Pub Date : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.088
Epigenetic processes, such as DNA methylation (DNAm), show potential as biological markers and mechanisms underlying gene-environment interplay in the prediction of psychiatric and other brain-based phenotypes. However, we still know surprisingly little about how peripheral epigenetic patterns relate to individual differences in the brain itself. An increasingly popular approach to address this is by combining epigenetic and neuroimaging data; yet, research is almost entirely comprised of cross-sectional studies in adults, with modest sample sizes (median N = 80) and a lack of replication.
To bridge this gap, we present here the new Methylation, Imaging and NeuroDevelopment (MIND) Consortium. MIND aims to bring a developmental focus to the emerging field of Neuroimaging Epigenetics by (i) promoting collaborative, adequately powered developmental research via multi-cohort analyses; (ii) increasing scientific rigor through the establishment of shared pipelines and open science practices; and (iii) advancing our understanding of DNA methylation-brain dynamics at different developmental periods (from birth to emerging adulthood), by leveraging data from prospective, longitudinal pediatric studies.
MIND currently brings together 14 cohorts worldwide, comprising samples from North and South America, Europe, Africa and Australia, with (repeated) measures of DNAm in peripheral tissues (blood, buccal cells, and saliva) and neuroimaging by magnetic resonance imaging (MRI) across up to five time points across development (Npooled DNAm = 11,791; Npooled neuroimaging = 9,350; Npooled combined = 5,249). The MIND Consortium operates as an open network, welcoming researchers who have access to neuroimaging and epigenetic data (collected at 1+ time points before 18 years) to join.
In this talk, we introduce the consortium, presenting key characteristics of the samples and data types included. We discuss main considerations, challenges and opportunities in collaborative research on developmental neuroimaging epigenetics, including: (i) separating developmental from technical variability, (ii) modeling time-varying DNAm-brain associations in multi-cohort analyses, and (iii) addressing the dimensionality of neuroimaging epigenetic data. We conclude with key priorities for the consortium, current plans and future directions.
By triangulating associations across multiple developmental time points and study types, we aim to generate new insights about the dynamic relationship between peripheral DNA methylation and the brain, and to improve understanding of how these ultimately relate to neurodevelopmental and psychiatric phenotypes.
{"title":"INTRODUCING MIND: THE METHYLATION, IMAGING AND NEURODEVELOPMENT CONSORTIUM","authors":"","doi":"10.1016/j.euroneuro.2024.08.088","DOIUrl":"10.1016/j.euroneuro.2024.08.088","url":null,"abstract":"<div><div>Epigenetic processes, such as DNA methylation (DNAm), show potential as biological markers and mechanisms underlying gene-environment interplay in the prediction of psychiatric and other brain-based phenotypes. However, we still know surprisingly little about how peripheral epigenetic patterns relate to individual differences in the brain itself. An increasingly popular approach to address this is by combining epigenetic and neuroimaging data; yet, research is almost entirely comprised of cross-sectional studies in adults, with modest sample sizes (median N = 80) and a lack of replication.</div><div>To bridge this gap, we present here the new Methylation, Imaging and NeuroDevelopment (MIND) Consortium. MIND aims to bring a developmental focus to the emerging field of Neuroimaging Epigenetics by (i) promoting collaborative, adequately powered developmental research via multi-cohort analyses; (ii) increasing scientific rigor through the establishment of shared pipelines and open science practices; and (iii) advancing our understanding of DNA methylation-brain dynamics at different developmental periods (from birth to emerging adulthood), by leveraging data from prospective, longitudinal pediatric studies.</div><div>MIND currently brings together 14 cohorts worldwide, comprising samples from North and South America, Europe, Africa and Australia, with (repeated) measures of DNAm in peripheral tissues (blood, buccal cells, and saliva) and neuroimaging by magnetic resonance imaging (MRI) across up to five time points across development (Npooled DNAm = 11,791; Npooled neuroimaging = 9,350; Npooled combined = 5,249). The MIND Consortium operates as an open network, welcoming researchers who have access to neuroimaging and epigenetic data (collected at 1+ time points before 18 years) to join.