Manolopoulou Ioanna, Emerson Brent C
Department of Statistics, Duke University, Durham, NC 27708, USA.
J Comput Biol. 2012 Jun;19(6):745-55. doi: 10.1089/cmb.2012.0038.
Phylogeographic ancestral inference is issue frequently arising in population ecology that aims to understand the geographical roots and structure of species. Here, we specifically address relatively small scale mtDNA datasets (typically less than 500 sequences with fewer than 1000 nucleotides), focusing on ancestral location inference. Our approach uses a coalescent modelling framework projected onto haplotype trees in order to reduce computational complexity, at the same time adhering to complex evolutionary processes. Statistical innovations of the last few years have allowed for computationally feasible yet accurate inferences in phylogenetic frameworks. We implement our methods on a set of synthetic datasets and show how, despite high uncertainty in terms of identifying the root haplotype, estimation of the ancestral location naturally encompasses lower uncertainty, allowing us to pinpoint the Maximum A Posteriori estimates for ancestral locations. We exemplify our methods on a set of synthetic datasets and then combine our inference methods with the phylogeographic clustering approach presented in Manolopoulou et al. (2011) on a real dataset from weevils in the Iberian peninsula in order to infer ancestral locations as well as population substructure.
系统发育地理学的祖先推断是种群生态学中经常出现的问题，旨在了解物种的地理根源和结构。在这里，我们专门处理相对较小规模的线粒体DNA数据集（通常少于500个序列，核苷酸少于1000个），重点是祖先位置推断。我们的方法使用合并建模框架投射到单倍型树上，以降低计算复杂性，同时遵循复杂的进化过程。过去几年的统计创新使得在系统发育框架中进行计算上可行且准确的推断成为可能。我们在一组合成数据集上实现了我们的方法，并展示了如何尽管在识别根单倍型方面存在高度不确定性，但祖先位置的估计自然包含较低的不确定性，使我们能够确定祖先位置的最大后验估计。我们在一组合成数据集上举例说明了我们的方法，然后将我们的推断方法与马诺洛普洛等人（2011年）提出的系统发育地理聚类方法相结合，应用于伊比利亚半岛象鼻虫的真实数据集，以推断祖先位置以及种群亚结构。
