This tutorial is modified from Taming the BEAST tutorial Skyline plots.

Population dynamics influence the shape of the tree and consequently, the shape of the tree contains some information about past population dynamics. The so-called Skyline methods allow to extract this information from phylogenetic trees in a non-parametric manner. It is non-parametric since there is no underlying system of differential equations governing the inference of these dynamics.

In this tutorial we will look at a popular coalescent method, the Coalescent Bayesian Skyline plot (Drummond, Rambaut, Shapiro, & Pybus, 2005), to infer these dynamics from sequence data.

The programs used in this tutorial are listed below.

## Background: Classic and Generalized Plots

(Drummond, Rambaut, Shapiro, & Pybus, 2005) explained these concepts in the figure below:

1. A genealogy of five individuals sampled contemporaneously (top) together with its associated classic (middle) and generalized (bottom) skyline plots.
2. A genealogy of five individuals sampled at three different times (top) along with its associated classic (middle) and generalized (bottom) skyline plots.

In the classic skyline plots, the changes in effective population size coincide with coalescent events, resulting in a stepwise function with n − 2 change points and n − 1 population sizes, where n is the number of sampled individuals. In the generalized skyline plot, changes in effective population size coincide with some, but not necessarily all, coalescent events. The resulting stepwise function has m − 1 change points (1 ≤ m ≤ n−1) and m effective population sizes.

## The NEXUS alignment

The data is in a file called hcv.nex. By clicking the name of the file, it will be opened on your web browser, after which you can download the data by right-clicking on the main window, “Save Page As”, and saving the file as hcv.nex in the desired folder.

The dataset consists of an alignment of 63 Hepatitis C sequences sampled in 1993 in Egypt (Ray, Arthur, Carella, Bukh, & Thomas, 2000). This dataset has been used previously to test the performance of skyline methods (Drummond, Rambaut, Shapiro, & Pybus, 2005, and Stadler, Kuhnert, Bonhoeffer, & Drummond, 2013).

With an estimated 15-25%, Egypt has the highest Hepatits C prevalence in the world. In the mid 20th century, the prevalence of Hepatitis C increased drastically (see Figure 2 for estimates). We will try to infer this increase from sequence data.

## Constructing the scripts in LPhy Studio

LPhy Studio implements a GUI for users to specify and visualize probabilistic graphical models, as well as for simulating data under those models. These tasks are executed according to an LPhy script the user types (or loads) on LPhy Studio’s interactive terminal. If you are new to LPhy, we recommend you to read this introduction first, before you continue on this tutorial.

Please note if you are working in LPhyStudio, you do not need to add data and model keywords and curly brackets to define the code blocks. We are supposed to add the lines without the data { } and model { } to the command line console at the bottom of the window, where the data and model tabs in the GUI are used to specify which block we are working on.

Below, we will build an LPhy script in two parts, the data and the model blocks.

## Code

data {
L = D.nchar();
numGroups = 4;
taxa = D.taxa();
w = taxa.length()-1;
}
model {
π ~ Dirichlet(conc=[3.0, 3.0, 3.0, 3.0]);
rates ~ Dirichlet(conc=[1.0, 2.0, 1.0, 1.0, 2.0, 1.0]);
Q = gtr(rates=rates, freq=π);
γ ~ LogNormal(meanlog=0.0, sdlog=2.0);
r ~ DiscretizeGamma(shape=γ, ncat=4, replicates=L);
A ~ RandomComposition(k=numGroups, n=w);
θ1 ~ LogNormal(meanlog=9.0, sdlog=2.0);
Θ ~ ExpMarkovChain(firstValue=θ1, n=numGroups);
ψ ~ SkylineCoalescent(groupSizes=A, taxa=taxa, theta=Θ);
D ~ PhyloCTMC(Q=Q, mu=7.9E-4, siteRates=r, tree=ψ);
}

## Graphical Model

### Data block

The data { ... } block is necessary when we use LPhy Studio to prepare instruction input files for inference software (e.g., BEAST 2, RevBayes, etc.). The purpose of this block is to tell LPhy which nodes of our graphical model are to be treated as known constants (and not to be sampled by the inference software) because they are observed data. Elsewhere, this procedure has been dubbed “clamping” (Höhna et al., 2016).