</div><div>In this talk, we introduce the consortium, presenting key characteristics of the samples and data types included. We discuss main considerations, challenges and opportunities in collaborative research on developmental neuroimaging epigenetics, including: (i) separating developmental from technical variability, (ii) modeling time-varying DNAm-brain associations in multi-cohort analyses, and (iii) addressing the dimensionality of neuroimaging epigenetic data. We conclude with key priorities for the consortium, current plans and future directions.</div><div>By triangulating associations across multiple developmental time points and study types, we aim to generate new insights about the dynamic relationship between peripheral DNA methylation and the brain, and to improve understanding of how these ultimately relate to neurodevelopmental and psychiatric phenotypes.</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":"142442150","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
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":"","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":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":"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.2024.08.081
<div><div><strong>Background:</strong> Examining rare variants in multiplex pedigrees offers a complementary approach to the case-control study design to identify genes robustly associated with psychiatric disorders. Affected individuals within a pedigree are likely influenced by the same rare variant(s), which can simplify the disease-gene discovery process. Also, pedigrees are less sensitive to confounding from population stratification or environmental effects compared to unrelated cohorts. The goal of the Pedigree Sequencing Working Group of the Psychiatric Genomics Consortium (PGC) is to evaluate the contribution of rare variants from whole genome sequencing (WGS) in densely affected pedigrees. To date, we have collated WGS data from 310 individuals in 50 pedigrees across a range of psychiatric diagnoses. Here we give a progress update of the working group as well as describing novel methodologies developed for analysing pedigree-based WGS data.</div><div><strong>Methods:</strong> As an example of the above, we evaluated WGS data from 61 samples across ten pedigrees recruited from Utah multiply affected with schizophrenia or bipolar disorder. For single nucleotide variants (SNVs) and indels, we applied a simple filtering approach to identify plausible causal variants within each pedigree. We prioritised variants with a full co-segregation pattern (carried by all affected samples in-family and absent from unaffected and marry-in samples) or a reduced co-segregation pattern (carried by all but one affected sample in-family and absent from unaffected and marry-in samples). In addition, we applied an in-house Bayesian methodology known as BICEP to further identify variants of interest that would have been lost to the strict filtering. For copy number variants (CNVs), we applied our pedigree-aware consensus framework known as PECAN to call variants from the WGS data. We then applied a simple filtering prioritisation as before.</div><div><strong>Results:</strong> For the SNV/indel analysis, our filtering approach identified an ultra-rare, deleterious variant in ATP2B2 that had a reduced co-segregation pattern with schizophrenia. Recently, this gene was reported as significantly associated with bipolar disorder from a large case-control analysis of ultra-rare variants. Additionally, BICEP identified an ultra-rare variant in TTBK1 that perfectly co-segregated with schizophrenia. De novo pathogenic variants in this gene have been reported for childhood-onset schizophrenia. Finally, PECAN identified a rare, exonic deletion that perfectly co-segregates with schizophrenia in one of the pedigrees. The CNV overlaps PITRM1, which has been implicated in a complex phenotype including ataxia, developmental delay, and schizophrenia-like episodes in affected adults.</div><div><strong>Discussion:</strong> Our results highlight how pedigree-based analyses can provide a useful orthogonal approach to case-control strategies in identifying plausible risk genes for r
{"title":"PROGRESS UPDATE FROM THE PGC PEDIGREE SEQUENCING WORKING GROUP: RESULTS AND NOVEL METHODOLOGIES","authors":"","doi":"10.1016/j.euroneuro.2024.08.081","DOIUrl":"10.1016/j.euroneuro.2024.08.081","url":null,"abstract":"<div><div><strong>Background:</strong> Examining rare variants in multiplex pedigrees offers a complementary approach to the case-control study design to identify genes robustly associated with psychiatric disorders. Affected individuals within a pedigree are likely influenced by the same rare variant(s), which can simplify the disease-gene discovery process. Also, pedigrees are less sensitive to confounding from population stratification or environmental effects compared to unrelated cohorts. The goal of the Pedigree Sequencing Working Group of the Psychiatric Genomics Consortium (PGC) is to evaluate the contribution of rare variants from whole genome sequencing (WGS) in densely affected pedigrees. To date, we have collated WGS data from 310 individuals in 50 pedigrees across a range of psychiatric diagnoses. Here we give a progress update of the working group as well as describing novel methodologies developed for analysing pedigree-based WGS data.