Pub Date : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.036
The latest schizophrenia GWAS meta-analysis found 287 loci that reached genome-wide statistical significance (67,390 cases and 94,015 controls). In these loci, 120 genes were prioritized using fine-mapping, summary-based Mendelian Randomization (SMR), and enhancer-promoter interaction (via Hi-C). However, these methods only use information within a given locus, ignoring information from the rest of the genome. Combining locus-based approaches with tools that incorporate genome-wide information such as the Polygenic Priority Score (PoPS) have been shown to improve gene prioritization precision. To more accurately characterize genes that play a role in schizophrenia etiology, we prioritized 62 genes based on their distance to GWAS signals, PoPS, fine-mapped coding variants, and ultra-rare coding variant burden tests. We prioritized DRD2, the target of most approved antipsychotics, which was not highlighted by previous efforts. In addition, we prioritized 9 genes that are targeted by approved or investigational drugs and may therefore present drug repurposing opportunities. These included drugs targeting calcium channels (CACNA1C and CACNB2), glutamatergic receptors (GRIN2A and GRM3), and GABAB receptor (GABBR2). We highlighted 3 additional genes (PDE4B, VRK2, and PLCL2) in loci that are shared with a recent addiction GWAS. While it is challenging to assess psychotic symptoms in rodents, high-quality rodent addiction models exist for a wide range of substances. Modulation of these genes could be tested in rodent addiction models and, if successful, may warrant further testing in human clinical trials of addiction and/or schizophrenia. Adding to previous gene prioritization efforts, we hope that our list of prioritized genes will ultimately facilitate the development of new medicines for people living with schizophrenia.
{"title":"IDENTIFYING DRUG TARGETS FOR SCHIZOPHRENIA THROUGH GENE PRIORITIZATION","authors":"","doi":"10.1016/j.euroneuro.2024.08.036","DOIUrl":"10.1016/j.euroneuro.2024.08.036","url":null,"abstract":"<div><div>The latest schizophrenia GWAS meta-analysis found 287 loci that reached genome-wide statistical significance (67,390 cases and 94,015 controls). In these loci, 120 genes were prioritized using fine-mapping, summary-based Mendelian Randomization (SMR), and enhancer-promoter interaction (via Hi-C). However, these methods only use information within a given locus, ignoring information from the rest of the genome. Combining locus-based approaches with tools that incorporate genome-wide information such as the Polygenic Priority Score (PoPS) have been shown to improve gene prioritization precision. To more accurately characterize genes that play a role in schizophrenia etiology, we prioritized 62 genes based on their distance to GWAS signals, PoPS, fine-mapped coding variants, and ultra-rare coding variant burden tests. We prioritized DRD2, the target of most approved antipsychotics, which was not highlighted by previous efforts. In addition, we prioritized 9 genes that are targeted by approved or investigational drugs and may therefore present drug repurposing opportunities. These included drugs targeting calcium channels (CACNA1C and CACNB2), glutamatergic receptors (GRIN2A and GRM3), and GABAB receptor (GABBR2). We highlighted 3 additional genes (PDE4B, VRK2, and PLCL2) in loci that are shared with a recent addiction GWAS. While it is challenging to assess psychotic symptoms in rodents, high-quality rodent addiction models exist for a wide range of substances. Modulation of these genes could be tested in rodent addiction models and, if successful, may warrant further testing in human clinical trials of addiction and/or schizophrenia. Adding to previous gene prioritization efforts, we hope that our list of prioritized genes will ultimately facilitate the development of new medicines for people living with schizophrenia.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442132","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 : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.061
Mental health stigma takes many forms and remains a significant barrier to seeking help and achieving equitable conduct in the work environment. This may be particularly relevant within psychiatric genetics, where researchers, clinicians, and individuals with lived experience converge. Characterizing the nature, prevalence and impact of mental health stigma within the psychiatric genetics community will be an important step toward developing strategies to mitigate its effects and promote inclusivity. The ISPG Stigma Reduction Special Interest Group (SIG) aims to explore ISPG members' views and experiences regarding mental health stigma, with the goal of understanding areas in which the SIG might affect change.
The Stigma Reduction SIG developed a survey to capture ISPG members' experiences and perceptions of stigma related to mental health conditions, as well as their advice on how to address it. To assess stigma in the personal environment, we used a question from the Attribution Questionnaire (ref). For the remaining questions, we developed novel items, as there were no suitable validated questionnaires available to address the specific topics relevant to the psychiatric genetics community. The survey was constructed using Qualtrics software, with participants' anonymity ensured. We received ethical approval from the QIMR Berghofer Medical Research Institute. The survey was distributed electronically to ISPG members, including researchers, clinicians, and individuals with lived experience with mental health conditions.
During this talk, the (preliminary) results of the survey data will be presented.
The ISPG Stigma Reduction SIG plans to use the survey insights to develop targeted action points aimed at fostering a more inclusive environment. By sharing these findings at the World Congress of Psychiatric Genetics 2024, we hope to initiate a broader conversation on stigma reduction and inspire collaborative efforts to eliminate prejudice and discrimination against people with mental health conditions, including those working in the psychiatric genetics field.