In this block, we will either type strings representing values to be directly assigned to scalar variables, or use LPhy’s syntax to extract such values from LPhy objects, which might be read from file paths given by the user.

(Note that keyword data cannot be used to name variables because it is reserved for defining scripting blocks as outlined above.)

In order to start specifying the data { ... } block, make sure you type into the “data” tab of the command prompt, by clicking “data” at the bottom of LPhy Studio’s window.

In the script, taxa and L respectively stores the taxa from the alignment D and the length of D. numGroups = 4 sets the number of grouped intervals in the generalized Coalescent Bayesian Skyline plots, and w defines n − 1 effective population sizes, which is the same number of times at which coalescent events occur.

### Model block

The model { ... } block is the main protagonist of our scripts. This is where you will specify the many nodes and sampling distributions that characterize a probabilistic model.

(Note that keyword model cannot be used to name variables because it is reserved for defining scripting blocks as outlined above.)

In order to start specifying the model { ... } block, make sure you type into the “model” tab of the command prompt, by clicking “model” at the bottom of LPhy Studio’s window.

In this analysis, we will use the GTR model, which is the most general reversible model and estimates transition probabilities between individual nucleotides separately. That means that the transition probabilities between e.g. A and T will be inferred separately to the ones between A and C, however transition probabilities from A to C will be the same as C to A etc. The nucleotide equilibrium state frequencies π are estimated here.

Additionally, we allow for rate heterogeneity among sites. We do this by approximating the continuous rate distribution (for each site in the alignment) with a discretized gamma probability distribution (mean = 1), where the number of bins in the discretization ncat = 4 (normally between 4 and 6). The shape parameter will be estimated in this analysis.

As explained in (Yang, 2006), the shape parameter α is inversely related to the extent of rate variation at sites. If α > 1, the distribution is bell-shaped, meaning that most sites have intermediate rates around 1, while few sites have either very low or very high rates. In particular, when α → ∞, the distribution degenerates into the model of a single rate for all sites. If α ≤ 1, the distribution has a highly skewed L-shape, meaning that most sites have very low rates of substitution or are nearly ‘invariable’, but there are some substitution hotspots with high rates.

The sequences were all sampled in 1993 so we are dealing with a homochronous alignment and do not need to specify tip dates.

Because our sequences are contemporaneous (homochronous data), there is no information in our dataset to estimate the clock rate. We will use an estimate inferred in Pybus et al., 2001 to fix the clock rate. In this case all the samples were contemporaneous (sampled at the same time) and the clock rate is simply a scaling of the estimated tree branch lengths (in substitutions/site) into calendar time.

So, let’s set the clock rate $\mu$ to 0.00079 s/s/y

In addition, we define the priors for the following parameters:

1. the vector of effective population sizes Θ;
2. the relative rates of the GTR process rates;
3. the base frequencies π;
4. the shape of the discretized gamma distribution $\gamma$.

Here we setup a Markov chain of effective population sizes using ExpMarkovChain, and apply a LogNormal distribution to the first value of the chain. Please note that the first value Θ1 is measured from the tips according to (Drummond, Rambaut, Shapiro, & Pybus, 2005). The vector of group sizes A are positive integers randomly sampled by the function RandomComposition where the vector’s dimension equals to a constant numGroups, and they should sum to the number of coalescent events w.

### Questions

1. What are numGroups and w according to the Figure 1? And how to compute the number of coalescent events given the number of taxa?

2. How to change the above LPhy scripts to use the classic Skyline coalescent?

Tips: by default all group sizes in SkylineCoalescent function are 1 which is equivalent to the classic skyline coalescent.

## Producing BEAST XML using LPhyBEAST

BEAST 2 reads instructions about the data and the model from a user-provided .xml, which can be produced in a variety of ways. Our goal with LPhy is to make the preparation of the .xml as painless, clear and precise as possible. In order to achieve that, we will use a companion application, LPhyBEAST, as a bridge between the LPhy script we typed above and the .xml.

