Limits...
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).

Show MeSH

Related in: MedlinePlus

ROC curve for identifying sampled ancestors based on simulated data (transmission process).The posterior distribution of trees obtained from a Bayesian MCMC analysis of simulated sequence data can be used to detect sampled ancestors. We identify a node as being a sampled ancestor if the posterior probability that the node is a sampled ancestor is greater than some threshold. The curve is parameterised by the threshold and shows the trade-off between true positive rate (sensitivity) and false positive rate (specificity) for different values of the threshold (any increase in sensitivity will be accompanied by a decrease in specificity). The dashed diagonal line corresponds to a ‘random guess’ test. The closer the ROC curve to the upper-left boarder of the ROC space (the whole area of the graph), the more accurate the test. The optimal value of the threshold for this curve is 0.45.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4263412&req=5

pcbi-1003919-g006: ROC curve for identifying sampled ancestors based on simulated data (transmission process).The posterior distribution of trees obtained from a Bayesian MCMC analysis of simulated sequence data can be used to detect sampled ancestors. We identify a node as being a sampled ancestor if the posterior probability that the node is a sampled ancestor is greater than some threshold. The curve is parameterised by the threshold and shows the trade-off between true positive rate (sensitivity) and false positive rate (specificity) for different values of the threshold (any increase in sensitivity will be accompanied by a decrease in specificity). The dashed diagonal line corresponds to a ‘random guess’ test. The closer the ROC curve to the upper-left boarder of the ROC space (the whole area of the graph), the more accurate the test. The optimal value of the threshold for this curve is 0.45.

Mentions: We used the data simulated from the transmission process to perform the receiver operating characteristic (ROC) analysis of the sampled ancestor predictor, which makes a prediction relying on the posterior distribution of genealogies. A node is predicted to be a sampled ancestor with a probability calculated as a fraction of trees in the posterior sample in which the node is a sampled ancestor. Out of the 5225 total sampled nodes in all simulated trees (excluding the last sample in each tree because this cannot be a sampled ancestor), 1814 were sampled ancestors. The ROC curve constructed from this data and predictions obtained from the MCMC runs is shown in Figure 6.


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

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

ROC curve for identifying sampled ancestors based on simulated data (transmission process).The posterior distribution of trees obtained from a Bayesian MCMC analysis of simulated sequence data can be used to detect sampled ancestors. We identify a node as being a sampled ancestor if the posterior probability that the node is a sampled ancestor is greater than some threshold. The curve is parameterised by the threshold and shows the trade-off between true positive rate (sensitivity) and false positive rate (specificity) for different values of the threshold (any increase in sensitivity will be accompanied by a decrease in specificity). The dashed diagonal line corresponds to a ‘random guess’ test. The closer the ROC curve to the upper-left boarder of the ROC space (the whole area of the graph), the more accurate the test. The optimal value of the threshold for this curve is 0.45.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003919-g006: ROC curve for identifying sampled ancestors based on simulated data (transmission process).The posterior distribution of trees obtained from a Bayesian MCMC analysis of simulated sequence data can be used to detect sampled ancestors. We identify a node as being a sampled ancestor if the posterior probability that the node is a sampled ancestor is greater than some threshold. The curve is parameterised by the threshold and shows the trade-off between true positive rate (sensitivity) and false positive rate (specificity) for different values of the threshold (any increase in sensitivity will be accompanied by a decrease in specificity). The dashed diagonal line corresponds to a ‘random guess’ test. The closer the ROC curve to the upper-left boarder of the ROC space (the whole area of the graph), the more accurate the test. The optimal value of the threshold for this curve is 0.45.
Mentions: We used the data simulated from the transmission process to perform the receiver operating characteristic (ROC) analysis of the sampled ancestor predictor, which makes a prediction relying on the posterior distribution of genealogies. A node is predicted to be a sampled ancestor with a probability calculated as a fraction of trees in the posterior sample in which the node is a sampled ancestor. Out of the 5225 total sampled nodes in all simulated trees (excluding the last sample in each tree because this cannot be a sampled ancestor), 1814 were sampled ancestors. The ROC curve constructed from this data and predictions obtained from the MCMC runs is shown in Figure 6.

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
Related in: MedlinePlus