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.

摘要 自闭症等心理健康问题在人群中非常普遍，并造成极大的痛苦和 财务成本。然而，目前缺乏准确预测其诊断或预后的生物标志物。 预后。表征高维生物标志物对这种复合体易感性的贡献 障碍对于促进我们对其病因的理解和开发新的疾病至关重要。 治疗。由一组生物标志物解释的方差分数(FVE)是对 预测变量中包含的结果信息。它是许多心理活动中的一个基本量 与健康有关的研究，例如人类微生物组、蛋白质组学、基因表达等 FVE最重要的领域包括全基因组关联研究(GWAS)和 神经成像，这两个都是了解精神健康障碍的生物学基础的重要工具。GWAs拥有 通过对数百万个个体的质量-单变量关联成功地定位了数千个遗传因素 核苷酸多态(SNPs)，但最重要的关联，即使是聚集在一起，也只占 敏感度比例较小。为了评估GWAS中的信息量，SNP遗传度，h2SNP， 量化所有GWASSNPs中的FVE，而不考虑重要性。同样，大脑的FVE 成像测量捕捉到大脑中与精神疾病相关的变化，这又一次显示出高度的 分布式的。在遗传和脑成像领域，预测因子的数量都非常大，在 几千到几百万的数量级，远远大于受试者的数量。因此，特定的关联与 每个预测单元都无法估计，而且特定基因座的影响极难识别。在……里面 相比之下，即使只有单变量汇总统计数据可用，也可以从数据中可靠地估计FVE。 估计FVE需要为这些特殊的、高维数据设计复杂的统计方法。 在该方案中，我们提出了一种适用于高维数据的FVE估计的通用框架 包括GWAS和脑成像环境。我们发展了确立有效性的基础理论，并 FVE估计的一致性，开发新的方法来评估实际数据中所需的条件，以及 开发将FVE划分为更多本地组件的方法，以便了解 在自上而下的方法中对敏感性的贡献。我们将这些方法应用于青少年的大脑 认知发展(ABCD)研究，包括纵向、多模式脑成像、GWAS数据和 对11,875名年龄在9-10岁并持续到成年早期的参与者进行了与自闭症相关的评估。