Estimating Mutation Parameters, Population History and Genealogy Simultaneously From Temporally Spaced Sequence Data
Abstract
Molecular sequences obtained at different sampling times from populations of rapidly evolving pathogens and from ancient subfossil and fossil sources are increasingly available with modern sequencing technology. Here, we present a Bayesian statistical inference approach to the joint estimation of mutation rate and population size that incorporates the uncertainty in the genealogy of such temporally spaced sequences by using Markov chain Monte Carlo (MCMC) integration. The Kingman coalescent model is used to describe the time structure of the ancestral tree. We recover information about the unknown true ancestral coalescent tree, population size, and the overall mutation rate from temporally spaced data, that is, from nucleotide sequences gathered at different times, from different individuals, in an evolving haploid population. We briefly discuss the methodological implications and show what can be inferred, in various practically relevant states of prior knowledge. We develop extensions for exponentially growing population size and joint estimation of substitution model parameters. We illustrate some of the important features of this approach on a genealogy of HIV-1 envelope (env) partial sequences.
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A unique rate parameter cannot be assigned to each branch when estimating absolute rates or divergence dates because rate and time are inextricably linked, unless the sequences are noncontemporaneous and have known ages.
“Unfortunately, such an approach cannot be taken when estimating absolute rates or divergence dates because rate and time are inextricably linked, unless the sequences are noncontemporaneous and have known ages (Rambaut 2000;Drummond et al. 2002).”
From Accuracy of Rate Estimation Using Relaxed-Clock Models with a Critical Focus on the Early Metazoan Radiation (2005) · cites this paper as Drummond et al. 2002
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Relative Time Constraints Improve Molecular Dating
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- Authors
- Drummond, A. J., Nicholls, G. K., Rodrigo, A. G., & Solomon, W.
- Affiliations
- University of Auckland (NZ)
- Department of Statistics, University of Auckland 1001, Auckland, New Zealand
- Corresponding author
- Drummond, Alexei J
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2002-07-01
- Volume
- 161
- Issue
- 3
- Pages
- 1307-1320
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- OpenAlex
- W1915699870
- Cited by
- 1,059
- References
- 4
- Citations by year
- 2026: 25 · 2025: 33 · 2024: 38 · 2023: 27 · 2022: 50 · 2021: 40 · 2020: 37 · 2019: 45 · 2018: 33 · 2017: 39 · 2016: 51 · 2015: 56 · 2014: 71 · 2013: 52 · 2012: 73
- FWCI
- 11.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Genetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01gm059174-03)
- National Institute of General Medical Sciences (GM59174)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- mutation rate estimation, population size estimation, Kingman coalescent, Bayesian inference, Markov chain Monte Carlo, ancient DNA, HIV-1 envelope, haploid populations
- MeSH terms
- Decision Trees, Genealogy and Heraldry, Genetics, Population, Markov Chains, Models, Genetic, Monte Carlo Method, Mutation, Time
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