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.

摘要