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THE PGC-PSTD ANCESTRY WORKING GROUP: IMPROVING THE INCLUSION OF ADMIXED INDIVIDUALS IN PSYCHIATRIC GWAS PGC-PSTD 血统工作组:将混血儿更好地纳入精神疾病全球监测系统
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.016
Marcos Santoro , Jessica Mauer , Adam Maihofer , Nirav Shah , Caroline Nievergelt , Elizabeth Atkinson
The latest PGC-PTSD GWAS achieved a considerable increase in the sample sizes of non-European populations, including more than 50,000 African American (AA) and 13,000 Latin American (LA) individuals. One of the difficulties in analyzing these samples is that they are usually very admixed, with different patterns of admixture according to the region they have been collected. The PGC-PTSD Ancestry working group is focused on developing new tools to allow proper inclusion of admixed individuals in GWAS and to facilitate the use of these tools for LMIC collaborators. Previously, we developed Tractor, a gene discovery tool for admixed populations that uses Local Ancestry Inference (LAI) to allow the well-calibrated inclusion of these individuals in GWAS studies. Currently, one of the primary efforts is to comprehensively test strategies for LAI to provide guidelines for parameter setting and reference panel composition. For this, we have simulated admixed individuals considering different patterns of admixture (AA-2 way and LA-3 way), demographic models, software settings, genomic data types, and reference panels. These simulations allow us to define best practice guidelines for researchers to use when analyzing diverse populations to produce the highest accuracy results across ancestries.
In our tests, we observe that Amerindigenous ancestry tracts suffer from notably reduced accuracy as compared to European and African tracts. When miscalls occur, LAI error rates are most frequently in the direction of calling European ancestry in true Amerindigenous sites than other error modes. Though our investigations are directly responsive to realistic admixed Latin American cohort compositions, the trends we characterize are broadly useful to inform best practice for local ancestry inference across diverse admixed populations. In parallel with this initiative of improving LAI analysis accuracy, our working group is developing a friendly pipeline for Tractor so other consortia can apply this approach for large sample sizes. As a future perspective, the PGC-PTSD ancestry working group will also work on the development of new tools for post GWAS approaches as polygenic scores.
最新的 PGC-PTSD 基因组研究大大增加了非欧洲人群的样本量,其中包括 50,000 多名非洲裔美国人(AA)和 13,000 多名拉丁美洲人(LA)。分析这些样本的困难之一在于它们通常具有很强的混杂性,根据采集地区的不同,混杂模式也不同。PGC-PTSD 祖先工作组致力于开发新工具,以便将混血个体适当纳入 GWAS,并为 LMIC 合作者使用这些工具提供便利。在此之前,我们开发了 Tractor,这是一种针对混血人群的基因发现工具,它使用本地祖先推断(LAI)将这些个体纳入 GWAS 研究,并对其进行良好校准。目前,我们的主要工作之一是全面测试 LAI 的策略,为参数设置和参考面板组成提供指导。为此,我们考虑了不同的混杂模式(AA-2 方式和 LA-3 方式)、人口统计模型、软件设置、基因组数据类型和参考面板,对混杂个体进行了模拟。通过这些模拟,我们确定了最佳实践指南,供研究人员在分析不同人群时使用,以便在不同祖先间得出最高准确度的结果。在我们的测试中,我们发现美洲土著祖先区与欧洲和非洲祖先区相比,准确度明显降低。当误判发生时,LAI 误差率最常出现的方向是在真正的美洲土著地点判定欧洲人祖先,而不是其他误差模式。虽然我们的研究是直接针对实际的拉丁美洲混血人群组成的,但我们所描述的趋势对不同混血人群的当地祖先推断的最佳实践具有广泛的参考价值。在提高 LAI 分析准确性的同时,我们的工作小组还在为 Tractor 开发一个友好的管道,以便其他联盟可以将这种方法应用于大样本量。从未来的角度来看,PGC-PTSD 祖先工作组还将致力于开发用于后 GWAS 方法的新工具,如多基因评分。
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引用次数: 0
EXPLORING THE IMMUNOGENETIC BASIS OF POST-TRAUMATIC STRESS DISORDER 探索创伤后应激障碍的免疫遗传基础
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.017
Alice Braun , Adam X Maihofer , Seyma Katrinli , Georgia Panagiotaropoulou , Daniel Levey , Stephan Ripke , Joel Gelernter , Caroline Nievergelt , PGC PTSD Working Group
<div><div>Post-Traumatic Stress Disorder (PTSD) is a complex psychiatric condition that develops following exposure to traumatic experiences. Its core symptoms include intrusive thoughts, avoidant behavior, and a persistent state of hyperarousal. Although it is widely recognized that stress exacerbates inflammation across tissues, a growing body of evidence suggests a reciprocal relationship, with immune function influencing susceptibility to PTSD. This relationship may be driven by shared underlying biology, such as from pleiotropy. The most recent genome-wide association study (GWAS) of PTSD identified 95 risk loci, including endocrine and immune regulators, such as the major histocompatibility complex (MHC). The MHC harbors numerous genetic variants such as human leukocyte antigen (HLA) alleles that are crucial for immune function. However, the complex linkage disequilibrium structure of the MHC poses challenges in isolating individual signals through SNP-based imputation. Here, we present a large-scale cross-ancestry analysis assessing the association of HLA alleles with PTSD.</div><div>We conducted HLA imputation and association analysis in a genotyped sample of individuals of African, European, or Latin American ancestry from 38 studies included in the latest PTSD GWAS published by the Psychiatric Genomics Consortium. The outcome phenotype was assessed as either case-control or on a continuous scale (e.g. the PTSD Checklist for DSM-IV or V). The 1000 Genomes Reference Panel, comprised of individuals from African, East Asian, European, South Asian, and American populations was employed to impute around 350 HLA alleles via SHAPEIT5 and MINIMAC4. Additionally, we introduced 21 long-range HLA haplotypes into the reference. Regression analyses were conducted using PLINK 2.0, while the first five principal components were included as covariates to adjust for population stratification. Finally, we employed METAL, using the sample-size weighting approach, to meta-analyze results from dichotomous and continuous outcomes.</div><div>We have generated preliminary results in a multi-ancestry sample (N = 60,159) that highlight HLA-DRB1*01:01 as the top risk-conferring allele across three ancestries (Z = 3.255, P = 1.14e-03). HLA-DRB1*01:01 was also the most significant HLA allele (Z = 2.380, P = 1.73e-02) in the Latin American sample (n = 7,072). In the African sample (n = 14,883), HLA-B*51:01 (Z = 3.553, P = 3.82e-04) emerged as the most significant HLA allele, while in Europeans (n = 38,204), the most significant allele HLA-B*08:01 showed a negative association with PTSD (Z = -2.555, P = 1.06e-02).</div><div>Our association analysis has identified multiple HLA alleles nominally associated with PTSD, with HLA-DRB1*01:01 emerging as the most significant possibly risk-conferring variant across ancestries. Notably, a protective effect of HLA-B*08:01 has previously been observed in schizophrenia and bipolar disorder. In the next steps, we will conduct imput
