Population structure in whole-genome disease scans

全基因组疾病扫描中的人群结构

• 批准号: 7104738
• 负责人: David E Reich
• 金额: $ 33.9万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-19 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7104738
• 关键词: clinical research; genome

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.

描述（由申请人提供）：全基因组关联图谱及其检测导致常见疾病的遗传变异的所有理论能力终于变得实用。大多数分析这些研究数据的方法都设想在欧洲裔美国人等相对同质的人群中扫描数十万个 SNP。然而，也需要考虑人群之间存在的差异。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历史，这将导致“人群分层”，或者人群最近可能“混合”，就像非洲裔美国人和西班牙裔美国人的情况一样。我们建议开发人口子结构分析（PSSA）工具和方法来处理疾病绘图场景中的这些问题。 (1) 我们的首要目标是改进我们已经发布的用于混合物映射的方法和软件 (ANCESTRYMAP)。混合作图是一种在最近混合血统的人群（例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，其标记比同质人群所需的标记要少得多。在过去的两年中，在将混合映射转化为实用方法方面取得了巨大进步，我们期望继续扩展其适用性。 (2) 我们的第二个目标将解决这样的问题：除非开发出在推断后寻找关联的方法，否则具有数十万个 SNP 的全基因组关联扫描将严重损害其研究非洲裔或西班牙裔美国人等少数群体的能力。个体在基因组中每个点的祖先状态。 PSSA 的一个主要目标是建立能够在少数群体中进行全基因组关联扫描的方法。 (3)我们的第三个目标是提供一种新的方法来校正全基因组关联扫描中的群体分层。群体分层是指病例和对照之间血统的系统差异，这可能导致等位基因频率差异和假阳性关联。 在之前工作的基础上，我们引入了新的方法来测量和纠正分层。 我们相信我们的新技术将提供接近最佳的性能，并且计算效率高。 我们打算向科学界公开提供所有这些工具。