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<p>This study introduces population history learning by averaging sampled histories (PHLASH), a new method for inferring population history from whole-genome sequence data. It works by drawing random, low-dimensional projections of the coalescent intensity function from the posterior distribution of a pairwise sequentially Markovian coalescent-like model and averaging them together to form an accurate and adaptive estimator. On simulated data, PHLASH tends to be faster and have lower error than several competing methods, including SMC++, MSMC2 and FITCOAL. Moreover, it provides automatic uncertainty quantification and leads to new Bayesian testing procedures for detecting population structure and ancient bottlenecks. The key technical advance is a new algorithm for computing the score function (gradient of the log likelihood) of a coalescent hidden Markov model, which has the same computational cost as evaluating the log likelihood. PHLASH has been released as an easy-to-use Python software package and leverages graphics processing unit acceleration when available.</p>

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Accelerated Bayesian inference of population size history from recombining sequence data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月15日

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  • Jonathan Terhorst

摘要

This study introduces population history learning by averaging sampled histories (PHLASH), a new method for inferring population history from whole-genome sequence data. It works by drawing random, low-dimensional projections of the coalescent intensity function from the posterior distribution of a pairwise sequentially Markovian coalescent-like model and averaging them together to form an accurate and adaptive estimator. On simulated data, PHLASH tends to be faster and have lower error than several competing methods, including SMC++, MSMC2 and FITCOAL. Moreover, it provides automatic uncertainty quantification and leads to new Bayesian testing procedures for detecting population structure and ancient bottlenecks. The key technical advance is a new algorithm for computing the score function (gradient of the log likelihood) of a coalescent hidden Markov model, which has the same computational cost as evaluating the log likelihood. PHLASH has been released as an easy-to-use Python software package and leverages graphics processing unit acceleration when available.