{"title":"EXPLORING MENTAL HEALTH STIGMA IN THE CONTEXT OF PSYCHIATRIC GENETICS: INSIGHTS FROM THE ISPG STIGMA REDUCTION SIG SURVEY","authors":"","doi":"10.1016/j.euroneuro.2024.08.061","DOIUrl":"10.1016/j.euroneuro.2024.08.061","url":null,"abstract":"<div><div>Mental health stigma takes many forms and remains a significant barrier to seeking help and achieving equitable conduct in the work environment. This may be particularly relevant within psychiatric genetics, where researchers, clinicians, and individuals with lived experience converge. Characterizing the nature, prevalence and impact of mental health stigma within the psychiatric genetics community will be an important step toward developing strategies to mitigate its effects and promote inclusivity. The ISPG Stigma Reduction Special Interest Group (SIG) aims to explore ISPG members' views and experiences regarding mental health stigma, with the goal of understanding areas in which the SIG might affect change.</div><div>The Stigma Reduction SIG developed a survey to capture ISPG members' experiences and perceptions of stigma related to mental health conditions, as well as their advice on how to address it. To assess stigma in the personal environment, we used a question from the Attribution Questionnaire (ref). For the remaining questions, we developed novel items, as there were no suitable validated questionnaires available to address the specific topics relevant to the psychiatric genetics community. The survey was constructed using Qualtrics software, with participants' anonymity ensured. We received ethical approval from the QIMR Berghofer Medical Research Institute. The survey was distributed electronically to ISPG members, including researchers, clinicians, and individuals with lived experience with mental health conditions.</div><div>During this talk, the (preliminary) results of the survey data will be presented.</div><div>The ISPG Stigma Reduction SIG plans to use the survey insights to develop targeted action points aimed at fostering a more inclusive environment. By sharing these findings at the World Congress of Psychiatric Genetics 2024, we hope to initiate a broader conversation on stigma reduction and inspire collaborative efforts to eliminate prejudice and discrimination against people with mental health conditions, including those working in the psychiatric genetics field.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442137","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 : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.049
<div><div>Suicidal thoughts and behaviors, 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, and SD, conducted by the Psychiatric Genomics Consortium Suicide Working Group (PGC SUI).</div><div><strong>Methods:</strong> Data comprise 30 cohorts contributing to the SI GWAS (N cases=256,257, N controls=1,298,106), 42 cohorts contributing to the SA GWAS (N cases=73,087, N controls=1,327,350), and 6 cohorts contributing to the SD GWAS (N cases=6,775, N controls=841,216). Notably, these cohorts comprise individuals from four diverse genetic ancestry groups: admixed European ancestries (EUR), admixed African ancestries (AA), East Asian ancestries (EA) and admixed Latino ancestries (LAT). New phenotyping and analytic protocols have been developed by PGC SUI to ensure exceptional rigor and comparability across cohorts. GWAS meta-analyses will be conducted via inverse variance-weighted fixed effects models to identify novel genetic risk loci. Post-GWAS analyses include pathway, tissue and drug target enrichment, and examination of the SNP-heritabilities (h2SNP), and genetic relationships between SI, SA, and SD.</div><div>Preliminary analysis using the currently available SA data (SA cases = 47,174, controls = 941,010 from 26 cohorts) yielded a h2SNP of 5.6% (se = 0.003, p = 1.2e-68) and ten replicated and three novel genome-wide significant (GWS) loci, containing FYN, AIG1, and DCC. Eight GWS loci were identified in the EUR meta-analysis (h2SNP = 7%, se = 0.004) which replicated previous findings. No GWS loci were identified in the AA (h2SNP = 9.8%, se = 0.02), EA (h2SNP 5.1%, se = 0.04) or LAT (h2SNP = 10%, se =0.07) GWAS meta-analyses. We also identified significant enrichment in genes expressed in several brain tissues from GTEx and summary data-based Mendelian Randomization revealed two novel genes (GMPPB, FURIN) significantly associated with SA. This SA GWAS showed significant genetic correlations with published GWAS of SI (rg = 0.80, se = 0.04), SD (rg = 0.77, se = 0.05), and several psychiatric disorders (rgs = 0.26-0.70).</div><div>Additional data intake is almost complete within PGC SUI, and this presentation will share the final GWAS results and novel biological insights. Increased sample sizes in combination with streamlined protocols for phenotyping and analyzing suicidal tho
