Integrative analysis of multiomic datasets for discovery of molecular underpinnings of large-scale human brain networks

总结 脑成像计划正在获得越来越大和全面的神经成像和多组学- 例如基因组和转录组数据集。在人类神经科学中，对这些数据的现有分析往往 一方面，寻找认知、行为或疾病与基因组特性之间的联系， 或者大脑形态学和连通性。这些有价值的分析稳步推进了我们的 人类大脑功能的知识。但他们也留下了一个关键的差距，在我们的理解如何这一功能- 这种现象源于大脑进化、发育和组织的相互作用。 目前的建议将有助于填补这一空白，通过整合几个大的和间接获得的神经成像 和多组学数据集。它将通过将日益增加的丰富数据与现代统计数据相结合来实现这一目标。 方法，并与其研究人员在网络神经科学，计算生物学， 生物学，人类进化，数据协调，认知发展和衰老神经科学。 该提案将把遗传表达与几个关键大脑区域的大脑网络表型联系起来， 成千上万的基因和个体。它将通过采用和应用遗传模型来做到这一点。 基因表达（在基因组织表达项目获得的转录组数据上训练， 项目）与人类连接组项目和英国生物银行获得的神经成像基因组数据。 该提案将区分适应性和非适应性脑网络表型。中国也会这样做 通过量化这些表型的自然选择在最近的人类进化，使用古代DNA从 古人类，以测试与大脑网络变异相关的基因上的自然选择压力。 该提案将最终划定遗传基因表达和网络表型之间的关系 在典型和非典型的发展和老化。它将通过从大规模的遗传基因表达中 神经成像基因组数据集由青少年大脑认知发展研究，凸轮， 桥梁老龄化和神经科学中心，以及阿尔茨海默病神经成像倡议。它将链接 这种表达的变化与发育和衰老过程中脑网络表型的变化有关， 描绘精神病谱系症状或认知障碍的基因表达脑网络特征。 总的来说，该提案整合了大规模脑网络的进化、发展和组织， 工程.具体来说，它第一次将基因表达和大脑网络表型联系在一起， 在许多个体中的基因，并以这种方式开辟了一个新的方向，在神经影像基因组学。建议AD- 万斯发现神经科学通过分析，增强现有的基因组，转录组和神经免疫， 老化数据最后，通过传播作为这些分析的一部分而创建的所有软件和结果， 这最终加速了未来大型神经科学数据集的严格和可复制的集成。

SUMMARY Brain-mapping initiatives are acquiring increasingly large and comprehensive neuroimaging and multiomic— e.g. genomic and transcriptomic—datasets. Existing analyses of such data in human neuroscience tend to search for links between cognition, behavior or disease on the one hand, and properties of genomes, transcrip- tomes or brain morphology and connectivity on the other. Such valuable analyses have steadily advanced our knowledge of human brain function. But they have also left a critical gap in our understanding of how this func- tion arises from the interplay of brain evolution, development and organization. The present proposal will help fill this gap by integrating several large and disparately acquired neuroimaging and multiomic datasets. It will do so by combining the increasing availability of rich data, with modern statistical methods, and with complementary expertise of its investigators in network neuroscience, computational biol- ogy, human evolution, data harmonization, and cognitive developmental and aging neuroscience. The proposal will link heritable expression to brain-network phenotypes across several key brain regions for thousands of genes and in thousands of individuals. It will do so by adopting and applying models of heritable gene expression (trained on transcriptomic data acquired by the Gene Tissue Expression Project, and allied projects) to neuroimaging genomic data acquired by the Human Connectome Project and the UK Biobank. The proposal will then distinguish between adaptive and non-adaptive brain-network phenotypes. It will do so by quantifying the natural selection of these phenotypes in recent human evolution, using ancient DNA from archaic hominins, to test for natural-selection pressures on genes associated with brain-network variation. The proposal will finally delineate the relationship between heritable gene expression and network phenotypes in typical and atypical development and aging. It will do so by imputing heritable gene expression from large neuroimaging genomic datasets acquired by the Adolescent Brain Cognitive Development Study, the Cam- bridge Centre for Ageing and Neuroscience, and the Alzheimer's Disease Neuroimaging Initiative. It will link the variation in this expression to the variation of brain-network phenotypes in development and aging, and will delineate gene-expression brain-network signatures of psychosis-spectrum symptoms or cognitive impairment. Collectively, the proposal integrates the evolution, development, and organization of large-scale brain net- works. Specifically it links, for the first time, gene expression and brain-network phenotypes across several re- gions in many individuals, and in this way opens a new direction in neuroimaging genomics. The proposal ad- vances discovery neuroscience through analyses that enhance existing genomic, transcriptomic and neuroim- aging data. Finally, through dissemination of all software and results created as part of these analyses, the pro- posal ultimately accelerates future rigorous and reproducible integration of large neuroscience datasets.