创伤后应激障碍(PTSD)是一种复杂的精神疾病,是在遭受创伤后出现的症状。其核心症状包括侵入性思维、回避行为和持续的过度焦虑状态。尽管人们普遍认为压力会加剧各组织的炎症反应,但越来越多的证据表明两者之间存在互为因果的关系,免疫功能会影响创伤后应激障碍的易感性。这种关系可能是由共同的潜在生物学因素驱动的,如多生物效应(pleiotropy)。创伤后应激障碍的最新全基因组关联研究(GWAS)确定了 95 个风险基因位点,包括内分泌和免疫调节剂,如主要组织相容性复合体(MHC)。主要组织相容性复合体(MHC)中存在大量遗传变异,如对免疫功能至关重要的人类白细胞抗原(HLA)等位基因。然而,MHC 复杂的连锁不平衡结构给通过基于 SNP 的估算分离个体信号带来了挑战。我们从精神疾病基因组学联盟(Psychiatric Genomics Consortium)最新发布的创伤后应激障碍 GWAS 中纳入的 38 项研究中,对非洲、欧洲或拉丁美洲血统的基因分型样本进行了 HLA 估算和关联分析。结果表型以病例对照或连续量表(如 DSM-IV 或 V 的创伤后应激障碍核对表)的形式进行评估。由非洲、东亚、欧洲、南亚和美洲人群组成的 1000 基因组参照组通过 SHAPEIT5 和 MINIMAC4 估算了约 350 个 HLA 等位基因。此外,我们还在参照中引入了 21 个长程 HLA 单倍型。使用 PLINK 2.0 进行回归分析,同时将前五个主成分作为协变量来调整人群分层。最后,我们采用了 METAL,使用样本量加权法对二分法和连续法的结果进行了元分析。我们在多血统样本(N = 60,159)中得出的初步结果显示,HLA-DRB1*01:01 是三个血统中风险最大的等位基因(Z = 3.255,P = 1.14e-03)。在拉丁美洲样本(n = 7 072)中,HLA-DRB1*01:01 也是最重要的 HLA 等位基因(Z = 2.380,P = 1.73e-02)。在非洲样本(n = 14883)中,HLA-B*51:01(Z = 3.553,P = 3.82e-04)是最重要的 HLA 等位基因,而在欧洲样本(n = 38204)中,最重要的等位基因 HLA-B*08:01 与创伤后应激障碍呈负相关(Z = -2.我们的关联分析发现了多个与创伤后应激障碍名义上相关的 HLA 等位基因,其中 HLA-DRB1*01:01 是不同血统中最重要的风险诱导变体。值得注意的是,以前曾在精神分裂症和躁狂症中观察到 HLA-B*08:01 的保护作用。下一步,我们将对位于 MHC III 类区的补体成分 4 进行估算和关联分析,并进行条件分析以确定其独立效应。最后,我们还将纳入更多大规模样本,如百万退伍军人计划和英国生物库,以提高统计能力。
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引用次数: 0
COORDINATED EPISTASIS DETECTS HETEROGENOUS PATHWAYS ACROSS PSYCHIATRIC DISORDERS AND COMORBIDITIES 协调外显检测精神疾病和合并症的异质通路
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.077
Jolien Rietkerk , Morten Krebs , Lianyun Huang , Kajsa-Lotta Georgii Hellberg , IPSYCH Consortium , Thomas Werge , Andrew Schork , Andy Dahl , Na Cai
Cross-disorder analyses in psychiatry often center around genetic correlation, which quantifies the average similarity of genetic effects across two disorders. For a long time, this has been the only feasible approach, as most cohorts only collect data on a single disorder. However, few studies have examined the genetic architecture of comorbidity itself or how it relates to the genetic architecture of the individual disorders involved. In this study we set out to investigate the genetic architecture of comorbidity between psychiatric disorders in the iPSYCH2015 case-cohort study. This Danish register-based study contains comorbid cases for 10 pairs of five psychiatric disorders (schizophrenia (SCZ), bipolar disorder (BPD), major depressive disorder (MDD), autism (AUT) and attention deficit hyperactivity disorder (ADHD)), making it ideal for understanding comorbidity. We develop a novel framework to model both cross-disorder genetic sharing and the genetics of comorbidity based on the concept of Coordinated Epistasis (CE). Within this framework, we can identify synergistic and antagonistic interactions of Polygenic Risk Scores (PRS) across each disorder pair. We can also identify how these interactions impact individual disorders involved and delineate established theoretical models of comorbidity. In particular, we test one model of comorbidity where genetic effects distinguish comorbid cases from cases with only one disorder, which shows synergistic PRS interactions between ADHD-AUT comorbid cases and cases of either AUT or ADHD, which replicates in both iPSYCH2015 sub-cohorts: 2012 (P = 1.3E-02) and 2015i (P = 2.9E-02). We next apply our framework to family-based genetic scores (PA-FGRS), using recorded diagnoses from an average of 20 genetic relatives from the Danish medical registry. We find synergistic PA-FGRS interactions in comorbid ADHD-AUT (P = 1.1E-05), validating our PRS results. In summary, we perform the first comprehensive study on the genetics of comorbidity by extending the CE framework using a combination of PRS and PA-FGRS, and for the first time identify coordinated polygenic interactions contributing to cross-disorder genetic sharing and comorbidity among five psychiatric disorders.
精神病学的跨障碍分析通常以遗传相关性为中心,即量化两种障碍的遗传效应的平均相似性。长期以来,这是唯一可行的方法,因为大多数队列只收集单一疾病的数据。然而,很少有研究对合并症本身的遗传结构或其与所涉及的单个疾病的遗传结构之间的关系进行研究。在本研究中,我们着手调查 iPSYCH2015 病例队列研究中精神疾病合并症的遗传结构。这项基于丹麦登记册的研究包含五种精神疾病(精神分裂症(SCZ)、双相情感障碍(BPD)、重度抑郁障碍(MDD)、自闭症(AUT)和注意缺陷多动障碍(ADHD))中 10 对合并病例,因此非常适合了解合并症。我们根据协调外显(Coordinated Epistasis,CE)的概念,建立了一个新颖的框架,为跨障碍遗传共享和合并症遗传建模。在这一框架内,我们可以确定每对疾病的多基因风险评分(PRS)之间的协同和拮抗相互作用。我们还能确定这些相互作用如何影响所涉及的单个疾病,并划定已建立的合并症理论模型。特别是,我们测试了一种合并症模型,在该模型中,遗传效应将合并症病例与仅患有一种疾病的病例区分开来,该模型显示,ADHD-AUT 合并症病例与 AUT 或 ADHD 病例之间存在协同的 PRS 相互作用,这在 iPSYCH2015 两个子队列中都得到了复制:2012(P = 1.3E-02)和 2015i(P = 2.9E-02)。接下来,我们将我们的框架应用于基于家族的遗传评分(PA-FGRS),使用丹麦医疗登记册中平均 20 个遗传亲属的诊断记录。我们发现 PA-FGRS 在合并 ADHD-AUT 中具有协同作用(P = 1.1E-05),验证了我们的 PRS 结果。总之,我们通过结合使用 PRS 和 PA-FGRS 来扩展 CE 框架,首次对合并症的遗传学进行了全面研究,并首次确定了导致五种精神疾病的跨障碍遗传共享和合并症的协调多基因相互作用。
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引用次数: 0
SCIENCE COMMUNICATION: THE IMPORTANCE OF LANGUAGE IN A DIVERSE WORLD 科学传播:语言在多元化世界中的重要性
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.100
Helena Davies (Chair) , Abigail ter Kuile (Co-chair) , Helena Davies (Discussant)
Our aim as the PGC Outreach Committee is to improve visibility, accessibility, and understanding of psychiatric genetics amongst both the general public and the wider scientific community. But how accessible are we really? How easily interpreted is the information we share to non-scientists? And how can we improve?
