Pub Date : 2024-10-01DOI: 10.1016/j.euroneuro.2024.08.053
<div><div>As the technological landscape evolves, neuropsychiatric research increasingly relies on data-driven methodologies to uncover the mysteries of the human brain malfunction and genetic risk in neuropsychiatric disorders. This symposium brings together experts and stakeholders for a focused dialogue on the pressing challenges and innovative solutions within this rapidly progressing domain. Through a series of thought-provoking presentations, we will delve into the nuances of data integration, the intricacies of handling big data, and the critical aspects of ensuring research reproducibility and clinical applicability.</div><div>Dr. Chunyu Liu from the State University of New York (SUNY) Upstate Medical University will present a compelling discussion on the paramount importance of quality control in single-cell RNA-seq data. His presentation will underscore the necessity of stringent data evaluation and filtering protocols to bolster the precision of genomic analyses. The talk will provide participants with a solid foundation on the principles of data quality, which is pivotal for the reproducibility and credibility of research findings.</div><div>Dr. Zhongming Zhao from the University of Texas Health Science Center at Houston (UTHealth Houston) will present an in-depth exploration of cutting-edge machine-learning algorithms for the integration of omics, genetic, neuroimaging, and phenotypic data. His session will be centered on the elucidation of Alzheimer's Disease's molecular mechanisms and the identification of potential therapeutic targets. This presentation will demonstrate the transformative power of data analytics in shaping the future of neuropsychiatric therapeutics.</div><div>Dr. Peter Kochunov from the UTHealth Houston, will tackle the multifaceted challenges of data integration, scalability, and reproducibility within the scope of large-scale population imaging genetic studies. His expertise will offer a strategic guide for navigating the complexities of big data, ensuring the robustness of research outcomes, and bridging the gap between scientific discoveries and their clinical translation.</div><div>Dr. Xing-Ming Zhao from Fudan University will conclude the symposium with an insightful examination of the challenges and strategies related to data integration in psychiatric disorder research. His work will emphasize the significance of synthesizing diverse data streams, such as clinical assessments, neuroimaging, and genetic information, to achieve a comprehensive view of neuropsychiatric conditions. This session will highlight the potential of integrated data approaches to surmount the obstacles faced in the field.</div><div>Dr. Eric Gamazon from Vanderbilt University will serve as the discussant to summarize the talks and offer his own insight into harnessing large biobank data of diverse ancestries.</div><div>The symposium will weave these presentations into a coherent narrative, progressively building upon each topic to pro
随着技术的发展，神经精神疾病研究越来越依赖于数据驱动的方法来揭示人脑功能失常和神经精神疾病遗传风险的奥秘。本次研讨会汇聚了专家和利益相关者，就这一快速发展领域中的紧迫挑战和创新解决方案展开集中对话。通过一系列发人深省的演讲，我们将深入探讨数据整合的微妙之处、处理大数据的复杂性以及确保研究可重复性和临床适用性的关键方面。他的演讲将强调严格的数据评估和过滤协议对提高基因组分析精度的必要性。来自德克萨斯大学休斯顿健康科学中心（UTHealth Houston）的赵忠明博士将深入探讨用于整合 omics、遗传、神经影像和表型数据的前沿机器学习算法。他的演讲将围绕阐明阿尔茨海默病的分子机制和确定潜在治疗靶点展开。来自休斯顿UTHealth 的 Peter Kochunov 博士将探讨大规模群体成像基因研究中数据整合、可扩展性和可重复性等多方面的挑战。他的专业知识将为驾驭复杂的大数据、确保研究成果的稳健性以及弥合科学发现与临床转化之间的差距提供战略指导。来自复旦大学的赵兴明博士将以对精神疾病研究中与数据整合相关的挑战和策略的深入探讨结束本次研讨会。他的工作将强调综合不同数据流（如临床评估、神经影像学和遗传信息）以全面了解神经精神疾病的重要性。来自范德比尔特大学的 Eric Gamazon 博士将作为讨论者对发言进行总结，并就如何利用不同血统的大型生物库数据提出自己的见解。研讨会将把这些发言编织成一个连贯的叙事，在每个主题的基础上逐步展开，全面概述数据驱动的神经精神疾病研究目前面临的挑战和未来前景。本次研讨会将通过营造合作环境和激发创新思维来推动这一领域的发展。与会者将深刻了解神经精神疾病数据驱动研究中的关键问题。此外，他们还将获得多功能工具包，以提高研究水平，为推动神经精神科学的发展做出贡献。
{"title":"CURRENT CHALLENGES AND OPPORTUNITIES IN DATA-DRIVEN APPROACHES IN NEUROPSYCHIATRIC DISORDERS","authors":"","doi":"10.1016/j.euroneuro.2024.08.053","DOIUrl":"10.1016/j.euroneuro.2024.08.053","url":null,"abstract":"<div><div>As the technological landscape evolves, neuropsychiatric research increasingly relies on data-driven methodologies to uncover the mysteries of the human brain malfunction and genetic risk in neuropsychiatric disorders. This symposium brings together experts and stakeholders for a focused dialogue on the pressing challenges and innovative solutions within this rapidly progressing domain. Through a series of thought-provoking presentations, we will delve into the nuances of data integration, the intricacies of handling big data, and the critical aspects of ensuring research reproducibility and clinical applicability.