Avoidable false PSMC population size peaks occur across numerous studies

Leon Hilgers, Shenglin Liu, Axel Jensen, Thomas Brown, Trevor Cousins, Regev Schweiger, Katerina Guschanski, Michael Hiller
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Abstract

Inferring historical population sizes is key to identify drivers of ecological and evolutionary change, and crucial to predict the future of species on our rapidly changing planet. The pairwise sequentially Markovian coalescent (PSMC) method provided a revolutionary framework to reconstruct species demographic histories over millions of years based on the genome sequence of a single individual 1. Here, we detected and solved a common artifact in PSMC and related methods: recent population peaks followed by population collapses. Combining real and simulated genomes, we show that these peaks do not represent true population dynamics. Instead, ill-set default parameters cause false peaks in our own and published data, which can be avoided by adjusted parameter settings. Furthermore, we show that certain population structure changes can cause similar patterns. Newer methods like Beta-PSMC perform better, but do not always avoid this artifact. Our results suggest testing multiple parameters before interpreting recent population peaks followed by collapses, and call for the development of robust methods.
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许多研究都出现了可避免的 PSMC 群体规模假峰值
推断历史种群数量是确定生态和进化变化驱动因素的关键,也是预测我们这个瞬息万变星球上物种未来的关键。成对序列马尔可夫聚合(PSMC)方法提供了一个革命性的框架,可以根据单个个体的基因组序列重建数百万年的物种人口历史1。在这里,我们发现并解决了 PSMC 和相关方法中的一个常见现象:近期种群高峰后出现种群崩溃。结合真实基因组和模拟基因组,我们发现这些峰值并不代表真实的种群动态。相反,在我们自己的数据和已发表的数据中,设置不当的默认参数会导致错误的峰值,而通过调整参数设置就可以避免。此外,我们还发现某些种群结构变化也会导致类似的模式。Beta-PSMC 等较新的方法表现较好,但并不总能避免这种假象。我们的研究结果表明,在解释近期出现的种群峰值和随后的崩溃之前,应先测试多个参数,并呼吁开发稳健的方法。
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