important mechanistic layer to inform how metabol","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 29"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204376","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}
Pub Date : 2025-10-01Epub Date: 2025-10-03DOI: 10.1016/j.euroneuro.2025.08.524
Michael Talkowski , Jack Fu , Kyle Satterstrom , Harrison Brand , Eren Shen , Justin Lim , Lily Wang , David Cutler , Kaitlin Samocha , Elise Robinson , Joseph Buxbaum , Bernie Devlin , Kathryn Roeder , Stephan Sanders , Mark Daly
<div><div>New insights into genetic etiological factors underlying autism and related neurodevelopmental and neuropsychiatric conditions have progressed rapidly, driven by accelerating data aggregation and analytic innovations. Large-scale sequencing of rare coding variation has enabled robust gene discovery and deeper insights into biological mechanisms underlying human development and cognition. Here, we report the largest-to-date analysis of rare variants in autism, encompassing 62,470 individuals diagnosed with autism, including 38,545 probands with parental data from complete families. By integrating de novo and inherited data across single-nucloetide and copy number variation in this cohort via the TADA Bayesian model, we identify 257 genes robustly associated with autism at a false discovery rate (FDR) < 0.001. Not only do these genes recapitulate strong enrichment in pathways such as those involved in chromatin remodeling, development, and synaptic communication/signaling, but many of them have also recently been shown to be significantly associated in studies across a range of neuropsychiatric disorders.</div><div>We sought to contextualize these findings in the broader landscape of neuropsychiatric genetics by systematically aggregating our results gene and pathway level findings from large-scale studies of schizophrenia (SCHEMA; 24,248 cases, 97,322 controls), epilepsy (Epi25K; 20,979 cases, 33,444 controls), and bipolar disorder (BIPEX; 13,933 cases, 14,422 controls). Burden heritability regression reveals that autism harbors the greatest rare variant heritability (>3%), followed by schizophrenia and epilepsy (1–2%). We observe moderate genetic correlation between autism and each of schizophrenia, epilepsy, and bipolar disorder based on rare variants (∼0.2), while the highest rare variant genetic correlation is between schizophrenia and epilepsy (∼0.5), and in schizophrenia with bipolar disorder (∼0.4). Partitioning rare variant heritability reveals that 25% of rare variant heritability in autism resides in the 257 autism-associated genes, while the same genes account for ∼20% of rare variant heritability in epilepsy, but less than 10% in schizophrenia and bipolar disorder, suggesting both shared and distinct pathways of disruption. At a gene-set level, the autism associated genes are significantly more likely to also be associated with schizophrenia (Odds ratio [OR]=10.8, p=1.5e-14) and epilepsy (OR=11.6, p=1.1e-11), with weaker enrichment in bipolar disorder (OR=2.8, p=0.094). Finally, autism genes in chromatin, development, and synaptic signaling pathways are significantly enriched for genes associated with schizophrenia (OR=1.76, p=7.6e-2) and epilepsy (OR=2.22, p=3.2e-4).</div><div>Our findings highlight a core set of highly penetrant genes with impact across autism and neuropsychiatric phenotypes, while also revealing disorder-specific genetic architecture differences. Additional efforts to integrate these gene-level disco
{"title":"SHARED AND DISTINCT GENETIC ARCHITECTURES OF AUTISM AND NEUROPSYCHIATRIC DISORDERS","authors":"Michael Talkowski , Jack Fu , Kyle Satterstrom , Harrison Brand , Eren Shen , Justin Lim , Lily Wang , David Cutler , Kaitlin Samocha , Elise Robinson , Joseph Buxbaum , Bernie Devlin , Kathryn Roeder , Stephan Sanders , Mark Daly","doi":"10.1016/j.euroneuro.2025.08.524","DOIUrl":"10.1016/j.euroneuro.2025.08.524","url":null,"abstract":"<div><div>New insights into genetic etiological factors underlying autism and related neurodevelopmental and neuropsychiatric conditions have progressed rapidly, driven by accelerating data aggregation and analytic innovations. Large-scale sequencing of rare coding variation has enabled robust gene discovery and deeper insights into biological mechanisms underlying human development and cognition. Here, we report the largest-to-date analysis of rare variants in autism, encompassing 62,470 individuals diagnosed with autism, including 38,545 probands with parental data from complete families. By integrating de novo and inherited data across single-nucloetide and copy number variation in this cohort via the TADA Bayesian model, we identify 257 genes robustly associated with autism at a false discovery rate (FDR) < 0.001. Not only do these genes recapitulate strong enrichment in pathways such as those involved in chromatin remodeling, development, and synaptic communication/signaling, but many of them have also recently been shown to be significantly associated in studies across a range of neuropsychiatric disorders.