空间Lambda-Fleming-Viot模型与用于分析地理参照遗传数据的其他过程之间的联系
On the connections between the spatial Lambda-Fleming-Viot model and other processes for analysing geo-referenced genetic data
AI总结：
本文研究空间Lambda-Fleming-Viot模型与其他随机过程的联系，通过模拟证明其树生成过程可由出生-死亡模型近似，并指出布朗运动因栖息地边界效应而无法良好近似LV，同时提出了快速模拟算法。
摘要：
空间Lambda-Fleming-Viot模型（LV）在群体遗传学中的引入，主要得益于Alison Etheridge约十年前与Nick Barton和Amandine Véber合作的开创性工作（1,2）。LV模型为描述相关个体组成的群体沿空间连续体的演化提供了一个严谨的数学框架。它缓解了Wright和Malécot的距离隔离模型中的“环面之痛”问题，并且具有采样一致性，使其成为统计推断的首选工具。然而，关于LV与生成树及沿相应谱系空间坐标的其他随机过程之间的潜在联系，目前知之甚少。本文聚焦于LV的一个版本，其中谱系在无限小距离上以无限快的速度移动。通过模拟，我们表明所诱导的LV树生成过程可以被出生-死亡模型很好地近似。我们的结果还表明，用于建模谱系沿出生-死亡树运动的布朗运动通常不能很好地近似LV，这是因为栖息地边界效应在长期中发挥着越来越重要的作用。最后，我们描述了用于快速模拟LV模型时间向前和向后版本的高效算法。
英文摘要：
The introduction of the spatial Lambda-Fleming-Viot model (LV) in population genetics was mainly driven by the pioneering work of Alison Etheridge, in collaboration with Nick Barton and Amandine Véber about ten years ago (1,2). The LV model provides a sound mathematical framework for describing the evolution of a population of related individuals along a spatial continuum. It alleviates the "pain in the torus" issue with Wright and Malécot's isolation by distance model and is sampling consistent, making it a tool of choice for statistical inference. Yet, little is known about the potential connections between the LV and other stochastic processes generating trees and the spatial coordinates along the corresponding lineages. This work focuses on a version of the LV whereby lineages move infinitely rapidly over infinitely small distances. Using simulations, we show that the induced LV tree-generating process is well approximated by a birth-death model. Our results also indicate that Brownian motions modelling the movements of lineages along birth-death trees do not generally provide a good approximation of the LV due to habitat boundaries effects that play an increasingly important role in the long run. Finally, we describe efficient algorithms for fast simulation of the backward and forward in time versions of the LV model.
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。
