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 对这些疾病的了解，也指出了潜在的治疗目标。全基因组关联 研究(GWAS)通常用于研究复杂疾病，并在一系列研究中非常成功 例如，通过GWA，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，我们不知道疾病相关基因座背后的生物学机制， 因为因果变异和基因被连锁不平衡(LD)和困难的 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，用于综合分析儿童的遗传数据 神经精神疾病，以更好地了解潜在的基因和生物过程。(1)我们会 开发了一种将表达QTL(EQTL)数据与GWAS整合的方法。我们的方法扩展了流行的 转录组-智者联合研究(TWAS)。Twas的目标是通过有效地评估发现风险基因 基因的eQTL与感兴趣的表型的相关性。与标准相比，Twas有许多优势 基于单个变量的分析，例如，它减少了多个测试负担，并提供了 联想。然而，目前的TWA法容易出现假阳性结果。我们将开发一种 严格的统计框架，通过考虑变异的多效性效应来控制错误发现。(2) 精细作图是识别疾病相关基因座因果变异的统计方法。目前很好- 然而，作图方法往往不能缩小特定的因果变量范围。我们的方法是基于 根据观察，等位基因异质性(AH)，即许多变异破坏同一基因，是常见的。所以 我们可以借用精细图谱的统计框架，利用AH来识别风险基因。(3) 研究人员已经开发出工具来联合分析多个性状，以提高基因发现和 识别疾病的致病危险因素。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 驱动多个性状之间的遗传关系。此外，我们还将开发更精确的孟德尔语言 确定性状之间因果关系的随机化(MR)方法。(4)我们努力的一个关键组成部分是 开发用户友好的软件，使广泛的精神病学遗传学社区受益。

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.