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":"Ole Andreassen (Chair) , Helga Ask (Co-chair) , Nadine Parker (Discussant)","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":"87 ","pages":"Pages 42-43"},"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.2024.08.085
Alexander Neumann , Sara Sammallahti , Marta Cosin-Tomas , Sarah Reese , Henning Tiemeier , Stephanie London , Janine Felix , Charlotte Cecil
<div><div>DNA methylation (DNAm) is a developmentally dynamic epigenetic process, yet, most studies linking DNAm to health phenotypes measure DNAm only once. Thus, it is largely unknown (i) whether the relationship between DNAm and health outcomes varies across development (ii) at which developmental periods DNAm profiles could be most informative for these outcomes, and (iii) to what extent DNAm-health associations at one timepoint can be generalized to other timepoints.</div><div>In most pediatric population studies, DNAm is either measured in cord blood samples at birth and associated with a child outcome at a later timepoint (i.e. prospective epigenome-wide association study [EWAS]) or DNAm is measured from a blood sample at the same timepoint as the child outcome (i.e. cross-sectional EWAS). Recently, the Pregnancy And Childhood Epigenetics (PACE) Consortium published five multi-cohort EWAS meta-analyses that investigated DNAm using both designs in relation to the same child outcome, spanning mental and physical health domains, namely: ADHD, general psychopathology (measured as a latent factor; GPF), sleep duration, body mass index (BMI) and asthma.</div><div>Here, we re-analyzed the five PACE meta-analyses (Npooled=2178-4641, 26 cohorts) to explore timing effects on DNAm-health associations during development. For each outcome, we integrated results from the prospective EWAS (cord blood DNAm at birth) and the cross-sectional EWAS (whole blood DNAm in childhood) into a longitudinal meta-regression model. This model systematically quantified changes in effect sizes and statistical significance between timepoints, and we also explored a range of factors that may contribute to the observed temporal trends. We then correlated DNAm associations between timepoints (to assess generalizability of epigenetic signals from one timepoint to another) and across health outcomes (to explore presence of shared DNAm associations).</div><div>Our findings reveal three new insights: (i) across outcomes, effects sizes are larger when DNAm is measured in childhood and cross-sectionally associated with child health outcomes, compared to when DNAm is assessed at birth and prospectively associated with later health development; (ii) higher effect sizes do not necessarily translate into more significant findings, as associations also become noisier in childhood for most outcomes (i.e. showing larger standard errors); and (iii) DNAm signals are highly time-specific, while showing pleiotropy across health outcomes: regression coefficients at birth did not correlate with those in childhood with few exceptions.</div><div>Overall, our results suggest developmentally-specific associations between DNAm and child health outcomes, when assessing DNAm at birth vs childhood. This implies that EWAS results from one timepoint are unlikely to generalize to another, at least based on birth vs childhood comparisons. Longitudinal studies with repeated epigenetic assessments are direl
{"title":"EPIGENETIC TIMING EFFECTS ON CHILD DEVELOPMENTAL OUTCOMES: A LONGITUDINAL META-REGRESSION OF FINDINGS FROM THE PREGNANCY AND CHILDHOOD EPIGENETICS CONSORTIUM","authors":"Alexander Neumann , Sara Sammallahti , Marta Cosin-Tomas , Sarah Reese , Henning Tiemeier , Stephanie London , Janine Felix , Charlotte Cecil","doi":"10.1016/j.euroneuro.2024.08.085","DOIUrl":"10.1016/j.euroneuro.2024.08.085","url":null,"abstract":"<div><div>DNA methylation (DNAm) is a developmentally dynamic epigenetic process, yet, most studies linking DNAm to health phenotypes measure DNAm only once. Thus, it is largely unknown (i) whether the relationship between DNAm and health outcomes varies across development (ii) at which developmental periods DNAm profiles could be most informative for these outcomes, and (iii) to what extent DNAm-health associations at one timepoint can be generalized to other timepoints.</div><div>In most pediatric population studies, DNAm is either measured in cord blood samples at birth and associated with a child outcome at a later timepoint (i.e. prospective epigenome-wide association study [EWAS]) or DNAm is measured from a blood sample at the same timepoint as the child outcome (i.e. cross-sectional EWAS). Recently, the Pregnancy And Childhood Epigenetics (PACE) Consortium published five multi-cohort EWAS meta-analyses that investigated DNAm using both designs in relation to the same child outcome, spanning mental and physical health domains, namely: ADHD, general psychopathology (measured as a latent factor; GPF), sleep duration, body mass index (BMI) and asthma.