{"title":"GENOME-WIDE ASSOCIATION STUDIES OF SUICIDAL THOUGHTS AND BEHAVIORS: AN UPDATE FROM THE PSYCHIATRIC GENOMICS CONSORTIUM SUICIDE WORKING GROUP","authors":"","doi":"10.1016/j.euroneuro.2024.08.049","DOIUrl":"10.1016/j.euroneuro.2024.08.049","url":null,"abstract":"<div><div>Suicidal thoughts and behaviors, 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, and SD, conducted by the Psychiatric Genomics Consortium Suicide Working Group (PGC SUI).</div><div><strong>Methods:</strong> Data comprise 30 cohorts contributing to the SI GWAS (N cases=256,257, N controls=1,298,106), 42 cohorts contributing to the SA GWAS (N cases=73,087, N controls=1,327,350), and 6 cohorts contributing to the SD GWAS (N cases=6,775, N controls=841,216). Notably, these cohorts comprise individuals from four diverse genetic ancestry groups: admixed European ancestries (EUR), admixed African ancestries (AA), East Asian ancestries (EA) and admixed Latino ancestries (LAT). New phenotyping and analytic protocols have been developed by PGC SUI to ensure exceptional rigor and comparability across cohorts. GWAS meta-analyses will be conducted via inverse variance-weighted fixed effects models to identify novel genetic risk loci. Post-GWAS analyses include pathway, tissue and drug target enrichment, and examination of the SNP-heritabilities (h2SNP), and genetic relationships between SI, SA, and SD.</div><div>Preliminary analysis using the currently available SA data (SA cases = 47,174, controls = 941,010 from 26 cohorts) yielded a h2SNP of 5.6% (se = 0.003, p = 1.2e-68) and ten replicated and three novel genome-wide significant (GWS) loci, containing FYN, AIG1, and DCC. Eight GWS loci were identified in the EUR meta-analysis (h2SNP = 7%, se = 0.004) which replicated previous findings. No GWS loci were identified in the AA (h2SNP = 9.8%, se = 0.02), EA (h2SNP 5.1%, se = 0.04) or LAT (h2SNP = 10%, se =0.07) GWAS meta-analyses. We also identified significant enrichment in genes expressed in several brain tissues from GTEx and summary data-based Mendelian Randomization revealed two novel genes (GMPPB, FURIN) significantly associated with SA. This SA GWAS showed significant genetic correlations with published GWAS of SI (rg = 0.80, se = 0.04), SD (rg = 0.77, se = 0.05), and several psychiatric disorders (rgs = 0.26-0.70).</div><div>Additional data intake is almost complete within PGC SUI, and this presentation will share the final GWAS results and novel biological insights. Increased sample sizes in combination with streamlined protocols for phenotyping and analyzing suicidal tho","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442217","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 : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.105
While the longitudinal aspect of mental disorders is critical for investigating disease mechanisms and improving treatment, psychiatric genetics have mostly focused on cross-sectional data. Longitudinal datasets from diverse ancestries are paramount to make progress in understanding mental health and illnesses. Availability of trajectories of phenotypes covering premorbid and prodromal stages, and the course of illnesses, coupled with genetics and other biological material will enable us to chart how mental disorders develop, characterize resilience and treatment, allow population stratification, and pave the way for early detection.
This session will present four large diverse longitudinal datasets covering the lifespan – from childhood to old age. The presenters will describe the datasets and new methods developed to take advantage of the longitudinal aspects, and novel results highlighting the opportunities for the field.
Dr. Parekh will introduce the Norwegian Mother, Father and Child Cohort Study (MoBa), an ongoing study following children from birth. This talk will present FEMA (and FEMA-GWAS) statistical methods for longitudinal data and present results that highlight longitudinal, time dependent genetic effects.
Ms. Smith will introduce the Adolescent Brain Cognitive Development (ABCD) Study, an ongoing study on adolescents in the United States. This talk will showcase multimodal imaging-genetics results using FEMA as well as shared resources that will allow any investigator to perform real-time analyses in the ABCD Study.
Dr. Viswanath will introduce the Centre for Brain and Mind (CBM) - Accelerator program for Discovery in Brain disorders using Stem cells (ADBS), an ongoing study on adults in India. This talk will highlight the opportunities and present results linking neuroimaging and rare damaging variants in patients with psychiatric illnesses.
Dr. Namba will introduce the BioBank Japan (BBJ), an ongoing study with extensive registry, biological, laboratory examinations, and other information across a wide range of 47 diseases across the lifespan. This talk will showcase ongoing studies of genetic risk variants, and present opportunities for ongoing collaborative endeavors towards precision medicine.
Dr. Parker, the symposium discussant, will discuss how these lifespan datasets can be integrated and used to generate insights to advance our understanding of the neurobiology of psychiatric illnesses and the goals of precision psychiatry. We will conclude the symposium with remarks on how diverse lifespan datasets can provide valuable knowledge and provide novel opportunities for the field.