</div><div><strong>Methods:</strong> As an example of the above, we evaluated WGS data from 61 samples across ten pedigrees recruited from Utah multiply affected with schizophrenia or bipolar disorder. For single nucleotide variants (SNVs) and indels, we applied a simple filtering approach to identify plausible causal variants within each pedigree. We prioritised variants with a full co-segregation pattern (carried by all affected samples in-family and absent from unaffected and marry-in samples) or a reduced co-segregation pattern (carried by all but one affected sample in-family and absent from unaffected and marry-in samples). In addition, we applied an in-house Bayesian methodology known as BICEP to further identify variants of interest that would have been lost to the strict filtering. For copy number variants (CNVs), we applied our pedigree-aware consensus framework known as PECAN to call variants from the WGS data. We then applied a simple filtering prioritisation as before.</div><div><strong>Results:</strong> For the SNV/indel analysis, our filtering approach identified an ultra-rare, deleterious variant in ATP2B2 that had a reduced co-segregation pattern with schizophrenia. Recently, this gene was reported as significantly associated with bipolar disorder from a large case-control analysis of ultra-rare variants. Additionally, BICEP identified an ultra-rare variant in TTBK1 that perfectly co-segregated with schizophrenia. De novo pathogenic variants in this gene have been reported for childhood-onset schizophrenia. Finally, PECAN identified a rare, exonic deletion that perfectly co-segregates with schizophrenia in one of the pedigrees. The CNV overlaps PITRM1, which has been implicated in a complex phenotype including ataxia, developmental delay, and schizophrenia-like episodes in affected adults.</div><div><strong>Discussion:</strong> Our results highlight how pedigree-based analyses can provide a useful orthogonal approach to case-control strategies in identifying plausible risk genes for r","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":"142442144","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.2024.08.055
{"title":"HARNESSING GENOMIC DATA FOR PRECISION MEDICINE IN ALZHEIMER'S DISEASE: CHALLENGES AND OPPORTUNITIES","authors":"","doi":"10.1016/j.euroneuro.2024.08.055","DOIUrl":"10.1016/j.euroneuro.2024.08.055","url":null,"abstract":"","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":"142441953","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.2024.08.065
Antidepressants are the most common treatment for moderate or severe depression. Side effects are crucial indicators for antidepressants, but their expression varies widely among individuals.
In this study, we leveraged genetic and phenotypic data from self-reported questionnaires in the Genetic Link to Anxiety and Depression (GLAD) study to predict side effects and discontinuation (due to side effect) across three antidepressant classes (SSRI, SNRI, tricyclic antidepressants (TCA)) at the first and the last (most recent) year of prescription. About 260 predictors spanning genetic, clinical, comorbidity, demographic, and antidepressant information were included. XGBoost, random forest, cubist, elastic net, and support vector machine (with RBF and polynomial kernel) were trained, and their performance was compared.
The final dataset comprised 5358 individuals, with 4354 in the first and 3414 in the last year of prescription. The average prevalence of side effects and discontinuation was 74.1% and 28.7%, respectively. In the initial year, the best AUROC for predicting SSRI discontinuation and side effects were 0.65 and 0.60. In the last year of SSRI prescription, the highest AUROC reached 0.73 for discontinuation and 0.87 for side effects. Models for predicting discontinuation and side effects of SNRI and TCA showed comparable performance. The history of side effects and discontinuation of antidepressant use were the most influential predictors of the outcomes in the last year. When examining 30 common antidepressant side effect symptoms, most of them were differentially prevalent between antidepressant classes.
Our findings demonstrate the feasibility of predicting antidepressant side effects using a self-reported questionnaire, particularly for the last prescription. These results contribute valuable insights for the development of clinical decisions aimed at optimising treatment selection with enhanced tolerability.
