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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要由精神病基因组学等团体的国际开放科学合作推动联盟（PGC，其中 co-I Mullins 是主要分析师）两项全基因组关联研究（GWAS）以及神经精神疾病（NPD）的全外显子组和基因组测序研究随着样本量的增加，统计能力也随之增强。检测更多、更小的遗传对疾病风险的影响，称为多基因成分。现在的挑战是了解这些研究结果告诉我们有关 NPD 风险、病因学和生物学的信息。提出一套多特征分析方法，以确定潜在的潜在结构、因果关系我们广泛利用基因和性状网络以及丰富的数据衍生调控途径。凸优化方法既可以提高计算效率，又可以保证可重复性。在目标 1 中，我们将把各种 NPD 及其亚表型分解为共享的和独特的使用观察加权主成分的新颖凸公式的遗传成分分析 (PCA) 并开发扩展来处理样本重叠、连锁不平衡 (LD) 和在目标 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跨式基因调控网络大规模统一处理的染色质可及性和表达数量性状基因座（QTL）。将使用染色质构象数据规范顺式相互作用的估计，建立潜在遗传模型使用期望最大化（EM）方法和估计网络来混淆这些网络对基因和 NPD 进行分析，以确定最直接的原因（全基因组中的“核心”基因）在目标 3 中，我们将分析基因调控中罕见和常见的遗传关联。借用癌症基因组学的网络背景，我们将使用热扩散模型来传播。本地网络上基因和调控元件（RE）的统计信息，然后使用图聚类算法提取“热”子网络，对应于涉及的路径我们正在研究的 NPD 方法将在下面公开发布。源许可证在文档、教程和插图方面提供广泛的支持。我们希望能够支持未来的“后 GWAS”分析，利用遗传、亚表型和性状人类神经精神健康的网络基础，并最终为治疗指明道路干预措施。