Genetic variants affect brain gene expression and risks of psychiatric disorders

描述（由申请人提供）：精神疾病是影响人类最具破坏性的疾病之一，给个人、家庭和社会带来了巨大的负担。全基因组关联研究（GWAS）已经确定了数十种与精神疾病相关的常见单核苷酸多态性（snp），但这些snp中的大多数已被定位到基因间或内含子区域，并且在功能上未分类。本研究的总体目标是利用定量性状位点（QTL）的遗传定位，包括表达性QTL （eQTLs）、蛋白性QTL （pQTLs）和dna酶I敏感性QTL (dsQTLs)，绘制人类大脑中的非编码调控元件，然后利用QTL snp揭示GWAS发现的调控机制，发现新的风险基因。我们之前的研究表明，精神病学GWAS信号富含脑eQTL snp (eSNPs)，这些脑eSNPs可能具有功能并与疾病易感性有关。我们假设其他qtl也同样代表着其他水平的调控。因此，利用QTL定位，我们将确定影响大脑染色质可及性的snp (dsQTLs)，以及下游基因和蛋白质水平的变异（eQTLs和pQTLs）。我们将使用RNA-seq、微western阵列（MWAs）、逆相蛋白阵列（RPPAs）和dna -seq来分析432个死后大脑的前额叶皮层和小脑，以及分类的NeuN+和NeuN-核。利用最优反卷积方法，将所有脑测度划分为神经元测度和非神经元测度进行QTL定位。此后，我们将重新分析现有的7种精神疾病的GWAS数据，加上3种非精神疾病/特征作为对照，以了解神经元和非神经元QTL snp对疾病风险的贡献。我们还将在患者大脑中寻找转录本和蛋白质的差异表达，以及差异DNA敏感性，并使用这些分子测量来构建新的调控网络。这项综合研究代表了一种及时、新颖和有力的方法，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。它的定位是创造一个新的范例，整合大脑基因组学和精神病学遗传学，这是真正不同于目前的方法。

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.