Methods for Genome-wide Association Studies in Admixed Populations

混合人群全基因组关联研究的方法

• 批准号: 8281417
• 负责人: ALKES L PRICE
• 金额: $ 51.5万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-15 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8281417
• 关键词: Accounting; Address; Admixture; Affect; African; African American; Area; Asthma; Cardiovascular Diseases; Cardiovascular system; Cohort Studies; Collaborations; Commit; Communities; Complex; Computer software; DNA Resequencing; Data; Data Set; Databases; Disease; Ensure; European; Family; Family Research; Future; Genes; Genetic; Genetic Heterogeneity; Genome; Genome Scan; Genotype; Goals; Hawaii; Heart; Hispanics; Incidence; Individual; Inherited; Latino; Letters; Location; Maintenance; Maps; Meta-Analysis; Methods; Minority; Native Americans; Phenotype; Population; Principal Component Analysis; Publications; Research; Research Methodology; Research Personnel; Risk; Running; Sampling; Scanning; Signal Transduction; South Africa; Statistical Methods; Stratification; United States; Variant; Work; base; computer code; disorder risk; family structure; genetic variant; genome wide association study; health disparity; method development; programs; software development; statistics; tool

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) have been successful in identifying common genetic variants contributing to disease risk. However, nearly all of these studies have been conducted in populations of European ancestry. It is important to include other populations, because GWAS in Europeans are unlikely to detect risk variants that are common only in non-European populations. In the United States, the majority of individuals of non-European ancestry belong to admixed populations (e.g. African Americans or Latinos) that inherit ancestry from more than one continental population. Existing GWAS methods for admixed populations are inadequate, because they do not incorporate both SNP association and admixture association signals. Thus, if existing methods are applied, analyses will not be fully powered and important variants will be missed. For diseases with known population differences-such as cardiovascular disease in African Americans and asthma in Latinos-the need to develop methods that combine these signals is particularly pressing, because admixture association signals are likely to be particularly important. Here, we propose to develop a complete set of methods and software to combine SNP and admixture association signals in GWAS in admixed populations, while addressing questions such as imputation and choosing SNPs for replication. Our goal is to make fully powered association studies in populations of mixed ancestry as practical as studies in populations of homogeneous ancestry. In addition to African Americans, we will also develop methods for complex admixed populations (e.g. Latinos) that inherit ancestry from three or more continental populations, and for related individuals from admixed populations. Our methods research will be driven by empirical data, including over 10,000 African American samples and 2,400 Latino samples that will be genome-scanned by the CARe consortium, the Jackson Heart Study, and the Multiethnic Cohort Study. Our work will be applicable not only to GWAS in admixed populations, but also to meta-analyses of European and admixed populations, as well as future resequencing-based studies.

描述(由申请人提供)：全基因组关联研究(GWAS)已经成功地识别了导致疾病风险的常见遗传变异。然而，几乎所有这些研究都是在欧洲血统的人群中进行的。重要的是要包括其他人群，因为欧洲人的GWAS不太可能检测到只在非欧洲人群中常见的风险变异。在美国，大多数非欧洲血统的人属于混合人口(例如，非洲裔美国人或拉丁裔)，他们的祖先来自不止一个大陆人口。现有的用于混合种群的GWAS方法是不充分的，因为它们没有同时包含SNP关联和混合关联信号。因此，如果应用现有的方法，分析将不会得到充分的支持，重要的变种将被遗漏。对于已知人群差异的疾病--例如非裔美国人的心血管疾病和拉美裔人的哮喘--开发结合这些信号的方法的必要性尤为紧迫，因为混合关联信号可能特别重要。在这里，我们建议开发一套完整的方法和软件来结合混合种群中的SNP和混合关联信号，同时解决诸如分配和选择SNP用于复制的问题。我们的目标是使混合血统人群中的完全强大的关联研究与同种血统人群中的研究一样实用。除了非裔美国人外，我们还将为从三个或更多大陆人口继承祖先的复杂混合人口(例如拉丁裔)以及来自混合人口的相关个人开发方法。我们的方法研究将由经验数据驱动，包括超过10,000个非洲裔美国人样本和2,400个拉丁裔样本，这些样本将由CARE联盟、杰克逊心脏研究和多种族队列研究进行基因组扫描。我们的工作不仅适用于混合种群中的GWAS，也适用于欧洲和混合种群的荟萃分析，以及未来基于重新测序的研究。