{"title":"LONGITUDINAL GENETIC APPROACHES IN MENTAL HEALTH: INTERNATIONAL PERSPECTIVES AND OPPORTUNITIES","authors":"","doi":"10.1016/j.euroneuro.2024.08.105","DOIUrl":"10.1016/j.euroneuro.2024.08.105","url":null,"abstract":"<div><div>While the longitudinal aspect of mental disorders is critical for investigating disease mechanisms and improving treatment, psychiatric genetics have mostly focused on cross-sectional data. Longitudinal datasets from diverse ancestries are paramount to make progress in understanding mental health and illnesses. Availability of trajectories of phenotypes covering premorbid and prodromal stages, and the course of illnesses, coupled with genetics and other biological material will enable us to chart how mental disorders develop, characterize resilience and treatment, allow population stratification, and pave the way for early detection.</div><div>This session will present four large diverse longitudinal datasets covering the lifespan – from childhood to old age. The presenters will describe the datasets and new methods developed to take advantage of the longitudinal aspects, and novel results highlighting the opportunities for the field.</div><div>Dr. Parekh will introduce the Norwegian Mother, Father and Child Cohort Study (MoBa), an ongoing study following children from birth. This talk will present FEMA (and FEMA-GWAS) statistical methods for longitudinal data and present results that highlight longitudinal, time dependent genetic effects.</div><div>Ms. Smith will introduce the Adolescent Brain Cognitive Development (ABCD) Study, an ongoing study on adolescents in the United States. This talk will showcase multimodal imaging-genetics results using FEMA as well as shared resources that will allow any investigator to perform real-time analyses in the ABCD Study.</div><div>Dr. Viswanath will introduce the Centre for Brain and Mind (CBM) - Accelerator program for Discovery in Brain disorders using Stem cells (ADBS), an ongoing study on adults in India. This talk will highlight the opportunities and present results linking neuroimaging and rare damaging variants in patients with psychiatric illnesses.</div><div>Dr. Namba will introduce the BioBank Japan (BBJ), an ongoing study with extensive registry, biological, laboratory examinations, and other information across a wide range of 47 diseases across the lifespan. This talk will showcase ongoing studies of genetic risk variants, and present opportunities for ongoing collaborative endeavors towards precision medicine.</div><div>Dr. Parker, the symposium discussant, will discuss how these lifespan datasets can be integrated and used to generate insights to advance our understanding of the neurobiology of psychiatric illnesses and the goals of precision psychiatry. We will conclude the symposium with remarks on how diverse lifespan datasets can provide valuable knowledge and provide novel opportunities for the field.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442223","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 : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.077
Cross-disorder analyses in psychiatry often center around genetic correlation, which quantifies the average similarity of genetic effects across two disorders. For a long time, this has been the only feasible approach, as most cohorts only collect data on a single disorder. However, few studies have examined the genetic architecture of comorbidity itself or how it relates to the genetic architecture of the individual disorders involved. In this study we set out to investigate the genetic architecture of comorbidity between psychiatric disorders in the iPSYCH2015 case-cohort study. This Danish register-based study contains comorbid cases for 10 pairs of five psychiatric disorders (schizophrenia (SCZ), bipolar disorder (BPD), major depressive disorder (MDD), autism (AUT) and attention deficit hyperactivity disorder (ADHD)), making it ideal for understanding comorbidity. We develop a novel framework to model both cross-disorder genetic sharing and the genetics of comorbidity based on the concept of Coordinated Epistasis (CE). Within this framework, we can identify synergistic and antagonistic interactions of Polygenic Risk Scores (PRS) across each disorder pair. We can also identify how these interactions impact individual disorders involved and delineate established theoretical models of comorbidity. In particular, we test one model of comorbidity where genetic effects distinguish comorbid cases from cases with only one disorder, which shows synergistic PRS interactions between ADHD-AUT comorbid cases and cases of either AUT or ADHD, which replicates in both iPSYCH2015 sub-cohorts: 2012 (P = 1.3E-02) and 2015i (P = 2.9E-02). We next apply our framework to family-based genetic scores (PA-FGRS), using recorded diagnoses from an average of 20 genetic relatives from the Danish medical registry. We find synergistic PA-FGRS interactions in comorbid ADHD-AUT (P = 1.1E-05), validating our PRS results. In summary, we perform the first comprehensive study on the genetics of comorbidity by extending the CE framework using a combination of PRS and PA-FGRS, and for the first time identify coordinated polygenic interactions contributing to cross-disorder genetic sharing and comorbidity among five psychiatric disorders.
{"title":"COORDINATED EPISTASIS DETECTS HETEROGENOUS PATHWAYS ACROSS PSYCHIATRIC DISORDERS AND COMORBIDITIES","authors":"","doi":"10.1016/j.euroneuro.2024.08.077","DOIUrl":"10.1016/j.euroneuro.2024.08.077","url":null,"abstract":"<div><div>Cross-disorder analyses in psychiatry often center around genetic correlation, which quantifies the average similarity of genetic effects across two disorders. For a long time, this has been the only feasible approach, as most cohorts only collect data on a single disorder. However, few studies have examined the genetic architecture of comorbidity itself or how it relates to the genetic architecture of the individual disorders involved. In this study we set out to investigate the genetic architecture of comorbidity between psychiatric disorders in the iPSYCH2015 case-cohort study. This Danish register-based study contains comorbid cases for 10 pairs of five psychiatric disorders (schizophrenia (SCZ), bipolar disorder (BPD), major depressive disorder (MDD), autism (AUT) and attention deficit hyperactivity disorder (ADHD)), making it ideal for understanding comorbidity. We develop a novel framework to model both cross-disorder genetic sharing and the genetics of comorbidity based on the concept of Coordinated Epistasis (CE). Within this framework, we can identify synergistic and antagonistic interactions of Polygenic Risk Scores (PRS) across each disorder pair. We can also identify how these interactions impact individual disorders involved and delineate established theoretical models of comorbidity. In particular, we test one model of comorbidity where genetic effects distinguish comorbid cases from cases with only one disorder, which shows synergistic PRS interactions between ADHD-AUT comorbid cases and cases of either AUT or ADHD, which replicates in both iPSYCH2015 sub-cohorts: 2012 (P = 1.3E-02) and 2015i (P = 2.9E-02). We next apply our framework to family-based genetic scores (PA-FGRS), using recorded diagnoses from an average of 20 genetic relatives from the Danish medical registry. We find synergistic PA-FGRS interactions in comorbid ADHD-AUT (P = 1.1E-05), validating our PRS results. In summary, we perform the first comprehensive study on the genetics of comorbidity by extending the CE framework using a combination of PRS and PA-FGRS, and for the first time identify coordinated polygenic interactions contributing to cross-disorder genetic sharing and comorbidity among five psychiatric disorders.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442231","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 : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.100
Our aim as the PGC Outreach Committee is to improve visibility, accessibility, and understanding of psychiatric genetics amongst both the general public and the wider scientific community. But how accessible are we really? How easily interpreted is the information we share to non-scientists? And how can we improve?
