论文标题
迈向种群中基因组的更现实的模型：马尔可夫调制了马尔可夫合并
Towards more realistic models of genomes in populations: the Markov-modulated sequentially Markov coalescent
论文作者
论文摘要
联合理论的发展为人口遗传数据的统计推断铺平了道路。然而，在基因组时代，由于基础祖先重组图的复杂性，合并模型受到限制。 Markov Colescent（SMC）是一种启发式，可以在合并框架下对完整基因组进行建模。尽管它从几乎一个二倍体基因组中赋予了人口详细的人口统计学历史的推论，但SMC的当前实施对沿基因组的合并过程的均匀性做出了不切实际的假设，而忽略了诸如重组率等参数的内在空间变异性。在这里，我回顾了SMC模型的历史发展，并讨论了参数异质性的证据。然后，我调查了处理这种异质性的方法，重点是最近开发的SMC扩展。
The development of coalescent theory paved the way to statistical inference from population genetic data. In the genomic era, however, coalescent models are limited due to the complexity of the underlying ancestral recombination graph. The sequentially Markov coalescent (SMC) is a heuristic that enables the modelling of complete genomes under the coalescent framework. While it empowers the inference of detailed demographic history of a population from as few as one diploid genome, current implementations of the SMC make unrealistic assumptions about the homogeneity of the coalescent process along the genome, ignoring the intrinsic spatial variability of parameters such as the recombination rate. Here, I review the historical developments of SMC models and discuss the evidence for parameter heterogeneity. I then survey approaches to handle this heterogeneity, focusing on a recently developed extension of the SMC.