{"title":"PREDICTION OF ANTIDEPRESSANT SIDE EFFECTS IN THE GENETIC LINK TO ANXIETY AND DEPRESSION STUDY","authors":"","doi":"10.1016/j.euroneuro.2024.08.065","DOIUrl":"10.1016/j.euroneuro.2024.08.065","url":null,"abstract":"<div><div>Antidepressants are the most common treatment for moderate or severe depression. Side effects are crucial indicators for antidepressants, but their expression varies widely among individuals.</div><div>In this study, we leveraged genetic and phenotypic data from self-reported questionnaires in the Genetic Link to Anxiety and Depression (GLAD) study to predict side effects and discontinuation (due to side effect) across three antidepressant classes (SSRI, SNRI, tricyclic antidepressants (TCA)) at the first and the last (most recent) year of prescription. About 260 predictors spanning genetic, clinical, comorbidity, demographic, and antidepressant information were included. XGBoost, random forest, cubist, elastic net, and support vector machine (with RBF and polynomial kernel) were trained, and their performance was compared.</div><div>The final dataset comprised 5358 individuals, with 4354 in the first and 3414 in the last year of prescription. The average prevalence of side effects and discontinuation was 74.1% and 28.7%, respectively. In the initial year, the best AUROC for predicting SSRI discontinuation and side effects were 0.65 and 0.60. In the last year of SSRI prescription, the highest AUROC reached 0.73 for discontinuation and 0.87 for side effects. Models for predicting discontinuation and side effects of SNRI and TCA showed comparable performance. The history of side effects and discontinuation of antidepressant use were the most influential predictors of the outcomes in the last year. When examining 30 common antidepressant side effect symptoms, most of them were differentially prevalent between antidepressant classes.</div><div>Our findings demonstrate the feasibility of predicting antidepressant side effects using a self-reported questionnaire, particularly for the last prescription. These results contribute valuable insights for the development of clinical decisions aimed at optimising treatment selection with enhanced tolerability.</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":"142442041","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.2024.08.070
Genome-wide association studies (GWAS) to date have been able to leverage large sample sizes to identify genomic loci that contribute to risk for various psychiatric disorders. However, GWAS of copy number variants (CNVs) have prioritized identifying risk loci within European populations due to the lack of power in diverse ancestry groups. In this study, we called CNVs in a diverse group of samples to create CNV datasets for 2 additional ancestry groups: African/African American (AFR/AFAM) and Asian/Asian American (ASN/ASAM). SNPweights was used to infer genome-wide genetic ancestry for each sample. We were then able to boost power at specific loci by using a meta-analysis to combine EUR, AFR/AFAM, and ASN/ASAM CNV analyses (N=571,803).
Rare copy number variants have been implicated in a cross-disorder European cohort (N=537,466) that includes major psychiatric disorders such as autism (ASD), schizophrenia (SCZ), major depressive disorder (MDD), bipolar disorder (BD), post-traumatic stress disorder (PTSD), and attention-deficit/hyperactivity disorder (ADHD). This analysis was able to identify novel loci with the statistical power that comes with being the largest CNV study to date. Naturally, the inclusion of diverse samples in this analysis can further lead to novel discoveries. Additional CNV-GWAS were performed for cross-disorder datasets in AFR/AFAM (N=17,474) and ASN/ASAM (N=16,863) populations. Meta-analysis of all 3 populations used an inverse-variance weighting to account for the disparity of sample size between populations. We compared EUR CNV-GWAS and burden results with those from the meta-analysis as these were the most well-powered tests. The effect was a substantial increase in significance levels at specific loci that reached testable CNV frequencies in the diverse groups. Comparing the EUR analysis with the trans-ancestry analysis allows us to quantify the contribution of the diverse groups and provide insight into the genomic loci associated with psychiatric disorders in AFR/AFAM and ASN/ASAM populations once similar sample sizes are reached. This study highlights the importance of expanding diversity during data collection so that the genotype-phenotype relationships can benefit people worldwide.
