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Bayesian inference of sampled ancestor trees for epidemiology and fossil calibration.

Gavryushkina A, Welch D, Stadler T, Drummond AJ - PLoS Comput. Biol. (2014)

Bottom Line: We show that even if sampled ancestors are not of specific interest in an analysis, failing to account for them leads to significant bias in parameter estimates.We also apply the method to infer divergence times and diversification rates when fossils are included along with extant species samples, so that fossilisation events are modelled as a part of the tree branching process.Such modelling has many advantages as argued in the literature.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Auckland, Auckland, New Zealand; Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand.

ABSTRACT
Phylogenetic analyses which include fossils or molecular sequences that are sampled through time require models that allow one sample to be a direct ancestor of another sample. As previously available phylogenetic inference tools assume that all samples are tips, they do not allow for this possibility. We have developed and implemented a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to infer what we call sampled ancestor trees, that is, trees in which sampled individuals can be direct ancestors of other sampled individuals. We use a family of birth-death models where individuals may remain in the tree process after sampling, in particular we extend the birth-death skyline model [Stadler et al., 2013] to sampled ancestor trees. This method allows the detection of sampled ancestors as well as estimation of the probability that an individual will be removed from the process when it is sampled. We show that even if sampled ancestors are not of specific interest in an analysis, failing to account for them leads to significant bias in parameter estimates. We also show that sampled ancestor birth-death models where every sample comes from a different time point are non-identifiable and thus require one parameter to be known in order to infer other parameters. We apply our phylogenetic inference accounting for sampled ancestors to epidemiological data, where the possibility of sampled ancestors enables us to identify individuals that infected other individuals after being sampled and to infer fundamental epidemiological parameters. We also apply the method to infer divergence times and diversification rates when fossils are included along with extant species samples, so that fossilisation events are modelled as a part of the tree branching process. Such modelling has many advantages as argued in the literature. The sampler is available as an open-source BEAST2 package (https://github.com/CompEvol/sampled-ancestors).

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A tree sampled from the posterior of the HIV 1 dataset analysis.The tree exhibits three estimated sampled ancestors shown as red circles. The samples with positive posterior probabilities of being sampled ancestors are shown in colour (red for the nodes with evidence of being sampled ancestors and blue for other nodes with non-zero probabilities) with the posterior probabilities in round brackets.
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pcbi-1003919-g008: A tree sampled from the posterior of the HIV 1 dataset analysis.The tree exhibits three estimated sampled ancestors shown as red circles. The samples with positive posterior probabilities of being sampled ancestors are shown in colour (red for the nodes with evidence of being sampled ancestors and blue for other nodes with non-zero probabilities) with the posterior probabilities in round brackets.

Mentions: We chose a random tree among the trees in the posterior sample that have exactly these three nodes as sampled ancestors. The tree is shown in Figure 8. All three sampled ancestors are clustered within a clade of 16 (out of 62) samples, suggesting that this clade was more extensively sampled. The median of the posterior distribution of the number of sampled ancestors was 2 with 95% HPD interval . The removal probability was estimated to be 0.74 with 95% HPD interval , indicating a substantial reduction in the probability that infected patients remained able to cause further infections after they were diagnosed.


Bayesian inference of sampled ancestor trees for epidemiology and fossil calibration.

Gavryushkina A, Welch D, Stadler T, Drummond AJ - PLoS Comput. Biol. (2014)

A tree sampled from the posterior of the HIV 1 dataset analysis.The tree exhibits three estimated sampled ancestors shown as red circles. The samples with positive posterior probabilities of being sampled ancestors are shown in colour (red for the nodes with evidence of being sampled ancestors and blue for other nodes with non-zero probabilities) with the posterior probabilities in round brackets.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4263412&req=5

pcbi-1003919-g008: A tree sampled from the posterior of the HIV 1 dataset analysis.The tree exhibits three estimated sampled ancestors shown as red circles. The samples with positive posterior probabilities of being sampled ancestors are shown in colour (red for the nodes with evidence of being sampled ancestors and blue for other nodes with non-zero probabilities) with the posterior probabilities in round brackets.
Mentions: We chose a random tree among the trees in the posterior sample that have exactly these three nodes as sampled ancestors. The tree is shown in Figure 8. All three sampled ancestors are clustered within a clade of 16 (out of 62) samples, suggesting that this clade was more extensively sampled. The median of the posterior distribution of the number of sampled ancestors was 2 with 95% HPD interval . The removal probability was estimated to be 0.74 with 95% HPD interval , indicating a substantial reduction in the probability that infected patients remained able to cause further infections after they were diagnosed.

Bottom Line: We show that even if sampled ancestors are not of specific interest in an analysis, failing to account for them leads to significant bias in parameter estimates.We also apply the method to infer divergence times and diversification rates when fossils are included along with extant species samples, so that fossilisation events are modelled as a part of the tree branching process.Such modelling has many advantages as argued in the literature.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Auckland, Auckland, New Zealand; Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand.

ABSTRACT
Phylogenetic analyses which include fossils or molecular sequences that are sampled through time require models that allow one sample to be a direct ancestor of another sample. As previously available phylogenetic inference tools assume that all samples are tips, they do not allow for this possibility. We have developed and implemented a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to infer what we call sampled ancestor trees, that is, trees in which sampled individuals can be direct ancestors of other sampled individuals. We use a family of birth-death models where individuals may remain in the tree process after sampling, in particular we extend the birth-death skyline model [Stadler et al., 2013] to sampled ancestor trees. This method allows the detection of sampled ancestors as well as estimation of the probability that an individual will be removed from the process when it is sampled. We show that even if sampled ancestors are not of specific interest in an analysis, failing to account for them leads to significant bias in parameter estimates. We also show that sampled ancestor birth-death models where every sample comes from a different time point are non-identifiable and thus require one parameter to be known in order to infer other parameters. We apply our phylogenetic inference accounting for sampled ancestors to epidemiological data, where the possibility of sampled ancestors enables us to identify individuals that infected other individuals after being sampled and to infer fundamental epidemiological parameters. We also apply the method to infer divergence times and diversification rates when fossils are included along with extant species samples, so that fossilisation events are modelled as a part of the tree branching process. Such modelling has many advantages as argued in the literature. The sampler is available as an open-source BEAST2 package (https://github.com/CompEvol/sampled-ancestors).

Show MeSH