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: 10597054
• 负责人: Chunyu Liu
• 金额: $ 73.56万
• 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/10597054
• 关键词: Address; Affect; African; African ancestry; Algorithms; Asian ancestry; Autopsy; Biological; Biology; Brain; Brain Mapping; China; Chinese; Collection; Copy Number Polymorphism; Data; Data Set; Diagnosis; Disease; Disparate; Disparity population; East Asian; European; European ancestry; 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; 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; Race; Regulatory Element; Reporting; Resources; Rest; Sampling; Schizophrenia; Sex Bias; Signal Transduction; Single Nucleotide Polymorphism; Specificity; Spliced Genes; Structure; Testing; Tissues; Universities; Variant; 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 定量性状基因座（EQTL）可以成功解释其中一些遗传关联。遗传差异 但是，经常报告不同祖先种群之间的关联，但是，尚不清楚这种 人口差异源自不同的潜在风险基因或不同的等位基因频率和联系 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 EQTL代表的大脑内部可以解释GWAS疾病信号。人口结构 影响EQTL会影响GWAS。主要假设是GWAS和 EQTL是S-E-D关系，SNP-GENE表达序列的缩写。 GWAS信号的功能解释 依靠发现S-E-D关系。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体的SCZ GWAS结果提出 重大挑战。为了发现这些人口差异的原因，我们将开发一个转录组 来自东亚人（EA，n = 578）的新大脑收集的数据集与现有的样本相结合 Psychencode项目（EA，n = 18）。我们还将使用非洲血统的个人（AFR，n = 411）的数据 Psychencode项目。以及来自欧洲血统（EU）的数据，该数据主导着 Psychencode（n = 1,321）项目，我们将拥有世界三个主要人群的大脑转录数据。 我们的具体目的包括：1）将SNP与基因表达（S-E-D网络的S-E部分）相关联，我们将 开发和比较来自EA，AFR和 欧盟2）要将SNP表达连接到SCZ GWAS信号（S-E-D方面），我们将使用脑EQTL数据 解释EA，EU和AFR人群的SCZ GWA 脑基因表达； 3）开发一种可以从遗传上推断的新型跨种群的预胞素算法 调节基因表达，并识别患者差异表达的基因。该算法将使用 重新分析现有的PGC SCZ数据，并使用Vanderbilt大学数据复制发现。这项研究会 提高对人口多样性对SCZ风险的遗传贡献的理解。这对于开发至关重要 更精确的诊断和治疗方法是为了解决健康差异的各种人群的利益。