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

摘要 破译复杂性状的遗传基础是人类遗传学和精准医学的核心目标。 我的小组目前的研究旨在开发计算方法，填补两个方面的知识空白。 地区首先，全基因组关联（GWAS）的成功主要局限于 欧洲血统;少数民族的代表性不足，因此，我们对 少数民族人群疾病病因学落后。我们长期致力于开发和 采用新的分析框架，旨在加速少数群体的生物医学研究。 在我们现有工作的基础上，我们将为少数民族个体制定遗传风险评估方法 明智地利用跨种族和特定种族的信息。我们还将开发模型， 表征群体水平表型差异的遗传基础。第二个领域， 我们将平行研究，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的一个基本限制是， 它没有揭示DNA水平变异表现为表型的机制， 这是特别有问题的，因为大部分GWAS SNP福尔斯落入非编码区。映射 利用RNA表达的基因表达数量性状（eQTL）的研究提供了丰富的信息来源， 关于基因调控的信息。另一方面，转录后调控的遗传基础 其对复杂性状和疾病的影响也知之甚少。我们假设蛋白质组学数据 使我们能够了解转录后调控，蛋白质丰度提供了一个 RNA和表型之间的遗传标记。利用蛋白质组学数据，例如 通过GTEx产生，我们提出了新的分析方法来发现pQTL，使用蛋白质作为 疾病风险评估中的中间表型，并用于鉴定连接GWAS的候选蛋白质 基因座和表型。最终，我们设想，我们开发的整套方法，通过利用 多族裔群体和多组学数据，将有助于实施个性化预防 和干预战略的所有种族和民族的人。

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.