Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599, USA.
Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599, USA.
Schizophr Res. 2020 Mar;217:17-25. doi: 10.1016/j.schres.2019.03.007. Epub 2019 Mar 18.
Recent advances in our understanding of the genetic architecture of schizophrenia have shed light on the schizophrenia etiology. While common variation is one of the major genetic contributors, the majority of common variation reside in non-coding genome, posing a significant challenge in understanding the functional impact of this class of genetic variation. Functional genomic datasets that range from expression quantitative trait loci (eQTL) to chromatin interactions are critical to identify the potential target genes and functional consequences of non-coding variation. In this review, we discuss how three-dimensional chromatin landscape, identified by a technique called Hi-C, has facilitated the identification of potential target genes impacting schizophrenia risk. We outline key steps for Hi-C driven gene mapping, and compare Hi-C defined schizophrenia risk genes defined across developmental epochs and cell types, which offer rich insights into the temporal window and cellular etiology of schizophrenia. In contrast with a neurodevelopmental hypothesis in schizophrenia, Hi-C defined schizophrenia risk genes are postnatally enriched, suggesting that postnatal development is also important for schizophrenia pathogenesis. Moreover, Hi-C defined schizophrenia risk genes are highly expressed in excitatory neurons, highlighting excitatory neurons as a central cell type for schizophrenia. Further characterization of Hi-C defined schizophrenia risk genes demonstrated enrichment for genes that harbor loss-of-function variation in neurodevelopmental disorders, suggesting a shared genetic etiology between schizophrenia and neurodevelopmental disorders. Collectively, moving the search space from risk variants to the target genes lays a foundation to understand the neurobiological basis of schizophrenia.
近年来，我们对精神分裂症遗传结构的理解取得了一些进展，这为精神分裂症的病因学提供了一些线索。虽然常见变异是主要的遗传因素之一，但大多数常见变异都位于非编码基因组中，这给理解这一类遗传变异的功能影响带来了重大挑战。功能基因组数据集，从表达数量性状基因座（eQTL）到染色质相互作用，对于识别非编码变异的潜在靶基因和功能后果至关重要。在这篇综述中，我们讨论了称为 Hi-C 的技术如何确定三维染色质景观，从而有助于识别影响精神分裂症风险的潜在靶基因。我们概述了基于 Hi-C 的基因映射的关键步骤，并比较了跨越发育阶段和细胞类型定义的 Hi-C 定义的精神分裂症风险基因，这为精神分裂症的时间窗口和细胞病因提供了丰富的见解。与精神分裂症的神经发育假说相反，Hi-C 定义的精神分裂症风险基因在出生后富集，这表明出生后发育对于精神分裂症的发病机制也很重要。此外，Hi-C 定义的精神分裂症风险基因在兴奋性神经元中高度表达，突出了兴奋性神经元作为精神分裂症的核心细胞类型。对 Hi-C 定义的精神分裂症风险基因的进一步特征分析表明，这些基因富集了神经发育障碍中丧失功能变异的基因，这表明精神分裂症和神经发育障碍之间存在共同的遗传病因。总的来说，将搜索空间从风险变异转移到靶基因为理解精神分裂症的神经生物学基础奠定了基础。
