Estimating The Fraction of Variance Explained by Genetics and Neuroanatomy in Neuropsychiatric Conditions

估计神经精神疾病中遗传学和神经解剖学解释的方差分数

• 批准号: 10684184
• 负责人: Armin Schwartzman
• 金额: $ 67.55万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10684184
• 关键词: Adolescent; Alleles; Anatomy; Biological; Biological Markers; Brain; Brain imaging; Brain region; Complex; Computer software; Data; Diagnosis; Diffuse; Disease; Etiology; Financial cost; Funding; Gene Expression; Genetic; Genome; Heritability; Human Microbiome; Laboratories; Maps; Measures; Mental Health; Mental disorders; Methodology; Methods; Modeling; Neuroanatomy; Neurobiology; Outcome; Participant; Population; Predisposition; Prognosis; Proteomics; Regional Anatomy; Research; Single Nucleotide Polymorphism; Statistical Methods; Testing; United States National Institutes of Health; Validation; Variant; Work; aged; autism spectrum disorder; cognitive development; design; emerging adult; experience; genome wide association study; health assessment; high dimensionality; interest; multidimensional data; multimodality; neuroimaging; neuropsychiatric disorder; neuropsychiatry; novel; simulation; statistics; theories; tool

Abstract Mental health problems such as autism are highly prevalent in the population and incur great suffering and financial costs. Yet there is currently a dearth of biomarkers that accurately predict their diagnosis or prognosis. Characterizing the contributions of high-dimensional biomarkers to susceptibility of such complex disorders is critically important for advancing our understanding of their etiology and for developing new treatments. The fraction of variance explained (FVE) by a set of biomarkers is a measure of the total amount of information for an outcome contained in the predictor variables. It is a fundamental quantity in much of mental health-related research, e.g., human microbiome, proteomics, gene expression, etc. Canonical examples where the FVE is of fundamental interest include Genome-Wide Association Studies (GWAS) and neuroimaging, both crucial tools for understanding the biological basis of mental health disorders. GWAS have successfully mapped thousands of genetic factors by mass-univariate association of millions of single nucleotide polymorphisms (SNPs), but the top significant associations, even in aggregate, account for only a small proportion of susceptibility. To assess the amount of information in GWAS, the SNP-heritability, h2SNP, quantifies the FVE among all GWAS SNPs in aggregate, regardless of significance. Similarly, the FVE by brain imaging measures captures variation in the brain related to mental illness, which again appears to be highly distributed. In both the genetic and brain imaging domains, the number of predictors is extremely large, in the order of thousands to millions, far larger than the number of subjects. As a result, the specific associations with each predictor unit cannot be estimated, and effects of specific loci are extremely difficult to identify. In contrast, the FVE can be reliably estimated from data, even if only univariate summary statistics are available. Estimating FVE requires sophisticated statistical methods designed for these particular, high-dimensional data. In this proposal, we propose a general framework for FVE estimation, applicable to high-dimensional data including both GWAS and brain imaging settings. We develop foundational theory establishing the validity and consistency of FVE estimation, develop new methods for evaluating the required conditions in real data, and develop methods for partitioning FVE into more local components, allowing understanding of the distribution of contributions to susceptibility in a top-down approach. We apply these methods to the Adolescent Brain Cognitive Development (ABCD) Study, comprising longitudinal, multi-modal brain imaging, GWAS data, and autism-related assessments for 11,875 participants aged 9-10 at baseline and continuing into early adulthood.

抽象的 自闭症等心理健康问题在人群中非常普遍，遭受了巨大的痛苦， 财务成本。然而，目前还有一大批生物标志物可以准确预测其诊断或 预后。表征高维生物标志物对这种复杂性敏感性的贡献 疾病对于促进我们对他们的病因和发展新的理解至关重要 治疗。一组生物标志物所解释的差异的比例（FVE）是总数的量度 预测变量中包含的结果的信息。这是大部分精神的基本数量 与健康相关的研究，例如人类微生物组，蛋白质组学，基因表达等。规范实例 FVE具有根本关注的地方包括全基因组协会研究（GWAS）和 神经影像，这是理解心理健康障碍生物学基础的关键工具。 gwas有 通过群众群体关联成功地绘制了数千个遗传因素 核苷酸多态性（SNP），但最高的显着关联，即使在骨料中，也仅占一个 一小部分的敏感性。评估GWAS中的信息量，SNP可耐性H2SNP， 量化所有GWAS SNP之间的FVE，无论其重要性如何。同样，大脑的FVE 成像措施捕获了与精神疾病有关的大脑的变化，这似乎是高度的 分布式。在遗传和脑成像域中，预测因子的数量非常大，在 数千至数百万人的顺序远大于受试者的数量。结果，与 每个预测单元无法估算，并且特定基因座的影响极难识别。在 对比，即使只有单变量摘要统计信息，也可以从数据中可靠地估算FVE。 估计FVE需要为这些特定的高维数据设计的复杂统计方法。 在此提案中，我们提出了一个用于FVE估计的一般框架，适用于高维数据 包括GWAS和大脑成像设置。我们开发了建立有效性和的基础理论 FVE估计的一致性，开发用于评估实际数据中所需条件的新方法，以及 开发将FVE划分为更多本地组件的方法，从而了解 自上而下的方法中对易感性的贡献。我们将这些方法应用于青春期的大脑 认知发展（ABCD）研究，包括纵向，多模式脑成像，GWAS数据和 对基线时9-10岁的11,875名参与者的自闭症相关评估，并持续成年。