{"title":"META-ANALYSIS OF RARE CNV GENOME-WIDE ASSOCIATION STUDIES ACROSS MAJOR PSYCHIATRIC DISORDERS IN EUR, AFR/AFAM, AND ASN/ASAM POPULATIONS","authors":"","doi":"10.1016/j.euroneuro.2024.08.070","DOIUrl":"10.1016/j.euroneuro.2024.08.070","url":null,"abstract":"<div><div>Genome-wide association studies (GWAS) to date have been able to leverage large sample sizes to identify genomic loci that contribute to risk for various psychiatric disorders. However, GWAS of copy number variants (CNVs) have prioritized identifying risk loci within European populations due to the lack of power in diverse ancestry groups. In this study, we called CNVs in a diverse group of samples to create CNV datasets for 2 additional ancestry groups: African/African American (AFR/AFAM) and Asian/Asian American (ASN/ASAM). SNPweights was used to infer genome-wide genetic ancestry for each sample. We were then able to boost power at specific loci by using a meta-analysis to combine EUR, AFR/AFAM, and ASN/ASAM CNV analyses (N=571,803).</div><div>Rare copy number variants have been implicated in a cross-disorder European cohort (N=537,466) that includes major psychiatric disorders such as autism (ASD), schizophrenia (SCZ), major depressive disorder (MDD), bipolar disorder (BD), post-traumatic stress disorder (PTSD), and attention-deficit/hyperactivity disorder (ADHD). This analysis was able to identify novel loci with the statistical power that comes with being the largest CNV study to date. Naturally, the inclusion of diverse samples in this analysis can further lead to novel discoveries. Additional CNV-GWAS were performed for cross-disorder datasets in AFR/AFAM (N=17,474) and ASN/ASAM (N=16,863) populations. Meta-analysis of all 3 populations used an inverse-variance weighting to account for the disparity of sample size between populations. We compared EUR CNV-GWAS and burden results with those from the meta-analysis as these were the most well-powered tests. The effect was a substantial increase in significance levels at specific loci that reached testable CNV frequencies in the diverse groups. Comparing the EUR analysis with the trans-ancestry analysis allows us to quantify the contribution of the diverse groups and provide insight into the genomic loci associated with psychiatric disorders in AFR/AFAM and ASN/ASAM populations once similar sample sizes are reached. This study highlights the importance of expanding diversity during data collection so that the genotype-phenotype relationships can benefit people worldwide.</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":"142442046","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.2024.08.063
The Identical Depression Phenotyping Consortium consists of studies in the UK (Genetic Links to Anxiety and Depression or GLAD and UK Biobank), the Australian Genetics of Depression study, and the Biobanks Netherlands Internet Collaboration (BIONIC). The three studies are using the same method of phenotyping depression with detailed demographics, clinical record linkage, and data on over 130,000 cases of Major Depressive Disorder. We propose a symposium focused on advancing predictive models in MDD and its treatment, emphasizing the integration of polygenic scores, family history, and clinical data.
Wang will present on Joint Multi-Family History and Multi-Polygenic Score Prediction of Major Depressive Disorder. Machine learning integrating these factors in GLAD (9,927 MDD cases, 4,452 controls) revealed significant prediction accuracies for MDD, the number of recurrent MDD episodes. These findings were replicated in UK Biobank (40,667 MDD cases, 70,755 controls). Next, Li will present on incorporating genetic and clinical predictors for antidepressant side effects in > 5K cases from the GLAD study. By employing machine learning models, they achieved significant success in predicting side effects and discontinuation rates, particularly when integrating data from prior prescriptions. Huider will present on genetic analyses of MDD on behalf of the BIONIC consortium presents a large-scale genetic analyses of MDD and its symptoms to explore depression heterogeneity within the Netherlands, utilizing uniform in-depth phenotyping in > 30K cases. This ambitious project highlights the importance of large, homogeneous datasets in deciphering the complex genetics of depression. Finally, Mitchell will present on Using polygenic risk scores to characterise treatment resistant MDD in to explore the association of TRD with biological predictors such a polygenic score (PGS) and CYP2C19 and CYP2D16 metaboliser profiles, measured personality traits, and environmental predictors such as social support and exposure to stressful life events. Lastly, they tested for any gene-environment interactions across predictors. Their research identifies genetic factors that correlate with long-term treatment outcomes, providing a basis for personalized medicine in treating depression.