</div><div>Here, we re-analyzed the five PACE meta-analyses (Npooled=2178-4641, 26 cohorts) to explore timing effects on DNAm-health associations during development. For each outcome, we integrated results from the prospective EWAS (cord blood DNAm at birth) and the cross-sectional EWAS (whole blood DNAm in childhood) into a longitudinal meta-regression model. This model systematically quantified changes in effect sizes and statistical significance between timepoints, and we also explored a range of factors that may contribute to the observed temporal trends. We then correlated DNAm associations between timepoints (to assess generalizability of epigenetic signals from one timepoint to another) and across health outcomes (to explore presence of shared DNAm associations).</div><div>Our findings reveal three new insights: (i) across outcomes, effects sizes are larger when DNAm is measured in childhood and cross-sectionally associated with child health outcomes, compared to when DNAm is assessed at birth and prospectively associated with later health development; (ii) higher effect sizes do not necessarily translate into more significant findings, as associations also become noisier in childhood for most outcomes (i.e. showing larger standard errors); and (iii) DNAm signals are highly time-specific, while showing pleiotropy across health outcomes: regression coefficients at birth did not correlate with those in childhood with few exceptions.</div><div>Overall, our results suggest developmentally-specific associations between DNAm and child health outcomes, when assessing DNAm at birth vs childhood. This implies that EWAS results from one timepoint are unlikely to generalize to another, at least based on birth vs childhood comparisons. Longitudinal studies with repeated epigenetic assessments are direl","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Pages 34-35"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442147","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.2024.08.090
Alex Kwong , Mark Adams , Poppy Grimes , Gareth Griffith , Tim Morris , Kate Tilling , Andrew McIntosh
Genome Wide Association Studies (GWAS) have been vital to understanding the genetics of complex traits. However, the majority of GWAS use data from only one occasion, even in longitudinal studies with repeated assessments. Traits like depression are often subject to measurement error. Using a more precise and longitudinal phenotype of depression could reduce measurement error and increase power and precision in depression GWAS, further enhancing understanding of the genetics of depression.
We used data from the UK Biobank (max n=462,566) on the PHQ-2, a measure of depressive symptoms assessed up to 8 occasions over approximately 17 years. We tested a GWAS baseline model (a traditional cross-sectional GWAS that took the first observed assessment) against four other longitudinal GWAS models: 1) the mean of all the assessments, 2) a structural equation model (common factor model), 3) a precision-weighted shrinkage model and 4) a genomicSEM model. We also conducted analysis across multiple ancestries and performed out of sample polygenic prediction.
All longitudinal GWAS models outperformed the GWAS baseline model in European ancestries, with the most powerful model being the precision-weighted shrinkage model which identified 169 genome wide significant single nucleotide polymorphisms (SNPs). Importantly, this precision-weighted shrinkage method had an increase of 34 more lead SNPs (121% increase), 107 more independent significant SNPs (173% increase), 50 more mapped genes (35% increase) and a greater SNP heritability (35% increase), compared to the baseline model. Polygenic prediction into an external cohort also explained a greater proportion of variance (17% increase). There were no GWAS lead SNPs identified in the South Asian and African baseline models, but 2 and 7 novel lead SNPs in the precision-weighted shrinkage methods, respectively.
Leveraging repeated information within GWAS appears to improve power and precision to detect novel biological underpinnings in depression. This is likely due to a reduction in measurement error and increased power. These methods can be applied to other noisy traits within psychiatric genetics and could be useful for detecting novel loci in smaller studies.
