Center for Genetic Studies of Drug Abuse in Outbred Rats

项目4：摘要 虽然全基因组关联研究（GWAS）已经将许多遗传基因座与复杂疾病联系起来，但基因座 到目前为止，绘制的基因图只占影响这些表型的总遗传变异的一小部分。这 限制是共同的人类GWAS和GWAS在模式生物，如异质股票 (HS)老鼠是这个中心的重点。为了更好地捕捉基因信号，我们（项目4的实验室） Trey Ideker主任）和许多其他人都认为，GWAS的结果必须与基本的 分子和细胞生物学知识，如生物网络模型所捕获的。为此，我们将创建 计算分析工具来合成GWAS数据与分子网络信息， 计算遗传分析的现状。这些工具将在以下背景下进行基准测试和应用： 项目1、2和3所研究的多种药物滥用相关行为表型，以及 由单独资助的“附属赠款”研究。工作将沿着沿着三个具体目标进行：第一，我们将 成熟并应用网络传播技术进行基因关联分析。在最近的研究中， 网络传播已被证明可以大大提高识别可再现和功能性的能力， 遗传关联，同时也提供了具体的假设，作为潜在的分子机制 将基因型传递给表现型。我们还将扩展此方法以集成Transcript Wide Association 研究（TWAS）方法。其次，我们将开发分子网络作为GWAS翻译的工具 与药物滥用相关的大鼠和人类研究之间的结果。这一目标将依赖于保护 物种之间的分子途径，以找到与大鼠和人类相关的重叠机制 表型。第三，我们将建立在上述结果的基础上，开发一个层次化的参考模型， 这种基因变异与药物滥用有关。我们将探索这条途径在多大程度上 层次结构可用于构造深度人工神经网络（ANN），用于将基因型转化为 表型。这个系统，基于以前发表的原型芽殖酵母，将被扩展沿着 应用于哺乳动物遗传学的重要品系。如果成功，它不仅能做出准确的预测 从基因型的表型，它也将是可解释的，并燃料相关的机制假设， 开发新的药物滥用治疗方法。

Project 4: Summary While genome­wide association studies (GWAS) have linked many genetic loci to complex diseases, the loci mapped thus far account for a small fraction of the total genetic variation affecting these phenotypes. This limitation is common to both human GWAS and GWAS in model organisms such as the heterogeneous stock (HS) rats that are the focus of this center. To better capture the genetic signal, we (laboratory of Project 4 Director Trey Ideker) and many others have argued that GWAS results must be integrated with fundamental knowledge of molecular and cell biology, as captured by biological network models. To this end, we will create computational analysis tools to synthesize GWAS data with molecular network information, advancing the current state of computational genetic analysis. These tools will be benchmarked and applied in the context of diverse drug abuse­related behavioral phenotypes studied by Projects 1, 2, and 3, as well as phenotypes being studied by the separately funded “affiliated grants.” Work will progress along three Specific Aims: First, we will mature and apply the technique of network propagation for gene association analysis. In recent studies, network propagation has been shown to substantially boost power to identify reproducible and functional genetic associations, while also providing concrete hypotheses as to the underlying molecular mechanisms transmitting genotype to phenotype. We will also extend this method to integrate Transcript Wide Association Study (TWAS) approaches. Second, we will develop molecular networks as a tool for translation of GWAS results between rat and human studies related to drug abuse. This aim will rely on the conservation of molecular pathways between species to find overlapping mechanisms associated with both rat and human phenotypes. Third, we will build on the above results to develop a hierarchical reference model of pathways in which genetic variation is associated with drug abuse. We will explore the extent to which this pathway hierarchy can be used to structure a deep artificial neural network (ANN) for translation of genotype to phenotype. This system, based on a previously published prototype in budding yeast, will be extended along significant lines for application to mammalian genetics. If successful, it will not only make accurate predictions of phenotype from genotype, it will also be interpretable and fuel mechanistic hypotheses relevant to the development of novel treatments for drug abuse.