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

使用凸优化方法描述精神障碍相关变异的网络效应

• 批准号: 10504516
• 负责人: David Arthur Knowles
• 金额: $ 79.91万
• 依托单位: NEW YORK GENOME CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10504516
• 关键词: Accounting; Algorithms; Automobile Driving; Biological; Biology; Bipolar Disorder; Brain; Cell Nucleus; Cell model; Chromatin; Clinical; Code; Collaborations; Computer Analysis; Custom; Data; Developmental Delay Disorders; Diffusion; Disease; Documentation; Etiology; Formulation; Future; Gene Expression Regulation; Genes; Genetic; Genetic Processes; Genetic Risk; Genetic Variation; Genetic study; Genotype; Graph; Growth; Health; Heterogeneity; Human; International; Licensing; Linkage Disequilibrium; Major Depressive Disorder; Mental disorders; Methods; Modeling; Molecular Conformation; Nature; Network-based; Onset of illness; Pathogenesis; Pathway interactions; Pattern; Phenotype; Principal Component Analysis; Process; Quantitative Trait Loci; Rare Diseases; Regulator Genes; Regulatory Element; Regulatory Pathway; Reproducibility; Role; Sample Size; Sampling; Schizophrenia; Source; Structure; Therapeutic Intervention; Translating; Untranslated RNA; Variant; Work; associated symptom; autism spectrum disorder; cancer genomics; causal variant; cell type; computational suite; developmental disease; disorder risk; driver mutation; early onset disorder; exome sequencing; expectation; gene network; gene regulatory network; genetic association; genetic variant; genome sequencing; genome wide association study; improved; innovation; neuropsychiatric disorder; neuropsychiatry; novel; phenome; psychiatric genomics; rare variant; statistics; therapeutic target; trait; transcriptome sequencing

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.

项目摘要/摘要 由国际开放科学合作的驱动，诸如精神病基因组学之类的群体 财团（PGC，Co-I Mullins是领先的分析师），这两个基因组关联研究 （GWAS）以及神经精神疾病（NPD）的整个外显子组和基因组测序研究是 样本量迅速增加。随着样本量的增加，统计功率提高到 检测更多对疾病风险的遗传影响，称为多基因成分。这 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。我们在这里 提案一套用于确定潜在潜在结构，因果关系的多特征分析的方法 基因和性状的网络，以及丰富的数据衍生的调节途径。我们广泛使用 凸优化方法允许计算效率和保证可重复性。 在AIM 1中，我们将把广泛的NPD及其子表型分解为共享且独特的 使用新型凸的遗传成分观察到的加权主成分公式 分析（PCA）并开发扩展以处理样品重叠，链接二动（LD）和 不同的祖先。在AIM 2中，我们将扩展并自定义我们在因果网络推理上的现有工作 使用Biconvex优化来估计大脑中的顺式和反式基因调节网络 大规模处理的染色质可及性和表达定量性状位置（QTL）。我们 将使用染色质构象数据对CIS相互作用的估计进行正规化，建模潜在通用 这些网络中的混淆者使用期望最大化（EM）方法和估算网络 在基因和NPD上都可以确定最直接的原因（异族中的“核心”基因 模型）。在AIM 3中，我们将在其基因调节中分析罕见和常见的遗传关联 网络上下文。从癌症基因组学中借用，我们将使用热扩散模型传播 关于基因和调节元素（RES）的本地网络的统计信息，然后使用 图形聚类算法以提取“热”子网，对应于在该中实现的途径 NPD正在研究。我们开发的这些分析的方法将在 在文档，教程和小插图方面，源许可证具有广泛的支持。通过这个 我们希望赋予未来的“后GWAS”分析，以利用遗传，亚表征和特征 人类神经精神健康的潜在网络，并最终指向治疗的道路 干预措施。