Design and Analysis of 2 Stage GWAS Study Using Next Generation Sequence Technolo

使用下一代测序技术的 2 阶段 GWAS 研究的设计和分析

• 批准号: 8006910
• 负责人: Paul Marjoram
• 金额: $ 22.52万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-28 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8006910
• 关键词: Address; Biology; Data Set; Disease; Future; Genetic; Genetic Variation; Genomics; Guidelines; Individual; Pattern; Performance; Phenotype; Sampling; Staging; Technology; Testing; Translating; Variant; design; genome wide association study; next generation

DESCRIPTION (provided by applicant): One of the most important challenges facing biology today is to understand how genetic variation between individuals translates into variation in phenotype, such as disease status. While the recent wave of genome-wide association studies has led to the discovery of a number of variants associated with disease, the vast majority of that variation remains unexplained. This project proposes to develop guidelines for the next step, the sequencing of samples in the associated regions to determine their complete patterns of genetic variation and thereby identify smaller sets of strongly associated variants that are associated with disease. This will be undertaken by comparing a variety of design and analysis approaches on a range of test datasets, thereby assessing the performance and utility of those approaches for future studies. RELEVANCE: The genomic era carries the promise of allowing us to uncover the genetic courses of disease. While some progress has been made to date, a large number of the genetic causes of disease are yet to be determined. The next wave of studies to address this issue will use next-generation sequencing technologies. The purpose of our proposal is to provide guidelines for the design and analysis of these studies. Such guidelines will leverage the newer of those studies.

描述（由申请人提供）：当今生物学面临的最重要挑战之一是了解个体之间的遗传变异如何转化为表型的变异，例如疾病状态。虽然最近一波全基因组关联研究发现了许多与疾病相关的变异，但绝大多数变异仍然无法解释。该项目建议为下一步制定指南，即对相关区域的样本进行测序，以确定其遗传变异的完整模式，从而识别与疾病相关的较小的强相关变异集。这将通过在一系列测试数据集上比较各种设计和分析方法来进行，从而评估这些方法对未来研究的性能和实用性。 相关性：基因组时代有望让我们揭示疾病的遗传过程。尽管迄今为止已经取得了一些进展，但许多疾病的遗传原因仍有待确定。 解决这一问题的下一波研究将使用下一代测序技术。我们提案的目的是为这些研究的设计和分析提供指导。此类指南将利用这些最新研究。