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

摘要 心理健康问题，如自闭症，在人口中非常普遍，并造成巨大的痛苦， 财务费用。然而，目前缺乏生物标志物，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的易感性的贡献 疾病对于促进我们对其病因学的理解和开发新的 治疗。由一组生物标志物解释的方差分数（FVE）是生物标志物的总量的量度。 预测变量中包含的结果的信息。它是一个基本的数量在许多心理 与健康有关的研究，例如，人类微生物组学、蛋白质组学、基因表达等。 FVE具有根本意义的研究包括全基因组关联研究（GWAS）和 神经影像学，两者都是理解心理健康障碍生物学基础的重要工具。GWAS拥有 通过对数百万个单变量的质量单变量关联，成功地绘制了数千个遗传因子， 核苷酸多态性（SNPs），但最重要的关联，即使在一起，只占一个 小比例的敏感性。为了评估GWAS中的信息量，SNP遗传力，h2SNP， 量化所有GWAS SNP中的FVE，无论显著性如何。同样，大脑的FVE 成像测量捕捉到与精神疾病有关的大脑变化，这再次显示出高度 发放在遗传和脑成像领域，预测因子的数量非常大， 数以千计到数百万计，远远大于受试者的数量。因此，具体的关联 每个预测单元都不能被估计，并且特定位点的效应极难识别。在 相反，即使只有单变量汇总统计量可用，FVE也可以从数据中可靠地估计。 估计FVE需要为这些特定的高维数据设计的复杂统计方法。 在这个建议中，我们提出了一个一般框架FVE估计，适用于高维数据 包括GWAS和脑成像设置。我们发展基础理论，建立有效性， FVE估计的一致性，开发用于评估真实的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 对易感性的贡献。我们将这些方法应用于青少年大脑 认知发育（ABCD）研究，包括纵向、多模态脑成像、GWAS数据和 对11，875名9-10岁的参与者进行了自闭症相关评估，并持续到成年早期。

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.