A systematic review of media coverage and readability in genome-wide association studies, published earlier this year, concluded that the language used to describe genetics research is too complex to be understood by the public. Over 95% of the online news sites examined would require more than twelve years of formal education for a full understanding of their content. The importance of language, particularly in genetics research, can extend beyond ‘readability’ to even more fundamental issues. For instance, another recent systematic review emphasised the need for defining ancestry based on the type of data used for its measurement (e.g., “genetic ancestry”), as failure to do so can result in reduced clarity concerning the distinction between genetic and social identities.
This symposium will delve into the critical role of language in the effective communication of scientific concepts to diverse audiences. Our presenters will first each discuss what the importance of language in a diverse world means from their own unique perspective (10 minutes each). They will cover topics such as the importance of the choice of words in relation to genetic ancestry and other complex concepts in psychiatric genetics such as heritability, and the impact of language in discussions surrounding the lived experience of those with psychiatric disorders. Broadly, the presentations will highlight how we can bridge the gap between technical jargon and layman's terms, making complex ideas accessible to a broader audience including those living with psychiatric conditions and their families, as well as how we can more accurately use language in our communications within the scientific community.
We will then have a panel discussion (30 minutes) in which the presenters will share insights into, for example, some of the challenges they have faced in science communication, such as combating misinformation, and what they believe the consequences for our field will be if we do not carefully consider the role of accurate and responsible communication in psychiatric genetics. We will conclude the session with questions from the audience (15 minutes).
Ultimately, the symposium will demonstrate that effective science communication is a dynamic interplay of language, empathy, and engagement, and will encourage attendees to consider the impact of their words in shaping public perceptions and attitudes towards psychiatric genetics.
{"title":"SCIENCE COMMUNICATION: THE IMPORTANCE OF LANGUAGE IN A DIVERSE WORLD","authors":"","doi":"10.1016/j.euroneuro.2024.08.100","DOIUrl":"10.1016/j.euroneuro.2024.08.100","url":null,"abstract":"<div><div>Our aim as the PGC Outreach Committee is to improve visibility, accessibility, and understanding of psychiatric genetics amongst both the general public and the wider scientific community. But how accessible are we really? How easily interpreted is the information we share to non-scientists? And how can we improve?</div><div>A systematic review of media coverage and readability in genome-wide association studies, published earlier this year, concluded that the language used to describe genetics research is too complex to be understood by the public. Over 95% of the online news sites examined would require more than twelve years of formal education for a full understanding of their content. The importance of language, particularly in genetics research, can extend beyond ‘readability’ to even more fundamental issues. For instance, another recent systematic review emphasised the need for defining ancestry based on the type of data used for its measurement (e.g., “genetic ancestry”), as failure to do so can result in reduced clarity concerning the distinction between genetic and social identities.</div><div>This symposium will delve into the critical role of language in the effective communication of scientific concepts to diverse audiences. Our presenters will first each discuss what the importance of language in a diverse world means from their own unique perspective (10 minutes each). They will cover topics such as the importance of the choice of words in relation to genetic ancestry and other complex concepts in psychiatric genetics such as heritability, and the impact of language in discussions surrounding the lived experience of those with psychiatric disorders. Broadly, the presentations will highlight how we can bridge the gap between technical jargon and layman's terms, making complex ideas accessible to a broader audience including those living with psychiatric conditions and their families, as well as how we can more accurately use language in our communications within the scientific community.</div><div>We will then have a panel discussion (30 minutes) in which the presenters will share insights into, for example, some of the challenges they have faced in science communication, such as combating misinformation, and what they believe the consequences for our field will be if we do not carefully consider the role of accurate and responsible communication in psychiatric genetics. We will conclude the session with questions from the audience (15 minutes).</div><div>Ultimately, the symposium will demonstrate that effective science communication is a dynamic interplay of language, empathy, and engagement, and will encourage attendees to consider the impact of their words in shaping public perceptions and attitudes towards psychiatric genetics.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442266","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 : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.017