This symposium aims to showcase cutting-edge research that integrates genetic, familial, and clinical data to predict and manage major depressive disorder more effectively. Discussant Hatoum will consider the implications of integration of genetic prediction with machine learning approaches and the possibilities for clinical utility.
{"title":"THE IDENTICAL DEPRESSION PHENOTYPING CONSORTIUM: DECONSTRUCTION AND PREDICTION OF MDD AND TREATMENT RESPONSE","authors":"","doi":"10.1016/j.euroneuro.2024.08.063","DOIUrl":"10.1016/j.euroneuro.2024.08.063","url":null,"abstract":"<div><div>The Identical Depression Phenotyping Consortium consists of studies in the UK (Genetic Links to Anxiety and Depression or GLAD and UK Biobank), the Australian Genetics of Depression study, and the Biobanks Netherlands Internet Collaboration (BIONIC). The three studies are using the same method of phenotyping depression with detailed demographics, clinical record linkage, and data on over 130,000 cases of Major Depressive Disorder. We propose a symposium focused on advancing predictive models in MDD and its treatment, emphasizing the integration of polygenic scores, family history, and clinical data.</div><div>Wang will present on Joint Multi-Family History and Multi-Polygenic Score Prediction of Major Depressive Disorder. Machine learning integrating these factors in GLAD (9,927 MDD cases, 4,452 controls) revealed significant prediction accuracies for MDD, the number of recurrent MDD episodes. These findings were replicated in UK Biobank (40,667 MDD cases, 70,755 controls). Next, Li will present on incorporating genetic and clinical predictors for antidepressant side effects in > 5K cases from the GLAD study. By employing machine learning models, they achieved significant success in predicting side effects and discontinuation rates, particularly when integrating data from prior prescriptions. Huider will present on genetic analyses of MDD on behalf of the BIONIC consortium presents a large-scale genetic analyses of MDD and its symptoms to explore depression heterogeneity within the Netherlands, utilizing uniform in-depth phenotyping in > 30K cases. This ambitious project highlights the importance of large, homogeneous datasets in deciphering the complex genetics of depression. Finally, Mitchell will present on Using polygenic risk scores to characterise treatment resistant MDD in to explore the association of TRD with biological predictors such a polygenic score (PGS) and CYP2C19 and CYP2D16 metaboliser profiles, measured personality traits, and environmental predictors such as social support and exposure to stressful life events. Lastly, they tested for any gene-environment interactions across predictors. Their research identifies genetic factors that correlate with long-term treatment outcomes, providing a basis for personalized medicine in treating depression.</div><div>This symposium aims to showcase cutting-edge research that integrates genetic, familial, and clinical data to predict and manage major depressive disorder more effectively. Discussant Hatoum will consider the implications of integration of genetic prediction with machine learning approaches and the possibilities for clinical utility.</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":"142442139","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.2024.08.046
<div><div>The fields of autism and neurodevelopmental disorder (NDD) genetics are rapidly advancing. Catalyzed by the power of large cohorts and integration of all classes of de novo and inherited protein-coding variation, dozens of genes have emerged to harbor variants that confer high relative risk for autism, and hundreds of genes have been associated with NDDs more broadly. Through examination of protein-truncating variants (PTVs), predicted damaging missense variation, and copy number variants (CNVs), our prior analyses have begun to map the allelic diversity of perturbations within 72 autism-associated genes and 373 genes associated with NDDs, finding intriguing evidence of genes with significantly higher mutation rates and differences in the distribution of clinical phenotypes in autism compared to NDD (Fu et al., 2022; Satterstrom et al., 2020). Despite this progress, cohort sizes remain insufficient for disentangling the shared and distinct genetic architectures of autism, NDDs, and other neuropsychiatric conditions, as well as associating genes with more subtle impacts on neurodevelopment.