</div><div>Dr. Chunyu Liu from the State University of New York (SUNY) Upstate Medical University will present a compelling discussion on the paramount importance of quality control in single-cell RNA-seq data. His presentation will underscore the necessity of stringent data evaluation and filtering protocols to bolster the precision of genomic analyses. The talk will provide participants with a solid foundation on the principles of data quality, which is pivotal for the reproducibility and credibility of research findings.</div><div>Dr. Zhongming Zhao from the University of Texas Health Science Center at Houston (UTHealth Houston) will present an in-depth exploration of cutting-edge machine-learning algorithms for the integration of omics, genetic, neuroimaging, and phenotypic data. His session will be centered on the elucidation of Alzheimer's Disease's molecular mechanisms and the identification of potential therapeutic targets. This presentation will demonstrate the transformative power of data analytics in shaping the future of neuropsychiatric therapeutics.</div><div>Dr. Peter Kochunov from the UTHealth Houston, will tackle the multifaceted challenges of data integration, scalability, and reproducibility within the scope of large-scale population imaging genetic studies. His expertise will offer a strategic guide for navigating the complexities of big data, ensuring the robustness of research outcomes, and bridging the gap between scientific discoveries and their clinical translation.</div><div>Dr. Xing-Ming Zhao from Fudan University will conclude the symposium with an insightful examination of the challenges and strategies related to data integration in psychiatric disorder research. His work will emphasize the significance of synthesizing diverse data streams, such as clinical assessments, neuroimaging, and genetic information, to achieve a comprehensive view of neuropsychiatric conditions. This session will highlight the potential of integrated data approaches to surmount the obstacles faced in the field.</div><div>Dr. Eric Gamazon from Vanderbilt University will serve as the discussant to summarize the talks and offer his own insight into harnessing large biobank data of diverse ancestries.</div><div>The symposium will weave these presentations into a coherent narrative, progressively building upon each topic to pro","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":"142442129","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.2024.08.035
Genome wide association studies (GWAS) have identified hundreds of loci contributing to bipolar disorder (BD) risk. However, translating genome-wide significant (GWS) loci into causal genes and mechanisms for BD is challenging due to linkage disequilibrium (LD) between risk variants, and incomplete understanding of the non-coding regulatory mechanisms in the brain. Recently, the Psychiatric Genomics Consortium Bipolar Disorder Working Group has performed GWAS meta-analyses of BD in cohorts of European (N cases = 131,969), East Asian (N cases = 5,969), African American (N cases = 7,076) and Latino (N cases = 13,022) ancestries, as well as a multi-ancestry meta-analysis (Total N = 158,036 cases, N= 2,796,499 controls) by including datasets with different ascertainment strategies. These analyses led to the identification of 298 GWS risk loci for BD, further emphasizing the need to identify the true causal variants and elucidate their biological mechanisms at the cellular level.
Here, we implemented SuSiEx, a statistical fine-mapping method leveraging differences in the LD architecture among different genetic ancestries, to prioritize likely causal SNPs, within these 298 GWS risk loci for BD. Then, we mapped these SNPs to their relevant gene(s), and investigated their likely functional consequences by aggregating multiple lines of evidence: (i) integration of variant annotation and brain cell-type epigenomic data (PLAC-seq data), (ii) implementation of Summary data-based Mendelian Randomization (SMR) to functionally interpret the likely causal SNPs in the context of brain bulk tissue quantitative trait loci (QTLs) (expression, splicing and methylation QTLs), and (iii) refining the cell-type specific context of likely causal SNPs via SMR, by leveraging a novel (unpublished) resource of brain single nuclei eQTLs.
Our comprehensive fine-mapping analysis prioritized 113 likely causal SNPs, from 298 GWS loci for BD using LD estimates from all 4 represented populations in the multi-ancestry GWAS. By integrating expression, splicing or methylation QTLs, preliminary results based on a previous BD GWAS indicated that the following genes, among others, are strongly implicated in BD: FURIN, FADS1, DCC, MED24, TTC12, SP4, POU6F2, TRANK1, and DDRD2. Additionally, our preliminary results showed that fine-mapped SNPs for BD can mediate their likely causal effect in specific brain cell-types, specifically inhibitory and excitatory neurons. Taken together, the abovementioned genes represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Finally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, thus highlighting the potential clinical utility of fine-mapping.
