Generalizing Haplotype Models for Phylogenetics

该项目将开发一种新的计算方法的组合，以改进从个体之间遗传变异性的数据推断人类基因组历史。最近收集了大量关于区分一个人和另一个人的微小基因差异的数据。这些信息对于推断基因组在人类历史进程中发生了怎样的变化，以及理解随着时间的推移塑造它的特定生物过程是很有价值的。这些基本的科学结果反过来又与许多实际问题有关，这些问题涉及识别基因功能、促进未来的基因研究以及发现和减轻疾病的风险因素。然而，要优化利用这些信息，需要在遗传变异如何产生的数学模型以及使用这些模型分析真实序列数据所需的计算机算法方面取得新的进展。这项拟议的工作基于一项战略，通过将单倍型结构识别的理论工具与系统发育学工具结合起来，克服数据和当前计算能力的限制。单倍型结构识别工具找到在人类历史的很长一段时间内基本上保存下来的DNA片段，而系统发育学工具则推断不同的个体或群体如何相互关联。执行这一战略将涉及多学科的努力，将人口遗传学、计算机科学、数学和运筹学的技术结合在一起。除了直接的科学成果外，该项目还将通过创建新的课程材料和学生研究项目来开发人力资源，以帮助未来的科学家为在这些领域的交叉领域工作做准备。

This project will develop a combination of novel computational methods to improve inferences of human genomic history from data on genetic variability between individuals. Large amounts of data have recently been gathered on the small genetic differences that distinguish one person from another. Such information is valuable for inferring how the genome has changed over the course of human history and for understanding the specific biological processes that have shaped it over time. These basic scientific results in turn have relevance to many practical problems in identifying the functions of genes, facilitating future genetic studies, and finding and mitigating risk factors for disease. Making optimal use of this information will, however, require new advances in mathematical models of how genetic variations are produced and in the computer algorithms needed to use those models to analyze real sequence data. The proposed work is based on a strategy to overcome limitations of the data and current computational capabilities by combining theoretical tools for haplotype structure identification, which finds segments of DNA that have been largely conserved over long periods of human history, with tools for phylogenetics, which infers how different individuals or populations are related to one another. Executing this strategy will involve a multidisciplinary effort, bringing together techniques from population genetics, computer science, mathematics, and operations research. In addition to its direct scientific outputs, the project will develop human resources by creating new course material and student research projects to help prepare future scientists to work at the intersection of these fields.