Methods and Tools to Analyze Genetic Complexity

项目总结/摘要 从具有多维表型数据的遗传研究中制定生物模型需要新的 分析方法可以捕捉遗传系统的复杂性，同时还提供可验证的假设 变体活性。随着基因组规模数据资源的出现，这一挑战变得日益严峻 旨在确定遗传变异如何影响分子分辨率的生物过程。 这项提案通过利用遗传复杂性的两个互补方面来解决这一需求：多效性， 其中一个变体影响多种表型;上位性，其中多个变体相互作用以影响一个 表型。虽然在模式生物中广泛观察到，并且在人类数据中越来越多，但这些现象 很少被提炼成简洁的模型。我们的方法，称为多效性和上位性的联合分析 （CAPE），整合这些方面，以数学方式约束可能的遗传模型，并确定一个 最能描述多种表型的遗传网络。这是通过多元线性 回归，然后进行正式的重新参数化，将交互系数转换为有向边 每一个都代表着基因的抑制或增强。CAPE在以下方面取得了成功 模型系统，我们现在的目标是将方法扩展到复杂的遗传系统，包括更大的等位基因 多样性、性别和饮食差异。为此，我们将使用多样性远交（DO）小鼠群体， 基因型-组织表达（GTEx），以及ENCODE和路线图项目，以开发 复杂的基因调控我们将模拟遗传变异和表观遗传状态之间的调控相互作用 来解释基因网络并揭示控制基因表达的规则。为了促进合作， 社区使用，我们将开发开源软件工具。这些工具将包括基于R的软件 库，用于在人类和模型群体中执行CAPE分析，以及一套可视化工具， 研究人员探索和解释结果。我们的总体目标是开发新的方法来发现 基因调控和疾病风险的复杂和新颖的遗传机制，在这项工作的过程中， 发布用于复杂性状研究的分析和可视化工具。 该项目分为三个具体目标。目的1是发展推断遗传网络的方法， 影响高维数量性状的变异，并创建分析和可视化软件， 社区使用。目的2是将我们的方法应用于DO小鼠和人体组织中的基因表达模型。目的 3是整合表观遗传学和遗传学数据，以模拟遗传变异和染色质修饰 交互影响基因表达。

PROJECT SUMMARY/ABSTRACT Formulating biological models from genetic studies with multidimensional phenotype data requires new analytical methods that capture the complexity of genetic systems while also providing verifiable hypotheses of variant activity. This challenge has become increasingly acute with the advent of genome-scale data resources designed to determine how genetic variation affects biological processes at molecular resolution. This proposal addresses this need by leveraging two complementary aspects of genetic complexity: pleiotropy, in which one variant affects multiple phenotypes; and epistasis, in which multiple variants interact to affect one phenotype. Although widely observed in model organisms and increasingly in human data, these phenomena are rarely distilled into concise models. Our method, called Combined Analysis of Pleiotropy and Epistasis (CAPE), integrates these aspects to mathematically constrain possible genetic models and determine a genetic network that best describes the multiple phenotypes. This is achieved through multivariate linear regression followed by a formal reparametrization that translates interaction coefficients into directed edges between variants, each representing genetic suppression or enhancement. CAPE has proven successful in model systems and we now aim to extend the approach to complex genetic systems that include greater allelic diversity, sex, and dietary differences. To this end, we will use the Diversity Outbred (DO) mouse population, the Genotype-Tissue Expression (GTEx), and the ENCODE and Roadmap projects to developing models of complex gene regulation. We will model regulatory interactions between genetic variants and epigenetic states to interpret genetic networks and uncover rules that govern gene expression. To facilitate collaborations and community use, we will develop open-source software tools. These tools will include an R-based software library to perform CAPE analysis in human and model populations, and a suite of visualization tools to facilitate researcher exploration and interpretation of results. Our overall goal is to derive new methods to discover complex and novel genetic mechanisms of gene regulation and disease risk and, in the course of this work, release analytical and visualization tools for use in complex trait research. This project is divided into three specific aims. Aim 1 is to develop methods to infer networks of genetic variants that influence high-dimensional quantitative traits and create analytic and visualization software for community use. Aim 2 is to apply our methods to model gene expression in DO mice and human tissues. Aim 3 is to integrate epigenetic and genetic data to model how genetic variation and chromatin modifications interactively affect gene expression.