</div><div>We sought to contextualize these findings in the broader landscape of neuropsychiatric genetics by systematically aggregating our results gene and pathway level findings from large-scale studies of schizophrenia (SCHEMA; 24,248 cases, 97,322 controls), epilepsy (Epi25K; 20,979 cases, 33,444 controls), and bipolar disorder (BIPEX; 13,933 cases, 14,422 controls). Burden heritability regression reveals that autism harbors the greatest rare variant heritability (>3%), followed by schizophrenia and epilepsy (1–2%). We observe moderate genetic correlation between autism and each of schizophrenia, epilepsy, and bipolar disorder based on rare variants (∼0.2), while the highest rare variant genetic correlation is between schizophrenia and epilepsy (∼0.5), and in schizophrenia with bipolar disorder (∼0.4). Partitioning rare variant heritability reveals that 25% of rare variant heritability in autism resides in the 257 autism-associated genes, while the same genes account for ∼20% of rare variant heritability in epilepsy, but less than 10% in schizophrenia and bipolar disorder, suggesting both shared and distinct pathways of disruption. At a gene-set level, the autism associated genes are significantly more likely to also be associated with schizophrenia (Odds ratio [OR]=10.8, p=1.5e-14) and epilepsy (OR=11.6, p=1.1e-11), with weaker enrichment in bipolar disorder (OR=2.8, p=0.094). Finally, autism genes in chromatin, development, and synaptic signaling pathways are significantly enriched for genes associated with schizophrenia (OR=1.76, p=7.6e-2) and epilepsy (OR=2.22, p=3.2e-4).</div><div>Our findings highlight a core set of highly penetrant genes with impact across autism and neuropsychiatric phenotypes, while also revealing disorder-specific genetic architecture differences. Additional efforts to integrate these gene-level disco","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Pages 32-33"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204468","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}
Pub Date : 2025-10-01Epub Date: 2025-10-03DOI: 10.1016/j.euroneuro.2025.08.489
Peyton Coleman , Jeffrey Annis , Hiral Master , Lide Han , Kelsie Full , Evan Brittain , Douglas Ruderfer
Sleep disturbances are hallmark symptoms of most psychiatric conditions but have been historically difficult to assess and interpret. Recent advancements in wearable devices (such as Fitbits) have enabled more accurate collection of sleep data. However, the high granularity of this data (sometimes at the minute-level for 10+ years) makes it challenging to define and interpret sleep disturbances.
Neurobiological models of sleep regulation are well established but are not employed for precision psychiatry because collecting neurological measures (EEG, fMRI, polysomnography) is highly invasive and does not capture natural sleep behaviors. The current work aims to link observable digital health behaviors to these neurobiological sleep measures to create interpretable and actionable sleep phenotypes for precision psychiatry. We have relied on the two-process model of sleep regulation, which includes independent circadian and sleep pressure processes, to inform digital health behaviors. Both processes are know to be significantly disrupted in psychiatric conditions, particularly depression, which we have modeled using data from the All of Us (AoU) Research Program. AoU includes Fitbit sleep and activity data for 31,341 participants, half of whom have whole genome sequencing.
Our findings indicate that our wearable derived sleep behaviors are highly associated with depression diagnosis and severity. Notably, sleep pressure processes, previously examined only with EEG, such as REM latency and slow wave sleep proportion, are very predictive of depression diagnosis in our sample (Odds Ratios: REM latency: 1.48; SWS proportion: 0.86). This work demonstrates the value of generating interpretable wearable-derived sleep features, opening the door to large-scale genomic and phenomic studies of sleep behaviors' role in psychiatric conditions.