{"title":"USING REPEATED MEASURES TO IMPROVE THE PRECISION AND POWER OF GENOME-WIDE ASSOCIATION STUDIES (GWAS)","authors":"Alex Kwong , Mark Adams , Poppy Grimes , Gareth Griffith , Tim Morris , Kate Tilling , Andrew McIntosh","doi":"10.1016/j.euroneuro.2024.08.090","DOIUrl":"10.1016/j.euroneuro.2024.08.090","url":null,"abstract":"<div><div>Genome Wide Association Studies (GWAS) have been vital to understanding the genetics of complex traits. However, the majority of GWAS use data from only one occasion, even in longitudinal studies with repeated assessments. Traits like depression are often subject to measurement error. Using a more precise and longitudinal phenotype of depression could reduce measurement error and increase power and precision in depression GWAS, further enhancing understanding of the genetics of depression.</div><div>We used data from the UK Biobank (max n=462,566) on the PHQ-2, a measure of depressive symptoms assessed up to 8 occasions over approximately 17 years. We tested a GWAS baseline model (a traditional cross-sectional GWAS that took the first observed assessment) against four other longitudinal GWAS models: 1) the mean of all the assessments, 2) a structural equation model (common factor model), 3) a precision-weighted shrinkage model and 4) a genomicSEM model. We also conducted analysis across multiple ancestries and performed out of sample polygenic prediction.</div><div>All longitudinal GWAS models outperformed the GWAS baseline model in European ancestries, with the most powerful model being the precision-weighted shrinkage model which identified 169 genome wide significant single nucleotide polymorphisms (SNPs). Importantly, this precision-weighted shrinkage method had an increase of 34 more lead SNPs (121% increase), 107 more independent significant SNPs (173% increase), 50 more mapped genes (35% increase) and a greater SNP heritability (35% increase), compared to the baseline model. Polygenic prediction into an external cohort also explained a greater proportion of variance (17% increase). There were no GWAS lead SNPs identified in the South Asian and African baseline models, but 2 and 7 novel lead SNPs in the precision-weighted shrinkage methods, respectively.</div><div>Leveraging repeated information within GWAS appears to improve power and precision to detect novel biological underpinnings in depression. This is likely due to a reduction in measurement error and increased power. These methods can be applied to other noisy traits within psychiatric genetics and could be useful for detecting novel loci in smaller studies.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Pages 36-37"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442229","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.2024.08.095
William Reay (Chair) , Zachary Gerring (Co-chair)
New pharmacotherapies in psychiatry would likely reduce the immense burden that mental health conditions place on individuals and society at large. However, the pipeline to discover novel drugs for use in psychiatric disorders has remained unproductive, where a lack of objective biomarkers for psychiatric diagnoses and the assessment of treatment outcomes contributes to clinical trial failure. Therefore, new approaches are urgently needed to identify more suitable drug candidates for clinical trials. One approach is the integration of human genetic and molecular data to identify and prioritize existing drugs for human clinical trials, known as drug repurposing. This approach has previously been successfully applied in psychiatry and offers an avenue for expedited clinical translation compared to traditional drug discovery. For example, valproic acid was originally used for its anticonvulsant properties in epilepsy before its utility in bipolar disorder was uncovered. Despite the promise of drug repurposing in psychiatry, challenges remain arising from the immense biological heterogeneity of these phenotypes. The advent of well powered, genetic association studies and high throughput measurements on diverse molecular data types (e.g., gene expression) represent an opportunity to better understand the biological complexity of these phenotypes and accelerate successfully translating prospective repurposing candidates into clinical practice. In this symposium, we will outline some of the key methodological considerations and progress to date in genetically informed prioritization of repurposing candidates across psychiatric disorders. Specifically, we will discuss how integration of genetic association signals with multiomic data can reveal prospective opportunities for drug repurposing. Approaches to biologically interpret individual target genes in the context of the wider polygenic architecture of these disorders will also be outlined. Shared genetic liability and biological relationships between psychiatric disorders and somatic disorders across the rest of the body treated by existing drugs additionally can present supporting lines of evidence for prospective repurposing candidates. Finally, given the immense variability observed between individuals with the same diagnosis, we will discuss the prospects of using genetics to target drug repurposing opportunities with greater precision at the individual level. This symposium brings together a diverse set of international researchers that are working at the forefront of innovative approaches to identify opportunities for drug repurposing in psychiatry.