{"title":"MULTI-ANCESTRY FINE-MAPPING REFINES BIPOLAR DISORDER RISK GENES","authors":"","doi":"10.1016/j.euroneuro.2024.08.035","DOIUrl":"10.1016/j.euroneuro.2024.08.035","url":null,"abstract":"<div><div>Genome wide association studies (GWAS) have identified hundreds of loci contributing to bipolar disorder (BD) risk. However, translating genome-wide significant (GWS) loci into causal genes and mechanisms for BD is challenging due to linkage disequilibrium (LD) between risk variants, and incomplete understanding of the non-coding regulatory mechanisms in the brain. Recently, the Psychiatric Genomics Consortium Bipolar Disorder Working Group has performed GWAS meta-analyses of BD in cohorts of European (N cases = 131,969), East Asian (N cases = 5,969), African American (N cases = 7,076) and Latino (N cases = 13,022) ancestries, as well as a multi-ancestry meta-analysis (Total N = 158,036 cases, N= 2,796,499 controls) by including datasets with different ascertainment strategies. These analyses led to the identification of 298 GWS risk loci for BD, further emphasizing the need to identify the true causal variants and elucidate their biological mechanisms at the cellular level.</div><div>Here, we implemented SuSiEx, a statistical fine-mapping method leveraging differences in the LD architecture among different genetic ancestries, to prioritize likely causal SNPs, within these 298 GWS risk loci for BD. Then, we mapped these SNPs to their relevant gene(s), and investigated their likely functional consequences by aggregating multiple lines of evidence: (i) integration of variant annotation and brain cell-type epigenomic data (PLAC-seq data), (ii) implementation of Summary data-based Mendelian Randomization (SMR) to functionally interpret the likely causal SNPs in the context of brain bulk tissue quantitative trait loci (QTLs) (expression, splicing and methylation QTLs), and (iii) refining the cell-type specific context of likely causal SNPs via SMR, by leveraging a novel (unpublished) resource of brain single nuclei eQTLs.</div><div>Our comprehensive fine-mapping analysis prioritized 113 likely causal SNPs, from 298 GWS loci for BD using LD estimates from all 4 represented populations in the multi-ancestry GWAS. By integrating expression, splicing or methylation QTLs, preliminary results based on a previous BD GWAS indicated that the following genes, among others, are strongly implicated in BD: FURIN, FADS1, DCC, MED24, TTC12, SP4, POU6F2, TRANK1, and DDRD2. Additionally, our preliminary results showed that fine-mapped SNPs for BD can mediate their likely causal effect in specific brain cell-types, specifically inhibitory and excitatory neurons. Taken together, the abovementioned genes represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Finally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, thus highlighting the potential clinical utility of fine-mapping.</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":"142442131","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.2024.08.045
Autism is highly heritable and has been associated with multiple classes of genetic variation. Common genetic variation contributes substantially to autism. Previously, with 18,381 autistic individuals and 27,969 non-autistic individuals, five genome-wide significant loci were identified. Now with 38,717 autistic individuals and 232,725 non-autistic individuals, we report an updated genome-wide association study (GWAS) of autism with 12 genome-wide significant loci. We observe a moderate genetic correlation (0.675, SE=0.0434) between Europe-based (Nautistic=22,643; Nnon-autistic=204,389) and United States-based (Nautistic =16,074; Nnon-autistic=28,346) autism cohorts, which contributes to the decline of the estimated single nucleotide polymorphism (SNP) heritability (from 0.118 (SE=0.010) to 0.068 (SE=0.003)). The genetic correlation between autism with intellectual disability (ID) (Nautistic=6,590; Nnon-autistic= 43,071; h2=0.062; SE=0.012) and autism without ID (Nautistic=23,173; Nnon-autistic= 204,679; h2=0.089; SE=0.005) is 0.658 (SE=0.086). In the United States family-based cohorts, the genetic correlation between autism with ID (Nfamily=3,993; h2=0.159; SE=0.033) and autism without ID (Nfamily=4,357; h2=0.171; SE=0.031) is 0.812 (SE=0.157). Autism without ID was positively genetically correlated with educational attainment (0.163; P=4.84 × 10-11) and intelligence (0.233; P=1.95 × 10-11). Autism with ID genetically correlated with neither educational attainment (0.036; P=0.409) nor intelligence (-0.072; P=0.235). As ID alone is negatively genetically correlated with intelligence, the lack of correlation between autism with ID and intelligence strongly suggests that autism with ID is genetically different from ID alone. This difference has implications for both research and clinical nosology. Rare and de novo variants contribute substantially to autism in some individuals. Through rare variant analyses, 72 genes have been associated with autism at a genome-wide significant level to date. While de novo protein truncating variants (PTVs) and copy number deletions have been associated with autism, we report preliminary findings that the burden of inherited PTVs and copy number deletions among autistic individuals was elevated compared to their non-autistic siblings (P=4.00 × 10-5). Integration of multiple genetic factors will help us better understand the etiology of autism.