</div><div>To advance these boundaries, we present the largest to-date study of rare coding variants, consisting of 62,013 autistic individuals, including 38,088 probands and 9,567 unaffected siblings from complete trio and quartet families, respectively, and 23,925 additional autism cases without parental information contrasted against 26,931 controls. By aggregating across the Autism Sequencing Consortium (ASC), the Simons Simplex Collection (SSC), the Simons Foundation Powering Autism Research (SPARK), and individuals from a leading diagnostic laboratory (GeneDx), this dataset totals almost 200,000 individuals, nearly a three-fold increase over prior studies. When we stratified the clinically-referred GeneDx autistic probands by co-occurring DD/ID status, we found synonymous, missense, and PTV de novo mutation rates in autism probands without DD/ID from GeneDx that were nearly identical to individuals ascertained for a diagnosis of autism in the ASC, SSC, and SPARK research studies (0.296 vs 0.294, 0.767 vs 0.763, and 0.141 vs 0.145 respectively), while GeneDx autism probands with DD/ID exhibited mutation rates similar to those observed in previous research studies of DD.</div><div>Further analyses of these data solidified previous observations of significant enrichment of de novo PTVs among autism probands of 3x compared to siblings among the genes most intolerant to PTVs in the human genome (i.e., lowest decile of LOEUF from gnomAD). We have also incorporated Alpha Missense (AM) pathogenicity estimates to complement our prior MPC scores for predicting damaging missense variation and identifying de novo missense variants acting with effect sizes comparable to de novo PTVs in constrained genes, with analysis of regional missense constraint within genes ongoing. We further leveraged the TADA Bayesian statistical method to jointly model these data in
自闭症和神经发育障碍（NDD）遗传学领域发展迅速。在大型队列和整合各类新发和遗传蛋白编码变异的推动下，数十个基因中出现了可导致自闭症高相对风险的变异，数百个基因与更广泛的 NDD 相关。通过研究蛋白质截断变异（PTVs）、预测的破坏性错义变异和拷贝数变异（CNVs），我们之前的分析已开始绘制 72 个自闭症相关基因和 373 个 NDDs 相关基因中扰乱的等位基因多样性图谱，发现了基因突变率显著高于 NDD 的有趣证据，以及自闭症与 NDD 相比临床表型分布的差异（Fu 等人，2022 年；Satterstrom 等人，2020 年）。尽管取得了这些进展，但队列规模仍然不足以区分自闭症、NDD 和其他神经精神疾病的共同和不同遗传结构，也不足以将对神经发育有更微妙影响的基因联系起来。为了推进这些研究，我们展示了迄今为止最大规模的罕见编码变异研究，研究对象包括 62,013 名自闭症患者，其中包括 38,088 名原发性患者和 9,567 名未受影响的兄弟姐妹，他们分别来自完整的三人家庭和四人家庭，另外还有 23,925 名没有父母信息的自闭症病例与 26,931 名对照组患者。通过汇总自闭症测序联盟（ASC）、Simons Simplex Collection (SSC)、Simons Foundation Powering Autism Research (SPARK)以及一家领先的诊断实验室（GeneDx）的数据，该数据集的总人数接近 20 万，比之前的研究增加了近三倍。当我们将临床转介的GeneDx自闭症受试者按并发DD/ID状态进行分层时，我们发现GeneDx中无DD/ID的自闭症受试者的同义突变率、错义突变率和PTV从头突变率几乎与ASC、SSC和SPARK研究中确诊为自闭症的个体相同（分别为0.296 vs 0.294、0.767 vs 0.763和0.141 vs 0.145）。对这些数据的进一步分析证实了之前的观察结果，即在人类基因组中最不耐受PTVs的基因中（即：LOEUF的最低十分位数），3倍于同胞的自闭症疑似患者的从头PTVs显著富集、即 gnomAD 中 LOEUF 最低十分位数）。我们还纳入了阿尔法错义（AM）致病性估计，以补充我们先前的 MPC 评分，从而预测破坏性错义变异，并识别在受限基因中作用效应大小与新生 PTV 相当的新生错义变异，目前正在对基因内的区域错义受限进行分析。我们进一步利用 TADA 贝叶斯统计方法，在一个统一的框架内对这些数据进行联合建模，充分利用罕见 PTV、损伤性错义变异和 CNV 的遗传信息。这种方法发现了数百个与自闭症相关的基因，我们观察到，在新的相关基因中，除全新 PTV 外，其他变异类别的贡献率正在稳步上升。我们正在进行分析，以了解这些基因对自闭症和相关神经精神疾病的表型表现产生影响的基因网络、发育时间和生物功能。
{"title":"THE ALLELIC ARCHITECTURE OF RARE VARIATION IN AUTISM AND OTHER NEURODEVELOPMENTAL CONDITIONS","authors":"","doi":"10.1016/j.euroneuro.2024.08.046","DOIUrl":"10.1016/j.euroneuro.2024.08.046","url":null,"abstract":"<div><div>The fields of autism and neurodevelopmental disorder (NDD) genetics are rapidly advancing. Catalyzed by the power of large cohorts and integration of all classes of de novo and inherited protein-coding variation, dozens of genes have emerged to harbor variants that confer high relative risk for autism, and hundreds of genes have been associated with NDDs more broadly. Through examination of protein-truncating variants (PTVs), predicted damaging missense variation, and copy number variants (CNVs), our prior analyses have begun to map the allelic diversity of perturbations within 72 autism-associated genes and 373 genes associated with NDDs, finding intriguing evidence of genes with significantly higher mutation rates and differences in the distribution of clinical phenotypes in autism compared to NDD (Fu et al., 2022; Satterstrom et al., 2020). Despite this progress, cohort sizes remain insufficient for disentangling the shared and distinct genetic architectures of autism, NDDs, and other neuropsychiatric conditions, as well as associating genes with more subtle impacts on neurodevelopment.