Integrating neuroimaging and brain gene expression for functional characterization of psychiatric GWAS

项目总结/摘要 精神障碍是复杂的，使人衰弱的健康状况，但神经生物学原因和 这些疾病背后的病理生理学机制还不清楚。大规模的出现-- 大规模的全基因组关联研究（GWAS）已经能够识别出重要的、可靠的遗传 与精神疾病的联系然而，将GWAS位点转化为特定的因果驱动因子一直是困难的， 变体/基因，以提取用于识别治疗干预的可行靶标的机制见解。 神经生物学中间表型（NBIP）在理解大脑的结构和功能方面是非常宝贵的。 精神病理学风险升高的功能相关性，尽管潜在的过程和分子 观察到的NBIP的机制是难以捉摸的。基于遗传的估算的最新进展现在允许人们 从全基因组基因型数据推断中间表型遗传调节部分。我们的研究 研究小组已经成功地在精神疾病中采用了脑特异性转录组学估算方法， 确定新的基因和风险途径。 在这个建议中，我们试图提高神经影像遗传学和神经细胞学之间联系的生物分辨率。 精神病遗传学，通过创建基于脑特异性的多模式神经成像的新多基因模型， 可以应用于精神病GWAS的基因表达。在目标1中，我们将生成大脑转录组学 多模态神经成像的预测模型，并在独立的数据集中复制它们。在目标2中，我们将 进行成像转录组关联研究（ITWAS），以确定神经影像学与 在大脑特定的基因水平上研究精神障碍，并区分因果关系。我们的综合分析将 提高我们对NBIP和精神障碍风险的理解;因此，它们将提供机械的见解， 可能会推动新的诊断，跨诊断和治疗方法的识别。

PROJECT SUMMARY/ABSTRACT Mental disorders are complex, debilitating health conditions, yet the neurobiological causes and pathophysiological mechanisms underlying these disorders are not well understood. The emergence of large- scale genome-wide association studies (GWAS) has enabled identification of significant, reliable genetic associations to mental disorders. However, it has been difficult to translate GWAS loci into specific causal driver variants/genes to extract mechanistic insights for identifying actionable targets for therapeutic interventions. Neurobiological intermediate phenotypes (NBIPs) are invaluable in understanding the brain’s structural and functional correlates of elevated risk of psychopathology, although the underlying processes and molecular mechanisms for observed NBIPs are elusive. Recent advances in genetic-based imputation now allow one to infer genetically-regulated portions of intermediate phenotypes from genome-wide genotype data. Our research team has successfully employed brain-specific transcriptomic imputation approaches across mental disorders to identify novel genes and pathways of risk. In this proposal, we seek to increase the biological resolution of the link between neuroimaging genetics and psychiatric genetics by creating novel polygenic models of multimodal neuroimaging based on brain-specific gene expression that can be applied to psychiatric GWAS. In Aim 1, we will generate brain transcriptomic predictive models of multimodal neuroimaging and replicate them in independent datasets. In Aim 2, we will conduct Imaging Transcriptome-wide Association Studies (ITWAS) to identify neuroimaging associations with mental disorders at a brain-specific gene-level and distinguish the causal ones. Our integrative analyses will enhance our understanding of NBIPs and mental disorder risk; thus, they will provide mechanistic insights that may drive identification of novel diagnostic, trans-diagnostic and treatment approaches.