Enabling Imputation and CNV Analysis in Genetic Studies of African Americans

在非裔美国人的遗传研究中实现插补和 CNV 分析

• 批准号: 7830755
• 负责人: James Graham Wilson
• 金额: $ 48.91万
• 依托单位: UNIVERSITY OF MISSISSIPPI MED CTR
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7830755
• 关键词: Accounting; Address; Admixture; Affect; African; African American; Alleles; American; Area; Arts; Award; Biological; Biomedical Research; Body mass index; Caring; Chromosome Mapping; Chromosomes; Collaborations; Complex; Computer software; Copy Number Polymorphism; Crohn' s disease; DNA; Data; Databases; Development; Disease; Effectiveness; Europe; European; Exclusion; Frequencies; Gene Frequency; Generations; Genetic; Genetic Polymorphism; Genetic Variation; Genome; Genomics; Genotype; Goals; Heart; Human; Human Genome; Individual; International; Investigation; Latino; Location; Malignant Neoplasms; Malignant neoplasm of prostate; Maps; Medical; Meta-Analysis; Methodology; Methods; Minority; Multiple Sclerosis; Myocardial Infarction; Non-Insulin-Dependent Diabetes Mellitus; Pattern; Persons; Phase; Phenotype; Play; Population; Population Genetics; Process; Property; Public Domains; Recording of previous events; Research; Resources; Risk; Role; SNP genotyping; Sampling; Source; Stratification; Techniques; Testing; Variant; Work; base; clinical phenotype; cohort; genetic association; genetic resource; genetic variant; genome sequencing; genome wide association study; genome-wide; human disease; improved; insight; neutrophil; new technology; tool

DESCRIPTION (provided by applicant): Enabling imputation and CNV analysis in genetic studies of African Americans This application addresses broad Challenge Area (08) Genomics, and specific Challenge Topic, 08-HL-104: Assess genetic variation in African Americans and determine its effect on disease. Project summary Rapid progress is being made in identifying regions of the human genome that harbor disease-causing genetic variation. However, almost all genome-wide association (GWA) studies to date have been carried out in populations whose ancestry derives from a single continent, usually Europe. Populations like African Americans and Latino Americans have been excluded in most GWA studies, both because their mixed ancestry (from multiple continents) creates complexities for GWA analyses, and because enabling data resources for such populations are currently under-developed. The routine exclusion of populations with mixed ancestry from GWA studies is problematic, not only because this practice produces research findings that may be less relevant to minority populations, but also because it reduces the number of biological discoveries. Genetic variants that may be important in affecting disease in minority populations - for example due to high frequency in those populations, or to interaction with environmental or cultural factors - are less likely to be discovered in populations of European ancestry than in the populations in which they are most significant. Here we propose to develop methods and resources that enable effective, fully powered GWA studies in admixed populations. Two areas of modern genetics that are critical to GWA studies are imputation and the analysis of copy number variation (CNV). The extension of GWA studies beyond the polymorphisms that are directly typed to far-larger sets of variants ("imputation") has become a fundamental tool for extending the reach of such studies and integrating the results of multiple studies (in "meta-analysis") that have directly typed different sets of variants. It has also become clear in recent years that human genomes differ at large physical scales in the form of copy number variants (CNV) that extend for thousands of bases, and that such variation is frequently associated with disease. Fully determining the relevance of CNV to disease and phenotypic variation is becoming a core goal of GWA studies. In the work to be supported by this award, we will: ¿ Extend the best methodology for imputation to African Americans, producing public-domain software to execute these analyses, validating our methods, and providing a public-domain database of all known polymorphisms and their imputability in African American cohorts; ¿ Extend effective CNV data resources and imputation strategies to African Americans, including maps of CNV locations, allele frequencies, and LD properties in African American populations, by integrating data from CARe, the 1000 Genomes Project, and HapMap phase 3; ¿ Critically evaluate these methods, resources, and analyses by genotyping SNPs and CNVs in a large African-American cohort, the Jackson Heart Study (JHS), that is also being analyzed in CARe; ¿ Use these methods and resources to extend the reach of GWA studies in CARe to far more SNPs and CNVs. To validate our methods, we will genotype in JHS the disease-associated SNPs and CNVs that we discover by imputation In CARe. The work we propose will offer opportunities both to uncover genetic associations of medical importance to African Americans and to demonstrate the efficacy of advanced GWA studies in an admixed population. Although our work emphasizes genetic studies in African Americans, the methods we provide will also improve the reach and quality of GWA studies in all groups whose ancestry derives from multiple continents. Populations like Africans and Europeans that were separated from each other for thousands of generations differ in both the frequency of specific genetic variations and in the relationships these variations have to each other (sometimes called "linkage patterns"). We will characterize and use the linkage patterns in persons of either African or European ancestry to analyze DNA of African Americans, most of whom have ancestry from both of these populations. We will develop methods to use information from genetic variants that have been genotyped to predict data for variants that have not been genotyped, a process called "imputation," thus providing useful data for literally millions of untyped variants, and greatly increasing the power of efforts to find variants that contribute to human disease.