</div><div>To advance these boundaries, we present the largest to-date study of rare coding variants, consisting of 62,013 autistic individuals, including 38,088 probands and 9,567 unaffected siblings from complete trio and quartet families, respectively, and 23,925 additional autism cases without parental information contrasted against 26,931 controls. By aggregating across the Autism Sequencing Consortium (ASC), the Simons Simplex Collection (SSC), the Simons Foundation Powering Autism Research (SPARK), and individuals from a leading diagnostic laboratory (GeneDx), this dataset totals almost 200,000 individuals, nearly a three-fold increase over prior studies. When we stratified the clinically-referred GeneDx autistic probands by co-occurring DD/ID status, we found synonymous, missense, and PTV de novo mutation rates in autism probands without DD/ID from GeneDx that were nearly identical to individuals ascertained for a diagnosis of autism in the ASC, SSC, and SPARK research studies (0.296 vs 0.294, 0.767 vs 0.763, and 0.141 vs 0.145 respectively), while GeneDx autism probands with DD/ID exhibited mutation rates similar to those observed in previous research studies of DD.</div><div>Further analyses of these data solidified previous observations of significant enrichment of de novo PTVs among autism probands of 3x compared to siblings among the genes most intolerant to PTVs in the human genome (i.e., lowest decile of LOEUF from gnomAD). We have also incorporated Alpha Missense (AM) pathogenicity estimates to complement our prior MPC scores for predicting damaging missense variation and identifying de novo missense variants acting with effect sizes comparable to de novo PTVs in constrained genes, with analysis of regional missense constraint within genes ongoing. We further leveraged the TADA Bayesian statistical method to jointly model these data in ","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":"142442214","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.2024.08.028
With the availability of sufficiently large data from genome wide association analyses for varied phenotypes, a technique, Mendelian Randomization, has become common when searching for causal factors. Essentially, this technique uses genetic factors as proxies for modifiable exposures to explore causal relationships. There are several conditions, which are required for a valid andimpactful Mendelian Randomization estimation. In this symposium, we explore these conditions in more detail, in addition to providing some examples for meaningful explorations in psychiatric genetics.
{"title":"MENDELIAN RANDOMIZATION – WHAT ARE THE PROMISES?","authors":"","doi":"10.1016/j.euroneuro.2024.08.028","DOIUrl":"10.1016/j.euroneuro.2024.08.028","url":null,"abstract":"<div><div>With the availability of sufficiently large data from genome wide association analyses for varied phenotypes, a technique, Mendelian Randomization, has become common when searching for causal factors. Essentially, this technique uses genetic factors as proxies for modifiable exposures to explore causal relationships. There are several conditions, which are required for a valid andimpactful Mendelian Randomization estimation. In this symposium, we explore these conditions in more detail, in addition to providing some examples for meaningful explorations in 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":"142441635","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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