The past decade has seen a surge in the number of disease/trait-associated variants, largely because of the union of studies to share genetic data and the availability of electronic health records from large cohorts for research use. Variant discovery for neurological and neuropsychiatric genome-wide association studies, including schizophrenia, Parkinson's disease and Alzheimer's disease, has greatly benefitted; however, the translation of these genetic association results to interpretable biological mechanisms and models is lagging. Interpreting disease-associated variants requires knowledge of gene regulatory mechanisms and computational tools that permit integration of this knowledge with genome-wide association study results. Here, we summarize key conceptual advances in the generation of brain-relevant functional genomic annotations and amongst tools that allow integration of these annotations with association summary statistics, which together provide a new and exciting opportunity to identify disease-relevant genes, pathways and cell types in silico. We discuss the opportunities and challenges associated with these developments and conclude with our perspective on future advances in annotation generation, tool development and the union of the two.

中文翻译：

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使用与大脑相关的功能基因组注释来告知疾病建模。

在过去的十年中，与疾病/特征相关的变体的数量激增，主要是因为人们进行了研究以共享遗传数据，并获得了来自大型队列的电子健康记录以供研究使用。神经病学和神经精神病学全基因组关联研究的各种发现，包括精神分裂症，帕金森氏病和阿尔茨海默氏病，已大为受益。然而，将这些遗传关联结果转化为可解释的生物学机制和模型是滞后的。解释与疾病相关的变异需要基因调控机制和计算工具的知识，以使这些知识与全基因组关联研究结果相结合。这里，我们总结了与大脑相关的功能基因组注释生成中的关键概念进展，以及允许将这些注释与关联摘要统计信息进行整合的工具，这些工具共同提供了一个令人振奋的新机会，可以在计算机上识别与疾病相关的基因，途径和细胞类型。我们讨论了与这些开发相关的机遇和挑战，并以对注释生成，工具开发以及两者的结合方面的未来进展的观点作了总结。