A systematic review of media coverage and readability in genome-wide association studies, published earlier this year, concluded that the language used to describe genetics research is too complex to be understood by the public. Over 95% of the online news sites examined would require more than twelve years of formal education for a full understanding of their content. The importance of language, particularly in genetics research, can extend beyond ‘readability’ to even more fundamental issues. For instance, another recent systematic review emphasised the need for defining ancestry based on the type of data used for its measurement (e.g., “genetic ancestry”), as failure to do so can result in reduced clarity concerning the distinction between genetic and social identities.
This symposium will delve into the critical role of language in the effective communication of scientific concepts to diverse audiences. Our presenters will first each discuss what the importance of language in a diverse world means from their own unique perspective (10 minutes each). They will cover topics such as the importance of the choice of words in relation to genetic ancestry and other complex concepts in psychiatric genetics such as heritability, and the impact of language in discussions surrounding the lived experience of those with psychiatric disorders. Broadly, the presentations will highlight how we can bridge the gap between technical jargon and layman's terms, making complex ideas accessible to a broader audience including those living with psychiatric conditions and their families, as well as how we can more accurately use language in our communications within the scientific community.
We will then have a panel discussion (30 minutes) in which the presenters will share insights into, for example, some of the challenges they have faced in science communication, such as combating misinformation, and what they believe the consequences for our field will be if we do not carefully consider the role of accurate and responsible communication in psychiatric genetics. We will conclude the session with questions from the audience (15 minutes).
Ultimately, the symposium will demonstrate that effective science communication is a dynamic interplay of language, empathy, and engagement, and will encourage attendees to consider the impact of their words in shaping public perceptions and attitudes towards psychiatric genetics.
作为PGC外联委员会,我们的目标是提高精神遗传学在公众和广大科学界的知名度、可及性和理解度。但是,我们到底有多容易接近呢?我们分享给非科学家的信息有多容易解读?今年早些时候发表的一篇关于全基因组关联研究的媒体报道和可读性的系统综述得出结论:描述遗传学研究的语言过于复杂,公众难以理解。在接受审查的在线新闻网站中,超过 95% 的网站需要接受过 12 年以上的正规教育才能完全理解其内容。语言的重要性,尤其是在遗传学研究中的重要性,可能超出 "可读性 "的范畴,而涉及更根本的问题。例如,最近的另一篇系统综述强调了根据用于测量的数据类型(如 "遗传祖先")来定义祖先的必要性,因为如果不这样做,就会降低遗传身份与社会身份之间区别的清晰度。我们的发言人将首先从各自独特的角度讨论语言在多元化世界中的重要性(每人 10 分钟)。他们将讨论的话题包括:选择与遗传祖先和精神遗传学中其他复杂概念(如遗传率)相关的词语的重要性,以及语言在围绕精神疾病患者生活经历的讨论中的影响。从广义上讲,演讲将强调我们如何缩小专业术语与通俗用语之间的差距,让更多受众(包括精神疾病患者及其家人)了解复杂的概念,以及我们如何在科学界的交流中更准确地使用语言。然后,我们将进行小组讨论(30 分钟),发言人将分享他们在科学交流中遇到的一些挑战,例如如何与错误信息作斗争,以及如果我们不认真考虑准确和负责任的交流在精神病遗传学中的作用,他们认为会给我们的领域带来什么后果。最后,我们将以听众提问的形式结束本场会议(15 分钟)。本次研讨会将证明,有效的科学传播是语言、移情和参与的动态互动,并鼓励与会者考虑他们的言辞在塑造公众对精神遗传学的看法和态度方面所产生的影响。
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引用次数: 0
EVALUATING THE IMPACT OF BIOLOGICAL SEX ON ADHD PRESENTATION, PREVALENCE, AND GENETIC RISK 评估生理性别对 adhd 表现、发病率和遗传风险的影响
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.076
Sarah Guagliardo , Mischa Lundberg , Andrew Schork , Nancy Cox , Megan Shuey
<div><div>Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that impairs executive functioning, vigilance-attention, and motivation. Due to this, individuals with ADHD are at higher risk of addiction, poor academic and professional outcomes, and social deficits. Heterogeneity in presenting symptoms is well-established and may result in a delayed or missed diagnosis. The prevalence of ADHD is reported from two to seven times higher for males than females. The prevalence of ADHD appears consistent in childhood and adulthood. Only half of those diagnosed in childhood report persisting symptoms, implying many are first diagnoses as adults and those first diagnosed in adulthood tend towards a different symptom profile. Such sex and age trends may reflect protective effects of “female sex”, children “growing out of” ADHD, or adults experiencing a different clinical entity. However, others argue that the high heritability of ADHD (0.6-0.85) and similar genetic risk in females suggests that these trends may be due to a heterogenous expression of symptoms in response to the environment (e.g., modulated by the female or adult experience). We use Vanderbilt University Medical Center's (VUMC) biobank (n=3,285,882 electronic health records (EHR) and 119,750 genotyped samples) to analyze ADHD prevalence and genetic architecture. We observed the ADHD-associated ICD codes (n=38,419) were less frequent in EHR-recorded females relative to males (n=14,395 vs. 24,024) and the median age at first diagnosis was substantially older (21.72 years, IQR=20.96 vs.15.05 years, IQR=9.1). Among subset of European ancestry patient genotyped in VUMC (n=69,397), we observed an ADHD polygenic risk scores (PRS) was significant independent predictor of diagnosis, with stronger effects on females (males, beta= 13.47, p=0.03; females, beta=16.90, p=7.4e-7), and female cases having higher average PRS than male cases (p=0.04). In a sex-specific phenome-wide association study (PheWAS), the ADHD PRS was associated with similar phenotypes regardless of sex, including substance/tobacco use, other psychiatric disorders, obesity, diabetes mellitus, and respiratory problems. Our findings that female patients with ADHD appear to have higher genetic liability for the condition despite lower rates of diagnosis are consistent with previous studies. Additionally, ADHD PRS did not demonstrate differential comorbidity structures based on sex in VUMC. One explanation for this is that established genetic proxies of disease inadequately reflect the nuances of particular behaviors of ADHD subtypes, including but not limited to exhibition of externalizing hyperactive subtype (ADHD-H) opposed to internalizing inattentive subtype (ADHD-I), which is reported more frequently in females. Therefore, obtaining clinical diagnoses in females may require symptom manifestations that are largely overlapping with their male counterparts. Additional work in various EHR resources may shed
注意力缺陷多动障碍(ADHD)是一种神经发育障碍,会损害执行功能、警觉-注意力和动机。因此,注意力缺陷多动障碍患者有较高的成瘾风险,在学业和职业方面表现不佳,并存在社交障碍。表现症状的异质性是公认的,可能导致诊断延迟或漏诊。据报道,多动症的发病率男性是女性的 2 到 7 倍。多动症的发病率在儿童期和成年期似乎是一致的。在儿童期被诊断为多动症的人中,只有一半人报告症状持续存在,这意味着许多人是在成年后首次被诊断,而那些在成年后首次被诊断的人往往会有不同的症状特征。这种性别和年龄趋势可能反映了 "女性性别 "的保护作用、儿童 "成长出 "多动症或成人经历了不同的临床实体。不过,也有人认为,ADHD 的高遗传率(0.6-0.85)和女性相似的遗传风险表明,这些趋势可能是由于症状在环境中的异质性表现(如受女性或成人经历的影响)。我们利用范德比尔特大学医学中心(VUMC)的生物库(n=3,285,882 份电子健康记录(EHR)和 119,750 份基因分型样本)来分析多动症的患病率和遗传结构。我们观察到,与 ADHD 相关的 ICD 代码(n=38,419)在 EHR 记录的女性中出现的频率低于男性(n=14,395 vs. 24,024),而且首次诊断的中位年龄要大得多(21.72 岁,IQR=20.96 vs. 15.05 岁,IQR=9.1)。在 VUMC 进行基因分型的欧洲血统患者子集中(n=69,397),我们观察到多动症多基因风险评分(PRS)是诊断的重要独立预测因子,对女性的影响更大(男性,β=13.47,p=0.03;女性,β=16.90,p=7.4e-7),女性病例的平均多基因风险评分高于男性病例(p=0.04)。在一项性别特异性全表型关联研究(PheWAS)中,无论性别如何,ADHD PRS 都与相似的表型相关,包括药物/烟草使用、其他精神疾病、肥胖、糖尿病和呼吸系统问题。我们的研究结果表明,尽管多动症的诊断率较低,但女性多动症患者似乎具有更高的遗传易感性,这与之前的研究结果是一致的。此外,在弗吉尼亚大学医学院,ADHD PRS 并未显示出基于性别的不同合并症结构。造成这种情况的原因之一是,已有的疾病遗传替代物不能充分反映多动症亚型的特定行为的细微差别,包括但不限于外显多动亚型(ADHD-H)与内隐注意力不集中亚型(ADHD-I)的表现,后者在女性中的报告频率更高。因此,要对女性患者进行临床诊断,可能需要其症状表现在很大程度上与男性患者重叠。在各种电子病历资源方面开展的其他工作可能会进一步揭示 ADHD 相关合并表型的性别特异性趋势的复杂性和细微差别。
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引用次数: 0
JOINT MULTI-FAMILY HISTORY AND MULTI-POLYGENIC SCORE PREDICTION OF MAJOR DEPRESSIVE DISORDER 联合多家族史和多基因评分预测重度抑郁障碍
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.064
Rujia Wang , Helena Davies , Sanghyuck Lee , Jonathan Coleman , Raquel Iniesta , Thalia Eley , Gerome Breen
Major depressive disorder (MDD) is a complex psychiatric disorder influenced by genetic, social, and environmental factors. Family history and polygenic risk scores of MDD and related psychiatric disorders are strong predictors for MDD, while childhood trauma (CT) also plays a crucial role. This study aimed to jointly model the predictive effect of multi-family history (mFH), multi-PRS (mPRS), and childhood trauma on the development of MDD and the number of MDD episodes experienced. Our aim was to identify predictive model useful for stratification to more or less intensive treatment plans and interventions.
Data were obtained from the NIHR BioResource Genetic Links to Anxiety and Depression (GLAD) study and UK Biobank (UKB). MDD diagnosis followed DSM-V criteria using the same online mental health questionnaire data in GLAD and UKB. Family history (Yes/No) was reported for up to 22 psychiatric disorders. MegaPRS was used to calculate PRSs based on large genome-wide association studies. Reported childhood trauma was identified via the5-item childhood trauma screener questionnaire. Elastic net regression with nested cross-validation was applied.
In GLAD (9,927 MDD cases, 4,452 controls), mFH explained 16.85% of MDD variance, followed by CT (10.62%), demographics (9.92%), and mPRS (7.73%). All predictors together explained 33.87% of MDD variance, with corresponding areas under the receiver operating characteristic curve (AUC) of 0.84 and a positive predictive value (PPV) of 0.81. In UKB (40,667 MDD cases, 70,755 controls), mFH explained 13.56% of MDD variance, followed by demographics (5.95%), CT (5.87%), and mPRS (3.69%). Together, all predictors explained 23.68% of variance (AUC=0.74, PPV=0.66). The strongest individual predictor in both cohorts is family history of depression, followed by CT, sex, family history of anxiety, and PRS for depression. The modal number of MDD episodes among MDD cases is ≥ 13 episodes in GLAD, compared to 1 episode in UKB. Additionally, the mean age of onset is 21 years in GLAD and 33 years in UKB. When the model was applied to other MDD phenotypes, all predictors accounted for 25.80% of the variances for the number of MDD episodes and 8.41% for age of onset in GLAD, and 11.92% and 6.01% in UKB, respectively.
Integrating multi-family history, multi-PRS, childhood trauma, and demographics enhances MDD prediction. The prediction model performs effectively in both severe MDD cohort (GLAD) and population-based cohort (UKB), suggesting its potential generalizability to broader populations. The strongest predictors are family history of depression and childhood trauma, both of which are easily measurable in clinical settings. Furthermore, the model trained for MDD prediction also proves to be a strong predictor for the number of MDD episodes and age of onset, indicating its effectiveness in predicting the severity of MDD.
