Integrating case-control transcriptomic and genetic data in admixed individuals to identify disease genes for schizophrenia and bipolar disorder

整合混合个体的病例对照转录组和遗传数据，以确定精神分裂症和双相情感障碍的疾病基因

• 批准号: 10681798
• 负责人: Roel A Ophoff
• 金额: $ 54.15万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-06 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10681798
• 关键词: Admixture; Affect; Alleles; Architecture; Biological; Biological Assay; Biological Factors; Bipolar Disorder; Blood specimen; Chromosome Mapping; Data; Data Set; Diagnosis; Disease; Disease susceptibility; Distal; European ancestry; Funding; Future; Gene Expression; Generations; Genes; Genetic; Genetic Predisposition to Disease; Genetic Research; Genetic Risk; Genetic Variation; Genome; Genomics; Heritability; Individual; Latin American; Latino; Latino Population; Link; Maps; Mediating; Methods; Molecular; National Institute of Mental Health; Neuroglia; Neurons; Patients; Population; Population Heterogeneity; Process; Reduce health disparities; Regulator Genes; Regulatory Element; Research; Resources; Schizophrenia; Side; Signal Transduction; Statistical Methods; Susceptibility Gene; Time; Tissues; Transcript; Translations; Underrepresented Populations; Validation; Variant; Vulnerable Populations; biobank; burden of illness; case control; causal variant; cell type; clinically relevant; data integration; differential expression; gene discovery; genetic risk factor; genetic variant; genome wide association study; genome-wide; genomic locus; genomic tools; health disparity; improved; insight; method development; novel; personalized genomic medicine; personalized medicine; predictive modeling; risk variant; schizophrenia risk; severe mental illness; single-cell RNA sequencing; tool; trait; transcriptome; transcriptome sequencing; transcriptomics; virtual

PROJECT SUMMARY Genome-wide association studies (GWAS) have identified hundreds of genomic loci that increase disease susceptibility for schizophrenia (SCZ) and bipolar disorder (BP). We and others have shown that integration of functional molecular data, including transcript abundance levels, can be used to decipher causal insights from GWAS-identified allelic variant to gene to disease susceptibility. The identification of genetic risk factors and causal interpretations provide the greatest potential for the future of personalized genomic medicine. These studies and advances, however, have mostly been performed in individuals of European ancestry, thereby exacerbating health disparities of most vulnerable populations of diverse and admixed genetic ancestries. At this time, NIMH is leading the effort for genetic discoveries in underrepresented populations through funding of large- scale GWAS projects of SCZ and BP, but it is necessary to simultaneously increase the genetic diversity of functional molecular data for data integration and deciphering causal biological effects involved in SCZ and BP. We will tackle the lack of diversity in integrative transcriptomic studies for the deciphering of genetic susceptibility of SCZ and BP in Latino admixed populations, through deliberate integration of novel statistical method development with new transcriptomic data generation. We will generate transcriptome data in 1,500 Latino individuals (500 SCZ cases, 500 BP cases, 500 controls) as a public resource to generate new insights about the molecular mechanisms that contribute to risk of SCZ and BP. At the same time, we will develop new statistical methods that leverage the admixture process to improve the power of mapping genetic effects on expression in Latino individuals. We will incorporate the local and global genetic ancestries within admixed individuals to increase power of transcriptome-wide association (TWAS) and expression quantitative trait score (eQTS) studies to identify novel susceptibility genes. We will integrate new transcriptomic data in Latino individuals with ongoing Latino GWAS of SCZ and BP involving tens of thousands of Latino individuals both to generate new biological hypotheses and to validate our new statistical methods. The most promising causal variants and susceptibility genes will undergo functional validation. Our efforts will result in a large public gene expression resource, novel statistical tools, and new biological findings validated in functional assays. Our findings may yield clinically relevant applications for diagnosis and treatment for SCZ and BP particularly in Latino individuals that have been traditionally underserved in large-scale genomic studies.

项目摘要 全基因组关联研究（GWAS）已经确定了数百个增加疾病的基因组位点 精神分裂症（SCZ）和双相情感障碍（BP）的易感性。我们和其他人已经表明， 功能性分子数据，包括转录本丰度水平，可用于解读 GWAS鉴定的等位基因变异基因对疾病的易感性。识别遗传风险因素， 因果解释为个性化基因组医学的未来提供了最大的潜力。这些 然而，研究和进展大多是在欧洲血统的个体中进行的，因此， 加剧了具有不同和混合遗传血统的最脆弱人群的健康差距。在这个 与此同时，NIMH正在通过资助大量的 规模的SCZ和BP GWAS项目，但有必要同时增加遗传多样性， 功能分子数据，用于数据整合和破译SCZ和BP中涉及的因果生物效应。 我们将解决缺乏多样性的综合转录组学研究破译遗传易感性 拉丁裔混合人群中SCZ和BP的比例，通过故意整合新的统计方法， 开发新的转录组数据生成。我们将生成1，500名拉丁裔的转录组数据 个人（500例SCZ病例，500例BP病例，500例对照）作为公共资源，以产生关于 导致SCZ和BP风险的分子机制。与此同时，我们将开发新的统计数据， 利用混合过程来提高绘制基因对表达的影响的能力的方法， 拉丁裔个人。我们将把本地和全球的遗传祖先混合在一起， 增加全转录组关联（TWAS）和表达数量性状评分（eQTS）研究功效 来鉴定新的易感基因。我们将整合拉丁美洲人的新转录组数据， SCZ和BP的拉丁裔GWAS涉及成千上万的拉丁裔个体， 假设和验证我们的新统计方法。最有希望的致病变异和易感性 基因将进行功能验证。我们的努力将产生一个大型的公共基因表达资源， 统计工具，以及在功能测定中验证的新生物学发现。我们的发现可能会在临床上产生 SCZ和BP诊断和治疗的相关应用，特别是在拉丁美洲人中， 传统上在大规模基因组研究中服务不足。