{"title":"THE POLYGENETIC ARCHITECTURE OF AUTISM","authors":"","doi":"10.1016/j.euroneuro.2024.08.045","DOIUrl":"10.1016/j.euroneuro.2024.08.045","url":null,"abstract":"<div><div>Autism is highly heritable and has been associated with multiple classes of genetic variation. Common genetic variation contributes substantially to autism. Previously, with 18,381 autistic individuals and 27,969 non-autistic individuals, five genome-wide significant loci were identified. Now with 38,717 autistic individuals and 232,725 non-autistic individuals, we report an updated genome-wide association study (GWAS) of autism with 12 genome-wide significant loci. We observe a moderate genetic correlation (0.675, SE=0.0434) between Europe-based (Nautistic=22,643; Nnon-autistic=204,389) and United States-based (Nautistic =16,074; Nnon-autistic=28,346) autism cohorts, which contributes to the decline of the estimated single nucleotide polymorphism (SNP) heritability (from 0.118 (SE=0.010) to 0.068 (SE=0.003)). The genetic correlation between autism with intellectual disability (ID) (Nautistic=6,590; Nnon-autistic= 43,071; h2=0.062; SE=0.012) and autism without ID (Nautistic=23,173; Nnon-autistic= 204,679; h2=0.089; SE=0.005) is 0.658 (SE=0.086). In the United States family-based cohorts, the genetic correlation between autism with ID (Nfamily=3,993; h2=0.159; SE=0.033) and autism without ID (Nfamily=4,357; h2=0.171; SE=0.031) is 0.812 (SE=0.157). Autism without ID was positively genetically correlated with educational attainment (0.163; P=4.84 × 10-11) and intelligence (0.233; P=1.95 × 10-11). Autism with ID genetically correlated with neither educational attainment (0.036; P=0.409) nor intelligence (-0.072; P=0.235). As ID alone is negatively genetically correlated with intelligence, the lack of correlation between autism with ID and intelligence strongly suggests that autism with ID is genetically different from ID alone. This difference has implications for both research and clinical nosology. Rare and de novo variants contribute substantially to autism in some individuals. Through rare variant analyses, 72 genes have been associated with autism at a genome-wide significant level to date. While de novo protein truncating variants (PTVs) and copy number deletions have been associated with autism, we report preliminary findings that the burden of inherited PTVs and copy number deletions among autistic individuals was elevated compared to their non-autistic siblings (P=4.00 × 10-5). Integration of multiple genetic factors will help us better understand the etiology of autism.</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":"142442213","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.2024.08.021
<div><div>Longitudinal prospective studies in high-risk populations are key for identifying pre-morbid risk factors for the development of psychopathology. The Australia-US collaborative Bipolar high-risk study comprises 3 groups of participants aged 12-30 years: ‘high-risk’ (with a sibling or parent with bipolar-I or -II), controls with no family history, and an unrelated group of BD-probands; with clinical, demographic and biological data. Familial risk is sometimes considered a surrogate for genetic risk (that is indexed via inherited DNA variants), but we know that this is a simplification of the ‘heritable’ component, which might comprise both direct and indirect genetic effects as well as the impact of family environment. We have used multiple analytic approaches to define and characterize features of disease risk, using neuroimaging, genomics and epigenomics. Analysis of magnetic resonance imaging (MRI) data from 217 unrelated Australian ‘Bipolar Kids and Sibs study’ participants (baseline n=217, follow-up n=152) finds accelerated cortical thinning over time (two scans, 2 years apart) in high-risk subjects (n=105) compared to controls (n=112), suggesting an early brain over-growth followed by normalisation towards the typical age of BD onset. Accelerated thickness and volume reductions over time were observed in ‘high-risk’ individuals across multiple cortical regions, relative to controls, including right lateral orbitofrontal thickness (β=.033, p < .001) and inferior frontal volume (β=.021, p < .001). We also find that bipolar polygenic risk (PsychArray) interacts with stress to increase suicide risk. We examined polygenic risk for both suicide attempt and risky behaviour on structural variance in cortical parcellations that have previously shown replicable associations with suicide attempts, finding that structural differences in the anterior cingulate, parahippocampal, and cuneus warrant further investigation as potential biomarkers for suicide attempts, particularly within the context of BD. Examination of epigenetic markers (450k/EPIC array) shows that genome-wide methylation patterns are broadly impacted by polygenic risk; highlighting an important interplay between genomically inherited risk and the potential biological encoding of environmental exposures. We are now collecting a 3rd MRI scan to capture nonlinear cortical developmental trajectories, and a 2nd blood sample to extend our baseline epigenetic work, derive serum measures and examine mRNA transcription patterns as potential biomarkers of emergent psychopathology. Brain regions associated with both genetic and clinical measures of psychopathology may serve as viable biomarkers, with clinical utility for the identification of individuals who are at greatest risk of developing psychopathology or suicidal intent. Future work will enable integration of these features into a prediction model of disease, to identify biological subgroups on the trajectory towards mental il
