Integrative Approaches to Understanding Genetic Basis of Neuropsychiatric Diseases

项目摘要 识别神经精神疾病的易感基因和变异不仅有助于我们的研究， 了解这些疾病，而且还指出了潜在的治疗靶点。全基因组关联 研究（GWAS）通常用于研究复杂疾病，并且在一系列研究中非常成功。 例如，通过GWAS，超过100个基因座与精神分裂症的风险相关。 然而，在大多数情况下，我们不知道疾病相关位点的生物学机制， 因为致病变异体和基因被连锁不平衡（LD）和 解释大多数遗传变异的功能效应。 该项目的目标是开发新的统计方法，用于遗传数据的综合分析， 神经精神疾病，以更好地了解潜在的基因和生物过程。(1)我们将 开发一种方法来整合表达QTL（eQTL）数据与GWAS。我们的方法扩展了流行的 Transcriptome-Wise Association Studies（TWAS）TWAS旨在通过有效评估， 基因的eQTL与目的表型的相关性。TWAS比标准的有许多优点 基于单一变异的分析，例如，它减少了多重测试负担，并提供了 协会.然而，目前的TWAS方法易受假阳性结果的影响。我们将开发一个 严格的统计框架，通过解释变体的多效性效应来控制错误的发现。（二） 精细定位是识别疾病相关基因座中的因果变异的统计方法。现行罚款- 然而，定位方法往往不能缩小特定因果变异的范围。我们的做法是根据 等位基因异质性（AH），即破坏相同基因的许多变体，是常见的。所以 我们可以利用AH来识别风险基因，借用精细定位的统计框架。（三） 研究人员已经开发出联合分析多种性状的工具，以提高基因发现的能力， 以确定疾病的因果风险因素。然而，现有的方法通常是基于成对的 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过程 在多个性状之间驱动遗传关系。此外，我们将开发更精确的孟德尔 随机化（MR）方法来确定性状之间的因果关系。(4)我们努力的一个关键组成部分是 开发用户友好的软件，使广大的精神病遗传学界受益。

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.