重度抑郁障碍(MDD)是一种受遗传、社会和环境因素影响的复杂精神疾病。多发性抑郁症及相关精神障碍的家族史和多基因风险评分是预测多发性抑郁症的有力因素,而儿童创伤(CT)也起着至关重要的作用。本研究旨在联合模拟多家族史(mFH)、多PRS(mPRS)和童年创伤对 MDD 发病和 MDD 发作次数的预测作用。我们的目的是确定预测模型,以便对强化或非强化治疗计划和干预措施进行分层。数据来自美国国立卫生研究院生物资源与焦虑和抑郁的遗传联系(GLAD)研究和英国生物库(UKB)。MDD诊断遵循DSM-V标准,使用GLAD和UKB中相同的在线心理健康问卷数据。报告了多达 22 种精神疾病的家族史(是/否)。MegaPRS 用于计算基于大型全基因组关联研究的 PRS。报告的童年创伤通过 5 项童年创伤筛查问卷确定。在 GLAD(9927 例 MDD 病例,4452 例对照)中,mFH 解释了 16.85% 的 MDD 变异,其次是 CT(10.62%)、人口统计学(9.92%)和 mPRS(7.73%)。所有预测因子加在一起可解释 33.87% 的 MDD 变异,相应的接收器操作特征曲线下面积 (AUC) 为 0.84,阳性预测值 (PPV) 为 0.81。在 UKB(40667 例 MDD 病例,70755 例对照)中,mFH 可解释 13.56% 的 MDD 变异,其次是人口统计学(5.95%)、CT(5.87%)和 mPRS(3.69%)。所有预测因子加在一起可解释 23.68% 的变异(AUC=0.74,PPV=0.66)。在两个队列中,最强的个体预测因子是抑郁症家族史,其次是 CT、性别、焦虑症家族史和抑郁症 PRS。在GLAD中,MDD病例的平均发作次数≥13次,而在UKB中为1次。此外,GLAD 的平均发病年龄为 21 岁,UKB 为 33 岁。当该模型应用于其他 MDD 表型时,在 GLAD 中,所有预测因子分别占 MDD 发作次数方差的 25.80%和发病年龄方差的 8.41%,在 UKB 中分别占 11.92% 和 6.01%。该预测模型在严重MDD队列(GLAD)和基于人群的队列(UKB)中都表现良好,这表明该模型可能适用于更广泛的人群。最强的预测因素是抑郁症家族史和童年创伤,这两个因素在临床环境中都很容易测量。此外,为预测 MDD 而训练的模型对 MDD 的发作次数和发病年龄也有很强的预测作用,这表明该模型在预测 MDD 的严重程度方面非常有效。
{"title":"JOINT MULTI-FAMILY HISTORY AND MULTI-POLYGENIC SCORE PREDICTION OF MAJOR DEPRESSIVE DISORDER","authors":"Rujia Wang ,&nbsp;Helena Davies ,&nbsp;Sanghyuck Lee ,&nbsp;Jonathan Coleman ,&nbsp;Raquel Iniesta ,&nbsp;Thalia Eley ,&nbsp;Gerome Breen","doi":"10.1016/j.euroneuro.2024.08.064","DOIUrl":"10.1016/j.euroneuro.2024.08.064","url":null,"abstract":"<div><div>Major depressive disorder (MDD) is a complex psychiatric disorder influenced by genetic, social, and environmental factors. Family history and polygenic risk scores of MDD and related psychiatric disorders are strong predictors for MDD, while childhood trauma (CT) also plays a crucial role. This study aimed to jointly model the predictive effect of multi-family history (mFH), multi-PRS (mPRS), and childhood trauma on the development of MDD and the number of MDD episodes experienced. Our aim was to identify predictive model useful for stratification to more or less intensive treatment plans and interventions.</div><div>Data were obtained from the NIHR BioResource Genetic Links to Anxiety and Depression (GLAD) study and UK Biobank (UKB). MDD diagnosis followed DSM-V criteria using the same online mental health questionnaire data in GLAD and UKB. Family history (Yes/No) was reported for up to 22 psychiatric disorders. MegaPRS was used to calculate PRSs based on large genome-wide association studies. Reported childhood trauma was identified via the5-item childhood trauma screener questionnaire. Elastic net regression with nested cross-validation was applied.</div><div>In GLAD (9,927 MDD cases, 4,452 controls), mFH explained 16.85% of MDD variance, followed by CT (10.62%), demographics (9.92%), and mPRS (7.73%). All predictors together explained 33.87% of MDD variance, with corresponding areas under the receiver operating characteristic curve (AUC) of 0.84 and a positive predictive value (PPV) of 0.81. In UKB (40,667 MDD cases, 70,755 controls), mFH explained 13.56% of MDD variance, followed by demographics (5.95%), CT (5.87%), and mPRS (3.69%). Together, all predictors explained 23.68% of variance (AUC=0.74, PPV=0.66). The strongest individual predictor in both cohorts is family history of depression, followed by CT, sex, family history of anxiety, and PRS for depression. The modal number of MDD episodes among MDD cases is ≥ 13 episodes in GLAD, compared to 1 episode in UKB. Additionally, the mean age of onset is 21 years in GLAD and 33 years in UKB. When the model was applied to other MDD phenotypes, all predictors accounted for 25.80% of the variances for the number of MDD episodes and 8.41% for age of onset in GLAD, and 11.92% and 6.01% in UKB, respectively.</div><div>Integrating multi-family history, multi-PRS, childhood trauma, and demographics enhances MDD prediction. The prediction model performs effectively in both severe MDD cohort (GLAD) and population-based cohort (UKB), suggesting its potential generalizability to broader populations. The strongest predictors are family history of depression and childhood trauma, both of which are easily measurable in clinical settings. Furthermore, the model trained for MDD prediction also proves to be a strong predictor for the number of MDD episodes and age of onset, indicating its effectiveness in predicting the severity of MDD.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Pages 24-25"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
COLLABORATIVE STUDY OF THE COMBINED EFFECTS OF RARE CNVS AND POLYGENIC RISK ON PSYCHIATRIC TRAITS 关于罕见 CNVs 和多基因风险对精神特征的综合影响的合作研究
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.072
Molly Sacks , Marieke Klein , Omar Shanta , Mohammad Ahangari , Oanh Hong , Jeff MacDonald , Bhooma Thiruvahindrapuram , Sebastien Jacquemont , Tim Bigdeli , Matthew Oetjens , Mart Kals , Stephen 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 , Genes to Mental Health Network
Recurrent CNVs are known to be major risk factors in neuropsychiatric disorders, yet phenotypic variability between carriers of the same CNV remains unexplained. A possible explanation is that the effect of a CNV depends on genetic background, which can be quantified by a polygenic risk score (PRS). Using data from the PGC and UKBB (combined n=543,111), this project aims to characterize the individual and combined effects of recurrent CNVs and PRS on six major psychiatric disorders: ADHD, ASD, BD, PTSD, MDD, and SCZ.
We first quantified the effects of 42 recurrent CNV loci across all 6 disorders using logistic regression. After Bonferroni correction, 24 loci had a significant association with at least one disorder in either the deletion or duplication. We next documented evidence for the traditional liability threshold model of disease risk; cases carrying CNVs with weak main effects had higher PRS than cases carrying CNVs with strong main effects. This pattern was strongest for SCZ (p=2.13e-4) but was evident in BD as well. Additionally, we tested a composite CNV-PRS model, which demonstrates how PRS can be a useful tool for predicting outcomes in CNV carriers. For example, a carrier of 16p11.2 proximal duplication (a well-known SCZ association) is not at increased risk for SCZ if they have a low PRS-SCZ.
