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Drug Discovery and the Genetic and Biological Underpinnings of Schizophrenia 药物发现与精神分裂症的遗传学和生物学基础
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.042
David Collier
Genome-wide association and the analysis of rare genetic variants has provided great insights into the genetic basis of schizophrenia, with over 250 variants now identified. Evidence suggests that genetic evidence for association between a gene and disease (even for low risk, common variants) increased the probability of success as a drug target by two-fold or more (Minikel et al., 2024). However, there are several barriers to drug development using this information, including variant-to-gene mapping, target prioritisation, the validity of disease models, target tractability and the development of a therapeutic hypothesis. On the plus side, mapping of the genes within these loci have identified the dopamine D2 receptor (DRD2) gene, a major target of typical antipsychotic drugs, as well as a number of other genes that produce druggable or potentially druggable proteins (Kraft et al., 2024). In this presentation, potential pathways to drug development and stratification using genetics and genomics will be explored.
全基因组关联和罕见基因变异分析为研究精神分裂症的遗传基础提供了重要依据,目前已发现 250 多种变异。有证据表明,基因与疾病相关的遗传学证据(即使是低风险、常见的变异)将药物靶点的成功概率提高了两倍或更多(Minikel 等人,2024 年)。然而,利用这些信息进行药物开发还存在一些障碍,包括变异基因间的映射、目标优先级的确定、疾病模型的有效性、目标的可及性以及治疗假设的提出。从好的方面来看,这些基因座内的基因图谱已经确定了多巴胺 D2 受体(DRD2)基因,这是典型抗精神病药物的一个主要靶点,还确定了产生可药物治疗或潜在药物治疗蛋白的许多其他基因(Kraft 等人,2024 年)。本讲座将探讨利用遗传学和基因组学进行药物开发和分层的潜在途径。
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引用次数: 0
WHAT DO WE KNOW ABOUT OFFSPRING AT RISK FOR BIPOLAR DISORDER AND WHAT REMAINS TO BE DISCOVERED 我们对有可能患躁郁症的后代了解多少?
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.020
John Nurnberger , Melvin McInnis , Janice Fullerton , Philip Mitchell , Howard Edenberg , Leslie Hulvershorn , Emma Stapp , Holly Wilcox , Neera Ghaziuddin , Masoud Kamali , Gloria Roberts
<div><div>The High Risk paradigm is inherently a genetic epidemiologic study, similar to twin studies, adoption studies, and family studies. The questions posed are: What does the disorder look like before its onset? and What determines the transition between the risk state and onset of illness? These questions relate to the developmental expression of the risk genotype and interactions between risk and resilience.</div><div>Many studies have been done using this paradigm in bipolar disorder (BD). The particular sample we have been following comprises 307 young people at risk (first-degree relative of a proband with BD) and 166 controls, followed over the past 15 years. Ascertainment sites in the US are Indiana University School of Medicine, Johns Hopkins University, the University of Michigan and Washington University in St. Louis. We have collaborated with the University of New South Wales in Sydney, Australia. Similar methods of assessment (KSADS-BP and DIGS) have been used in all sites, along with a battery of self-assessment instruments. All participants have provided blood for DNA. All have had GWAS completed and polygenic risk scores calculated and 103 have DNA methylation data. A series of follow-up assessments were carried out over 2007-2020.</div><div>Diagnostic outcome is related to polygenic risk (Fullerton et al, 2015 PMID <span><span>26178159</span><svg><path></path></svg></span>; Zwicker et al, 2023 PMID <span><span>36856707</span><svg><path></path></svg></span>, Freeman et al, unpublished). Polygenic risk interacts with history of trauma to increase the chances of attempted suicide in high-risk offspring (Wilcox et al, 2017 PMID <span><span>29173741</span><svg><path></path></svg></span>). Polygenic risk also interacts dramatically with perceived pattern of family interaction (Stapp et al, 2023 PMID <span><span>37378048</span><svg><path></path></svg></span>).