Dissecting the Multivariate Genetic Architecture of Psychiatric Diseases

剖析精神疾病的多变量遗传结构

• 批准号: 10401847
• 负责人: Michel Guillaume Nivard
• 金额: $ 64.38万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 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/10401847
• 关键词: Address; Atlases; Behavior; Behavioral; Bioinformatics; Biological; Categories; Circadian Rhythms; Communities; Complex; Computer software; Data; Derivation procedure; Diagnostic; Dimensions; Disease; 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; Meta-Analysis; 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; 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

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.

项目摘要 精神疾病是高度多基因的，在整个方面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知识 中间机制。在大型财团，基于注册表的努力和国家 生物库，全基因组关联研究（GWAS）精神疾病和相关定量 表型在获得检测可再现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产以及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 与精神疾病和遗传相关的地图相关，已迅速扩大本体论 跨精神病和社会共享遗传结构的描述性知识网络， 行为和生物学特征。为了更充分利用这种不断增长的GWAS研究 输出，我们最近引入了基因组结构等分模型（基因组SEM； Grotzinger等，，，， 2019;自然人类行为），一种用于多元建模的分析框架和相关软件 使用GWAS汇总数据的遗传结构来自不同或未知的重叠度的样本。这 该R01提案的主要目标是将基因组SEM扩展并进一步发展为正式 研究跨精神病疾病及其同等重要的遗传风险共享 - 遗传 它们之间的区别。我们将（1）确定跨遗传共享的经诊断维度 精神疾病，并测试与生物学和生物学和生物学的遗传关联中的共同点和差异 潜在的跨遗传风险的社会心理维度； （2）确定基因集和类别 为跨疾病和/或特定疾病特定的遗传变异而造成不成比例的风险共享； （3） 正式区分障碍特定基因症； （4）大大扩展了 当前在基因组SEM软件中可用的方法满足遗传学社区不断增长的需求。 性别分层的GWAS摘要数据的可用性将使我们能够检查收敛性和分歧 跨男性和女性的关联和多元遗传结构的模式。而且，我们会的 合并用于建模跨种族数据的前沿方法，随着更多 多样化的GWAS样品可用。该项目将构成最全面的审讯 主要精神疾病的共同且特定的遗传结构及其与他们的关系 潜在跨切割遗传风险的生物学和社会心理维度，并将提供扩大的 一套新颖，用户友好，免费，开源工具，可为整个遗传学社区提供服务。