To increase power to detect statistical interactions between CNVs and PRS, we conducted a meta-analysis of CNVxPRS effects on BMI and height in four biobanks: UK Biobank, Estonian Biobank, Geisinger Health, and Million Veterans Program, (n=975,408). Of the 32 CNVs that were sufficiently powered for this analysis (n > 225), 3 had nominally significant (p < .05) interactions with PRS on BMI. In all three cases, the sign on the interaction was the same as the main effect of the CNV, suggesting that these interactions are synergistic. When we collapsed CNVs by their main effect direction, we saw a significant negative interaction between the BMI decreasing CNVs and PRS (p=9.98e-4). These interactions were robust to rescaling of the BMI response variable via inverse normalization or Box-Cox. We observed no significant interactions for Height.
Taken together, these analyses demonstrate that the effect of recurrent CNVs is moderated by PRS. In addition to emphasizing the importance of considering genetic background when studying the effects of rare variants, this study also demonstrates that genetic factors may have non-additive effects on complex traits.
众所周知,复发性 CNV 是神经精神疾病的主要风险因素,但同一 CNV 携带者之间的表型差异仍无法解释。一种可能的解释是,CNV 的影响取决于遗传背景,而遗传背景可以通过多基因风险评分(PRS)来量化。本项目利用 PGC 和 UKBB(合计 n=543,111)的数据,旨在描述复发性 CNV 和 PRS 对六种主要精神疾病的个体和综合影响:我们首先使用逻辑回归量化了 42 个复发性 CNV 位点对所有 6 种疾病的影响。经过Bonferroni校正后,24个基因位点在缺失或重复中至少与一种疾病有显著关联。接下来,我们记录了疾病风险传统责任阈值模型的证据;携带弱主效应 CNV 的病例比携带强主效应 CNV 的病例具有更高的 PRS。这种模式在 SCZ 中最为明显(p=2.13e-4),但在 BD 中也很明显。此外,我们还测试了 CNV-PRS 复合模型,该模型展示了 PRS 如何成为预测 CNV 携带者结局的有用工具。例如,16p11.2近端重复(众所周知与SCZ有关)的携带者如果PRS-SCZ较低,则患SCZ的风险不会增加。为了提高检测CNV与PRS之间统计学相互作用的能力,我们对四个生物库中CNVxPRS对BMI和身高的影响进行了荟萃分析:英国生物库、爱沙尼亚生物库、Geisinger Health 和百万退伍军人计划(n=975,408)。在本分析充分支持的 32 个 CNV(n >225)中,有 3 个与 PRS 对 BMI 的交互作用具有名义上的显著性(p <.05)。在所有三个案例中,交互作用的符号与 CNV 的主效应相同,表明这些交互作用具有协同作用。当我们将 CNV 按其主效应方向折叠时,我们发现 BMI 下降的 CNV 与 PRS 之间存在显著的负交互作用(p=9.98e-4)。这些交互作用对通过反向归一化或盒-柯克斯(Box-Cox)对 BMI 响应变量进行重新缩放是稳健的。总之,这些分析表明,复发性 CNV 的影响受到 PRS 的调节。除了强调在研究罕见变异的影响时考虑遗传背景的重要性外,这项研究还证明了遗传因素可能对复杂性状产生非加性影响。
{"title":"COLLABORATIVE STUDY OF THE COMBINED EFFECTS OF RARE CNVS AND POLYGENIC RISK ON PSYCHIATRIC TRAITS","authors":"Molly Sacks ,&nbsp;Marieke Klein ,&nbsp;Omar Shanta ,&nbsp;Mohammad Ahangari ,&nbsp;Oanh Hong ,&nbsp;Jeff MacDonald ,&nbsp;Bhooma Thiruvahindrapuram ,&nbsp;Sebastien Jacquemont ,&nbsp;Tim Bigdeli ,&nbsp;Matthew Oetjens ,&nbsp;Mart Kals ,&nbsp;Stephen H. Scherer ,&nbsp;Jonathan Sebat ,&nbsp;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 ,&nbsp;Genes to Mental Health Network","doi":"10.1016/j.euroneuro.2024.08.072","DOIUrl":"10.1016/j.euroneuro.2024.08.072","url":null,"abstract":"<div><div>Recurrent CNVs are known to be major risk factors in neuropsychiatric disorders, yet phenotypic variability between carriers of the same CNV remains unexplained. A possible explanation is that the effect of a CNV depends on genetic background, which can be quantified by a polygenic risk score (PRS). Using data from the PGC and UKBB (combined n=543,111), this project aims to characterize the individual and combined effects of recurrent CNVs and PRS on six major psychiatric disorders: ADHD, ASD, BD, PTSD, MDD, and SCZ.</div><div>We first quantified the effects of 42 recurrent CNV loci across all 6 disorders using logistic regression. After Bonferroni correction, 24 loci had a significant association with at least one disorder in either the deletion or duplication. We next documented evidence for the traditional liability threshold model of disease risk; cases carrying CNVs with weak main effects had higher PRS than cases carrying CNVs with strong main effects. This pattern was strongest for SCZ (p=2.13e-4) but was evident in BD as well. Additionally, we tested a composite CNV-PRS model, which demonstrates how PRS can be a useful tool for predicting outcomes in CNV carriers. For example, a carrier of 16p11.2 proximal duplication (a well-known SCZ association) is not at increased risk for SCZ if they have a low PRS-SCZ.</div><div>To increase power to detect statistical interactions between CNVs and PRS, we conducted a meta-analysis of CNVxPRS effects on BMI and height in four biobanks: UK Biobank, Estonian Biobank, Geisinger Health, and Million Veterans Program, (n=975,408). Of the 32 CNVs that were sufficiently powered for this analysis (n &gt; 225), 3 had nominally significant (p &lt; .05) interactions with PRS on BMI. In all three cases, the sign on the interaction was the same as the main effect of the CNV, suggesting that these interactions are synergistic. When we collapsed CNVs by their main effect direction, we saw a significant negative interaction between the BMI decreasing CNVs and PRS (p=9.98e-4). These interactions were robust to rescaling of the BMI response variable via inverse normalization or Box-Cox. We observed no significant interactions for Height.</div><div>Taken together, these analyses demonstrate that the effect of recurrent CNVs is moderated by PRS. In addition to emphasizing the importance of considering genetic background when studying the effects of rare variants, this study also demonstrates that genetic factors may have non-additive effects on complex traits.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Pages 28-29"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442048","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}
引用次数: 0
USING REPEATED MEASURES TO IMPROVE THE PRECISION AND POWER OF GENOME-WIDE ASSOCIATION STUDIES (GWAS) 使用重复测量法提高全基因组关联研究(GWAS)的精度和功率
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.090
Alex Kwong , Mark Adams , Poppy Grimes , Gareth Griffith , Tim Morris , Kate Tilling , Andrew McIntosh
Genome Wide Association Studies (GWAS) have been vital to understanding the genetics of complex traits. However, the majority of GWAS use data from only one occasion, even in longitudinal studies with repeated assessments. Traits like depression are often subject to measurement error. Using a more precise and longitudinal phenotype of depression could reduce measurement error and increase power and precision in depression GWAS, further enhancing understanding of the genetics of depression.