<div><div>Post-Traumatic Stress Disorder (PTSD) is a complex psychiatric condition that develops following exposure to traumatic experiences. Its core symptoms include intrusive thoughts, avoidant behavior, and a persistent state of hyperarousal. Although it is widely recognized that stress exacerbates inflammation across tissues, a growing body of evidence suggests a reciprocal relationship, with immune function influencing susceptibility to PTSD. This relationship may be driven by shared underlying biology, such as from pleiotropy. The most recent genome-wide association study (GWAS) of PTSD identified 95 risk loci, including endocrine and immune regulators, such as the major histocompatibility complex (MHC). The MHC harbors numerous genetic variants such as human leukocyte antigen (HLA) alleles that are crucial for immune function. However, the complex linkage disequilibrium structure of the MHC poses challenges in isolating individual signals through SNP-based imputation. Here, we present a large-scale cross-ancestry analysis assessing the association of HLA alleles with PTSD.</div><div>We conducted HLA imputation and association analysis in a genotyped sample of individuals of African, European, or Latin American ancestry from 38 studies included in the latest PTSD GWAS published by the Psychiatric Genomics Consortium. The outcome phenotype was assessed as either case-control or on a continuous scale (e.g. the PTSD Checklist for DSM-IV or V). The 1000 Genomes Reference Panel, comprised of individuals from African, East Asian, European, South Asian, and American populations was employed to impute around 350 HLA alleles via SHAPEIT5 and MINIMAC4. Additionally, we introduced 21 long-range HLA haplotypes into the reference. Regression analyses were conducted using PLINK 2.0, while the first five principal components were included as covariates to adjust for population stratification. Finally, we employed METAL, using the sample-size weighting approach, to meta-analyze results from dichotomous and continuous outcomes.</div><div>We have generated preliminary results in a multi-ancestry sample (N = 60,159) that highlight HLA-DRB1*01:01 as the top risk-conferring allele across three ancestries (Z = 3.255, P = 1.14e-03). HLA-DRB1*01:01 was also the most significant HLA allele (Z = 2.380, P = 1.73e-02) in the Latin American sample (n = 7,072). In the African sample (n = 14,883), HLA-B*51:01 (Z = 3.553, P = 3.82e-04) emerged as the most significant HLA allele, while in Europeans (n = 38,204), the most significant allele HLA-B*08:01 showed a negative association with PTSD (Z = -2.555, P = 1.06e-02).</div><div>Our association analysis has identified multiple HLA alleles nominally associated with PTSD, with HLA-DRB1*01:01 emerging as the most significant possibly risk-conferring variant across ancestries. Notably, a protective effect of HLA-B*08:01 has previously been observed in schizophrenia and bipolar disorder. In the next steps, we will conduct imput
{"title":"EXPLORING THE IMMUNOGENETIC BASIS OF POST-TRAUMATIC STRESS DISORDER","authors":"","doi":"10.1016/j.euroneuro.2024.08.017","DOIUrl":"10.1016/j.euroneuro.2024.08.017","url":null,"abstract":"<div><div>Post-Traumatic Stress Disorder (PTSD) is a complex psychiatric condition that develops following exposure to traumatic experiences. Its core symptoms include intrusive thoughts, avoidant behavior, and a persistent state of hyperarousal. Although it is widely recognized that stress exacerbates inflammation across tissues, a growing body of evidence suggests a reciprocal relationship, with immune function influencing susceptibility to PTSD. This relationship may be driven by shared underlying biology, such as from pleiotropy. The most recent genome-wide association study (GWAS) of PTSD identified 95 risk loci, including endocrine and immune regulators, such as the major histocompatibility complex (MHC). The MHC harbors numerous genetic variants such as human leukocyte antigen (HLA) alleles that are crucial for immune function. However, the complex linkage disequilibrium structure of the MHC poses challenges in isolating individual signals through SNP-based imputation. Here, we present a large-scale cross-ancestry analysis assessing the association of HLA alleles with PTSD.</div><div>We conducted HLA imputation and association analysis in a genotyped sample of individuals of African, European, or Latin American ancestry from 38 studies included in the latest PTSD GWAS published by the Psychiatric Genomics Consortium. The outcome phenotype was assessed as either case-control or on a continuous scale (e.g. the PTSD Checklist for DSM-IV or V). The 1000 Genomes Reference Panel, comprised of individuals from African, East Asian, European, South Asian, and American populations was employed to impute around 350 HLA alleles via SHAPEIT5 and MINIMAC4. Additionally, we introduced 21 long-range HLA haplotypes into the reference. Regression analyses were conducted using PLINK 2.0, while the first five principal components were included as covariates to adjust for population stratification. Finally, we employed METAL, using the sample-size weighting approach, to meta-analyze results from dichotomous and continuous outcomes.</div><div>We have generated preliminary results in a multi-ancestry sample (N = 60,159) that highlight HLA-DRB1*01:01 as the top risk-conferring allele across three ancestries (Z = 3.255, P = 1.14e-03). HLA-DRB1*01:01 was also the most significant HLA allele (Z = 2.380, P = 1.73e-02) in the Latin American sample (n = 7,072). In the African sample (n = 14,883), HLA-B*51:01 (Z = 3.553, P = 3.82e-04) emerged as the most significant HLA allele, while in Europeans (n = 38,204), the most significant allele HLA-B*08:01 showed a negative association with PTSD (Z = -2.555, P = 1.06e-02).</div><div>Our association analysis has identified multiple HLA alleles nominally associated with PTSD, with HLA-DRB1*01:01 emerging as the most significant possibly risk-conferring variant across ancestries. Notably, a protective effect of HLA-B*08:01 has previously been observed in schizophrenia and bipolar disorder. In the next steps, we will conduct imput","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441742","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 : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.076
