Haplotype-Based Association Modeling for Whole-Genome Scan and Candidate Gene Studies

用于全基因组扫描和候选基因研究的基于单倍型的关联建模

• 批准号: 0504726
• 负责人: Jung-Ying Tzeng
• 金额: $ 7.5万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-15 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0504726&HistoricalAwards=false
• 关键词: Haplotype; Based; Association; Modeling; Whole

AbstractPrincipal Investigators: Tzeng, Jung-Ying Proposal Number: DMS-0504726 Institution: North Carolina State University Proposal Title: Haplotype-based Association Modeling for Whole-Genome Scan and Candidate Gene Studies Identifying genes responsible for human diseases can illuminate significant insight to the detection, treatment and prevention of these diseases. In the search for genes underlying human complex diseases, haplotype-based association analysis has been recognized as a tool with high resolution and, more importantly, potentially great power for identifying modest etiological effects of genes. However, in practice, its efficacy has not been as successful as expected in theory; one primary cause is that such analysis requires a large number of parameters in order to capture the abundant haplotype varieties. While high degrees of freedom can hinder the power of identifying modest genetic effects on complex diseases, the need to incorporate covariates of other risk factors further worsens the degrees-of-freedom problem in mapping genes for complex diseases. To tackle this issue, the proposed work constructs an efficient and powerful model-based framework for association analysis at haplotype level. The central focus of the methods development is on the efficient use of haplotype information in a model-based framework, and different strategies of reducing haplotype complexity are considered at different research stages to optimize efficiency. For screening-stage analyses, the PI constructs approaches based on haplotype similarity of pair-wise comparison in a regression platform to detect chromosomal regions that are likely to harbor disease genes. For refinement-stage analyses, the PI develops evolutionary-based methods of haplotype grouping to identify specific disease-associated haplotypes, and integrate new dimension-reduction techniques into existing regression methods.The proposed work aims not only to provide novel association tools in complex gene mapping, but also develop a routine method for case-control studies and offer a methodological foundation for future advancement. With the availability of a better statistical tool, scientists can advance their understanding of complex diseases, and design better diagnostic and therapeutic strategies that improve human health.

摘要主要研究者： 曾荣英提案编号：DMS-0504726机构：北卡罗来纳州州立大学提案题目：基于单体型的关联建模用于全基因组扫描和候选基因研究 确定人类疾病的基因可以为这些疾病的检测、治疗和预防提供重要的见解。 在寻找人类复杂疾病的基因基础上，基于单体型的关联分析已被公认为具有高分辨率的工具，更重要的是，潜在的巨大力量，用于识别基因的适度病因学影响。 然而，在实践中，它的功效并没有像理论上预期的那样成功;一个主要原因是这种分析需要大量的参数以捕获丰富的单倍型品种。虽然高自由度可能会阻碍识别复杂疾病的适度遗传效应的能力，但需要纳入其他风险因素的协变量，进一步解决了复杂疾病基因定位中的自由度问题。为了解决这个问题，建议的工作构建了一个高效和强大的基于模型的框架，在单倍型水平的关联分析。方法开发的中心焦点是在基于模型的框架中有效利用单体型信息，并在不同的研究阶段考虑降低单体型复杂性的不同策略以优化效率。 对于筛选阶段分析，PI构建方法基于回归平台中成对比较的单倍型相似性，以检测可能含有疾病基因的染色体区域。 对于精细化阶段的分析，PI开发了基于进化的单倍型分组方法，以确定特定的疾病相关的单倍型，并将新的降维技术集成到现有的回归方法中。拟议的工作不仅旨在为复杂的基因定位提供新的关联工具，而且还为病例对照研究开发了一种常规方法，并为未来的发展提供了方法学基础。有了更好的统计工具，科学家们就可以加深对复杂疾病的理解，并设计出更好的诊断和治疗策略来改善人类健康。