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密医学的中心目标。 我的团队目前的研究目标是开发计算方法，以填补两个方面的知识空白 区域。首先，全基因组联合的成功很大程度上局限于 欧洲人后裔；少数族裔代表不足，因此，我们对 少数民族人群的疾病病因学落后。我们长期致力于开发和 应用新的分析框架，旨在加快少数群体的生物医学研究。 在现有工作的基础上，我们将开发针对少数族裔个体的遗传风险评估方法。 它明智地利用了跨种族和特定种族的信息。我们还将为 表征种群水平表型差异背后的遗传基础。第二个区域，即 我们将并行调查，是利用分子表型，特别是蛋白质组学数据，来阐明 Gwas基因座与疾病结局的生物学关系。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 尤其有问题，因为很大一部分GWASSNPs属于非编码区。映射 利用RNA表达的基因表达数量性状(EQTL)提供了丰富的 关于基因调控的信息。另一方面，转录后调控的遗传基础 而且它对复杂的特征和疾病的影响还知之甚少。我们假设蛋白质组学数据 使我们能够了解转录后调控，而蛋白质丰度提供了 在RNA和表型之间连锁的可遗传标记。利用蛋白质组数据，比如那些 通过GTEx生成的，我们提出了新的分析方法来发现pQTL，将蛋白质用作 在疾病风险评估中的中间表型，以及用于识别连接GWAs的候选蛋白 基因座和表型。最终，我们设想我们开发的方法集合，通过利用 多民族队列和多组学数据，将有助于实施个体化预防 并为所有种族和民族的人制定干预战略。

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.