Transcriptional consequences of structural variation in brains of schizophrenia patients

项目总结 精神分裂症最强烈的遗传学发现之一是大脑结构变异(SV)过多 与对照相比，病例中罕见的缺失和重复形式。虽然有几个基因和基因集 目前尚不存在支持这一发现的特定生物学假说。 此外，由于基因分型阵列的分辨率有限，仅对所有SV的一小部分进行了研究 (最常用的技术)来检测较短的删除和重复以及更复杂的SV 作为反转。通过转录活性的测量来理解SV的功能作用的努力已经 只关注少数几个大型的、反复发生的事件，但这种过剩的很大一部分是由SV组成的 分布在整个基因组中。此外，大多数已发表的研究都将血液中的表情变化视为 而不是直接评估这些事件在大脑中的功能后果，这是最 SCZ的相关组织。这项提案将利用NIMH资助的1018个全基因组的现有数据集 对人类死后大脑进行了测序和转录分析，以及39个外显子组测序和 转录谱人诱导多能干细胞(HiPSC)来源的神经前体细胞和 以帮助解释SV在SCZ患者大脑中的功能作用。 具体地说，AIM 1将应用最新的方法从基因分型阵列中发现SV，Exome- 测序数据和全基因组测序数据，构建一套高质量、全面的SV 所有1,057个大脑样本。目标2将寻求量化SV对大脑表达的影响，并 大小、频率、基因命中比例和SV类型等程度特征起作用。我们期待着 这些数据将有助于在没有转录组数据的样本中推断SV的转录后果。目标3是 重点研究了SV和SV驱动的转录活动如何与SCZ风险相关。这一目标包括评估 一套全面的SV对SCZ风险的贡献。最后，这个目标将使用基因-基因共表达和 先前在SCZ中涉及到基因，以识别增加SCZ风险的SV的转录信号。 在这笔赠款结束时，我们将编制一份关于突变的全面调查 尸脑SCZ大型数据集的SV谱。我们将提供一个详细的检查 SV如何改变人脑和HiPSC来源神经细胞的转录，包括比较 这将有助于评估这些细胞用于进一步的转录遗传学研究。最后， 我们将评估SV对SCZ风险的贡献，并探索功能签名的存在 这映射到SCZ病例与对照组相比SV增加。此赠款创建的数据和结果 将广泛用于解释在其他转录研究中，特别是在转录研究中，sv在表达上的作用 大脑。此外，还可以跟踪疾病相关转录特征的成功识别。 此外，在HiPSC衍生的神经细胞中，可以操纵和测试细胞表型结果。

PROJECT SUMMARY One of the strongest genetic findings in schizophrenia is an excess of structural variation (SV) in the form of rare deletions and duplications in cases compared to controls. While a few genes and gene sets have been implicated by SV there does not currently exist a specific biological hypothesis underlying this finding. Further, only a small proportion of all SV has been studied due to the limited resolution of genotyping arrays (the most commonly used technology) to detect shorter deletions and duplications and more complex SV such as inversions. Efforts to understand a functional role of SV through measures of transcriptional activity have focused only on a few large, recurrent events, yet a large proportion of this excess is comprised of SV distributed across the genome. Further, most published work has looked at expression changes in blood as opposed to directly assessing the functional consequences of these events in the brain, which is the most relevant tissue for SCZ. This proposal will leverage existing NIMH funded datasets of 1,018 whole genome sequenced and transcriptionally profiled human post-mortem brains, as well as 39 exome-sequenced and transcriptionally profiled human induced pluripotent stem cell (hiPSC) derived neural progenitor cells and neurons of SCZ cases and controls to help interpret the functional role of SV in the brains of SCZ patients. Specifically, Aim 1 will apply the latest methods to discover SV from genotyping arrays, exome- sequencing data and whole genome sequencing data to build a high quality and comprehensive set of SV across all 1,057 brain samples. Aim 2 will look to quantify the effect SV has on expression in the brain and to what degree features such as size, frequency, proportion of gene hit and type of SV play a role. We anticipate these data will help infer transcriptional consequences of SV in samples without transcriptome data. Aim 3 is focused on how SV and SV driven transcriptional activity relate to SCZ risk. This aim includes assessing the contribution of a comprehensive set of SV to SCZ risk. Finally, this aim will use gene-gene co-expression and previously implicated genes in SCZ to identify transcriptional signatures of SV that increase risk of SCZ. At the conclusion of this grant we will have compiled a comprehensive survey of the mutational spectrum of SV in a large SCZ dataset of post-mortem brains. We will have provided a detailed examination of how SV alters transcription in the human brain and in hiPSC derived neural cells including a comparison between the two that will help assess the use of these cells for further genetic studies of transcription. Finally, we will have assessed the contribution of SV to SCZ risk and explored the existence of a functional signature that maps onto increased SV in SCZ cases compared to controls. The data and results created by this grant will be broadly useful in interpreting the role SV on expression in other transcriptional studies particularly in the brain. In addition, successful identification of a disease relevant transcriptional signature could be followed up further in hiPSC derived neural cells that can be manipulated and tested for cellular phenotypic consequences.