Statistics and Algorithms in Evolution

聚结理论是数学进化理论的一个分支，最早开始于80年代初。与以前的方法相比，它的方法提供了一种计算效率高的遗传样本统计分析，例如，Wright-Fisher模型。由于HapMap计划正在绘制人类单体型图，它对理解全基因组关联研究具有意义，从而确定导致人类疾病的单核苷酸多态性（SNP）。最近在结合理论研究的活跃领域包括人类重组率的测定，特别是所谓的重组热点的预测。重组热点的定位是解释连锁不平衡的关键，它影响着全基因组关联研究数据的解释。 我们正在研究聚结理论，并开发一个可扩展的计算机模拟聚结的框架。 Tewari博士正在学习教科书中的聚结理论的基础数学，并吸收相关文献。 他目前正在编写一套详细记录的Java程序，目的是对合并过程以及重组等相关过程进行面向对象的建模。

Coalescent theory is a branch of mathematical evolutionary theory that began as recently as the early 80's. Its methods provide a computationally efficient statistical analysis of genetic samples compared to previous methods, e.g., simulation of the Wright-Fisher model. Because of the HapMap Project, which is elaborating a haplotype map of human beings, it has implications for the understanding of genome-wide associations studies, and thereby the determination of single nucleotide polymorphisms (SNPs) that contribute to human disease. Active areas of recent research in coalescent theory include the determination of human recombination rates, and in particular the prediction of so-called recombination hot-spots. The location of recombination hot-spots is critical to interpreting linkage disequilibrium, which influences the interpretation of data from genome-wide association studies. We are studying coalescent theory and developing an extensible framework for simulating coalescence by computer. Dr. Tewari is studying the basic mathematics of coalescent theory from textbooks and absorbing the relevant literature. He is presently writing a suite of carefully documented Java programs, with the aim of object-oriented modeling of the coalescent process, and related processes like recombination.