Gene Expression Regulation in Brains of East Asian, African, and European Descent Explains Schizophrenia GWAS in Diverse Populations.

摘要 精神病学遗传学家发现了数百种常见的单核苷酸多态(SNPs) 与精神分裂症相关(SCZ)通过全基因组关联研究(GWAS)。脑部表情 数量性状基因座(EQTL)可以成功地解释其中的一些遗传关联。基因上的差异 然而，不同祖先群体之间的联系经常被报道，然而，还不知道这种联系 群体差异源于不同的潜在风险基因或不同的等位基因频率和连锁 相同风险基因的不平衡。我们的中心假设是基因表达的遗传调节 在大脑中，以eQTL为代表的信号可以解释这种疾病。人口结构 影响eQTL，就像它影响GWAS一样。主要的假设是GWAS的生物学基础和 EQTL是S-E-D关系的缩写，是单核苷酸多态性-基因表达紊乱的简称。Gwas信号的功能解释 依赖于S-E-D关系的发现。由于缺乏来自非人类群体的脑转录组数据 欧洲血统，解释SCZ GWAS结果的变异在其他人群中不常见呈现一种 这是一个巨大的挑战。为了发现这些种群差异的原因，我们将开发一个转录组 来自东亚人(EA，N=578)的新脑部收集的数据集与现有 心理编码项目(EA，N=18)。我们还将使用非洲血统的个人数据(AFR，N=411)。 《心理密码》项目。以及来自欧洲人后裔(EU)的数据，欧盟在 心理编码(N=1,321)计划，我们将拥有世界上三个主要人群的大脑转录数据。 我们的具体目标包括：1)将SNPs与基因表达联系起来(S-E-D网络中的S-鄂部)，我们将 建立并比较EA、AFR和AFR三个群体死后脑的eQTL和共表达网络 2)为了将SNP表达与SCZ Gwas信号联系起来(S-E-D方面)，我们将使用脑eQTL数据来 解释EA、EU和AFR人群的SCZ GWA，并确定SCZ风险基因座也是 脑基因表达；3)开发一种新的跨种群Predixcan算法，可以从基因上推断 调控基因表达，并找出在患者中差异表达的基因。将使用该算法 重新分析现有的PGC SCZ数据，并使用范德比尔特大学的数据来复制这些发现。这项研究将 提高对群体多样性对SCZ风险的遗传贡献的理解。它是发展的关键 更准确的诊断和治疗，造福于不同的人群，解决健康差距。

Abstract Psychiatric geneticists have discovered hundreds of common single nucleotide polymorphisms (SNPs) associated with schizophrenia (SCZ) through genome-wide association studies (GWAS). Brain expression quantitative trait loci (eQTL) can successfully explain some of those genetic associations. Differences in genetic association between disparate ancestral populations are often reported, however, it is not known whether such population differences originate from different underlying risk genes or from different allele frequencies and linkage disequilibrium of the same risk genes. Our central hypothesis is that genetic regulation of gene expression within brains, as represented by eQTL, can explain the disease GWAS signals. Population structure influences eQTL as it influences GWAS. The major assumption is that the biological foundation of GWAS and eQTL is the S-E-D relationship, short for SNP-Gene Expression-Disorder. Functional interpretation of GWAS signals relies on the discovery of S-E-D relationships. Due to a lack of brain transcriptome data from populations of non- European descent, interpreting SCZ GWAS results for variants uncommon in other populations presents a significant challenge. To discover the causes of these population differences, we will develop a transcriptome dataset of a new brain collection from East Asians (EA, N = 578) combined with samples from the existing PsychENCODE project (EA, N = 18). We will also use data of individuals of African ancestry (AFR, N = 411) from the PsychENCODE projects. Along with data from those of European descent (EU), which dominates the PsychENCODE (N =1,321) projects, we will have brain transcription data of three major populations in the world. Our specific aims include: 1) to relate SNPs to gene expression (the S-E portion of the S-E-D networks), we will develop and compare eQTL and coexpression networks of postmortem brains from three populations, EA, AFR and EU; 2) to connect SNP-expression to SCZ GWAS signals (the S-E-D aspect), we will use brain eQTL data to explain SCZ GWAS of EA, EU and AFR populations and to identify SCZ risk loci that also serve as regulators of brain gene expression; 3) to develop a novel cross-population predixcan algorithm that can infer genetically regulated gene expression, and identify those differentially expressed in patients. The algorithm will be used to re-analyze existing PGC SCZ data, and use Vanderbilt University data to replicate the findings. This study will improve the understanding of the genetic contribution of population diversity to SCZ risk. It is critical for developing more precise diagnoses and treatments for the benefit of diverse populations, for addressing health disparity.