对高危人群进行纵向前瞻性研究，是确定精神病理学发展的病前风险因素的关键。澳大利亚和美国合作开展的躁郁症高危人群研究包括三组 12-30 岁的参与者："高危人群"（兄弟姐妹或父母中有一人患有躁郁症 I 或 II）、无家族史的对照组和无亲属关系的躁郁症患者组；这些参与者都有临床、人口统计学和生物学数据。家族风险有时被认为是遗传风险的替代物（通过遗传的 DNA 变异来表示），但我们知道，这只是对 "遗传 "成分的简化，"遗传 "成分可能包括直接和间接的遗传效应以及家庭环境的影响。我们采用多种分析方法，利用神经影像学、基因组学和表观基因组学来定义和描述疾病风险特征。通过分析217名无亲属关系的澳大利亚 "双相儿童和兄弟姐妹研究 "参与者的磁共振成像（MRI）数据（基线人数为217人，随访人数为152人）发现，与对照组（人数为112人）相比，高风险受试者（人数为105人）的大脑皮层随着时间的推移（两次扫描，间隔2年）加速变薄，这表明大脑在早期过度生长，随后在双相情感障碍的典型发病年龄趋于正常。与对照组相比，"高危 "人群的多个皮质区域的厚度和体积随着时间的推移加速减少，包括右侧眶额叶厚度（β=.033，p <.001）和下额叶体积（β=.021，p <.001）。我们还发现，躁郁症多基因风险（PsychArray）与压力相互作用，增加了自杀风险。我们对自杀未遂和危险行为的多基因风险进行了研究，研究结果表明，前扣带回、海马旁和楔叶的结构差异作为自杀未遂的潜在生物标志物值得进一步研究，尤其是在双相情感障碍的背景下。对表观遗传标记物（450k/EPIC 阵列）的研究表明，全基因组的甲基化模式受到多基因风险的广泛影响；这凸显了基因遗传风险与环境暴露的潜在生物编码之间的重要相互作用。目前，我们正在收集第三次核磁共振成像扫描结果，以捕捉大脑皮层的非线性发育轨迹，并收集第二次血液样本，以扩展我们的基线表观遗传学工作，得出血清测量结果，并研究 mRNA 转录模式，作为新出现的精神病理学的潜在生物标志物。与精神病理学遗传和临床测量相关的脑区可作为可行的生物标志物，在临床上用于识别最有可能发展成精神病理学或自杀意图的个体。未来的工作将能够把这些特征整合到疾病预测模型中，以确定精神疾病轨迹上的生物亚群。
{"title":"YOUTH AT RISK OF BIPOLAR DISORDER: TRACKING TRAJECTORIES, OUTCOMES AND BIOMARKERS USING NEUROIMAGING, GENOMICS AND EPIGENOMICS","authors":"","doi":"10.1016/j.euroneuro.2024.08.021","DOIUrl":"10.1016/j.euroneuro.2024.08.021","url":null,"abstract":"<div><div>Longitudinal prospective studies in high-risk populations are key for identifying pre-morbid risk factors for the development of psychopathology. The Australia-US collaborative Bipolar high-risk study comprises 3 groups of participants aged 12-30 years: ‘high-risk’ (with a sibling or parent with bipolar-I or -II), controls with no family history, and an unrelated group of BD-probands; with clinical, demographic and biological data. Familial risk is sometimes considered a surrogate for genetic risk (that is indexed via inherited DNA variants), but we know that this is a simplification of the ‘heritable’ component, which might comprise both direct and indirect genetic effects as well as the impact of family environment. We have used multiple analytic approaches to define and characterize features of disease risk, using neuroimaging, genomics and epigenomics. Analysis of magnetic resonance imaging (MRI) data from 217 unrelated Australian ‘Bipolar Kids and Sibs study’ participants (baseline n=217, follow-up n=152) finds accelerated cortical thinning over time (two scans, 2 years apart) in high-risk subjects (n=105) compared to controls (n=112), suggesting an early brain over-growth followed by normalisation towards the typical age of BD onset. Accelerated thickness and volume reductions over time were observed in ‘high-risk’ individuals across multiple cortical regions, relative to controls, including right lateral orbitofrontal thickness (β=.033, p < .001) and inferior frontal volume (β=.021, p < .001). We also find that bipolar polygenic risk (PsychArray) interacts with stress to increase suicide risk. We examined polygenic risk for both suicide attempt and risky behaviour on structural variance in cortical parcellations that have previously shown replicable associations with suicide attempts, finding that structural differences in the anterior cingulate, parahippocampal, and cuneus warrant further investigation as potential biomarkers for suicide attempts, particularly within the context of BD. Examination of epigenetic markers (450k/EPIC array) shows that genome-wide methylation patterns are broadly impacted by polygenic risk; highlighting an important interplay between genomically inherited risk and the potential biological encoding of environmental exposures. We are now collecting a 3rd MRI scan to capture nonlinear cortical developmental trajectories, and a 2nd blood sample to extend our baseline epigenetic work, derive serum measures and examine mRNA transcription patterns as potential biomarkers of emergent psychopathology. Brain regions associated with both genetic and clinical measures of psychopathology may serve as viable biomarkers, with clinical utility for the identification of individuals who are at greatest risk of developing psychopathology or suicidal intent. Future work will enable integration of these features into a prediction model of disease, to identify biological subgroups on the trajectory towards mental il","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":"142441626","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.2024.08.024
Expression quantitative locus (eQTL) mapping provides deep insights into the function of disease-associated variants from Genome-wide association studies (GWAS). However, previous studies and our research reported misalignment between eQTLs and GWAS signals, likely due to bulk eQTLs mapped in non-pathology-relevant contexts and lack of cell-type resolution. Alternatively, unraveling the links between cis-regulatory elements (CREs) and genes in various cellular contexts offers an independent strategy to associate GWAS variants with their target genes beyond eQTL mapping. The state-of-the-art approaches, such as experimental assays (e.g., Promoter Capture Hi-C and macro-C) and computational models (e.g., ABC and EpiMap), provide linking resources based on different pieces of evidence, however, are each confined to limited brain cell types or cellular states.
