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arXiv 0901.2867q-bio.PE
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面向具有人口统计结构种群模型的序贯马尔可夫溯祖算法

Sequential Markov coalescent algorithms for population models with demographic structure

A. Eriksson, B. Mahjani, B. Mehlig

AI总结:

本文在瓶颈、分化和两岛屿迁移模型中评估序贯马尔可夫溯祖算法,发现其通常近似良好,但在低基因流分隔种群抽样时会显著低估基因历史相关性。

摘要:

我们分析了具有人口统计结构种群的序贯马尔可夫溯祖算法,分别针对瓶颈模型、种群分化模型以及带迁移的两岛屿模型展开研究。序贯马尔可夫溯祖方法是由 McVean 和 Cardin,以及 Marjoram 和 Wall 提出的一种溯祖过程近似方法。在该算法框架内,对于从种群中随机抽样的两个个体,我们计算了其最近共同祖先时间之间的相关性,以及两个不同基因座对应的连锁概率,这两个基因座之间的重组率为 R。我们发现,在具有人口统计结构的模型中,序贯马尔可夫溯祖方法总体上能很好地近似溯祖过程。一个例外情形是个体从因基因流减弱而分隔的种群中抽样得到;在这种情况下,基因历史相关性可能被显著低估。我们解释了出现该现象的原因。

英文摘要:

We analyse sequential Markov coalescent algorithms for populations with demographic structure: for a bottleneck model, a population-divergence model, and for a two-island model with migration. The sequential Markov coalescent method is an approximation to the coalescent suggested by McVean and Cardin, and Marjoram and Wall. Within this algorithm we compute, for two individuals randomly sampled from the population, the correlation between times to the most recent common ancestor and the linkage probability corresponding to two different loci with recombination rate R between them. We find that the sequential Markov coalescent method approximates the coalescent well in general in models with demographic structure. An exception is the case where individuals are sampled from populations separated by reduced gene flow. In this situation, the gene-history correlations may be significantly underestimated. We explain why this is the case.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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