{"title":"ACCELERATING DRUG REPURPOSING IN PSYCHIATRY USING GENETICS","authors":"William Reay (Chair) , Zachary Gerring (Co-chair)","doi":"10.1016/j.euroneuro.2024.08.095","DOIUrl":"10.1016/j.euroneuro.2024.08.095","url":null,"abstract":"<div><div>New pharmacotherapies in psychiatry would likely reduce the immense burden that mental health conditions place on individuals and society at large. However, the pipeline to discover novel drugs for use in psychiatric disorders has remained unproductive, where a lack of objective biomarkers for psychiatric diagnoses and the assessment of treatment outcomes contributes to clinical trial failure. Therefore, new approaches are urgently needed to identify more suitable drug candidates for clinical trials. One approach is the integration of human genetic and molecular data to identify and prioritize existing drugs for human clinical trials, known as drug repurposing. This approach has previously been successfully applied in psychiatry and offers an avenue for expedited clinical translation compared to traditional drug discovery. For example, valproic acid was originally used for its anticonvulsant properties in epilepsy before its utility in bipolar disorder was uncovered. Despite the promise of drug repurposing in psychiatry, challenges remain arising from the immense biological heterogeneity of these phenotypes. The advent of well powered, genetic association studies and high throughput measurements on diverse molecular data types (e.g., gene expression) represent an opportunity to better understand the biological complexity of these phenotypes and accelerate successfully translating prospective repurposing candidates into clinical practice. In this symposium, we will outline some of the key methodological considerations and progress to date in genetically informed prioritization of repurposing candidates across psychiatric disorders. Specifically, we will discuss how integration of genetic association signals with multiomic data can reveal prospective opportunities for drug repurposing. Approaches to biologically interpret individual target genes in the context of the wider polygenic architecture of these disorders will also be outlined. Shared genetic liability and biological relationships between psychiatric disorders and somatic disorders across the rest of the body treated by existing drugs additionally can present supporting lines of evidence for prospective repurposing candidates. Finally, given the immense variability observed between individuals with the same diagnosis, we will discuss the prospects of using genetics to target drug repurposing opportunities with greater precision at the individual level. This symposium brings together a diverse set of international researchers that are working at the forefront of innovative approaches to identify opportunities for drug repurposing in psychiatry.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 39"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442261","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.2024.08.101
Allan Kalungi , Segun Fatumo
Psychiatric genomics research tools frequently depend on terminology and notions that are predominantly derived from Western viewpoints, specifically designed for populations speaking English in Europe and the United States of America. Nevertheless, there is an increasing interest in incorporating African populations into genetic studies, as African genetic data possess significant potential for enhancing discovery in psychiatric genetics research. However, this undertaking has unique difficulties, including inefficiently conveying intricate genetic and psychiatric ideas and terminology to participants using their native African languages. The absence of obvious counterparts for terms such as "trauma" or "genome" necessitates the need for unique strategies to overcome linguistic barriers.
In 2011, we established the Uganda Genome Resource (UGR) – a well-characterized genomic database with a range of phenotypes for communicable and non-communicable diseases and risk factors generated from the Uganda General Population Cohort (GPC), a population-based open cohort. The UGR comprises genotype data on ∼5,000 and whole-genome sequence data on ∼2,000 Ugandan GPC individuals from 10 ethno-linguistic groups. We have since extended UGR to include studies focusing primarily on mental health conditions including major depressive disorder, post-traumatic stress disorder, generalized anxiety disorder, alcohol misuse and suicidality, among others.
To mitigate against the barrier poised by research tools which were developed in a foreign language to the participants, first, we engage the service of a professional linguistic translator to ensure accurate translation of all study materials. Additionally, we provide cultural sensitivity training to researchers to ensure respectful and ethical interactions with participants from diverse ethno-linguistic backgrounds. Secondly, following the translated study material, we set up a series of workshop including mental health experts and leading psychiatric geneticists and local scientists to agree on the translated content. Thirdly, we ask an independent local scientist to conduct a reverse translation of the study materials to ensure accuracy and consistency in the translated versions. This thorough process helps to minimize any potential misunderstandings or misinterpretations that may arise during the research study.
