Collaborative Research: SEIII: Estimating Haplotype Frequencies

复杂疾病的病因涉及多基因和环境因素。由于每个单独的基因座只是整个画面的一小部分，基于一个或几个基因座上的变异与疾病结果相关的关联研究可能会错过重大的更大规模的关联。一种可能更具启发性的有吸引力的替代方案是将疾病结果与选定基因组区域的单倍型之间的相关性建立在关联研究的基础上，无论是基于几个基因座还是基于更大范围的单倍型，关联研究的先决条件是在特定人群中估计单倍型频率的准确方法。健康人群的单倍型频率和受影响个体的单倍型频率之间的差异可能是微妙的。因此，获得单倍型频率的准确估计对于疾病关联研究是极其重要的。估计单倍型频率不是一项简单的任务，因为目前的测序方法可能会产生噪声或不完整的数据，并且通常会产生基因类型，其对单倍型的分辨是模糊的。现有的单倍型频率估计方法主要是启发式的，它们只适用于短基因组区域内同质群体中无关个体的大样本。任何偏离这些条件的情况都可能导致不准确的估计。该项目的主要目标是开发在不同条件下有效和准确的单倍型频率估计工具，并将这些方法与疾病关联研究的新工具相结合。特别是，提出了以下活动：开发在短基因组和长基因组区域进行单倍型频率估计的准确、高效和稳健的方法；扩展这些方法以处理小样本量和由于种群亚结构导致的偏离Hardy-Weinberg平衡的情况，并将家系信息纳入单倍型频率估计器；将所产生的工具与用于疾病关联研究的系统化工具相结合，该系统工具使用对单倍型频率估计器的多次调用来自动寻找候选基因；并启动一个网络服务器，允许遗传学家上传他们的数据，并通过网络服务器实时运行项目中开发的程序。该项目的直接影响将是减少关联研究所需的样本量，从而在相同的预算限制下进行更多研究。这反过来将导致对复杂疾病的更好理解，这可能会加快诊断和治疗工具的搜索。该项目中介绍的数学模型可能有助于揭示单倍型结构和进化。此外，该项目将解决优化问题和统计学习问题，这些问题可能超出遗传学的范围。该项目的不同任务包括算法设计和实现、软件集成和生物建模。因此，有广泛的活动，适合所有级别的学生。这将使学生们有机会接触到涉及计算机科学、统计学、遗传学和数学的多学科研究。在这个项目中开发的方法将被整合到加州大学圣迭戈分校的生物信息学课程中，材料将以PowerPoint演示文稿的形式在网上公开提供。在这个项目中开发的软件将与现有的公开可用的网络服务器HAP集成。

The etiology of complex diseases involves multiple genes and environmental factors. Since each individual gene locus is only a small part of the whole picture, association studies based on correlating variation at one or a few gene loci to disease outcomes may miss significant larger-scale associations. An attractive alternative that may be more revealing is to base association studies on correlations between disease outcomesand haplotypes across selected genomic regions, A prerequisite for association studies, whether they are based on a few loci or on larger-scale haplotypes, is an accurate method for haplotype frequency estimation in a given population. The differences between the haplotype frequencies in a healthy population and in a population ofaffected individuals may be subtle. Thus, getting an accurate estimate for the haplotype frequencies is extremely important for disease association studies. Estimating haplotype frequencies is a non-trivial task because current sequencing methods may produce noisy or incomplete data and typically yield genotypes, whose resolution into pairs of haplotypes is ambiguous. Existing methods for haplotype frequency estimation are mainly heuristic in nature, and they are only suitable for large samples of unrelated individuals from a homogenous population over short genomic regions. Any deviation from these conditions may result in inaccurate estimates. The main goal of this project is to develop efficient and accurate tools for haplotype frequency estimation under different conditions, and to integrate these methods with novel tools for disease association studies. In particular, the following activities are proposed: develop accurate, efficient and robust methods for haplotype frequency estimation over short and long genomic regions; extend these methods to deal with small sample size and deviations from Hardy-Weinberg equilibrium due to population substructure, and incorporate pedigreeinformation into the haplotype frequency estimator; integrate the resulting tools with a systematic tool for disease association studies that looks for candidate loci automatically using multiple calls to the haplotype frequency estimator; and launch a web server that will allow geneticists to upload their data and run the programs developed in the project on the fly through the web server.The direct effect of the project would be to reduce the sample size needed for association studies, thus making more studies possible under the same budget constraints. This in turn will lead to a better understanding of complex diseases, which may speed up the search for diagnosis and treatment tools. The mathematical models introduced in this project may shed light on haplotype structure and on evolution. Furthermore, the project will address optimization problems and statistical learningproblems that may be of use beyond the scope of genetics. The diverse tasks of this project include algorithm design and implementation, software integration and biological modeling. Thus, there is a wide range of activities that are suitable for students of all levels. This will give students an exciting exposure to multidisciplinary research involving computer science, statistics, genetics and mathematics. The methods developed in this project will be integrated in bioinformatics courses at UCSD, andthe material will be publicly available as PowerPoint presentations on the web.The software developed in this project will be integrated with the existing publicly available web server HAP.