Genetic Architecture of Complex Traits in Admixed Populations

摘要：通过全基因组关联研究（GWAS）鉴定与人类健康和疾病相关的数千种遗传变异，点燃了将遗传发现转化为临床和公共卫生实践的希望。在未来的几年里，基于外显子组和全基因组序列的GWAS将继续推动复杂遗传学领域的发展。然而，GWAS的成功主要局限于欧洲后裔。对少数民族人群疾病病因学的了解仍然有限，特别是在具有混合大陆血统的人群中，如非洲裔美国人和西班牙裔美国人，矛盾的是，他们承受着不成比例的疾病负担。填补这一知识空白的主要障碍是缺乏大量的少数民族人口队列，这需要检测常见复杂疾病中影响不大的无数基因。随着我们转向基于测序的关联研究，这个问题可能会加剧。通过为每个少数民族群体建立一个足够强大且表型良好的队列，并孤立地分析每个群体，这种蛮力解决这个问题的方法既不可行，也不有效。必须探索新的分析策略，以提高少数民族人口GWAS的效率。本研究的长期目标是开发新的定量方法来了解混合人群中复杂疾病的病因学，并将这些知识转化为有效的临床和公共卫生实践，从而有助于消除种族健康差异。本应用程序的目的是开发统计和计算方法，使全基因组信息，如基因型和测序数据，可用于估计种群之间遗传结构的共享和独特组成部分。这一目标通过追求三个具体目标来实现：(1)表征种群之间遗传结构的重叠，(2)客观评估混合种群中遗传对种族健康差异的贡献，以及(3)通过自适应地同化种群间的信息，开发一种对代表性不足的种族群体进行个体风险预测的方法。拟议的研究具有创新性，因为它促进并实现了GWAS向多种族范式的过渡，在这种范式中，可以明智地利用欧洲GWAS结果的大量、现有和未充分利用的资源来加速少数民族人群的疾病研究。这项研究意义重大，因为它将提供对所有人群复杂性状的遗传结构的综合理解，同时确定最需要种族特异性预防和干预策略的地方。

DESCRIPTION (provided by applicant): Abstract The identification of thousands of genetic variants associated with human health and disease through genome- wide association studies (GWAS) has kindled the hope of translating genetic findings into clinical and public health practices. In the next few years, exome and full genome sequence-based GWAS will continue to pro- pel the field of complex genetics. The success of GWAS, however, has been largely confined to populations of European descent. Understanding of disease etiology in minority populations remains limited, especially in populations with mixed continental ancestries such as African Americans and Hispanics who, paradoxically, suffer from disproportionate disease burdens. Chief among the barriers in filling this knowledge gap is the lack of large minority population cohorts, which are required to detect the myriad genes of modest effect underlying common, complex diseases. The problem is likely exacerbated as we moved towards sequencing- based association studies. The brute force solution to this problem, by establishing an adequately powered and well-phenotyped cohort for every minority population, and analyzing each population in isolation, is neither feasible nor efficient. New analytic strategies must be explored to improve the efficiencies of GWAS in minority populations. The long-term goals of this research are to develop novel quantitative methods for understanding the etiol- ogy of complex diseases in admixed populations, and to translate this knowledge into effective clinical and public health practices, thereby contributing to the elimination of ethnic health disparity. Th objective of this application is to develop statistical and computational methods whereby genome-wide information, such as genotype and sequencing data, can be used to estimate both shared and unique components of the genetic architectures between populations. This objective is met by pursing three Specific Aims: (1) characterize the overlap in genetic architecture between populations, (2) objectively assess the genetic contribution to ethnic health disparities in an admixed population, and (3) develop an approach for individual risk prediction in an under-represented ethnic group by adaptively assimilating information across populations. The proposed re- search is innovative because it promotes and enables a transition toward a multi-ethnic paradigm in GWAS, in which the large, existing and underused resource of European GWAS results can be judiciously leveraged to accelerate disease studies in minority populations. This research is significant because it will provide an integrated understanding of the genetic architecture of complex traits in all human populations, and at the same time identify where ethnicity-specific prevention and intervention strategies are most needed.