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Dissecting the Multivariate Genetic Architecture of Psychiatric Diseases

剖析精神疾病的多变量遗传结构

基本信息

批准号：
10627791
负责人：
Michel Guillaume Nivard
金额：
$ 63.36万
依托单位：
UNIVERSITY OF TEXAS AT AUSTIN
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-17 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10627791
关键词：
Address Atlases Behavior Behavioral Bioinformatics Biological Categories Circadian Rhythms Communities Complex Computer software Data Derivation procedure Diagnostic Dimensions Disease Equation Ethnic Origin Exhibits Female Fright Genes Genetic Genetic Drift Genetic Models Genetic Risk Genetic Variation Genomics Goals Heritability Human Characteristics Individual Investments Joints Knowledge Life Magnetic Resonance Imaging Mathematics Measurement Mental disorders Methods Modeling National Institute of Mental Health Nature Neurons Ontology Outcome Output Patient Self-Report Pattern Phenotype Prevention Publications Registries Reproducibility Research Research Domain Criteria Risk Risk Taking Sampling Sampling Studies Schizophrenia Secure Specific qualifier value System Testing Tissues Update Variant alcohol use disorder base biobank biological sex cell type data resource disorder risk functional/structural genomics gene conservation genetic architecture genetic association genome wide association study genome-wide insight interest male novel open source open source tool pleiotropism psychiatric comorbidity psychogenetics psychosocial research study risk sharing sex simulation social tool trait user-friendly

项目摘要

PROJECT SUMMARY Psychiatric disorders are highly polygenic, exhibit a complex pattern of genetic correlations across the full spectrum of diagnostic categories. Genetic risk for psychiatric disorders acts via a poorly understood set of intermediate mechanisms. With substantial investments in large consortia, registry-based efforts, and national biobanks, genome-wide association studies (GWAS) of psychiatric disorders and related quantitative phenotypes have made substantial strides in attaining the power needed to detect reproducible genetic associations, estimate genome-wide chip heritabilities, and estimate genetic correlations between traits. Combined with bioinformatic approaches, GWAS efforts have produced insights into tissues and cell types relevant to psychiatric disease, and atlases of genetic correlations have rapidly expanded the ontological network of descriptive knowledge of shared genetic architecture across psychiatric diseases and social, behavioral, and biological traits. In order to more fully capitalize on this growing corpus of GWAS research output, we have recently introduced Genomic Structural Equating Modeling (Genomic SEM; Grotzinger et al., 2019; Nature Human Behaviour), an analytic framework and associated software for multivariate modelling of genetic architecture using GWAS summary data from samples of varying or unknown degrees of overlap. The primary goal of this R01 proposal is to capitalize on and further develop Genomic SEM to formally investigate genetic risk sharing across psychiatric disorders and- equally importantly- genetic differentiation between them. We will (1) identify transdiagnostic dimensions of genetic sharing across psychiatric disorders, and test for commonalities and divergence in genetic associations with biological and psychosocial dimensions of potentially cross-cutting genetic risk; (2) Identify gene sets and categories that contribute disproportionately to risk sharing across disorders and/or to disorder-specific genetic variation; (3) Formally distinguish disorder-general from disorder-specific Loci; and (4) Considerably expand the suite of methods currently available in Genomic SEM software to meet increasing demand by the genetics community. The availability of sex-stratified GWAS summary data will allow us to examine convergent and divergent patterns of association and multivariate genetic architecture across males and females. Moreover, we will incorporate cutting edge methods for modeling trans-ethnic data, which will be of increasing value as more diverse GWAS samples become available. This project will constitute the most comprehensive interrogation of the shared and disorder-specific genetic architecture of major psychiatric disorders and their relationships to biological and psychosocial dimensions of potentially cross-cutting genetic risk, and will provide an expanded suite of novel, user friendly, free, open-source tools that serve the entire genetics community.

项目概要精神疾病是高度多基因的，在整个系统中表现出复杂的遗传相关模式。诊断类别的范围。精神疾病的遗传风险通过一组鲜为人知的机制发挥作用中间机制。通过对大型财团、基于注册管理机构的努力和国家的大量投资生物库、精神疾病的全基因组关联研究 (GWAS) 和相关定量表型在获得检测可重复遗传所需的能力方面取得了长足的进步关联，估计全基因组芯片遗传力，并估计性状之间的遗传相关性。与生物信息学方法相结合，GWAS 的努力产生了对组织和细胞类型的见解与精神疾病相关，遗传相关图谱迅速扩展了本体论跨精神疾病和社会的共享遗传结构的描述性知识网络，行为和生物学特征。为了更充分地利用不断增长的 GWAS 研究资料库输出，我们最近引入了基因组结构等同建模（Genomic SEM；Grotzinger 等人， 2019； Nature Human Behaviour），用于多变量建模的分析框架和相关软件使用来自不同或未知重叠程度的样本的 GWAS 汇总数据的遗传结构。这该 R01 提案的主要目标是利用并进一步开发基因组 SEM，以正式研究精神疾病之间的遗传风险共享，以及同样重要的遗传风险他们之间的区别。我们将（1）确定基因共享的跨诊断维度精神疾病，并测试与生物和精神疾病的遗传关联的共性和差异潜在交叉遗传风险的心理社会维度； (2) 识别基因组和类别不成比例地促进跨疾病的风险分担和/或疾病特异性遗传变异； (3) 正式区分一般疾病基因座和特定疾病基因座； (4) 大幅扩展套件基因组 SEM 软件中当前可用的方法可满足遗传学界日益增长的需求。性别分层 GWAS 摘要数据的可用性将使我们能够检查趋同性和趋异性男性和女性之间的关联模式和多元遗传结构。此外，我们将结合对跨种族数据进行建模的尖端方法，随着更多人的参与，这将具有越来越大的价值多种 GWAS 样本可供使用。该项目将构成对主要精神疾病的共享和疾病特异性遗传结构及其与潜在交叉遗传风险的生物和心理社会维度，并将提供扩大的一套新颖、用户友好、免费、开源的工具，为整个遗传学界服务。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Interactions between Polygenic Scores and Environments: Methodological and Conceptual Challenges.

DOI：
10.15195/v7.a19
发表时间：
2020-09
期刊：
SOCIOLOGICAL SCIENCE
影响因子：
3.4
作者：
Domingue, Benjamin;Trejo, Sam;Armstrong-Carter, Emma;Tucker-Drob, Elliot
通讯作者：
Tucker-Drob, Elliot

Multivariate genomic architecture of cortical thickness and surface area at multiple levels of analysis.

DOI：
10.1038/s41467-023-36605-x
发表时间：
2023-02-20
期刊：
NATURE COMMUNICATIONS
影响因子：
16.6
作者：
Grotzinger, Andrew D.;Mallard, Travis T.;Liu, Zhaowen;Seidlitz, Jakob;Ge, Tian;Smoller, Jordan W.
通讯作者：
Smoller, Jordan W.

No effects of siblings and twin testosterone transfer on autistic traits.