</div><div>Different patterns of comorbidity predicted major mood disorder in high-risk offspring, including both internalizing and externalizing disorders during childhood and early adolescence. These patterns of comorbidity are related to distinct polygenic signatures (Fullerton et al, unpublished).</div><div>In the literature, studies focused specifically on the emergence of BD have implicated subthreshold hypomanic/manic symptomatology as a premorbid indicator of developing illness (Hafeman et al, 2017 PMID <span><span>28678992</span><svg><path></path></svg></span>). Study of early symptomatic indicators in high-risk offspring have also implicated sleep disorders (Duffy et al, 2019 PMID <span><span>30525908</span><svg><path></path></svg></span>) and this may relate to premorbid disruption of circadian rhythms.</div><div>Although BD presents clinically as a distinctive syndrome, the genetic predisposition and developmental course of the disorder is complex. Risk for BD may be expressed phenotypically as an internalizing disorder, an externalizing disorder, or a disruption of
高风险模式本质上是一种遗传流行病学研究,类似于双胞胎研究、领养研究和家庭研究。提出的问题是疾病在发病前是什么样的? 是什么决定了风险状态与发病之间的转变?这些问题与风险基因型的发育表现以及风险与恢复力之间的相互作用有关。我们一直在跟踪的特定样本包括 307 名处于风险中的年轻人(躁狂症患者的直系亲属)和 166 名对照组,这些样本在过去 15 年中一直在跟踪。美国的确定地点包括印第安纳大学医学院、约翰霍普金斯大学、密歇根大学和圣路易斯华盛顿大学。我们还与澳大利亚悉尼的新南威尔士大学进行了合作。所有研究地点都采用了类似的评估方法(KSADS-BP 和 DIGS)以及一系列自我评估工具。所有参与者都提供了 DNA 血液。所有参与者都完成了基因组学分析并计算了多基因风险评分,103 人获得了 DNA 甲基化数据。诊断结果与多基因风险有关(Fullerton 等人,2015 年,PMID 26178159;Zwicker 等人,2023 年,PMID 36856707;Freeman 等人,未发表)。多基因风险与创伤史相互作用,增加了高风险后代企图自杀的几率(Wilcox 等人,2017 PMID 29173741)。多基因风险还与感知到的家庭互动模式有显著的相互作用(Stapp et al, 2023 PMID 37378048)。不同的合并症模式预示着高风险后代的主要情绪障碍,包括儿童期和青春期早期的内化障碍和外化障碍。这些合并症模式与不同的多基因特征有关(Fullerton 等人,未发表)。在文献中,专门针对 BD 出现的研究表明,阈下躁狂/狂躁症状是发病前的指标(Hafeman 等人,2017 PMID 28678992)。对高危后代早期症状指标的研究也牵涉到睡眠障碍(Duffy et al, 2019 PMID 30525908),这可能与病前昼夜节律紊乱有关。虽然BD在临床上表现为一种独特的综合征,但该疾病的遗传易感性和发育过程却很复杂。虽然 BD 在临床上表现为一种独特的综合征,但其遗传倾向和发育过程是复杂的。BD 风险在表型上可能表现为内化障碍、外化障碍或昼夜节律紊乱。成人躁狂症可能是单相躁狂症,也可能是双相躁狂症,病程和治疗反应受发病前和合并症的影响。米切尔博士和富勒顿博士对其中的一些大脑机制进行了描述。
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引用次数: 0
MENDELIAN RANDOMIZATION – WHAT ARE THE PROMISES? 门德尔随机化--有哪些承诺?
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.028
Sibylle Schwab (Chair) , Naomi Wray (Co-chair and Discussant)
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.
随着针对各种表型的全基因组关联分析提供了足够多的数据,一种名为 "孟德尔随机化"(Mendelian Randomization)的技术已成为寻找因果关系的常用方法。从本质上讲,这种技术使用遗传因素作为可改变暴露的替代物来探索因果关系。要进行有效且有影响的孟德尔随机化估计,需要满足几个条件。在本次研讨会上,我们将更详细地探讨这些条件,并举例说明精神遗传学中的一些有意义的探索。
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引用次数: 0
NEW INSIGHTS INTO THE GENETIC ETIOLOGY AND THERAPEUTIC TARGETS OF SCHIZOPHRENIA 精神分裂症遗传病因学和治疗目标的新见解
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.039
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引用次数: 0
POLYGENIC ARCHITECTURE AND DISEASE RISK PREDICTION 多基因结构和疾病风险预测
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.008
Na Cai (Speaker)
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引用次数: 0
PREDICTION OF ANTIDEPRESSANT SIDE EFFECTS IN THE GENETIC LINK TO ANXIETY AND DEPRESSION STUDY 焦虑和抑郁遗传关联研究中抗抑郁药副作用的预测
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.065
Danyang Li , Yuhao Lin , Helena Davies , Evangelos Vassos , Raquel Iniesta , Gerome Breen
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.