{"title":"BRIDGING LINGUISTIC AND CULTURAL DIVIDES IN PSYCHIATRIC GENOMICS RESEARCH: LESSONS FROM UGANDA","authors":"Allan Kalungi , Segun Fatumo","doi":"10.1016/j.euroneuro.2024.08.101","DOIUrl":"10.1016/j.euroneuro.2024.08.101","url":null,"abstract":"<div><div>Psychiatric genomics research tools frequently depend on terminology and notions that are predominantly derived from Western viewpoints, specifically designed for populations speaking English in Europe and the United States of America. Nevertheless, there is an increasing interest in incorporating African populations into genetic studies, as African genetic data possess significant potential for enhancing discovery in psychiatric genetics research. However, this undertaking has unique difficulties, including inefficiently conveying intricate genetic and psychiatric ideas and terminology to participants using their native African languages. The absence of obvious counterparts for terms such as \"trauma\" or \"genome\" necessitates the need for unique strategies to overcome linguistic barriers.</div><div>In 2011, we established the Uganda Genome Resource (UGR) – a well-characterized genomic database with a range of phenotypes for communicable and non-communicable diseases and risk factors generated from the Uganda General Population Cohort (GPC), a population-based open cohort. The UGR comprises genotype data on ∼5,000 and whole-genome sequence data on ∼2,000 Ugandan GPC individuals from 10 ethno-linguistic groups. We have since extended UGR to include studies focusing primarily on mental health conditions including major depressive disorder, post-traumatic stress disorder, generalized anxiety disorder, alcohol misuse and suicidality, among others.</div><div>To mitigate against the barrier poised by research tools which were developed in a foreign language to the participants, first, we engage the service of a professional linguistic translator to ensure accurate translation of all study materials. Additionally, we provide cultural sensitivity training to researchers to ensure respectful and ethical interactions with participants from diverse ethno-linguistic backgrounds. Secondly, following the translated study material, we set up a series of workshop including mental health experts and leading psychiatric geneticists and local scientists to agree on the translated content. Thirdly, we ask an independent local scientist to conduct a reverse translation of the study materials to ensure accuracy and consistency in the translated versions. This thorough process helps to minimize any potential misunderstandings or misinterpretations that may arise during the research study.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 41"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442267","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.2024.08.038
Tao Li (Chair) , Fang Liu (Co-chair) , Zafiris Daskalakis (Discussant)
This symposium will feature cutting-edge research that leverages diverse genomic approaches to elucidate the complex etiology of schizophrenia. The presentations will span large-scale genetic association studies, novel sequencing technologies, and investigations of the gut microbiome - all with the goal of advancing our fundamental understanding of this debilitating psychiatric disorder.
The symposium will begin with a report on the largest genome-wide association study (GWAS) of schizophrenia conducted in an Eastern Asian population to date. The speaker will present findings on newly identified risk loci that provide insights into the unique genetic architecture of schizophrenia in this understudied ancestral group. Next, researchers will share results from a study utilizing long-read sequencing technology to comprehensively catalog de novo mutations in schizophrenia parent-offspring trios. This high-resolution approach has uncovered rare, disruptive variants missed by short-read sequencing that may confer substantial risk for the disorder. The symposium will also feature an investigation of the gut microbiome and its potential role in schizophrenia pathogenesis. The speaker will discuss metagenomic analyses revealing distinct microbiota signatures associated with the disease state, suggesting gut-brain axis mechanisms worthy of further exploration. Finally, the symposium will conclude with functional experiments probing the biological impact of D2R-DISC1 complex on antipsychotic treatment. The speaker will share findings from a comprehensive investigation using both patient-derived samples and mouse models of schizophrenia. Through a combination of proteomic analyses, pharmacological manipulations, and advanced molecular techniques, the researchers have uncovered novel insights into how the D2R-DISC1 signaling axis contribute to treatment response in schizophrenia.
Collectively, this symposium will showcase innovative genomic and experimental approaches that are revolutionizing our understanding of schizophrenia's complex etiology. The findings presented will inspire new hypotheses and accelerate the translation of genetic discoveries into improved diagnostic tools and targeted therapeutic strategies.
