Genetic variants affect brain gene expression and risks of psychiatric disorders

摘要 精神疾病是影响人类的最具破坏性的疾病之一， 给个人、家庭和社会带来巨大负担。全基因组关联研究 （GWAS）已经确定了几十种常见的单核苷酸多态性（SNP）， 与精神疾病相关，但大多数SNPs已被定位为 基因间或内含子区域，并且在功能上未分类。本建议的总体目标 研究是利用数量性状基因座（QTL），包括表达QTL的遗传作图 （eQTL）、蛋白质QTL（pQTL）和DNA酶I敏感性QTL（dsQTL），以定位非编码 调控元件，然后利用QTL SNPs来揭示调控基因， GWAS发现的潜在机制和发现新的风险基因。 我们以前的研究表明，精神病GWAS信号丰富的大脑 eQTL SNPs（eSNPs），这些大脑的eSNPs可能是功能性的，并有助于 疾病易感性。我们假设其他的QTL将类似地代表其他水平的 调控因此，使用QTL作图，我们将鉴定影响染色体可及性的SNP， 脑（dsQTL），和下游基因和蛋白质水平的变化（eQTL和pQTL）。我们将 使用RNA-seq、微蛋白质阵列（MWAs）、反相蛋白质阵列（RPPA），以及 DNase-seq分析了432个死后大脑的前额叶皮层和小脑，沿着 分类的NeuN+和NeuN-核。使用最佳解卷积方法，所有大脑测量 将被划分为神经元和非神经元的QTL定位措施。我们将 此后，重新分析现有的GWAS数据的七种精神疾病，加上三个非 精神疾病/性状作为对照，以了解神经元和非神经元的贡献。 神经元QTL SNP与疾病风险的关系。我们还将寻找转录本的差异表达 和蛋白质，以及不同的DNA敏感性，在病人的大脑，并使用这些 构建新型调控网络的分子措施。这项综合研究代表了一个 及时，新颖和强大的方法，将改变我们对大脑基因组学的理解 以及精神疾病的遗传风险。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， 接近。

Abstract 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.