We used data from the UK Biobank (max n=462,566) on the PHQ-2, a measure of depressive symptoms assessed up to 8 occasions over approximately 17 years. We tested a GWAS baseline model (a traditional cross-sectional GWAS that took the first observed assessment) against four other longitudinal GWAS models: 1) the mean of all the assessments, 2) a structural equation model (common factor model), 3) a precision-weighted shrinkage model and 4) a genomicSEM model. We also conducted analysis across multiple ancestries and performed out of sample polygenic prediction.
All longitudinal GWAS models outperformed the GWAS baseline model in European ancestries, with the most powerful model being the precision-weighted shrinkage model which identified 169 genome wide significant single nucleotide polymorphisms (SNPs). Importantly, this precision-weighted shrinkage method had an increase of 34 more lead SNPs (121% increase), 107 more independent significant SNPs (173% increase), 50 more mapped genes (35% increase) and a greater SNP heritability (35% increase), compared to the baseline model. Polygenic prediction into an external cohort also explained a greater proportion of variance (17% increase). There were no GWAS lead SNPs identified in the South Asian and African baseline models, but 2 and 7 novel lead SNPs in the precision-weighted shrinkage methods, respectively.
Leveraging repeated information within GWAS appears to improve power and precision to detect novel biological underpinnings in depression. This is likely due to a reduction in measurement error and increased power. These methods can be applied to other noisy traits within psychiatric genetics and could be useful for detecting novel loci in smaller studies.
全基因组关联研究(GWAS)对于了解复杂性状的遗传学至关重要。然而,大多数 GWAS 只使用了一次的数据,即使是在重复评估的纵向研究中也是如此。像抑郁症这样的性状往往存在测量误差。使用更精确的抑郁症纵向表型可以减少测量误差,提高抑郁症 GWAS 的功率和精确度,从而进一步加深对抑郁症遗传学的理解。我们使用了英国生物库(UK Biobank)(最多 n=462,566)中关于 PHQ-2 的数据,PHQ-2 是一种抑郁症状测量方法,在大约 17 年的时间里进行了 8 次评估。我们测试了一个 GWAS 基线模型(传统的横断面 GWAS,采用第一次观察到的评估结果)和其他四个纵向 GWAS 模型:1)所有评估的平均值;2)结构方程模型(共同因素模型);3)精确加权收缩模型;4)基因组SEM模型。在欧洲血统中,所有纵向 GWAS 模型的表现都优于 GWAS 基线模型,其中最强大的模型是精确加权收缩模型,该模型确定了 169 个全基因组重要的单核苷酸多态性(SNPs)。重要的是,与基线模型相比,这种精确加权收缩方法增加了 34 个先导 SNPs(增加 121%)、107 个独立重要 SNPs(增加 173%)、50 个映射基因(增加 35%)和更大的 SNP 遗传性(增加 35%)。外部队列的多基因预测也解释了更大比例的变异(增加 17%)。在南亚和非洲基线模型中没有发现 GWAS 引导 SNP,但在精确加权收缩方法中分别发现了 2 个和 7 个新的引导 SNP。利用 GWAS 中的重复信息似乎提高了发现抑郁症新生物学基础的能力和精确度,这可能是由于测量误差的减少和能力的提高。这些方法可应用于精神遗传学中的其他噪声性状,并可用于在较小规模的研究中检测新的基因座。
{"title":"USING REPEATED MEASURES TO IMPROVE THE PRECISION AND POWER OF GENOME-WIDE ASSOCIATION STUDIES (GWAS)","authors":"Alex Kwong ,&nbsp;Mark Adams ,&nbsp;Poppy Grimes ,&nbsp;Gareth Griffith ,&nbsp;Tim Morris ,&nbsp;Kate Tilling ,&nbsp;Andrew McIntosh","doi":"10.1016/j.euroneuro.2024.08.090","DOIUrl":"10.1016/j.euroneuro.2024.08.090","url":null,"abstract":"<div><div>Genome Wide Association Studies (GWAS) have been vital to understanding the genetics of complex traits. However, the majority of GWAS use data from only one occasion, even in longitudinal studies with repeated assessments. Traits like depression are often subject to measurement error. Using a more precise and longitudinal phenotype of depression could reduce measurement error and increase power and precision in depression GWAS, further enhancing understanding of the genetics of depression.</div><div>We used data from the UK Biobank (max n=462,566) on the PHQ-2, a measure of depressive symptoms assessed up to 8 occasions over approximately 17 years. We tested a GWAS baseline model (a traditional cross-sectional GWAS that took the first observed assessment) against four other longitudinal GWAS models: 1) the mean of all the assessments, 2) a structural equation model (common factor model), 3) a precision-weighted shrinkage model and 4) a genomicSEM model. We also conducted analysis across multiple ancestries and performed out of sample polygenic prediction.</div><div>All longitudinal GWAS models outperformed the GWAS baseline model in European ancestries, with the most powerful model being the precision-weighted shrinkage model which identified 169 genome wide significant single nucleotide polymorphisms (SNPs). Importantly, this precision-weighted shrinkage method had an increase of 34 more lead SNPs (121% increase), 107 more independent significant SNPs (173% increase), 50 more mapped genes (35% increase) and a greater SNP heritability (35% increase), compared to the baseline model. Polygenic prediction into an external cohort also explained a greater proportion of variance (17% increase). There were no GWAS lead SNPs identified in the South Asian and African baseline models, but 2 and 7 novel lead SNPs in the precision-weighted shrinkage methods, respectively.</div><div>Leveraging repeated information within GWAS appears to improve power and precision to detect novel biological underpinnings in depression. This is likely due to a reduction in measurement error and increased power. These methods can be applied to other noisy traits within psychiatric genetics and could be useful for detecting novel loci in smaller studies.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Pages 36-37"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ACCELERATING DRUG REPURPOSING IN PSYCHIATRY USING GENETICS 利用遗传学加速精神病学的药物再利用
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.095
William Reay (Chair) , Zachary Gerring (Co-chair)
New pharmacotherapies in psychiatry would likely reduce the immense burden that mental health conditions place on individuals and society at large. However, the pipeline to discover novel drugs for use in psychiatric disorders has remained unproductive, where a lack of objective biomarkers for psychiatric diagnoses and the assessment of treatment outcomes contributes to clinical trial failure. Therefore, new approaches are urgently needed to identify more suitable drug candidates for clinical trials. One approach is the integration of human genetic and molecular data to identify and prioritize existing drugs for human clinical trials, known as drug repurposing. This approach has previously been successfully applied in psychiatry and offers an avenue for expedited clinical translation compared to traditional drug discovery. For example, valproic acid was originally used for its anticonvulsant properties in epilepsy before its utility in bipolar disorder was uncovered. Despite the promise of drug repurposing in psychiatry, challenges remain arising from the immense biological heterogeneity of these phenotypes. The advent of well powered, genetic association studies and high throughput measurements on diverse molecular data types (e.g., gene expression) represent an opportunity to better understand the biological complexity of these phenotypes and accelerate successfully translating prospective repurposing candidates into clinical practice. In this symposium, we will outline some of the key methodological considerations and progress to date in genetically informed prioritization of repurposing candidates across psychiatric disorders. Specifically, we will discuss how integration of genetic association signals with multiomic data can reveal prospective opportunities for drug repurposing. Approaches to biologically interpret individual target genes in the context of the wider polygenic architecture of these disorders will also be outlined. Shared genetic liability and biological relationships between psychiatric disorders and somatic disorders across the rest of the body treated by existing drugs additionally can present supporting lines of evidence for prospective repurposing candidates. Finally, given the immense variability observed between individuals with the same diagnosis, we will discuss the prospects of using genetics to target drug repurposing opportunities with greater precision at the individual level. This symposium brings together a diverse set of international researchers that are working at the forefront of innovative approaches to identify opportunities for drug repurposing in psychiatry.