{"title":"UNCOVERING THE GENETIC AND BIOLOGICAL UNDERPINNINGS OF SCHIZOPHRENIA","authors":"Tao Li (Chair) , Fang Liu (Co-chair) , Zafiris Daskalakis (Discussant)","doi":"10.1016/j.euroneuro.2024.08.038","DOIUrl":"10.1016/j.euroneuro.2024.08.038","url":null,"abstract":"<div><div>This symposium will feature cutting-edge research that leverages diverse genomic approaches to elucidate the complex etiology of schizophrenia. The presentations will span large-scale genetic association studies, novel sequencing technologies, and investigations of the gut microbiome - all with the goal of advancing our fundamental understanding of this debilitating psychiatric disorder.</div><div>The symposium will begin with a report on the largest genome-wide association study (GWAS) of schizophrenia conducted in an Eastern Asian population to date. The speaker will present findings on newly identified risk loci that provide insights into the unique genetic architecture of schizophrenia in this understudied ancestral group. Next, researchers will share results from a study utilizing long-read sequencing technology to comprehensively catalog de novo mutations in schizophrenia parent-offspring trios. This high-resolution approach has uncovered rare, disruptive variants missed by short-read sequencing that may confer substantial risk for the disorder. The symposium will also feature an investigation of the gut microbiome and its potential role in schizophrenia pathogenesis. The speaker will discuss metagenomic analyses revealing distinct microbiota signatures associated with the disease state, suggesting gut-brain axis mechanisms worthy of further exploration. Finally, the symposium will conclude with functional experiments probing the biological impact of D2R-DISC1 complex on antipsychotic treatment. The speaker will share findings from a comprehensive investigation using both patient-derived samples and mouse models of schizophrenia. Through a combination of proteomic analyses, pharmacological manipulations, and advanced molecular techniques, the researchers have uncovered novel insights into how the D2R-DISC1 signaling axis contribute to treatment response in schizophrenia.</div><div>Collectively, this symposium will showcase innovative genomic and experimental approaches that are revolutionizing our understanding of schizophrenia's complex etiology. The findings presented will inspire new hypotheses and accelerate the translation of genetic discoveries into improved diagnostic tools and targeted therapeutic strategies.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 13"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442312","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.2024.08.018
Philip Mitchell (Chair) , John Nurnberger (Co-chair) , Fernando Goes (Discussant)
Bipolar disorder (BD) is a highly familial condition, with a heritability of at least 70%, and with10-15% of first-degree relatives developing this illness. Unlike schizophrenia, there is no distinct prodromal (ultra high-risk) syndrome, making development of early intervention treatment programs difficult. Studies of high-risk young people with a family history of BD provide the potential for identifying clinical and/or biological changes that either predate or occur early in the development of BD, thereby presenting targets for early intervention therapies. This symposium will focus on what has been learnt from over a decade of BD high-risk studies globally, and what remains unknown or uncertain. Speakers will address these issues from both their own cohorts and the field more broadly. This symposium will comprise presentations by:
i)
John Nurnberger - Emeritus Professor, University of Indiana, USA. He led the NIMH Genetics Initiative Bipolar Group. His group has been active in the Bipolar Genome Study Consortium and in the Psychiatric Genomics Consortium. He established the US BD high-risk consortium, with sites at the universities of Indianapolis and Michigan, as well as Johns Hopkins University;
ii)
Janice Fullerton - Principal Research Scientist at Neuroscience Research Australia and Conjoint Associate Professor, University of New South Wales in Sydney, Australia. Jan will present genetics and neuroimaging data from the Australian Bipolar Kids and Sibs high-risk cohort;
iii)
Kathryn Freeman is a Research Assistant on the FORBOW Project, and PhD Student in Medical Neuroscience at Dalhousie University, Canada. Kate will present on bipolar disorder offspring findings from the FORBOW study; and
iv)
Philip Mitchell - Professor of Psychiatry at the University of New South Wales in Sydney who established the Australian Bipolar Kids and Sibs high-risk cohort. He will present findings from the 10-year follow-up of this sample.
{"title":"OVER A DECADE OF STUDIES IN BIPOLAR DISORDER HIGH RISK POPULATIONS: WHAT HAVE WE LEARNT AND WHAT ARE THE GAPS?","authors":"Philip Mitchell (Chair) , John Nurnberger (Co-chair) , Fernando Goes (Discussant)","doi":"10.1016/j.euroneuro.2024.08.018","DOIUrl":"10.1016/j.euroneuro.2024.08.018","url":null,"abstract":"<div><div>Bipolar disorder (BD) is a highly familial condition, with a heritability of at least 70%, and with10-15% of first-degree relatives developing this illness. Unlike schizophrenia, there is no distinct prodromal (ultra high-risk) syndrome, making development of early intervention treatment programs difficult. Studies of high-risk young people with a family history of BD provide the potential for identifying clinical and/or biological changes that either predate or occur early in the development of BD, thereby presenting targets for early intervention therapies. This symposium will focus on what has been learnt from over a decade of BD high-risk studies globally, and what remains unknown or uncertain. Speakers will address these issues from both their own cohorts and the field more broadly. This symposium will comprise presentations by:<ul><li><span>i)</span><span><div>John Nurnberger - Emeritus Professor, University of Indiana, USA. He led the NIMH Genetics Initiative Bipolar Group. His group has been active in the Bipolar Genome Study Consortium and in the Psychiatric Genomics Consortium. He established the US BD high-risk consortium, with sites at the universities of Indianapolis and Michigan, as well as Johns Hopkins University;</div></span></li><li><span>ii)</span><span><div>Janice Fullerton - Principal Research Scientist at Neuroscience Research Australia and Conjoint Associate Professor, University of New South Wales in Sydney, Australia. Jan will present genetics and neuroimaging data from the Australian Bipolar Kids and Sibs high-risk cohort;</div></span></li><li><span>iii)</span><span><div>Kathryn Freeman is a Research Assistant on the FORBOW Project, and PhD Student in Medical Neuroscience at Dalhousie University, Canada. Kate will present on bipolar disorder offspring findings from the FORBOW study; and</div></span></li><li><span>iv)</span><span><div>Philip Mitchell - Professor of Psychiatry at the University of New South Wales in Sydney who established the Australian Bipolar Kids and Sibs high-risk cohort. He will present findings from the 10-year follow-up of this sample.</div></span></li></ul></div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 5"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441673","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.2024.08.056