抗抑郁药是治疗中度或重度抑郁症最常用的药物。在这项研究中,我们利用焦虑和抑郁的遗传联系(GLAD)研究中自我报告问卷中的遗传和表型数据,预测了三种抗抑郁药(SSRI、SNRI、三环类抗抑郁药(TCA))在处方第一年和最后一年(最近一年)的副作用和停药(由于副作用)情况。该研究纳入了约 260 个预测因子,涵盖遗传、临床、合并症、人口统计学和抗抑郁药信息。对 XGBoost、随机森林、立方体、弹性网和支持向量机(RBF 和多项式核)进行了训练,并比较了它们的性能。副作用和停药的平均发生率分别为 74.1%和 28.7%。在第一年,预测 SSRI 停药和副作用的最佳 AUROC 分别为 0.65 和 0.60。在处方 SSRI 的最后一年,预测停药和副作用的最高 AUROC 分别为 0.73 和 0.87。预测 SNRI 和 TCA 的停药和副作用的模型表现相当。有副作用史和停用抗抑郁药是对去年治疗结果影响最大的预测因素。我们的研究结果表明,使用自我报告问卷预测抗抑郁药副作用是可行的,尤其是对最近一次处方的副作用。这些结果为临床决策的制定提供了有价值的见解,旨在优化治疗选择,提高耐受性。
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引用次数: 0
META-ANALYSIS OF RARE CNV GENOME-WIDE ASSOCIATION STUDIES ACROSS MAJOR PSYCHIATRIC DISORDERS IN EUR, AFR/AFAM, AND ASN/ASAM POPULATIONS 对欧洲、非洲/非洲医学会和亚洲医学会/亚洲医学会人群中主要精神疾病的罕见 CNV 全基因组关联研究的荟萃分析
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.070
Omar Shanta , Worrawat Engchuan , Jeff MacDonald , Marieke Klein , Bhooma Thiruvahindrapuram , Adam Maihofer , Molly Sacks , Mohammad Ahangari , Sebastien Jacquemont , Kimberley Kendall , Ida Sonderby , Guillaume Huguet , Steven H. Scherer , Jonathan Sebat , The Bipolar Disorder, Schizophrenia, Post-Traumatic Stress Disorder, Attention-Deficit/Hyperactivity Disorder, Major Depressive Disorder, Autism Spectrum Disorder and Copy Number Variation Working groups of the Psychiatric Genomics Consortium
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.
迄今为止,全基因组关联研究(GWAS)能够利用大样本量来确定导致各种精神疾病风险的基因组位点。然而,拷贝数变异(CNVs)的全基因组关联研究由于缺乏对不同祖先群体的研究,一直优先考虑确定欧洲人群中的风险位点。在这项研究中,我们调用了一组不同样本中的 CNVs,为另外两个祖先群体创建了 CNV 数据集:非洲/非裔美国人(AFR/AFAM)和亚洲/亚裔美国人(ASN/ASAM)。SNPweights 用于推断每个样本的全基因组遗传祖先。然后,我们利用荟萃分析将欧洲人、非洲裔美国人/非洲裔美国人和亚裔美国人/亚裔美国人的 CNV 分析结合起来(N=571,803),从而提高了特定位点的分析能力。罕见拷贝数变异已牵涉到一个跨障碍的欧洲队列(N=537,466),其中包括自闭症(ASD)、精神分裂症(SCZ)、重度抑郁障碍(MDD)、双相情感障碍(BD)、创伤后应激障碍(PTSD)和注意力缺陷/多动障碍(ADHD)等主要精神障碍。这项分析能够发现新的基因座,其统计能力是迄今为止最大的 CNV 研究所具备的。当然,将不同的样本纳入这项分析还能进一步带来新的发现。我们还对AFR/AFAM(样本数=17,474)和ASN/ASAM(样本数=16,863)人群的交叉紊乱数据集进行了CNV-GWAS分析。对所有 3 个人群的 Meta 分析都采用了反方差加权法,以考虑不同人群样本量的差异。我们将 EUR CNV-GWAS 和负担结果与荟萃分析的结果进行了比较,因为这些是最有效的检测方法。结果显示,在不同群体中,达到可检测 CNV 频率的特定位点的显著性水平大幅提高。将EUR分析与跨种群分析进行比较,可以量化不同群体的贡献,并在样本量达到类似规模后,深入了解与AFR/AFAM和ASN/ASAM人群精神障碍相关的基因组位点。这项研究强调了在数据收集过程中扩大多样性的重要性,从而使基因型与表型之间的关系造福于全世界的人们。
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引用次数: 0