精神病学中的新药物疗法很可能会减轻精神疾病给个人和整个社会带来的巨大负担。然而,发现用于治疗精神疾病的新药的渠道一直没有成果,其中缺乏用于精神疾病诊断和治疗效果评估的客观生物标志物导致了临床试验的失败。因此,迫切需要新的方法来为临床试验确定更合适的候选药物。其中一种方法是整合人类基因和分子数据,为人类临床试验识别现有药物并确定其优先次序,即所谓的药物再利用。这种方法此前已成功应用于精神病学领域,与传统的药物发现相比,它为加快临床转化提供了一条途径。例如,丙戊酸最初用于治疗癫痫的抗惊厥特性,后来才发现它可用于治疗躁郁症。尽管精神病学中的药物再利用前景广阔,但由于这些表型的生物异质性巨大,挑战依然存在。动力充足的遗传关联研究和对不同分子数据类型(如基因表达)的高通量测量的出现,为更好地了解这些表型的生物学复杂性并加快将潜在的再利用候选药物成功转化为临床实践提供了机会。在本次研讨会上,我们将概述一些关键的方法学考虑因素,以及迄今为止在跨精神疾病的基因知情优先再利用候选药物方面取得的进展。具体来说,我们将讨论遗传关联信号与多组学数据的整合如何揭示药物再利用的潜在机会。此外,我们还将概述在这些疾病更广泛的多基因结构背景下从生物学角度解释单个目标基因的方法。此外,现有药物所治疗的精神疾病和身体其他部位的躯体疾病之间的共同遗传责任和生物学关系也能为潜在的再利用候选药物提供支持性证据。最后,鉴于同一诊断的个体之间存在巨大差异,我们将讨论利用遗传学在个体水平上更精确地瞄准药物再利用机会的前景。本次研讨会汇集了国际上不同的研究人员,他们都站在创新方法的最前沿,为精神病学的药物再利用寻找机会。
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引用次数: 0
BRIDGING LINGUISTIC AND CULTURAL DIVIDES IN PSYCHIATRIC GENOMICS RESEARCH: LESSONS FROM UGANDA 在精神疾病基因组学研究中弥合语言和文化鸿沟:乌干达的经验教训
IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY Pub Date : 2024-10-01 DOI: 10.1016/j.euroneuro.2024.08.101
Allan Kalungi , Segun Fatumo
Psychiatric genomics research tools frequently depend on terminology and notions that are predominantly derived from Western viewpoints, specifically designed for populations speaking English in Europe and the United States of America. Nevertheless, there is an increasing interest in incorporating African populations into genetic studies, as African genetic data possess significant potential for enhancing discovery in psychiatric genetics research. However, this undertaking has unique difficulties, including inefficiently conveying intricate genetic and psychiatric ideas and terminology to participants using their native African languages. The absence of obvious counterparts for terms such as "trauma" or "genome" necessitates the need for unique strategies to overcome linguistic barriers.
In 2011, we established the Uganda Genome Resource (UGR) – a well-characterized genomic database with a range of phenotypes for communicable and non-communicable diseases and risk factors generated from the Uganda General Population Cohort (GPC), a population-based open cohort. The UGR comprises genotype data on ∼5,000 and whole-genome sequence data on ∼2,000 Ugandan GPC individuals from 10 ethno-linguistic groups. We have since extended UGR to include studies focusing primarily on mental health conditions including major depressive disorder, post-traumatic stress disorder, generalized anxiety disorder, alcohol misuse and suicidality, among others.
To mitigate against the barrier poised by research tools which were developed in a foreign language to the participants, first, we engage the service of a professional linguistic translator to ensure accurate translation of all study materials. Additionally, we provide cultural sensitivity training to researchers to ensure respectful and ethical interactions with participants from diverse ethno-linguistic backgrounds. Secondly, following the translated study material, we set up a series of workshop including mental health experts and leading psychiatric geneticists and local scientists to agree on the translated content. Thirdly, we ask an independent local scientist to conduct a reverse translation of the study materials to ensure accuracy and consistency in the translated versions. This thorough process helps to minimize any potential misunderstandings or misinterpretations that may arise during the research study.
精神病基因组学研究工具经常依赖于主要源自西方观点的术语和概念,这些术语和概念是专门为讲英语的欧洲和美国人设计的。尽管如此,人们对将非洲人群纳入遗传学研究的兴趣与日俱增,因为非洲遗传学数据在提高精神病遗传学研究发现方面具有巨大潜力。然而,这项工作有其独特的困难,包括无法有效地用非洲母语向参与者传达复杂的遗传学和精神病学观点和术语。2011 年,我们建立了乌干达基因组资源(UGR)--一个特征明确的基因组数据库,其中包含一系列传染性和非传染性疾病的表型以及风险因素,这些数据来自乌干达普通人群队列(GPC),这是一个基于人群的开放式队列。UGR 包括来自 10 个民族语言群体的 5,000 多名乌干达普通人群的基因型数据和 2,000 多名乌干达普通人群的全基因组序列数据。此后,我们将 UGR 扩展到主要关注精神健康状况的研究,包括重度抑郁障碍、创伤后应激障碍、广泛性焦虑障碍、酒精滥用和自杀倾向等。为了减少以外语开发的研究工具给参与者带来的障碍,首先,我们聘请了专业的语言翻译服务,以确保所有研究材料的准确翻译。此外,我们还为研究人员提供文化敏感性培训,以确保他们与来自不同民族语言背景的参与者进行相互尊重和符合道德规范的互动。其次,在翻译好研究材料后,我们会召开一系列研讨会,包括心理健康专家、知名精神遗传学家和当地科学家,就翻译内容达成一致意见。第三,我们请一位独立的当地科学家对研究材料进行逆向翻译,以确保翻译版本的准确性和一致性。这一彻底的过程有助于最大限度地减少研究过程中可能出现的任何误解或曲解。
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引用次数: 0
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European Neuropsychopharmacology
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