Integrative Approaches to Understanding Genetic Basis of Neuropsychiatric Diseases

了解神经精神疾病遗传基础的综合方法

• 批准号: 10224033
• 负责人: Xin He
• 金额: $ 49.54万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-17 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10224033
• 关键词: Accounting; Address; Affect; Alleles; Automobile Driving; Biological; Biological Process; Biology; Code; Communities; Complex; Computer software; Cost of Illness; DNA; Data; Development; Disease; Enhancers; Exposure to; Factor Analysis; Gene Expression; Genes; Genetic; Genotype; Goals; Heterogeneity; Joints; Knowledge; Lead; Learning; Light; Link; Linkage Disequilibrium; Medical; Mendelian randomization; Mental disorders; Methods; Modeling; Molecular; Outcome; Pathway interactions; Patients; Phenotype; Play; Quantitative Trait Loci; RNA Splicing; Regulation; Research; Research Personnel; Risk Factors; Role; Sampling; Schizophrenia; Site; Software Tools; Statistical Methods; Susceptibility Gene; Techniques; Testing; Time; Translating; Variant; Work; autism spectrum disorder; base; causal variant; cohort; computerized tools; data to knowledge; disorder risk; experience; flexibility; gene discovery; genetic analysis; genetic variant; genome wide association study; genomic data; genomic locus; improved; interest; neuropsychiatric disorder; novel; open source; pleiotropism; predictive modeling; psychogenetics; risk variant; schizophrenia risk; therapeutic target; tool; trait; transcriptome; user friendly software

Project Summary Identifying the susceptibility genes and variants of neuro-psychiatric diseases will not only contribute to our understanding of these diseases, but also point to potential therapeutic targets. Genome-wide association studies (GWAS) are commonly used to study complex diseases, and have been highly successful in a range of disorder, for instance, more than 100 loci have been associated with the risk of Schizophrenia through GWAS. Nevertheless, in most cases, we do not know the biological mechanisms underlying disease associated loci, because the causal variants and genes are obscured by linkage disequilibrium (LD) and by the difficulty of interpreting functional effects of most genetic variants. The goal of this project is to develop novel statistical methods for integrative analysis of genetic data of neuropsychiatric diseases to better understand the underlying genes and biological processes. (1) We will develop a method to integrate expression QTL (eQTL) data with GWAS. Our method extends the popular Transcriptome-Wise Association Studies (TWAS). TWAS aims to discover risk genes, by effectively assessing the correlation of eQTLs of a gene with the phenotype of interest. TWAS has many advantages over standard single variant-based analysis, e.g. it reduces multiple testing burden and provides biological contexts of associations. However, current TWAS methods are susceptible to false positive findings. We will develop a rigorous statistical framework to control false discoveries by accounting for pleiotropic effects of variants. (2) Fine-mapping is the statistical approach to identifying causal variants in disease-associated loci. Current fine- mapping methods, however, are often not able to narrow down specific causal variants. Our approach is based on the observation that allelic heterogeneity (AH), i.e. many variants disrupting the same gene, is common. So we can leverage AH to identify risk genes, borrowing the statistical framework of fine-mapping. (3) Researchers have developed tools to joint analyze multiple traits to improve the power of gene discovery and to identify causal risk factors of diseases. Existing approaches, however, are often based on pair-wise analysis. We will develop a powerful statistical framework to better understand common biological processes driving genetic relationships among multiple traits. Additionally, we will develop more accurate Mendelian Randomization (MR) method to identify causal relationship among traits. (4) A key component of our effort is the development of user-friendly software that could benefit the broad psychiatric genetics community.

项目摘要 识别神经精神疾病的敏感性基因和变体不仅会促进我们 了解这些疾病，但也指出潜在的治疗靶标。全基因组关联 研究（GWAS）通常用于研究复杂疾病，并且在一系列 例如，障碍通过GWAS有100多个基因座与精神分裂症的风险有关。 然而，在大多数情况下，我们不知道与疾病相关的基因座的生物学机制， 因为因果变体和基因被连锁不平衡（LD）遮盖 解释大多数遗传变异的功能效应。 该项目的目的是开发新颖的统计方法，以整合遗传数据的整合 神经精神疾病，以更好地了解潜在的基因和生物学过程。 （1）我们会的 开发一种将表达式QTL（EQTL）数据与GWAS集成的方法。我们的方法扩展了流行 通过转录组协会研究（TWA）。 TWA旨在通过有效评估来发现风险基因 基因的EQTL与感兴趣的表型的相关性。 Twas比标准具有许多优势 基于单个变体的分析，例如它减轻了多个测试负担，并提供了 协会。但是，当前的TWA方法容易受到假阳性发现的影响。我们将发展一个 严格的统计框架通过考虑变体的多效效应来控制虚假发现。 （2） 精细映射是识别疾病相关基因座因果变异的统计方法。当前细 但是，映射方法通常无法缩小特定因果变体。我们的方法是基于 关于等位基因异质性（AH）的观察，即许多破坏同一基因的变体是常见的。所以 我们可以利用AH来识别风险基因，并借用精细映射的统计框架。 （3） 研究人员开发了联合分析多种特征的工具，以提高基因发现的力量和 确定疾病的因果风险因素。但是，现有的方法通常是基于配对的 分析。我们将开发一个强大的统计框架，以更好地了解常见的生物学过程 推动多种特征之间的遗传关系。此外，我们将开发更准确的门德利人 随机化（MR）方法以识别性状之间的因果关系。 （4）我们努力的关键组成部分是 可以使广泛的精神病遗传学社区受益的用户友好软件的开发。