THE ALLELIC ARCHITECTURE OF RARE VARIATION IN AUTISM AND OTHER NEURODEVELOPMENTAL CONDITIONS 自闭症和其他神经发育疾病中罕见变异的等位基因结构
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.046
Jack Fu , F. Kyle Satterstrom , Kirsty McWalter , Harrison Brand , Robert Kueffner , David Cutler , Kaitlin Samocha , Elise Robinson , Joseph Buxbaum , Bernie Devlin , Kathryn Roeder , Paul Kruszka , Stephan Sanders , Mark Daly , Michael Talkowski
<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。
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引用次数: 0
THE IDENTICAL DEPRESSION PHENOTYPING CONSORTIUM: DECONSTRUCTION AND PREDICTION OF MDD AND TREATMENT RESPONSE 相同抑郁表型联盟:解构和预测 MDD 及治疗反应
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.063
Gerome Breen (Chair) , Brittany Mitchell (Co-chair) , Alexander Hatoum (Discussant)
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.
同种抑郁症表型研究联盟由英国的研究(焦虑和抑郁的遗传链接或 GLAD 和英国生物库)、澳大利亚抑郁症遗传学研究和荷兰生物库互联网合作(BIONIC)组成。这三项研究采用相同的方法对抑郁症进行表型分析,包括详细的人口统计学数据、临床记录链接以及超过 13 万例重度抑郁症病例的数据。我们提议召开一次专题讨论会,重点讨论 MDD 及其治疗的预测模型,强调多基因评分、家族史和临床数据的整合。在GLAD(9927例MDD病例,4452例对照)中整合了这些因素的机器学习显示,对MDD、MDD反复发作次数的预测准确率很高。这些发现在英国生物库(40667 例 MDD 病例,70755 例对照)中得到了验证。接下来,Li 将介绍在 GLAD 研究的 5K 个病例中纳入抗抑郁药物副作用的遗传和临床预测因素。通过采用机器学习模型,他们在预测副作用和停药率方面取得了巨大成功,尤其是在整合先前处方数据时。Huider将代表BIONIC联盟介绍MDD的遗传分析,该联盟利用对> 3万个病例的统一深入表型,对MDD及其症状进行了大规模遗传分析,以探索荷兰国内抑郁症的异质性。这一雄心勃勃的项目凸显了大型同质数据集在解读抑郁症复杂遗传学方面的重要性。最后,米切尔将发表题为 "使用多基因风险评分来描述耐药性MDD "的报告,探讨TRD与生物预测因素(如多基因评分(PGS)、CYP2C19和CYP2D16代谢物特征)、人格特征测量结果以及环境预测因素(如社会支持和生活压力事件)之间的关系。最后,他们测试了各种预测因素之间的基因与环境之间的相互作用。他们的研究确定了与长期治疗效果相关的遗传因素,为治疗抑郁症的个性化医疗提供了基础。本次研讨会旨在展示整合遗传、家族和临床数据的前沿研究,以便更有效地预测和管理重度抑郁障碍。讨论者 Hatoum 将探讨基因预测与机器学习方法相结合的意义以及临床应用的可能性。
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引用次数: 0
ETHICAL AND POLICY ISSUES IN A DIVERSE WORLD 多样化世界中的伦理和政策问题
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.058
Todd Lencz (Chair) , Julia Sealock (Co-chair) , Anna Docherty (Discussant)
This will be the Symposium presented by the Ethics, Positions, and Public Policy Committee.
这将是伦理、立场和公共政策委员会举办的专题讨论会。
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引用次数: 0
期刊
European Neuropsychopharmacology
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