Delineation of genetic architecture underlying complex traits at molecular, individual and population levels

在分子、个体和群体水平上描绘复杂性状背后的遗传结构

• 批准号: 9901591
• 负责人: Hua Tang
• 金额: $ 35.33万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9901591
• 关键词: Area; Biological; Biomedical Research; Chromosome Mapping; Complex; Computing Methodologies; DNA; Data; Disease; Disease Outcome; Ethnic Origin; Etiology; European; Future; Gene Expression; Gene Expression Regulation; Genetic; Genetic Determinism; Genetic Medicine; Genetic Transcription; Genotype-Tissue Expression Project; Goals; Health Benefit; Heritability; Human Genetics; Individual; Knowledge; Link; Methods; Minority; Modeling; Molecular; Multiomic Data; Phenotype; Population; Post-Transcriptional Regulation; Precision Health; Preventive Intervention; Proteins; Proteomics; RNA; Race; Research; Resources; Risk Assessment; Source; Untranslated RNA; Variant; Work; cohort; design; disorder risk; falls; genetic architecture; genetic risk assessment; genome wide association study; individualized prevention; insight; link protein; molecular phenotype; novel; precision medicine; success; trait

Abstract Deciphering the genetic basis of complex traits is a central goal of human genetics and precision medicine. Current research in my group aims to develop computational approaches that fill knowledge gaps in two areas. First, the success of genome-wide associations (GWAS) has largely confined to populations of European descent; minority individuals are under-represented, and as a result, our understanding of disease etiology in minority populations lags behind. We have a long-standing commitment to develop and apply novel analytic frameworks, which aim to accelerate biomedical research in minority populations. Building upon our existing work, we will develop genetic risk assessment approaches for minority individuals that judiciously leverage both trans-ethnic and ethnic-specific information. We will also develop models for characterizing the genetic basis underlying population-level phenotypic differences. A second area, which we will investigate in parallel, is to use molecular phenotypes, in particular proteomics data, to elucidate the biological relationship between GWAS loci and disease outcomes. A fundamental limitation of GWAS is that it does not reveal the mechanisms through which DNA-level variation manifests into phenotypes; this is particularly problematic because a large fraction of GWAS SNPs falls into non-coding regions. The mapping of gene expression quantitative traits (eQTL) using RNA expression has provided a rich source of information regarding gene regulation. On the other hand, the genetic basis of post-transcriptional regulation and its impact on complex traits and diseases is poorly understood. We hypothesize that proteomics data allows us to gain understanding about post-transcriptional regulation, and protein abundance provides a heritable marker linking between RNA and phenotypes. Making use of proteomic data, such as those generated through GTEx, we propose novel analytic approaches for uncovering pQTLs, for using protein as intermediate phenotype in disease risk assessment, and for identifying candidate proteins that link GWAS loci and phenotype. Ultimately, we envision that the ensemble of methods we develop, by capitalizing on multi-ethnic cohorts and multi-omics data, will contribute to the implementation of individualized prevention and intervention strategies for people of all races and ethnicities.

抽象的 解读复杂性状的遗传基础是人类遗传学和精确医学的核心目标。 我小组的当前研究旨在开发填补两个知识差距的计算方法 区域。首先，全基因组协会的成功（GWAS）在很大程度上仅限于 欧洲下降；少数派个人的人数不足，因此我们对 少数民族的疾病病因落后。我们长期以来一直致力于发展和 应用新颖的分析框架，旨在加速少数群体的生物医学研究。 在我们现有的工作的基础上，我们将为少数民族个人开发遗传风险评估方法 明智地利用跨种族和特定民族的信息。我们还将为 表征遗传基础种群水平的表型差异。第二个区域 我们将同时研究，是使用分子表型，特别是蛋白质组学数据，以阐明 GWAS基因座与疾病结局之间的生物学关系。 GWAS的基本限制是 它没有揭示DNA级变异表现为表型的机制。这是 特别是有问题的，因为很大一部分GWAS SNP属于非编码区域。映射 使用RNA表达的基因表达定量性状（EQTL）提供了丰富的来源 有关基因调节的信息。另一方面，转录后调节的遗传基础 它对复杂性状和疾病的影响知之甚少。我们假设蛋白质组学数据 允许我们了解转录后调节，蛋白质丰度提供了 RNA和表型之间连接的可遗传标记。利用蛋白质组学数据，例如 通过GTEX生成，我们提出了用于使用PQTLS的新型分析方法，用于使用蛋白 疾病风险评估中的中间表型，并用于识别与GWAS联系的候选蛋白 基因座和表型。最终，我们设想我们开发的方法合奏，通过大写 多种族同群和多派对数据将有助于实施个性化的预防 以及针对各种种族和种族的人们的干预策略。