Paper List

Journal: ArXiv Preprint
Published: Unknown
BioinformaticsEvolutionary Biology

Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood

Department of Statistics, University of Georgia, Athens, 30601, USA

Greg M. Ellison, Liang Liu
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The 30-Second View

IN SHORT: This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimation for large phylogenomic datasets.

Innovation (TL;DR)

  • Methodology Introduces two Adjusted Pairwise Likelihood (APW) formulations (APW1 and APW2) that use asymptotic moment-matching weights to correct composite likelihoods within a Bayesian MCMC framework.
  • Methodology Demonstrates that APW methods reduce computational cost by more than an order of magnitude compared to full-likelihood methods while maintaining comparable accuracy in node-age estimation.
  • Methodology Shows that APW methods exhibit greater robustness to fossil misplacement and prior misspecification due to the reduced sensitivity of composite likelihoods to local calibration errors.

Key conclusions

  • APW methods produce node-age estimates statistically comparable to full-likelihood methods across diverse simulation scenarios, with reduced sensitivity to local calibration errors.
  • Applied to a genome-scale avian dataset, APW recovered divergence time patterns consistent with recent studies while achieving a >10x reduction in computational cost.
  • The robustness of APW to fossil misplacement stems from the composite likelihood's inherent property of being less sensitive to errors in individual calibration points, as demonstrated in simulations modeling various prior misspecifications.
Background and Gap： Current Bayesian relaxed-clock methods are computationally prohibitive for large genomic datasets and produce divergence-time estimates highly sensitive to uncertainties or errors in fossil placement and prior specification.

Abstract: Estimating divergence times from molecular sequence data is central to reconstructing the evolutionary history of lineages. Although Bayesian relaxed-clock methods provide a principled framework for incorporating fossil information, their dependence on repeated evaluations of the full phylogenetic likelihood makes them computationally demanding for large genomic datasets. Furthermore, because disagreements in divergence-time estimates often arise from uncertainty or error in fossil placement and prior specification, there is a need for methods that are both computationally efficient and robust to fossil-calibration uncertainty. In this study, we introduce fast and accurate alternatives based on the phylogenetic pairwise composite likelihood, presenting two adjusted pairwise likelihood (APW) formulations that employ asymptotic moment-matching weights to better approximate the behavior of the full likelihood within a Bayesian MCMC framework. Extensive simulations across diverse fossil-calibration scenarios show that APW methods produce node-age estimates comparable to those obtained from the full likelihood while offering greater robustness to fossil misplacement and prior misspecification, due to the reduced sensitivity of composite likelihoods to local calibration errors. Applied to a genome-scale dataset of modern birds, APW methods recover divergence time patterns consistent with recent studies, while reducing computational cost by more than an order of magnitude. Overall, our results demonstrate that adjusted pairwise likelihoods provide a calibration-robust and computationally efficient framework for Bayesian node dating, especially suited for large phylogenomic datasets and analyses in which fossil priors may be uncertain or imperfectly placed.