{"title":"INTERPRETABLE WEARABLE-DERIVED SLEEP BEHAVIORS FOR DEPRESSION AT SCALE","authors":"Peyton Coleman , Jeffrey Annis , Hiral Master , Lide Han , Kelsie Full , Evan Brittain , Douglas Ruderfer","doi":"10.1016/j.euroneuro.2025.08.489","DOIUrl":"10.1016/j.euroneuro.2025.08.489","url":null,"abstract":"<div><div>Sleep disturbances are hallmark symptoms of most psychiatric conditions but have been historically difficult to assess and interpret. Recent advancements in wearable devices (such as Fitbits) have enabled more accurate collection of sleep data. However, the high granularity of this data (sometimes at the minute-level for 10+ years) makes it challenging to define and interpret sleep disturbances.</div><div>Neurobiological models of sleep regulation are well established but are not employed for precision psychiatry because collecting neurological measures (EEG, fMRI, polysomnography) is highly invasive and does not capture natural sleep behaviors. The current work aims to link observable digital health behaviors to these neurobiological sleep measures to create interpretable and actionable sleep phenotypes for precision psychiatry. We have relied on the two-process model of sleep regulation, which includes independent circadian and sleep pressure processes, to inform digital health behaviors. Both processes are know to be significantly disrupted in psychiatric conditions, particularly depression, which we have modeled using data from the All of Us (AoU) Research Program. AoU includes Fitbit sleep and activity data for 31,341 participants, half of whom have whole genome sequencing.</div><div>Our findings indicate that our wearable derived sleep behaviors are highly associated with depression diagnosis and severity. Notably, sleep pressure processes, previously examined only with EEG, such as REM latency and slow wave sleep proportion, are very predictive of depression diagnosis in our sample (Odds Ratios: REM latency: 1.48; SWS proportion: 0.86). This work demonstrates the value of generating interpretable wearable-derived sleep features, opening the door to large-scale genomic and phenomic studies of sleep behaviors' role in psychiatric conditions.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 18"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204456","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}
Pub Date : 2025-10-01Epub Date: 2025-10-03DOI: 10.1016/j.euroneuro.2025.08.529
Sarah Colbert , the Suicide Working Group of the Psychiatric Genomics Consortium , Douglas Ruderfer , Anna Docherty , Niamh Mullins
<div><h3>Background</h3><div>Suicidality phenotypes, specifically suicidal ideation (SI), suicide attempt (SA) and suicide death (SD), are substantially heritable, with twin and family studies estimating heritabilities in the range of 30-55%. Recently, genome-wide association studies (GWAS) have reached sufficient sample sizes to conduct well-powered analyses, leading to the identification of 4, 12 and 2 loci associated with SI, SA, and SD, respectively. Importantly, these phenotypes show strong, yet incomplete, genetic correlations with each other, motivating genetic studies of each phenotype separately to understand their underlying biology and the progression from one to the next. Here, we present an update on the progress of the latest and most extensive GWAS of SI, SA, SD, and suicidal behavior (SB; SA + SD) conducted by the Psychiatric Genomics Consortium Suicide Working Group (PGC SUI).</div></div><div><h3>Methods</h3><div>Data comprise 37 cohorts contributing to the SI GWAS (N cases=259,747, N controls=1,309,943), 46 cohorts contributing to the SA GWAS (N cases=64,993, N controls=1,269,037), and 7 cohorts contributing to the SD GWAS (N cases=9,197, N controls=668,162). The SB GWAS included 49 cohorts with SA and/or SD data (N cases=75,300, N controls=1,311,895). Notably, these cohorts comprise individuals from five diverse genetic ancestry groups: European ancestry (EUR), African ancestry (AA), East Asian ancestry (EA), Central South Asian ancestry (CSA), and Latino ancestry (LAT). Standardized phenotyping and analytic protocols were employed by PGC SUI to ensure exceptional rigor and comparability across cohorts. GWAS meta-analyses were conducted via inverse variance-weighted fixed effects models to identify genetic risk loci. Post-GWAS analyses included examination of the SNP-heritabilities (h2SNP), and genetic relationships between SI, SA, SB, and SD.