Collaborative Research: SEIII: Estimating Haplotype Frequencies

复杂疾病的病因涉及多个基因和环境因素。由于每个单独的基因位点只是整个图景的一小部分，基于将一个或几个基因位点的变异与疾病结果相关联的关联研究可能会错过显著的大规模关联。一个有吸引力的替代方案，可能是更有启发性的基础上的关联研究疾病outcomesandhaplotypes之间的相关性在选定的基因组区域，关联研究的先决条件，无论是基于少数位点或大规模的单倍型，是一个准确的方法，在给定的人群中的单倍型频率估计。健康人群和患病人群中的单倍型频率之间的差异可能是微妙的。因此，准确估计单倍型频率对于疾病关联研究是极其重要的。估计单倍型频率是一项重要的任务，因为目前的测序方法可能产生噪声或不完整的数据，并且通常产生基因型，其对单倍型对的分辨率是模糊的。现有的单倍型频率估计方法本质上主要是启发式的，并且它们仅适用于来自短基因组区域的同质群体的大样本无关个体。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进行单倍型频率估计的有效和准确的工具，并将这些方法与疾病关联研究的新工具相结合。具体而言，提出了以下活动：发展准确、有效和稳健的方法，用于短和长基因组区域上的单倍型频率估计;将这些方法扩展到处理小样本大小和由于群体子结构而偏离Hardy-Weinberg平衡的情况，并将谱系信息纳入单倍型频率估计;将所得工具与用于疾病关联研究的系统工具整合，所述系统工具使用对单倍型频率估计器的多次调用自动寻找候选基因座;并启动一个网络服务器，允许遗传学家上传他们的数据，并通过网络服务器运行该项目开发的程序。该项目的直接影响将是减少关联研究所需的样本量，从而在同样的预算限制下可以进行更多的研究。这反过来将导致更好地了解复杂的疾病，这可能会加快寻找诊断和治疗工具。本计画中所介绍的数学模型，可能有助于了解单倍型的结构与演化。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，有一个广泛的活动，适合所有级别的学生。这将使学生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地亚哥分校的生物信息学课程中，并且材料将在网络上以PowerPoint演示的形式公开提供。在这个项目中开发的软件将与现有的公开提供的网络服务器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.