Genetic variants affect brain gene expression and risks of psychiatric disorders

描述(申请人提供)：精神疾病是影响人类人口的一些最具破坏性的疾病，给个人、家庭和社会带来巨大负担。全基因组关联研究已经发现了数十种常见的与精神疾病相关的单核苷酸多态(SNPs)，但这些SNPs中的大多数已经被定位到基因间或内含子区域，并且在功能上没有分类。这项研究的总体目标是利用数量性状基因座(QTL)的遗传图谱，包括表达QTL(EQTL)、蛋白质QTL(PQTL)和DNase I敏感性QTL(DsQTL)，在人脑中定位非编码调控元件，然后利用QTL SNPs揭示GWAs发现的调控机制和发现新的危险基因。我们以前的研究表明，精神疾病的Gwas信号中富含大脑eQTL SNPs(ESNPs)，这些eSNPs可能具有功能并与疾病易感性有关。我们假设，其他QTL将同样代表其他水平的监管。因此，利用QTL作图，我们将确定影响大脑染色质可及性的SNP(DsQTL)，以及下游基因和蛋白质水平的变异(eQTL和pQTL)。我们将使用RNA-SEQ、微蛋白质阵列、反相蛋白质阵列(RPPAs)和DNase-SEQ来描述432例死后大脑的前额叶皮质和小脑，以及分类的Neun和Neun核。使用最优去卷积方法，所有的脑测量将被划分为神经元和非神经元测量，用于QTL定位。此后，我们将重新分析现有的七种精神疾病的Gwas数据，以及三种非精神疾病/特征作为对照，以了解神经性和非神经性QTL SNP对疾病风险的贡献。我们还将寻找转录本和蛋白质的差异表达，以及患者大脑中不同的DNA敏感性，并使用这些分子措施来构建新的调控网络。这项综合性研究代表了一种及时、新颖和强大的方法，它将改变我们对大脑基因组学和精神疾病遗传风险的理解。它的定位是创建一种新的范式，用于整合大脑基因组学和精神病学遗传学，这与目前的方法真正不同。

DESCRIPTION (provided by applicant): Mental illnesses are some of the most devastating diseases affecting human populations, placing a huge burden on individuals, families and society. Genome-wide association studies (GWAS) have identified dozens of common single nucleotide polymorphisms (SNPs) that are associated with psychiatric diseases, but a majority of those SNPs have been mapped to intergenic or intronic regions and are functionally unclassified. The overall goal of this proposed study is to use genetic mapping of quantitative trait loci (QTL), including expression QTLs (eQTLs), protein QTLs (pQTLs), and DNase I sensitivity QTLs (dsQTLs), to map non-coding regulatory elements in human brain, then to use the QTL SNPs to uncover regulatory mechanisms underlying GWAS findings and to discover novel risk genes. Our previous studies have shown that psychiatric GWAS signals are enriched with brain eQTL SNPs (eSNPs), and these brain eSNPs are likely to be functional and contribute to disease susceptibilities. We hypothesize that other QTLs will similarly represent other levels of regulation. So, using QTL mapping, we will identify SNPs affecting chromatin accessibility in brain (dsQTLs), and downstream gene and protein level variations (eQTLs and pQTLs). We will use RNA-seq, micro-western arrays (MWAs), reverse phase protein arrays (RPPAs), and DNase-seq to profile prefrontal cortex and cerebellum of 432 postmortem brains, along with sorted NeuN+ and NeuN- nuclei. Using the optimal deconvolution method, all brain measures will be partitioned into neuronal and non-neuronal measures for QTL mapping. We will thereafter re-analyze existing GWAS data for seven psychiatric diseases, plus three non- psychiatric diseases/traits as controls, to understand the contributions of neuronal- and non- neuronal QTL SNPs to disease risks. We will also look for differential expressions of transcripts and proteins, as well as for differential DNA sensitivities, in patient brains, and use these molecular measures to construct novel regulatory networks. This integrative study represents a timely, novel and powerful approach that will transform our understanding of brain genomics and the genetic risks of psychiatric diseases. It is positioned to create a new paradigm for integrating brain genomics and psychiatric genetics that are truly distinct from current approaches.