LPhyBEAST is distributed as a BEAST 2 package, we can use an application called Package Manager, which is distributed with BEAST 2 together, to install it. To start LPhyBEAST, we have to use the script lphybeast. Some technical guides can help you to start.

In our hcv_coal.lphy script, the alignment file is assumed to locate under the folder tutorials/data/. So we need to go to the tutorials folder, which is normally where the LPhy is installed, run LPhyBEAST as below and check the end of message to find where is the generated XML.

Let us run LPhyBEAST now:

# BEAST_DIR="/Applications/BEAST2"
cd ~/WorkSpace/linguaPhylo/tutorials/
\$BEAST_DIR/bin/lphybeast -l 40000000 hcv_coal.lphy


## Running BEAST

After LPhyBEAST generates a BEAST 2 .xml file (e.g., hcv_coal.xml), we can point BEAST 2 to it, which will then start the inferential MCMC analysis.

BEAST 2 will write its outputs to disk into text files specified in the .xml file (specific paths can be passed in, but in their absence BEAST 2 will write the outputs in the same directory from where it was called).

BEAST 2 will also output the progress of the analysis and some summaries to the screen, like this:

                         BEAST v2.6.7, 2002-2020
Bayesian Evolutionary Analysis Sampling Trees
Designed and developed by
Remco Bouckaert, Alexei J. Drummond, Andrew Rambaut & Marc A. Suchard

Centre for Computational Evolution
University of Auckland
r.bouckaert@auckland.ac.nz
alexei@cs.auckland.ac.nz

Institute of Evolutionary Biology
University of Edinburgh
a.rambaut@ed.ac.uk

David Geffen School of Medicine
University of California, Los Angeles
msuchard@ucla.edu

http://beast2.org/

http://github.com/CompEvol/beast2

BEAST developers:
Alex Alekseyenko, Trevor Bedford, Erik Bloomquist, Joseph Heled,
Sebastian Hoehna, Denise Kuehnert, Philippe Lemey, Wai Lok Sibon Li,
Gerton Lunter, Sidney Markowitz, Vladimir Minin, Michael Defoin Platel,
Oliver Pybus, Tim Vaughan, Chieh-Hsi Wu, Walter Xie

Thanks to:
Roald Forsberg, Beth Shapiro and Korbinian Strimmer

File: hcv_coal.xml seed: 1630290833514 threads: 1

...

...
38000000     -6632.7087     -6161.6478      -471.0609         0.2063         0.3178         0.2259         0.2499         0.0461         0.3619         0.0614         0.0340         0.4518         0.0445         0.3966              1             10              2             49      1847.5418      8278.3538       788.9602       243.4601 1m19s/Msamples
40000000     -6635.1459     -6184.2080      -450.9379         0.1935         0.3251         0.2391         0.2421         0.0541         0.3255         0.0663         0.0406         0.4662         0.0471         0.3508             13             32             10              7      7759.0760        88.4127       139.5266       295.8700 1m19s/Msamples

Operator                                       Tuning    #accept    #reject      Pr(m)  Pr(acc|m)
DeltaExchangeOperator(A.deltaExchange)        3.18924     177760     541485    0.01800    0.24715
ScaleOperator(Theta.scale)                    0.28842     229995     650541    0.02201    0.26120
ScaleOperator(gamma.scale)                    0.63167      81407     252858    0.00834    0.24354
DeltaExchangeOperator(pi.deltaExchange)       0.07328     144605     576445    0.01800    0.20055
Exchange(psi.narrowExchange)                        -    2453146    3545681    0.14993    0.40894
ScaleOperator(psi.rootAgeScale)               0.63607      56804     276244    0.00834    0.17056
ScaleOperator(psi.scale)                      0.71569    1416928    4579768    0.14993    0.23628
SubtreeSlide(psi.subtreeSlide)               60.10339     575882    5421860    0.14993    0.09602 Try decreasing size to about 30.052
Uniform(psi.uniform)                                -    2410120    3584371    0.14993    0.40206
Exchange(psi.wideExchange)                          -      51775    5949658    0.14993    0.00863
WilsonBalding(psi.wilsonBalding)                    -      86216    5907884    0.14993    0.01438
DeltaExchangeOperator(rates.deltaExchange)    0.08060     129011     899557    0.02573    0.12543

