直接从双等位基因遗传标记推断物种树：在完全合并分析中筛选基因树
Inferring Species Trees Directly from Biallelic Genetic Markers: Bypassing Gene Trees in a Full Coalescent Analysis
摘要：
多物种的结合提供了一个优雅的理论框架，估计物种树和物种的人口统计学遗传标记。然而，多物种合并模型的实际应用受到限制，需要对每个遗传标记可能的所有基因树进行整合或采样。在这里，我们描述了一个多项式时间算法，计算一个物种树的可能性直接从标记下的有限位点突变模型，有效地整合所有可能的基因树。该方法适用于独立的（非连锁的）双等位基因标记，如间隔良好的单核苷酸多态性（SNP），我们已经实现了SNAPP，马尔可夫链蒙特卡洛采样器推断物种树，分歧日期，和人口规模。我们报告的结果，从模拟实验和1997年扩增片段长度多态性（AFLP）位点的分析，在69个个体从6个物种的{ \em Ourisia}（新西兰本地毛地黄）。
英文摘要：
The multi-species coalescent provides an elegant theoretical framework for estimating species trees and species demographics from genetic markers. Practical applications of the multi-species coalescent model are, however, limited by the need to integrate or sample over all gene trees possible for each genetic marker. Here we describe a polynomial-time algorithm that computes the likelihood of a species tree directly from the markers under a finite-sites model of mutation, effectively integrating over all possible gene trees. The method applies to independent (unlinked) biallelic markers such as well-spaced single nucleotide polymorphisms (SNPs), and we have implemented it in SNAPP, a Markov chain Monte-Carlo sampler for inferring species trees, divergence dates, and population sizes. We report results from simulation experiments and from an analysis of 1997 amplified fragment length polymorphism (AFLP) loci in 69 individuals sampled from six species of {\em Ourisia} (New Zealand native foxglove).
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