随机谱系的有效重构：从理论到实践的一些步骤
Efficient Reconstruction of Stochastic Pedigrees: Some Steps From Theory to Practice
摘要：
在一个现存的种群中，现存的个体提供了多少关于其祖先谱系的信息？Kim，Mossel，Ramnarayan和Turner（2020）最近的工作在一些简化的假设下研究了这个问题，包括随机交配，固定长度的遗传块和足够大的创始人口。他们表明，在这些条件下，如果后代的平均数量是一个足够大的常数，那么它是可能的，以恢复大部分的系谱结构和遗传内容的算法，他们命名为REC-GEN. We有兴趣研究的性能REC-GEN根据模型生成的模拟数据。作为第一步，我们改进了算法的运行时间。然而，我们观察到，即使是更快的版本的算法没有做好在任何模拟恢复超过2代的谱系。我们声称，这是由于近亲繁殖存在于任何设置中的算法可以运行，即使是模拟数据。为了支持这一说法，我们表明，算法的一个主要步骤，称为祖先重建，准确地执行在一个理想化的设置，没有近亲繁殖，但在随机交配种群表现不佳。为了克服REC-GEN的不良行为，我们引入了一个基于置信传播的启发式算法，该算法考虑了近亲繁殖，并且在我们的模拟中表现得更好。
英文摘要：
In an extant population, how much information do extant individuals provide on the pedigree of their ancestors? Recent work by Kim, Mossel, Ramnarayan and Turner (2020) studied this question under a number of simplifying assumptions, including random mating, fixed length inheritance blocks and sufficiently large founding population. They showed that under these conditions if the average number of offspring is a sufficiently large constant, then it is possible to recover a large fraction of the pedigree structure and genetic content by an algorithm they named REC-GEN. We are interested in studying the performance of REC-GEN on simulated data generated according to the model. As a first step, we improve the running time of the algorithm. However, we observe that even the faster version of the algorithm does not do well in any simulations in recovering the pedigree beyond 2 generations. We claim that this is due to the inbreeding present in any setting where the algorithm can be run, even on simulated data. To support the claim we show that a main step of the algorithm, called ancestral reconstruction, performs accurately in a idealized setting with no inbreeding but performs poorly in random mating populations. To overcome the poor behavior of REC-GEN we introduce a Belief-Propagation based heuristic that accounts for the inbreeding and performs much better in our simulations.
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。