Peter Kochunov , Tom Nichols , John Blangero , Sarah Medland , David Glahn , Elliot Hong
Worldwide efforts have led to large and inclusive imaging genetics datasets enabling examination of the contribution of genetic and environmental factors to development, clinical course and treatment effectiveness in psychiatric disorders. These datasets combine high-resolution neuroimaging and genetic data in large and inclusive samples. Classical genetic analyses can help to parse the variance in disorder-related brain patterns into additive genetic, specific SNP, household and environmental causes. Performing these inquiries at full imaging and genetic resolution is a formidable computational task where the computational complexity of classical genetic analyses rises as a square or cube of the sample size. We describe fast, non-iterative simplifications to accelerate classical variance component (VC) methods including heritability, genetic correlation, and genome-wide association in dense and complex empirical pedigrees derived in samples such as UKBB, HCP and ABCD. These approaches linearize computational effort while maintaining approximation fidelity (r∼0.95) with VC results and take advantage of parallel computing provided by central and graphics processing units (CPU and GPU). We show that the new approaches can help to tract the nature vs. nurture interaction in the development of major depressive disorder and psychosis in the longitudinal datasets such as ABCD. We also show how specific genetic risk factors for Alzheimer disease can interact with environment leading to development of brain patterns that are predictive of the risk of development of dementia.
{"title":"RESOLVING THE CHALLENGES OF BIG-DATA IMAGING GENETICS ANALYSIS TO UNDERSTAND GENETIC AND ENVIRONMENTAL RISK FACTORS IN PSYCHIATRIC DISORDERS","authors":"Peter Kochunov , Tom Nichols , John Blangero , Sarah Medland , David Glahn , Elliot Hong","doi":"10.1016/j.euroneuro.2024.08.056","DOIUrl":"10.1016/j.euroneuro.2024.08.056","url":null,"abstract":"<div><div>Worldwide efforts have led to large and inclusive imaging genetics datasets enabling examination of the contribution of genetic and environmental factors to development, clinical course and treatment effectiveness in psychiatric disorders. These datasets combine high-resolution neuroimaging and genetic data in large and inclusive samples. Classical genetic analyses can help to parse the variance in disorder-related brain patterns into additive genetic, specific SNP, household and environmental causes. Performing these inquiries at full imaging and genetic resolution is a formidable computational task where the computational complexity of classical genetic analyses rises as a square or cube of the sample size. We describe fast, non-iterative simplifications to accelerate classical variance component (VC) methods including heritability, genetic correlation, and genome-wide association in dense and complex empirical pedigrees derived in samples such as UKBB, HCP and ABCD. These approaches linearize computational effort while maintaining approximation fidelity (r∼0.95) with VC results and take advantage of parallel computing provided by central and graphics processing units (CPU and GPU). We show that the new approaches can help to tract the nature vs. nurture interaction in the development of major depressive disorder and psychosis in the longitudinal datasets such as ABCD. We also show how specific genetic risk factors for Alzheimer disease can interact with environment leading to development of brain patterns that are predictive of the risk of development of dementia.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Pages 21-22"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442226","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.2024.08.025
Shansha Peng , Chunyu Liu
Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal technology for dissecting cellular heterogeneity and function. In an effort to assess the consistency and rigor of quality control (QC) measures across scRNA-seq studies, we systematically reviewed publications from high-impact journals, including Cell, Nature, Science, and their major sister journals. Our analysis revealed a lack of standardization in QC procedures, with significant variability in the parameters employed. Despite general agreement on the necessity of certain QC steps, such as the removal of low-quality cells and the detection of doublets, the specific criteria for these steps were often arbitrarily defined and not universally applied. Notably, the assessment of ambient RNA contamination and the precision of gene expression measurements were frequently overlooked, potentially leading to the inclusion of spurious data in downstream analyses. To address these inconsistencies, we propose a revised set of QC procedures and parameters, which yielded distinct results compared to the original publications when applied to existing datasets. Moreover, we also assessed the performance of the existing data-driven QC tools in distinguishing the low-quality cells from the high-quality cells. Our findings underscore the urgent need for a standardized approach to QC in scRNA-seq to ensure the reliability and reproducibility of biological insights derived from this powerful technology.
