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Additive Uncorrelated Relaxed Clock Models for the Dating of Genomic Epidemiology Phylogenies.

Authors
  • Didelot, Xavier1, 2
  • Siveroni, Igor3
  • Volz, Erik M3
  • 1 School of Life Sciences, University of Warwick, Coventry, United Kingdom. , (United Kingdom)
  • 2 Department of Statistics, University of Warwick, Coventry, United Kingdom. , (United Kingdom)
  • 3 Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom. , (United Kingdom)
Type
Published Article
Journal
Molecular Biology and Evolution
Publisher
Oxford University Press
Publication Date
Jan 04, 2021
Volume
38
Issue
1
Pages
307–317
Identifiers
DOI: 10.1093/molbev/msaa193
PMID: 32722797
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Phylogenetic dating is one of the most powerful and commonly used methods of drawing epidemiological interpretations from pathogen genomic data. Building such trees requires considering a molecular clock model which represents the rate at which substitutions accumulate on genomes. When the molecular clock rate is constant throughout the tree then the clock is said to be strict, but this is often not an acceptable assumption. Alternatively, relaxed clock models consider variations in the clock rate, often based on a distribution of rates for each branch. However, we show here that the distributions of rates across branches in commonly used relaxed clock models are incompatible with the biological expectation that the sum of the numbers of substitutions on two neighboring branches should be distributed as the substitution number on a single branch of equivalent length. We call this expectation the additivity property. We further show how assumptions of commonly used relaxed clock models can lead to estimates of evolutionary rates and dates with low precision and biased confidence intervals. We therefore propose a new additive relaxed clock model where the additivity property is satisfied. We illustrate the use of our new additive relaxed clock model on a range of simulated and real data sets, and we show that using this new model leads to more accurate estimates of mean evolutionary rates and ancestral dates. © The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

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