Addressing this challenge, our study proposes a machine-learning approach to predict CRE-gene associations by combining protein-protein interactions and transcription factor (TF) binding predictions based on ATAC-seq, an assay measuring genomic accessibility. This computational approach facilitates the discovery of CRE-gene connections across different contexts (combinations of cell types and various conditions) whenever ATAC-seq data are available, enriching our understanding of the cis-regulatory networks between TF-CRE-gene.
We have amassed over 130 cell-sorted and single-cell ATAC-seq datasets encompassing a variety of brain cell types—excitatory neurons, inhibitory neurons, oligodendrocytes, oligodendrocyte progenitor cells (OPCs), astrocytes, microglia, immune cells, and brain vascular cells—under a range of conditions including chemical perturbations, genetic modifications, infections, and disease status. Utilizing this extensive data collection and our integrative pipeline, we have constructed an atlas of TF-CRE-gene linking, namely cEpiNets. We finally employ the atlas to evaluate the enrichment of GWAS signals in CRE modules under various cellular contexts and to prioritize target genes and key drivers across a spectrum of neuropsychiatric disorders.
{"title":"LEVERAGING CONTEXT-SPECIFIC EPIGENOMIC REGULATORY NETWORKS (EPINETS) TO DISSECT THE GENETICS OF NEUROPSYCHIATRIC DISORDERS","authors":"","doi":"10.1016/j.euroneuro.2024.08.024","DOIUrl":"10.1016/j.euroneuro.2024.08.024","url":null,"abstract":"<div><div>Expression quantitative locus (eQTL) mapping provides deep insights into the function of disease-associated variants from Genome-wide association studies (GWAS). However, previous studies and our research reported misalignment between eQTLs and GWAS signals, likely due to bulk eQTLs mapped in non-pathology-relevant contexts and lack of cell-type resolution. Alternatively, unraveling the links between cis-regulatory elements (CREs) and genes in various cellular contexts offers an independent strategy to associate GWAS variants with their target genes beyond eQTL mapping. The state-of-the-art approaches, such as experimental assays (e.g., Promoter Capture Hi-C and macro-C) and computational models (e.g., ABC and EpiMap), provide linking resources based on different pieces of evidence, however, are each confined to limited brain cell types or cellular states.</div><div>Addressing this challenge, our study proposes a machine-learning approach to predict CRE-gene associations by combining protein-protein interactions and transcription factor (TF) binding predictions based on ATAC-seq, an assay measuring genomic accessibility. This computational approach facilitates the discovery of CRE-gene connections across different contexts (combinations of cell types and various conditions) whenever ATAC-seq data are available, enriching our understanding of the cis-regulatory networks between TF-CRE-gene.</div><div>We have amassed over 130 cell-sorted and single-cell ATAC-seq datasets encompassing a variety of brain cell types—excitatory neurons, inhibitory neurons, oligodendrocytes, oligodendrocyte progenitor cells (OPCs), astrocytes, microglia, immune cells, and brain vascular cells—under a range of conditions including chemical perturbations, genetic modifications, infections, and disease status. Utilizing this extensive data collection and our integrative pipeline, we have constructed an atlas of TF-CRE-gene linking, namely cEpiNets. We finally employ the atlas to evaluate the enrichment of GWAS signals in CRE modules under various cellular contexts and to prioritize target genes and key drivers across a spectrum of neuropsychiatric disorders.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441628","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.2024.08.027
{"title":"INNATE AND ADAPTIVE IMMUNITY IN PSYCHIATRY: INSIGHTS FROM GENETIC ASSOCIATION STUDIES AND PERIPHERAL BLOOD IMMUNOPHENOTYPING","authors":"","doi":"10.1016/j.euroneuro.2024.08.027","DOIUrl":"10.1016/j.euroneuro.2024.08.027","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":"142441634","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.2024.08.080
<div><div>The majority of human genes maintain normal biological function when they become haploid due to a genomic deletion. However, pathogenicity may still arise when the remaining allele is affected by additional functional variation. Here, we describe analytical strategies for examining a specific type of compound heterozygosity, namely the co-occurrence of a deletion and a sequence-level variant affecting the other allele, hereafter referred to as deletion compound heterozygosity (DelCH). We report preliminary results in using these strategies to assess DelCH in Autism Spectrum Disorder (ASD).</div><div>We analyzed whole-genome sequencing data from MSSNG, Simons Simplex Collection, and SPARK cohorts (collectively 11,636 autistic individuals and 22,962 family members).</div><div>We developed multiple analytical strategies to examine rare (event rate < 1%) DelCH:<ul><li><span>1)</span><span><div>The burden analysis uses conditional logistic regression for group-level comparisons of DelCH rates between a) probands and their deletion-transmitting parents, with inherited deletion as a random effect variable, or b) probands and their family members, with family ID as a random effect variable;</div></span></li><li><span>2)</span><span><div>The transmission disequilibrium test (TDT) compares the rates with which deletion-non-transmitting parents transmit sequence-level variants within genes affected by deletions to their autistic offspring. Association is indicated by transmission of non-synonymous variants at a rate higher than predicted by chance. This approach was repeated in unaffected siblings as an additional control analysis.