Population structure in whole-genome disease scans

描述(由申请人提供)：全基因组关联图谱凭借其所有理论能力来检测导致常见疾病的遗传变异，终于变得实用起来。大多数分析这些研究数据的方法都设想了在相对同质的人群中进行数十万个SNP的扫描，比如欧洲裔美国人。然而，也需要考虑到人类群体之间存在的差异。即使在一个相对同质的人口中，病例和对照可能有不同的祖先历史，这将导致“人口分层”，或者人口可能最近被“混杂”，就像非洲裔美国人和西班牙裔美国人的情况一样。我们建议开发用于人口亚结构分析(PSSA)的工具和方法来处理疾病地图场景中的这些问题。 (1)我们的首要目标将是改进我们已经出版的混合物制图方法和软件(ANCESTRYMAP)。混合作图是一种在最近混血的群体中进行全基因组关联研究的方法，例如非洲裔或西班牙裔美国人，其标记远远少于同种群体所需的标记。在过去的两年里，在将混合映射转化为实用方法方面取得了长足的进步，我们预计它的适用性将继续扩大。 (2)我们的第二个目标将解决这样一个问题，即对数十万个SNP进行全基因组关联扫描，将严重影响它们研究非洲或西班牙裔美国人等少数群体的能力，除非开发出在基因组中每个点推断个人祖先状态后寻找关联的方法。PSSA的一个关键目标是建立方法，允许在少数群体中进行全动力全基因组关联扫描。 (3)我们的第三个目标是为校正全基因组关联扫描中的种群分层提供一种新的方法。人群分层指的是病例和对照之间祖先的系统性差异，这可能导致等位基因频率差异和假阳性关联。在前人工作的基础上，我们引入了测量和修正分层的新方法。我们相信，我们的新技术将提供接近最佳的功率，并且在计算上将是高效的。我们打算向科学界公开提供所有这些工具。

DESCRIPTION (provided by applicant): Whole-genome association mapping, with all its theoretical power to detect genetic variants that contribute to common disease, is finally becoming practical. Most methods for analyzing data from these studies have envisioned scans with hundreds of thousands of SNPs in a relatively homogeneous population such as European Americans. However, the differences that exist among human populations also need to be taken into account. Even in a population that is relatively homogeneous, cases and controls may have different ancestral histories, which will result in "population stratification", or the population may be recently "admixed" as is the case for African-Americans and Hispanics. We propose to develop tools & methods for Population Substructure Analysis (PSSA) to deal with these issues in a disease-mapping scenario. (1) Our first aim will be to improve our already published methods and software (ANCESTRYMAP) for admixture mapping. Admixture mapping is a method for carrying out a genome-wide association study in a population of recent mixed ancestry such as African or Hispanic Americans, with far fewer markers than are needed for a homogeneous population. In the past two years great strides have been made in turning admixture mapping into a practical method, and we expect to continue to extend its applicability. (2) Our second aim will address the problem that whole-genome association scans with hundreds of thousands of SNPs will be severely compromised in their power to study a minority population such as African or Hispanic Americans unless methods are developed that search for association after inferring an individual's ancestry state at each point in the genome. A key aim of PSSA is to build methods that allow fully-powered whole-genome association scans in minority groups. (3) Our third aim will be to provide a novel approach for correcting of population stratification in whole-genome association scans. Population stratification refers to systematic differences in ancestry between cases and controls, which can lead to allele frequency differences and false-positive associations. Building on previous work we introduce new methods to measure and correct for stratification. We believe our new techniques will provide near-optimal power, and will be computationally efficient. We intend to make all these tools publicly available for the scientific community.