Horticultural Crops Research Laboratory, USDA Agricultural Research Service, Corvallis, Oregon 97330, USA.
Annu Rev Phytopathol. 2011;49:249-67. doi: 10.1146/annurev-phyto-072910-095246.
Given human population growth and accelerated global trade, the rate of emergence of exotic plant pathogens is bound to increase. Understanding the processes that lead to the emergence of new pathogens can help manage emerging epidemics. Novel tools for analyzing population genetic variation can be used to infer the evolutionary history of populations or species, allowing for the unprecedented reconstruction of the demographic history of pathogens. Specifically, recent advances in the application of coalescent, maximum likelihood (ML), and Bayesian methods to population genetic data combined with increasing availability of affordable sequencing and parallel computing have created the opportunity to apply these methods to a broad range of questions regarding the evolution of emerging pathogens. These approaches are particularly powerful when used to test multiple competing hypotheses. We provide several examples illustrating how coalescent analysis provides critical insights into understanding migration pathways as well as processes of divergence, speciation, and recombination.
鉴于人口增长和全球贸易加速，外来植物病原体的出现率势必会增加。了解导致新病原体出现的过程有助于管理新出现的传染病。用于分析种群遗传变异的新工具可用于推断种群或物种的进化历史，从而能够以前所未有的方式重建病原体的人口历史。具体而言，最近在将合并、最大似然 (ML) 和贝叶斯方法应用于种群遗传数据方面取得的进展，加上测序和并行计算成本的降低，为应用这些方法解决有关新兴病原体进化的广泛问题创造了机会。当用于测试多个相互竞争的假设时，这些方法特别有效。我们提供了几个示例，说明了合并分析如何为理解迁移途径以及分化、物种形成和重组过程提供关键见解。