</div></span></li></ul></div><div>Each strategy has different strengths and weaknesses. The first burden analysis (1a) achieves perfect matching of deleted sequence and unambiguous phasing of variants but is restricted to proband-parent pairs. The second burden analysis (1b) benefits from a larger sample size but cannot distinguish between de novo and inherited variation. In addition, unambiguous phasing is possible only for SNVs within deletion boundaries. In contrast, while the TDT (2) can include SNVs outside deletion boundaries, thereby increasing statistical power, de novo events are not analyzed.</div><div>Our preliminary findings show variability in results as a function of the analytical strategy. Findings from the burden analysis suggest a modest enrichment of DelCH in ASD which was inversely proportional to the variant frequency thresholds applied.</div><div>Given that the mechanism consists of two rare events at the same locus, on the population level the role of DelCH in ASD etiology is likely modest, requiring large samples sizes for sufficient statistical power. In addition to this “lightning striking twice”, data preparation is demanding, as every subject has unique deletion regions in which sequence-level variants on the other allele are tallied. Variant selection metrics include allele frequency thres
{"title":"WHAT IS THE IMPACT OF COMPOUND HETEROZYGOUS EVENTS INVOLVING DELETIONS AND SEQUENCE-LEVEL VARIANTS IN AUTISM?","authors":"","doi":"10.1016/j.euroneuro.2024.08.080","DOIUrl":"10.1016/j.euroneuro.2024.08.080","url":null,"abstract":"<div><div>The majority of human genes maintain normal biological function when they become haploid due to a genomic deletion. However, pathogenicity may still arise when the remaining allele is affected by additional functional variation. Here, we describe analytical strategies for examining a specific type of compound heterozygosity, namely the co-occurrence of a deletion and a sequence-level variant affecting the other allele, hereafter referred to as deletion compound heterozygosity (DelCH). We report preliminary results in using these strategies to assess DelCH in Autism Spectrum Disorder (ASD).</div><div>We analyzed whole-genome sequencing data from MSSNG, Simons Simplex Collection, and SPARK cohorts (collectively 11,636 autistic individuals and 22,962 family members).</div><div>We developed multiple analytical strategies to examine rare (event rate < 1%) DelCH:<ul><li><span>1)</span><span><div>The burden analysis uses conditional logistic regression for group-level comparisons of DelCH rates between a) probands and their deletion-transmitting parents, with inherited deletion as a random effect variable, or b) probands and their family members, with family ID as a random effect variable;</div></span></li><li><span>2)</span><span><div>The transmission disequilibrium test (TDT) compares the rates with which deletion-non-transmitting parents transmit sequence-level variants within genes affected by deletions to their autistic offspring. Association is indicated by transmission of non-synonymous variants at a rate higher than predicted by chance. This approach was repeated in unaffected siblings as an additional control analysis.</div></span></li></ul></div><div>Each strategy has different strengths and weaknesses. The first burden analysis (1a) achieves perfect matching of deleted sequence and unambiguous phasing of variants but is restricted to proband-parent pairs. The second burden analysis (1b) benefits from a larger sample size but cannot distinguish between de novo and inherited variation. In addition, unambiguous phasing is possible only for SNVs within deletion boundaries. In contrast, while the TDT (2) can include SNVs outside deletion boundaries, thereby increasing statistical power, de novo events are not analyzed.</div><div>Our preliminary findings show variability in results as a function of the analytical strategy. Findings from the burden analysis suggest a modest enrichment of DelCH in ASD which was inversely proportional to the variant frequency thresholds applied.</div><div>Given that the mechanism consists of two rare events at the same locus, on the population level the role of DelCH in ASD etiology is likely modest, requiring large samples sizes for sufficient statistical power. In addition to this “lightning striking twice”, data preparation is demanding, as every subject has unique deletion regions in which sequence-level variants on the other allele are tallied. Variant selection metrics include allele frequency thres","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":"142442143","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.2024.08.092
Prevalence of depression is increasing, especially amongst adolescents and young adults, representing a key risk period where intervention is critical. When using Mendelian randomisation (MR) to identify causal risk factors for depression, estimates are limited to average lifetime effects, rather than being specific to developmental stages.
