Generalizing Haplotype Models for Phylogenetics

该项目将开发一种新的计算方法的组合，以改善从个体之间的遗传变异数据推断人类基因组历史。 最近，人们收集了大量关于将一个人与另一个人区分开来的微小遗传差异的数据。 这些信息对于推断基因组在人类历史进程中如何变化以及理解随着时间的推移塑造基因组的特定生物过程非常有价值。 这些基本的科学成果反过来又与许多实际问题有关，如确定基因的功能，促进未来的遗传学研究，以及发现和减轻疾病的风险因素。 然而，要最佳利用这些信息，就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的序列数据所需的计算机算法方面取得新的进展。 拟议的工作是基于一种克服数据和当前计算能力局限性的策略，方法是将单倍型结构鉴定的理论工具与遗传学工具相结合，单倍型结构鉴定发现在人类历史的长时期内基本上保守的DNA片段，遗传学工具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，汇集了人口遗传学，计算机科学，数学和运筹学的技术。 除了直接的科学产出外，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在这些领域的交叉点上工作。

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.