</div></div><div><h3>Results</h3><div>The SI GWAS yielded a h2SNP of 2.1% (se=0.001) and 13 GWS loci (6 novel). The SA GWAS yielded a h2SNP of 5.6% (se=0.003) and 37 GWS loci (22 novel). The SB GWAS yielded a h2SNP of 5.7% (se=0.003) and in addition to 34 GWS loci overlapping with the SA GWAS, it identified a further 19 GWS loci (3 novel). The multi-ancestry GWAS of SD yielded a h2SNP of 4.8% (se=0.007), but no GWS loci were identified. However, the EUR SD meta-analysis (h2SNP=5.1%, se=0.008) identified 2 novel GWS loci. We observed strong genetic correlations (rg) between all suicidality phenotypes, although notably all were significantly lower than 1. The highest genetic correlations were observed between SI and SA (rg=0.88, se=0.03) and SB (rg=0.88, se=0.02), while genetic correlations were lower between SD and SI (rg=0.70, se=0.08), SA (rg=0.73, se=0.06) and SB (rg=0.73, se=0.04).</div></div><div><h3>Discussion</h3><div>Increased sample sizes in combination with streamlined protocols for phenotyping and analyzing have yielded novel loci associated with suicidality phenotypes and provid
{"title":"GENOME-WIDE ASSOCIATION STUDIES OF SUICIDALITY PHENOTYPES: AN UPDATE FROM THE PSYCHIATRIC GENOMICS CONSORTIUM SUICIDE WORKING GROUP","authors":"Sarah Colbert , the Suicide Working Group of the Psychiatric Genomics Consortium , Douglas Ruderfer , Anna Docherty , Niamh Mullins","doi":"10.1016/j.euroneuro.2025.08.529","DOIUrl":"10.1016/j.euroneuro.2025.08.529","url":null,"abstract":"<div><h3>Background</h3><div>Suicidality phenotypes, specifically suicidal ideation (SI), suicide attempt (SA) and suicide death (SD), are substantially heritable, with twin and family studies estimating heritabilities in the range of 30-55%. Recently, genome-wide association studies (GWAS) have reached sufficient sample sizes to conduct well-powered analyses, leading to the identification of 4, 12 and 2 loci associated with SI, SA, and SD, respectively. Importantly, these phenotypes show strong, yet incomplete, genetic correlations with each other, motivating genetic studies of each phenotype separately to understand their underlying biology and the progression from one to the next. Here, we present an update on the progress of the latest and most extensive GWAS of SI, SA, SD, and suicidal behavior (SB; SA + SD) conducted by the Psychiatric Genomics Consortium Suicide Working Group (PGC SUI).</div></div><div><h3>Methods</h3><div>Data comprise 37 cohorts contributing to the SI GWAS (N cases=259,747, N controls=1,309,943), 46 cohorts contributing to the SA GWAS (N cases=64,993, N controls=1,269,037), and 7 cohorts contributing to the SD GWAS (N cases=9,197, N controls=668,162). The SB GWAS included 49 cohorts with SA and/or SD data (N cases=75,300, N controls=1,311,895). Notably, these cohorts comprise individuals from five diverse genetic ancestry groups: European ancestry (EUR), African ancestry (AA), East Asian ancestry (EA), Central South Asian ancestry (CSA), and Latino ancestry (LAT). Standardized phenotyping and analytic protocols were employed by PGC SUI to ensure exceptional rigor and comparability across cohorts. GWAS meta-analyses were conducted via inverse variance-weighted fixed effects models to identify genetic risk loci. Post-GWAS analyses included examination of the SNP-heritabilities (h2SNP), and genetic relationships between SI, SA, SB, and SD.</div></div><div><h3>Results</h3><div>The SI GWAS yielded a h2SNP of 2.1% (se=0.001) and 13 GWS loci (6 novel). The SA GWAS yielded a h2SNP of 5.6% (se=0.003) and 37 GWS loci (22 novel). The SB GWAS yielded a h2SNP of 5.7% (se=0.003) and in addition to 34 GWS loci overlapping with the SA GWAS, it identified a further 19 GWS loci (3 novel). The multi-ancestry GWAS of SD yielded a h2SNP of 4.8% (se=0.007), but no GWS loci were identified. However, the EUR SD meta-analysis (h2SNP=5.1%, se=0.008) identified 2 novel GWS loci. We observed strong genetic correlations (rg) between all suicidality phenotypes, although notably all were significantly lower than 1. The highest genetic correlations were observed between SI and SA (rg=0.88, se=0.03) and SB (rg=0.88, se=0.02), while genetic correlations were lower between SD and SI (rg=0.70, se=0.08), SA (rg=0.73, se=0.06) and SB (rg=0.73, se=0.04).</div></div><div><h3>Discussion</h3><div>Increased sample sizes in combination with streamlined protocols for phenotyping and analyzing have yielded novel loci associated with suicidality phenotypes and provid","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"99 ","pages":"Page 35"},"PeriodicalIF":6.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204472","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}
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