<div><div>Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that impairs executive functioning, vigilance-attention, and motivation. Due to this, individuals with ADHD are at higher risk of addiction, poor academic and professional outcomes, and social deficits. Heterogeneity in presenting symptoms is well-established and may result in a delayed or missed diagnosis. The prevalence of ADHD is reported from two to seven times higher for males than females. The prevalence of ADHD appears consistent in childhood and adulthood. Only half of those diagnosed in childhood report persisting symptoms, implying many are first diagnoses as adults and those first diagnosed in adulthood tend towards a different symptom profile. Such sex and age trends may reflect protective effects of “female sex”, children “growing out of” ADHD, or adults experiencing a different clinical entity. However, others argue that the high heritability of ADHD (0.6-0.85) and similar genetic risk in females suggests that these trends may be due to a heterogenous expression of symptoms in response to the environment (e.g., modulated by the female or adult experience). We use Vanderbilt University Medical Center's (VUMC) biobank (n=3,285,882 electronic health records (EHR) and 119,750 genotyped samples) to analyze ADHD prevalence and genetic architecture. We observed the ADHD-associated ICD codes (n=38,419) were less frequent in EHR-recorded females relative to males (n=14,395 vs. 24,024) and the median age at first diagnosis was substantially older (21.72 years, IQR=20.96 vs.15.05 years, IQR=9.1). Among subset of European ancestry patient genotyped in VUMC (n=69,397), we observed an ADHD polygenic risk scores (PRS) was significant independent predictor of diagnosis, with stronger effects on females (males, beta= 13.47, p=0.03; females, beta=16.90, p=7.4e-7), and female cases having higher average PRS than male cases (p=0.04). In a sex-specific phenome-wide association study (PheWAS), the ADHD PRS was associated with similar phenotypes regardless of sex, including substance/tobacco use, other psychiatric disorders, obesity, diabetes mellitus, and respiratory problems. Our findings that female patients with ADHD appear to have higher genetic liability for the condition despite lower rates of diagnosis are consistent with previous studies. Additionally, ADHD PRS did not demonstrate differential comorbidity structures based on sex in VUMC. One explanation for this is that established genetic proxies of disease inadequately reflect the nuances of particular behaviors of ADHD subtypes, including but not limited to exhibition of externalizing hyperactive subtype (ADHD-H) opposed to internalizing inattentive subtype (ADHD-I), which is reported more frequently in females. Therefore, obtaining clinical diagnoses in females may require symptom manifestations that are largely overlapping with their male counterparts. Additional work in various EHR resources may shed
{"title":"EVALUATING THE IMPACT OF BIOLOGICAL SEX ON ADHD PRESENTATION, PREVALENCE, AND GENETIC RISK","authors":"","doi":"10.1016/j.euroneuro.2024.08.076","DOIUrl":"10.1016/j.euroneuro.2024.08.076","url":null,"abstract":"<div><div>Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that impairs executive functioning, vigilance-attention, and motivation. Due to this, individuals with ADHD are at higher risk of addiction, poor academic and professional outcomes, and social deficits. Heterogeneity in presenting symptoms is well-established and may result in a delayed or missed diagnosis. The prevalence of ADHD is reported from two to seven times higher for males than females. The prevalence of ADHD appears consistent in childhood and adulthood. Only half of those diagnosed in childhood report persisting symptoms, implying many are first diagnoses as adults and those first diagnosed in adulthood tend towards a different symptom profile. Such sex and age trends may reflect protective effects of “female sex”, children “growing out of” ADHD, or adults experiencing a different clinical entity. However, others argue that the high heritability of ADHD (0.6-0.85) and similar genetic risk in females suggests that these trends may be due to a heterogenous expression of symptoms in response to the environment (e.g., modulated by the female or adult experience). We use Vanderbilt University Medical Center's (VUMC) biobank (n=3,285,882 electronic health records (EHR) and 119,750 genotyped samples) to analyze ADHD prevalence and genetic architecture. We observed the ADHD-associated ICD codes (n=38,419) were less frequent in EHR-recorded females relative to males (n=14,395 vs. 24,024) and the median age at first diagnosis was substantially older (21.72 years, IQR=20.96 vs.15.05 years, IQR=9.1). Among subset of European ancestry patient genotyped in VUMC (n=69,397), we observed an ADHD polygenic risk scores (PRS) was significant independent predictor of diagnosis, with stronger effects on females (males, beta= 13.47, p=0.03; females, beta=16.90, p=7.4e-7), and female cases having higher average PRS than male cases (p=0.04). In a sex-specific phenome-wide association study (PheWAS), the ADHD PRS was associated with similar phenotypes regardless of sex, including substance/tobacco use, other psychiatric disorders, obesity, diabetes mellitus, and respiratory problems. Our findings that female patients with ADHD appear to have higher genetic liability for the condition despite lower rates of diagnosis are consistent with previous studies. Additionally, ADHD PRS did not demonstrate differential comorbidity structures based on sex in VUMC. One explanation for this is that established genetic proxies of disease inadequately reflect the nuances of particular behaviors of ADHD subtypes, including but not limited to exhibition of externalizing hyperactive subtype (ADHD-H) opposed to internalizing inattentive subtype (ADHD-I), which is reported more frequently in females. Therefore, obtaining clinical diagnoses in females may require symptom manifestations that are largely overlapping with their male counterparts. Additional work in various EHR resources may shed ","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441954","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 : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.064
Major depressive disorder (MDD) is a complex psychiatric disorder influenced by genetic, social, and environmental factors. Family history and polygenic risk scores of MDD and related psychiatric disorders are strong predictors for MDD, while childhood trauma (CT) also plays a crucial role. This study aimed to jointly model the predictive effect of multi-family history (mFH), multi-PRS (mPRS), and childhood trauma on the development of MDD and the number of MDD episodes experienced. Our aim was to identify predictive model useful for stratification to more or less intensive treatment plans and interventions.