{"title":"STANDARDIZE QC PROCEDURE FOR SCRNA-SEQ","authors":"Shansha Peng , Chunyu Liu","doi":"10.1016/j.euroneuro.2024.08.025","DOIUrl":"10.1016/j.euroneuro.2024.08.025","url":null,"abstract":"<div><div>Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal technology for dissecting cellular heterogeneity and function. In an effort to assess the consistency and rigor of quality control (QC) measures across scRNA-seq studies, we systematically reviewed publications from high-impact journals, including Cell, Nature, Science, and their major sister journals. Our analysis revealed a lack of standardization in QC procedures, with significant variability in the parameters employed. Despite general agreement on the necessity of certain QC steps, such as the removal of low-quality cells and the detection of doublets, the specific criteria for these steps were often arbitrarily defined and not universally applied. Notably, the assessment of ambient RNA contamination and the precision of gene expression measurements were frequently overlooked, potentially leading to the inclusion of spurious data in downstream analyses. To address these inconsistencies, we propose a revised set of QC procedures and parameters, which yielded distinct results compared to the original publications when applied to existing datasets. Moreover, we also assessed the performance of the existing data-driven QC tools in distinguishing the low-quality cells from the high-quality cells. Our findings underscore the urgent need for a standardized approach to QC in scRNA-seq to ensure the reliability and reproducibility of biological insights derived from this powerful technology.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 9"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441629","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.2024.08.054
Rujia Dai , Ming Zhang , Tianyao Chu , Richard Kopp , Chunling Zhang , Kefu Liu , Yue Wang , Xusheng Wang , Chao Chen , Chunyu Liu
Single-cell/nuclei RNA sequencing (sc/snRNA-seq) is widely used for profiling cell-type gene expression in brain research. An important but frequently underappreciated issue is the data quality in terms of precision and accuracy. We evaluated precision using data from 14 human brain studies with a total of 3,483,905 cells from 297 individuals, with technical replicates based on random grouping of cells of the same type from the same individual. We also evaluated accuracy with sample-matched scRNA-seq and pooled-cell RNA-seq data of cultured mononuclear phagocytes from four species. Low precision and accuracy at the single-cell level across all evaluated data were observed. Cell number was highlighted as a key factor determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-seq. A high missing rate is likely the cause of the quantification quality problem. Downstream analysis results are severely affected by the expression quality issue. Many false findings can be produced when the noises are not properly controlled. This study underscores the necessity of sequencing enough cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification. Pseudo-bulk aggregation of expression data over cells of the same type is required when the high-quality expression quantification is desired.
{"title":"PRECISION AND ACCURACY IN SINGLE-CELL RNA-SEQ","authors":"Rujia Dai , Ming Zhang , Tianyao Chu , Richard Kopp , Chunling Zhang , Kefu Liu , Yue Wang , Xusheng Wang , Chao Chen , Chunyu Liu","doi":"10.1016/j.euroneuro.2024.08.054","DOIUrl":"10.1016/j.euroneuro.2024.08.054","url":null,"abstract":"<div><div>Single-cell/nuclei RNA sequencing (sc/snRNA-seq) is widely used for profiling cell-type gene expression in brain research. An important but frequently underappreciated issue is the data quality in terms of precision and accuracy. We evaluated precision using data from 14 human brain studies with a total of 3,483,905 cells from 297 individuals, with technical replicates based on random grouping of cells of the same type from the same individual. We also evaluated accuracy with sample-matched scRNA-seq and pooled-cell RNA-seq data of cultured mononuclear phagocytes from four species. Low precision and accuracy at the single-cell level across all evaluated data were observed. Cell number was highlighted as a key factor determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-seq. A high missing rate is likely the cause of the quantification quality problem. Downstream analysis results are severely affected by the expression quality issue. Many false findings can be produced when the noises are not properly controlled. This study underscores the necessity of sequencing enough cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification. Pseudo-bulk aggregation of expression data over cells of the same type is required when the high-quality expression quantification is desired.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 21"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442133","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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