Genetic neuroscience: How human genes and alleles shape neuronal phenotypes

遗传学研究已经确定了许多与精神疾病有显著关联的特定基因座。 然而，除非我们能够开发出有用的方法，系统地将遗传信息转化为 虽然我们对大脑疾病的神经生物学见解，但有一种危险，即昂贵而来之不易的遗传学发现将 不能用于理解病理生理学和产生重要的治疗假设。 我们的合作，跨学科的努力的目标是开发强大的，可推广的方法， 发现精神疾病的风险变量如何在多个水平上塑造神经生物学过程， 分析，并确定其失调的疾病的基础过程。为此，我们建议 开发新的实验和推理系统，以弥合人类遗传学和 实验生物学我们的目标是确定跨越遗传，分子， 和神经系统的细胞水平。 我们的跨学科团队将开发新的实验系统，测量遗传影响， 分析水平（RNA，蛋白质和细胞功能，包括生理学）， 控制方式。我们将利用新兴的细胞系统，包括三维皮质球体 和类器官，以及全新的“培养皿中的群体”实验系统，该系统收集来自 在一个共享的环境中的数百名供体，在表型读出时推断供体身份。的 对此类系统的分析反过来又需要复杂的推理策略和计算机的新想法 科学我们建议开发和广泛共享实验和计算资源，包括细胞 线条、方法、数据集和分析工具。 这项工作的成功完成将确定多种精神疾病的关键神经生物学过程， 疾病，并加强了许多其他科学家在自己的工作中建立这种联系。我们希望这样做 创造一种新的跨学科科学，通过结合数据驱动的优势， 人类遗传学与新兴实验系统的力量-改变了人类- 遗传学的线索可以帮助我们了解疾病的机制。

Genetic studies have identified many specific loci with significant associations to psychiatric disorders. However, unless we can develop useful approaches for systematically turning genetic information into neurobiological insights about brain disorders, there is a danger that costly and hard-won genetic findings will not be exploitable to understand pathophysiology and generate important therapeutic hypotheses. The goal of our collaborative, interdisciplinary effort is to develop powerful, generalizable approaches for discovering how risk variants for psychiatric disorders shape neurobiological processes at multiple levels of analysis, and to identify the processes whose dysregulation underlies disease. To do this, we propose to develop new experimental and inferential systems to bridge a longstanding gap between human genetics and experimental biology. We aim to identify biological causes and effects that span the genetic, molecular, and cellular levels of the nervous system. Our interdisciplinary team will develop new experimental systems that measure genetic influences across levels of analysis (RNA, proteins, and cellular function including physiology) in precise, scalable, well- controlled ways. We will make use of emerging cellular systems including three-dimensional cortical spheroids and organoids, and radically novel “population in a dish” experimental systems that collect data on cells from hundreds of donors in a shared environment, inferring donor identity at the time of phenotypic readout. The analysis of such systems in turn requires sophisticated inferential strategies and new ideas from computer science. We propose to develop and widely share experimental and computational resources, including cell lines, methods, datasets, and analytic tools. The successful completion of this work will identify key neurobiological processes for multiple psychiatric disorders, and fortify many other scientists in making such connections in their own work. We hope in so doing to create a new kind of interdisciplinary science that – by combining the strengths of data-driven, unbiased human genetics with the power of emerging experimental systems – transforms the rate at which human- genetic leads lead to insights about disease mechanisms.