Data were obtained from the NIHR BioResource Genetic Links to Anxiety and Depression (GLAD) study and UK Biobank (UKB). MDD diagnosis followed DSM-V criteria using the same online mental health questionnaire data in GLAD and UKB. Family history (Yes/No) was reported for up to 22 psychiatric disorders. MegaPRS was used to calculate PRSs based on large genome-wide association studies. Reported childhood trauma was identified via the5-item childhood trauma screener questionnaire. Elastic net regression with nested cross-validation was applied.
In GLAD (9,927 MDD cases, 4,452 controls), mFH explained 16.85% of MDD variance, followed by CT (10.62%), demographics (9.92%), and mPRS (7.73%). All predictors together explained 33.87% of MDD variance, with corresponding areas under the receiver operating characteristic curve (AUC) of 0.84 and a positive predictive value (PPV) of 0.81. In UKB (40,667 MDD cases, 70,755 controls), mFH explained 13.56% of MDD variance, followed by demographics (5.95%), CT (5.87%), and mPRS (3.69%). Together, all predictors explained 23.68% of variance (AUC=0.74, PPV=0.66). The strongest individual predictor in both cohorts is family history of depression, followed by CT, sex, family history of anxiety, and PRS for depression. The modal number of MDD episodes among MDD cases is ≥ 13 episodes in GLAD, compared to 1 episode in UKB. Additionally, the mean age of onset is 21 years in GLAD and 33 years in UKB. When the model was applied to other MDD phenotypes, all predictors accounted for 25.80% of the variances for the number of MDD episodes and 8.41% for age of onset in GLAD, and 11.92% and 6.01% in UKB, respectively.
Integrating multi-family history, multi-PRS, childhood trauma, and demographics enhances MDD prediction. The prediction model performs effectively in both severe MDD cohort (GLAD) and population-based cohort (UKB), suggesting its potential generalizability to broader populations. The strongest predictors are family history of depression and childhood trauma, both of which are easily measurable in clinical settings. Furthermore, the model trained for MDD prediction also proves to be a strong predictor for the number of MDD episodes and age of onset, indicating its effectiveness in predicting the severity of MDD.
{"title":"JOINT MULTI-FAMILY HISTORY AND MULTI-POLYGENIC SCORE PREDICTION OF MAJOR DEPRESSIVE DISORDER","authors":"","doi":"10.1016/j.euroneuro.2024.08.064","DOIUrl":"10.1016/j.euroneuro.2024.08.064","url":null,"abstract":"<div><div>Major depressive disorder (MDD) is a complex psychiatric disorder influenced by genetic, social, and environmental factors. Family history and polygenic risk scores of MDD and related psychiatric disorders are strong predictors for MDD, while childhood trauma (CT) also plays a crucial role. This study aimed to jointly model the predictive effect of multi-family history (mFH), multi-PRS (mPRS), and childhood trauma on the development of MDD and the number of MDD episodes experienced. Our aim was to identify predictive model useful for stratification to more or less intensive treatment plans and interventions.</div><div>Data were obtained from the NIHR BioResource Genetic Links to Anxiety and Depression (GLAD) study and UK Biobank (UKB). MDD diagnosis followed DSM-V criteria using the same online mental health questionnaire data in GLAD and UKB. Family history (Yes/No) was reported for up to 22 psychiatric disorders. MegaPRS was used to calculate PRSs based on large genome-wide association studies. Reported childhood trauma was identified via the5-item childhood trauma screener questionnaire. Elastic net regression with nested cross-validation was applied.</div><div>In GLAD (9,927 MDD cases, 4,452 controls), mFH explained 16.85% of MDD variance, followed by CT (10.62%), demographics (9.92%), and mPRS (7.73%). All predictors together explained 33.87% of MDD variance, with corresponding areas under the receiver operating characteristic curve (AUC) of 0.84 and a positive predictive value (PPV) of 0.81. In UKB (40,667 MDD cases, 70,755 controls), mFH explained 13.56% of MDD variance, followed by demographics (5.95%), CT (5.87%), and mPRS (3.69%). Together, all predictors explained 23.68% of variance (AUC=0.74, PPV=0.66). The strongest individual predictor in both cohorts is family history of depression, followed by CT, sex, family history of anxiety, and PRS for depression. The modal number of MDD episodes among MDD cases is ≥ 13 episodes in GLAD, compared to 1 episode in UKB. Additionally, the mean age of onset is 21 years in GLAD and 33 years in UKB. When the model was applied to other MDD phenotypes, all predictors accounted for 25.80% of the variances for the number of MDD episodes and 8.41% for age of onset in GLAD, and 11.92% and 6.01% in UKB, respectively.</div><div>Integrating multi-family history, multi-PRS, childhood trauma, and demographics enhances MDD prediction. The prediction model performs effectively in both severe MDD cohort (GLAD) and population-based cohort (UKB), suggesting its potential generalizability to broader populations. The strongest predictors are family history of depression and childhood trauma, both of which are easily measurable in clinical settings. Furthermore, the model trained for MDD prediction also proves to be a strong predictor for the number of MDD episodes and age of onset, indicating its effectiveness in predicting the severity of MDD.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442040","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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