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

东亚、非洲和欧洲血统大脑中的基因表达调控解释了不同人群中的精神分裂症 GWAS。

• 批准号: 10382057
• 负责人: Chunyu Liu
• 金额: $ 78.64万
• 依托单位: UPSTATE MEDICAL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-25 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10382057
• 关键词: Address; Affect; African; African ancestry; Algorithms; Autopsy; Biological; Biology; Brain; Brain Mapping; China; Chinese; Collection; Copy Number Polymorphism; Data; Data Set; Diagnosis; Disease; East Asian; European; Foundations; Gene Expression; Gene Expression Regulation; Gene Frequency; Genes; Genetic; Genetic Risk; Genetic Transcription; Genetic study; Genotype; Heritability; Human; Individual; Learning; Linkage Disequilibrium; Maps; Mediator of activation protein; Mendelian randomization; Mental disorders; Methods; Mutation; Ontology; Pathway interactions; Patients; Population; Population Analysis; Population Heterogeneity; Population Study; Precision therapeutics; Protein Isoforms; Quantitative Trait Loci; RNA Splicing; Regulatory Element; Reporting; Resources; Rest; Sampling; Schizophrenia; Sex Bias; Signal Transduction; Single Nucleotide Polymorphism; Specificity; Spliced Genes; Structure; Testing; Tissues; Universities; Ursidae Family; Variant; base; brain cell; brain tissue; causal variant; cell type; differential expression; disorder risk; diverse data; epigenomics; frontal lobe; gene function; genetic architecture; genetic association; genome wide association study; genomic locus; health disparity; health disparity populations; improved; insight; novel; precision medicine; preservation; psychogenetics; risk variant; schizophrenia risk; transcriptome; transcriptomics

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.

抽象的 精神病遗传学家发现了数百种常见的单核苷酸多态性 (SNP) 通过全基因组关联研究（GWAS）与精神分裂症（SCZ）相关。大脑表达 数量性状基因座（eQTL）可以成功解释其中一些遗传关联。遗传差异 不同祖先群体之间的联系经常被报道，然而，尚不清楚这种联系是否存在。 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控 以 eQTL 为代表的大脑内部的基因可以解释疾病的 GWAS 信号。人口结构 影响 eQTL 就像影响 GWAS 一样。主要假设是 GWAS 的生物学基础和 eQTL 是 S-E-D 关系，是 SNP-基因表达-紊乱的缩写。 GWAS 信号的功能解释 依赖于 S-E-D 关系的发现。由于缺乏非非人群的大脑转录组数据 欧洲血统，解释其他人群中不常见变异的 SCZ GWAS 结果提出了 重大挑战。为了发现这些群体差异的原因，我们将开发一个转录组 来自东亚人（EA，N = 578）的新大脑集合的数据集与现有样本相结合 PsychENCODE 项目（EA，N = 18）。我们还将使用来自以下国家的非洲血统个体（AFR，N = 411）的数据： PsychENCODE 项目。以及来自欧洲血统 (EU) 的数据，这些人在 PsychENCODE（N = 1,321）项目，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 SNP 与基因表达（S-E-D 网络的 S-E 部分）联系起来，我们将 开发并比较 EA、AFR 和 3 个群体死后大脑的 eQTL 和共表达网络 欧盟； 2) 为了将 SNP 表达连接到 SCZ GWAS 信号（S-E-D 方面），我们将使用大脑 eQTL 数据 解释 EA、EU 和 AFR 人群的 SCZ GWAS，并确定 SCZ 风险位点，这些位点也可作为监管者 大脑基因表达； 3）开发一种新颖的跨群体predixcan算法，可以进行遗传推断 调节基因表达，并识别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研究结果。这项研究将 提高对人口多样性对 SCZ 风险的遗传贡献的理解。对发展至关重要 更精确的诊断和治疗，造福不同人群，解决健康差距。