Center for Genetic Studies of Drug Abuse in Outbred Rats

项目4：总结 虽然全基因组关联研究(GWAS)已经将许多遗传基因座与复杂的疾病联系在一起，但这些基因座 到目前为止被定位的基因只占影响这些表型的全部遗传变异的一小部分。这 在模式生物中，如异质种群中，人类GWAs和GWAs的局限性是共同的 (HS)作为该中心焦点的大鼠。为了更好地捕获遗传信号，我们(项目4的实验室 主任Trey Ideker)和其他许多人认为，GWAS的结果必须与基础 通过生物网络模型获取的分子和细胞生物学知识。为此，我们将创建 利用分子网络信息合成GWAS数据的计算分析工具，推动了 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研究的与药物滥用相关的不同行为表型，以及 由单独出资的“附属助学金”进行研究。工作将沿着三个具体目标推进：一是 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显著提高识别可重复性和功能性的能力 遗传关联，同时也提供了关于潜在分子机制的具体假设 将基因型传递给表型。我们还将扩展此方法以集成文本广域关联 研究(TWAS)方法。其次，我们将开发分子网络作为GWAS翻译的工具 与药物滥用有关的大鼠和人类研究结果之间的差异。这一目标将依赖于保护 物种间寻找与老鼠和人类相关的重叠机制的分子途径 表型。第三，我们将在上述结果的基础上，开发一个分层的路径参考模型 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 层次结构可以用来构建一个深层人工神经网络(ANN)，用于将基因型转换为 表型。该系统基于之前在萌芽酵母上发表的原型，将继续扩展 应用于哺乳动物遗传学的重要线索。如果成功，它不仅将做出准确的预测 来自基因的表型，它也将是可解释的，并推动与 开发治疗药物滥用的新方法。

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.