Genetic Architecture of Complex Traits in Admixed Populations

混合群体中复杂性状的遗传结构

• 批准号: 8840960
• 负责人: Hua Tang
• 金额: $ 24.28万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-03-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8840960
• 关键词: African; African American; American; Architecture; Base Sequence; Clinical; Complex; Computing Methodologies; Data; Disease; Ensure; Ethnic Origin; Ethnic group; Etiology; European; Future; Genes; Genetic; Genetic Research; Genetic study; Genome; Genomics; Genotype; Goals; Health; Hispanic Americans; Hispanics; Human; Individual; Intervention; Kindling (Neurology); Knowledge; Left; Medicine; Methods; Minority; Minority Groups; Molecular; Phenotype; Population; Prevention strategy; Public Health Practice; Recording of previous events; Research; Research Personnel; Resources; Risk; Sample Size; Solutions; Statistical Methods; Time; Translating; United States; Variant; abstracting; base; burden of illness; cohort; design; disorder prevention; disorder risk; exome; experience; genetic predictors; genetic risk factor; genetic variant; genome sequencing; genome wide association study; genome-wide; health disparity; improved; innovation; insight; meetings; non-genetic; novel; novel strategies; risk variant; success; tool; trait

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.

描述（申请人提供）：摘要通过基因组研究（GWAS）鉴定了数千种与人类健康和疾病相关的遗传变异，这激发了人们希望将遗传发现转化为临床和公共卫生实践的希望。在接下来的几年中，基于基因组序列的GWAS将继续为复杂的遗传学领域提供研究。然而，GWAS的成功在很大程度上仅限于欧洲血统的种群。对少数族裔疾病病因的理解仍然有限，尤其是在诸如非裔美国人和西班牙裔美国人等混合大陆祖先的人群中，他们自相矛盾地遭受了不成比例的疾病负担。填补这一知识差距的障碍的主要是缺乏少数族裔人群，这是检测常见，复杂疾病的无数基因所必需的。当我们朝着基于测序的关联研究迈进时，问题可能会加剧。通过为每个少数族裔建立足够的动力和良好的型人群，并孤立地分析每个人群的蛮力解决方案是不可行的，也不是有效的。必须探索新的分析策略，以提高GWAS在少数群体中的效率。这项研究的长期目标是开发新颖的定量方法，以理解混合群体中复杂疾病的基因，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。该应用程序的目标是开发统计和计算方法，从而将全基因组的信息（例如基因型和测序数据）用于估计种群之间遗传体系结构的共享和独特组成部分。通过追求三个具体目的来实现这一目标：（1）表征人口之间的遗传结构的重叠，（2）客观地评估混合群体中种族健康差异的遗传贡献，（3）通过在人群中适应群体的信息来适应人口群中的个人风险预测方法。拟议的搜索是创新的，因为它可以促进并使GWAS的多种族范式过渡，其中大型，现有和未充分利用的欧洲GWAS结果资源可以被明智地利用，以加速少数群体的疾病研究。这项研究之所以重要，是因为它将对所有人类种群中复杂性状的遗传结构提供综合的理解，同时确定最需要的特定民族的预防和干预策略。