Delineating the network effects of mental disorder-associated variants using convex optimization methods

项目总结/摘要 在国际开放科学合作的推动下， 财团（PGC，其中co-I Mullins是一个领先的分析师）都是全基因组关联研究 （GWAS）和神经精神疾病（NPD）的全外显子组和基因组测序研究 样本量迅速增加。随着样本量的增加， 检测出更多的、更小的基因对疾病风险的影响，称为多基因成分。的 现在的挑战是了解这些发现告诉我们有关NPD风险、病因和生物学的信息。这里我们 提出了一套多性状分析的方法，以确定潜在的潜在结构，因果关系， 基因和性状的网络，以及丰富的数据衍生的调控途径。我们广泛使用 凸优化方法，允许计算效率和保证再现性。 在目标1中，我们将广泛的NPD及其亚表型分解为共享和独特的 基于新的凸加权主成分的遗传成分 分析（PCA）和开发扩展来处理样本重叠，连锁不平衡（LD）， 不同的祖先。在目标2中，我们将扩展和定制我们现有的因果网络推理工作 使用双凸优化来估计大脑中的顺式和反式基因调控网络， 大规模均匀加工的染色质可及性和表达数量性状位点（QTL）。我们 将使用染色质构象数据，模型潜在的遗传 使用期望最大化（EM）方法和估计网络 为了确定最直接的原因（全基因组中的“核心”基因）， 模型）。在目标3中，我们将分析罕见和常见的遗传关联，在他们的基因调控中， 网络环境。借用癌症基因组学，我们将使用热扩散模型来传播 在基因和调控元件（RE）上的局部网络上的统计信息，然后使用 图聚类算法提取“热”子网络，对应的路径牵连在 正在研究NPD。我们为这些分析开发的方法将在 源代码许可证，在文档、教程和插图方面提供广泛的支持。通过这个 我们希望能够在未来的“后GWAS”分析中利用基因、亚表型和性状， 人类神经精神健康的基础网络，并最终指出了治疗方法 干预措施。

PROJECT SUMMARY/ABSTRACT Driven by international open scientific collaboration through groups such as the Psychiatric Genomics Consortium (PGC, in which co-I Mullins is a leading analyst) both genome-wide association studies (GWAS) and whole exome and genome sequencing studies of neuropsychiatric disorders (NPDs) are rapidly increasing in sample size. With this increased sample size comes increased statistical power to detect many more, smaller genetic effects on disease risk, known as the polygenic component. The challenge now is to understand what these findings tell us about NPD risk, etiology and biology. Here we propose a suite of methods for multi-trait analysis to determine underlying latent structure, causal networks of genes and traits, and enriched data-derived regulatory pathways. We make extensive use of convex optimization methods that allow both computational efficiency and guarantees on reproducibility. In Aim 1 we will decompose a wide range of NPDs and their subphenotypes into shared and unique genetic components using a novel convex formulation of observed-weighted principal components analysis (PCA) and develop extensions to handle sample overlap, linkage disequilibrium (LD), and different ancestries. In Aim 2 we will extend and customize our existing work on causal network inference using biconvex optimization to estimate both cis and trans gene regulatory networks in the brain using large-scale uniformly processed chromatin accessibility and expression quantitative trait loci (QTLs). We will regularize estimates of cis interactions using chromatin conformation data, model latent genetic confounders in these networks using an expectation-maximization (EM) approach and estimate networks over both genes and NPDs in order to determine the most direct causes (“core” genes in the omnigenic model). In Aim 3 we will analyze both rare and common genetic associations in their gene regulatory network context. Borrowing from cancer genomics, we will use heat diffusion models to propagate statistical information on the local network over both genes and regulatory elements (REs) and then use graph clustering algorithms to extract “hot” subnetworks, corresponding to pathways implicated in the NPD under study. The methods we develop for these analyses will be made publicly available under source licenses with extensive support in terms of documentation, tutorials, and vignettes. Through this we hope to empower future “post-GWAS” analyses that can leverage the genetic, subphenotype and trait networks underlying human neuropsychiatric health, and eventually point the way to therapeutic interventions.