NSF Postdoctoral Research Fellowship in Biology for FY 2008

这一行动为NSF 2008财年博士后研究奖学金提供了资金。该奖学金支持亚当·D·利奇的一项研究和培训计划，题为“从基因树到人口统计学：一种近似的贝叶斯计算方法”。这项研究的主办机构是加州大学戴维斯分校和伦敦大学学院，赞助科学家分别是布鲁斯·兰纳拉博士和杨子恒博士。系统发育学正在进入一个新时代，在这个时代，研究人员将有机会将基因组规模的数据集应用于非模式生物的研究。需要新的计算方法和相应的软件工具来处理这些更大、更复杂的生物数据集。这项研究正在开发一个近似贝叶斯计算(ABC)统计框架，用于估计人口统计参数(祖先和当代种群大小、基因流动水平和物种/种群分化时间)，该框架基于基因树汇总统计。使用以基因树汇总统计为条件的ABC模拟来估计人口统计学参数是很有吸引力的，因为许多遗传研究产生了有助于基因树估计的数据，创造了丰富的生物信息学数据来源。培训目标包括生物信息学、计算生物学、理论种群遗传学和统计系统发育学方面的广泛培训。这种跨学科的生物信息学研究将在数学、统计学、计算生物学、理论种群遗传学和统计系统发育学领域之间建立联系，同时保持对进化生物学概念的中心关注。这种新的统计方法和配套的软件工具将适应生命树中许多嵌套层次的复杂生物学问题。

This action funds an NSF Postdoctoral Research Fellowship for FY 2008. The fellowship supports a research and training plan entitled "From Gene Trees to Population Demography: An Approximate Bayesian Computation Approach" for Adam D. Leache. The host institutions for this research are University of California, Davis, and University College London, and the sponsoring scientists are Dr. Bruce Rannala and Dr. Ziheng Yang, respectively. Phylogenetics is entering a new era in which researchers will have the opportunity to apply genomic-scale data sets to the study of non-model organisms. Novel computational methods and the accompanying software tools are required to handle these larger, more complex biological data sets. This research is developing an approximate Bayesian computation (ABC) statistical framework for estimating demographic parameters (ancestral and contemporary population sizes, levels of gene flow, and species/population divergence times) that is based on gene tree summary statistics. Estimating demographic parameters using ABC simulations conditional on gene tree summary statistics is appealing, because many genetic studies generate data conducive to gene tree estimation, creating a rich source of bioinformatics data. Training objectives include extensive training in bioinformatics, computational biology, theoretical population genetics, and statistical phylogenetics. This interdisciplinary bioinformatics research will build links between the fields of mathematics, statistics, computational biology, theoretical population genetics, and statistical phylogenetics, while at the same time maintaining a central focus on concepts in evolutionary biology. This new statistical approach and accompanying software tools will accommodate complex biological problems across many nested levels in the tree of life.