Methods. We have combined trajectories of depressive symptoms with MR to identify developmentally specific risk factors. We used repeated measures of depressive symptoms (short Moods and Feelings Questionnaire) in the ALSPAC cohort, with 11 repeated assessments covering ages 9 to 27 years. First, we used a repeated measures multi-level model (MLM) to describe the average trajectory of depressive symptoms. Linear splines split by knot points were used to explain the non-linear pattern of growth. Second, we used latent class analysis to explore heterogeneity in depression trajectories. Third, we combined both trajectory models with genetic instruments for depression (positive control) and with modifiable risk factors for depression.
Our models included 44,611 repeated assessments of sMFQ from 6,422 unique individuals. Our best fitting MLM trajectory had three linear splines corresponding to puberty (9-14.5 years), adolescence (14.5-21 years) and early adulthood (21-27 years). Latent classes were stable low, decreasing, transient, increasing and stable high. Positive control genetic instrument for MDD predicted trajectories, most strongly membership into the increasing and stable high class. Genetic instruments for BMI and educational attainment were not associated with change in population average depressive symptoms at any of the different developmental stages nor with class membership. This could suggest no causal effects of these risk factors at these developmental stages, or low power.
We are continuing to develop our methods, test power and incorporate additional risk factors. We believe that combining outcome trajectories with MR analyses has wide ranging application to improve specificity of causal effects and recommendations for intervention development.
{"title":"COMBINING MENDELIAN RANDOMISATION WITH DEPRESSION TRAJECTORIES TO IDENTIFY DEVELOPMENTALLY SPECIFIC PREDICTORS OF CHANGE IN DEPRESSIVE SYMPTOMS","authors":"","doi":"10.1016/j.euroneuro.2024.08.092","DOIUrl":"10.1016/j.euroneuro.2024.08.092","url":null,"abstract":"<div><div>Prevalence of depression is increasing, especially amongst adolescents and young adults, representing a key risk period where intervention is critical. When using Mendelian randomisation (MR) to identify causal risk factors for depression, estimates are limited to average lifetime effects, rather than being specific to developmental stages.</div><div><strong>Methods.</strong> We have combined trajectories of depressive symptoms with MR to identify developmentally specific risk factors. We used repeated measures of depressive symptoms (short Moods and Feelings Questionnaire) in the ALSPAC cohort, with 11 repeated assessments covering ages 9 to 27 years. First, we used a repeated measures multi-level model (MLM) to describe the average trajectory of depressive symptoms. Linear splines split by knot points were used to explain the non-linear pattern of growth. Second, we used latent class analysis to explore heterogeneity in depression trajectories. Third, we combined both trajectory models with genetic instruments for depression (positive control) and with modifiable risk factors for depression.</div><div>Our models included 44,611 repeated assessments of sMFQ from 6,422 unique individuals. Our best fitting MLM trajectory had three linear splines corresponding to puberty (9-14.5 years), adolescence (14.5-21 years) and early adulthood (21-27 years). Latent classes were stable low, decreasing, transient, increasing and stable high. Positive control genetic instrument for MDD predicted trajectories, most strongly membership into the increasing and stable high class. Genetic instruments for BMI and educational attainment were not associated with change in population average depressive symptoms at any of the different developmental stages nor with class membership. This could suggest no causal effects of these risk factors at these developmental stages, or low power.</div><div>We are continuing to develop our methods, test power and incorporate additional risk factors. We believe that combining outcome trajectories with MR analyses has wide ranging application to improve specificity of causal effects and recommendations for intervention development.</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":"142442152","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.2024.08.034
{"title":"ANCESTRALLY DIVERSE SAMPLES IMPROVE FINE-MAPPING OF DEPRESSION-ASSOCIATED LOCI","authors":"","doi":"10.1016/j.euroneuro.2024.08.034","DOIUrl":"10.1016/j.euroneuro.2024.08.034","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":"142442130","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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