描述（由申请人提供）： 在非裔美国人的遗传研究中实现插补和CNV分析本申请涉及广泛的挑战领域（08）基因组学和特定的挑战主题08-HL-104：评估非裔美国人的遗传变异并确定其对疾病的影响。在确定人类基因组中存在致病遗传变异的区域方面正在取得快速进展。然而，迄今为止，几乎所有的全基因组关联（GWA）研究都是在祖先来自单一大陆（通常是欧洲）的人群中进行的。像非洲裔美国人和拉丁裔美国人这样的人群被排除在大多数GWA研究之外，这既是因为他们的混合血统（来自多个大陆）为GWA分析带来了复杂性，也是因为这些人群的数据资源目前还不发达。常规地将具有混合血统的人群排除在GWA研究之外是有问题的，不仅因为这种做法产生的研究结果可能与少数群体的相关性较低，而且还因为它减少了生物学发现的数量。在影响少数群体疾病方面可能很重要的遗传变异-例如，由于在这些群体中的高频率，或与环境或文化因素的相互作用-在欧洲血统的群体中发现的可能性比在它们最重要的群体中发现的可能性要小。在这里，我们建议开发的方法和资源，使有效的，充分的动力GWA研究混合人群。现代遗传学中对GWA研究至关重要的两个领域是插补和拷贝数变异（CNV）分析。GWA研究的扩展超出了直接分型的多态性到更大的变体集（“插补”），已经成为扩展这种研究的范围和整合直接分型不同变体集的多个研究（“荟萃分析”）的结果的基本工具。近年来，人类基因组在大的物理尺度上以延伸数千个碱基的拷贝数变异（CNV）的形式存在差异，并且这种变异经常与疾病相关。充分确定CNV与疾病和表型变异的相关性正成为GWA研究的核心目标。在这项由该奖项支持的工作中，我们将：将最好的归责方法扩展到非洲裔美国人，制作公共领域软件来执行这些分析，验证我们的方法，并提供所有已知多态性及其在非洲裔美国人队列中的可归责性的公共领域数据库;将有效的CNV数据资源和插补策略扩展到非洲裔美国人，包括非洲裔美国人人群中CNV位置、等位基因频率和LD特性的地图，通过整合来自CARe、1000个基因组计划和HapMap第3阶段的数据;通过在一个大型非洲裔美国人队列中对SNP和CNV进行基因分型，对这些方法、资源和分析进行批判性评估，杰克逊心脏研究（JHS）也在CARe中进行分析;使用这些方法和资源，将CARe中的GWA研究范围扩展到更多的SNP和CNV。为了验证我们的方法，我们将在JHS中对我们在CARe中通过插补发现的疾病相关SNP和CNV进行基因分型。我们提出的这项工作将提供机会，既可以揭示非裔美国人的医学重要性的遗传协会，也可以证明先进的GWA研究在混合人群中的有效性。虽然我们的工作强调非裔美国人的遗传研究，但我们提供的方法也将提高其祖先来自多个大陆的所有群体的GWA研究的范围和质量。像非洲人和欧洲人这样的群体，彼此分离了数千代，在特定遗传变异的频率和这些变异彼此之间的关系（有时称为“连锁模式”）上都有所不同。我们将描述并使用非洲或欧洲血统的人的连锁模式来分析非裔美国人的DNA，他们中的大多数人都有这两个人群的祖先。我们将开发方法，利用已进行基因分型的遗传变异的信息来预测尚未进行基因分型的变异数据，这一过程称为“插补”，从而为数百万未分型的变异提供有用的数据，并大大增加寻找导致人类疾病的变异的努力。