Population structure in whole-genome disease scans

描述（由申请人提供）：全基因组关联作图，其所有的理论力量，以检测遗传变异，有助于常见疾病，终于成为现实。大多数分析这些研究数据的方法都设想在相对同质的人群（如欧洲裔美国人）中使用数十万个SNP进行扫描。然而，也需要考虑到人口之间存在的差异。即使在相对同质的人群中，病例和对照也可能有不同的祖先史，这将导致“人群分层”，或者人群可能是最近“混合”的，就像非洲裔美国人和西班牙裔美国人的情况一样。我们建议开发人口亚结构分析（PSSA）的工具和方法，以处理这些问题的疾病映射方案。 (1)我们的第一个目标将是改进我们已经公布的方法和软件（ANCESTRYMAP）的混合映射。混合作图是一种在最近的混合血统人群（如非洲裔或西班牙裔美国人）中进行全基因组关联研究的方法，其标记数量远远少于同质人群所需的标记。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很大的进展，我们希望继续扩大其适用范围。 (2)我们的第二个目标将解决这样的问题，即具有数十万个SNP的全基因组关联扫描在研究少数群体（如非洲裔或西班牙裔美国人）的能力方面将受到严重损害，除非开发出在推断个体在基因组中的每个点的祖先状态之后搜索关联的方法。PSSA的一个关键目标是建立允许在少数群体中进行全基因组关联扫描的方法。 (3)我们的第三个目标将是提供一种新的方法来纠正全基因组关联扫描中的群体分层。人群分层是指病例组和对照组之间的系统性祖先差异，这可能导致等位基因频率差异和假阳性关联。 在以前工作的基础上，我们引入了新的方法来测量和校正分层。 我们相信我们的新技术将提供接近最佳的功率，并且将具有计算效率。 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.