Genetic Architecture of Complex Traits in Admixed Populations

描述(由申请人提供)：通过全基因组关联研究(GWAS)识别与人类健康和疾病相关的数千个遗传变异，点燃了将遗传发现转化为临床和公共卫生实践的希望。在接下来的几年里，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的成功在很大程度上局限于欧洲血统的人口。少数群体对疾病病因的了解仍然有限，特别是在非洲裔美国人和西班牙裔美国人等具有混合大陆血统的人群中，他们矛盾的是，他们遭受着不成比例的疾病负担。填补这一知识缺口的主要障碍之一是缺乏庞大的少数群体队列，他们需要检测常见、复杂疾病背后的无数适度影响的基因。随着我们转向基于序列的关联研究，这个问题可能会加剧。通过为每个少数民族人口建立一个有足够能力和良好表型的队列，并孤立地分析每个人口，对这个问题的蛮力解决方案既不可行，也不有效。必须探索新的分析战略，以提高少数群体中GWAs的效率。这项研究的长期目标是开发新的定量方法来了解混杂人群中复杂疾病的病因，并将这些知识转化为有效的临床和公共卫生实践，从而为消除种族健康差异做出贡献。这项应用的目标是开发统计和计算方法，从而可以使用基因组范围的信息，如基因和测序数据，来估计种群之间遗传结构的共享和独特组成部分。这一目标是通过追求三个具体目标来实现的：(1)描述群体之间的遗传结构重叠的特征；(2)客观地评估混合群体中对种族健康差异的遗传贡献；以及(3)通过自适应地吸收跨群体的信息，开发一种在代表性不足的民族群体中预测个人风险的方法。拟议的研究是创新的，因为它促进并实现了向GWAS多种族范式的过渡，在这种范式中，可以明智地利用大量的、现有的和未得到充分利用的欧洲GWAS结果资源来加速少数群体中的疾病研究。这项研究具有重要意义，因为它将提供对所有人类群体中复杂特征的遗传结构的综合理解，同时确定哪里最需要针对种族的预防和干预策略。

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.