Tuning: The value of the operator's tuning parameter, or '-' if the operator can't be optimized.
#accept: The total number of times a proposal by this operator has been accepted.
#reject: The total number of times a proposal by this operator has been rejected.
Pr(m): The probability this operator is chosen in a step of the MCMC (i.e. the normalized weight).
Pr(acc|m): The acceptance probability (#accept as a fraction of the total proposals for this operator).

Total calculation time: 3160.808 seconds
End likelihood: -6635.145974434814


## Analysing the BEAST output

Run the program called Tracer to analyze the output of BEAST. When the main window has opened, choose Import Trace File... from the File menu and select the file that BEAST has created called hcv_coal.log. You should now see a window like in 5.

For the reconstruction of the population dynamics, we need two files, the hcv_coal.log file and the hcv_coal.trees file. The log file contains the information about the group sizes and population sizes of each segment, while the trees file is needed for the times of the coalescent events.

Navigate to Analysis > Bayesian Skyline Reconstruction. From there open the tree log file. To get the correct dates in the analysis we should specify the Age of the youngest tip. In our case it is 1993, the year where all the samples were taken. If the sequences were sampled at different times (heterochronous data), the age of the youngest tip is the time when the most recent sample was collected.

Press OK to reconstruct the past population dynamics.

The output will have the years on the x-axis and the effective population size on the y-axis. By default, the y-axis is on a log-scale. If everything worked as it is supposed to work you will see a sharp increase in the effective population size in the mid 20th century, similar to what is seen below.

Note that the reconstruction will only work if the *.log and *.trees files contain the same number of states and both files were logged at the same frequency.

There are two ways to save the analysis, it can either be saved as a PDF file for displaying purposes or as a tab delimited file.

Navigate to File > Export Data Table. Enter the filename as hcv_coal.tsv and save the file. The exported file will have five rows, the time, the mean, median, lower and upper boundary of the 95% HPD interval of the estimates, which you can use to plot the data with other software (R, Matlab, etc).

### The parameterization and choosing the dimension

Please read the section of “The Coalescent Bayesian Skyline parameterization” and “Choosing the Dimension” from Taming the BEAST tutorial Skyline plots.

### Questions

1. How to choose the dimension for the Bayesian skyline analysis? How does the number of dimensions of effective population sizes affect the result?

2. What are the alternative models to deal with this dimension problem?

3. What does the Bayesian skyline plot in this analysis tell you?

## Some considerations for using skyline plots

In the coalescent, the time is modeled to go backwards, from present to past.

The coalescent skylines assume that the population is well-mixed. That is, they assume that there is no significant population structure and that the sequences are a random sample from the population. However, if there is population structure, for instance sequences were sampled from two different villages and there is much more contact within than between villages, then the results will be biased (Heller, Chikhi, & Siegismund, 2013). Instead a structured model should then be used to account for these biases.

## Programs used in this tutorial

The following software will be used in this tutorial:

• LPhy Studio - this software will specify and visualise models as well as simulate data from models defined in LPhy scripts. It is available for download from LPhy releases.
• LPhy BEAST - this software will construct an input file for BEAST. The installation guide and usage can be found from here.
• BEAST - this package contains the BEAST program, BEAUti, DensiTree, TreeAnnotator and other utility programs. This tutorial is written for BEAST v2.6.7 or higher version, which has support for multiple partitions. It is available for download from http://www.beast2.org.
• BEAST labs package - containing some generally useful stuff used by other packages.
• BEAST feast package - this is a small BEAST 2 package which contains additions to the core functionality.
• Tracer - this program is used to explore the output of BEAST (and other Bayesian MCMC programs). It graphically and quantitatively summarises the distributions of continuous parameters and provides diagnostic information. At the time of writing, the current version is v1.7.2. It is available for download from http://beast.community/tracer.
• FigTree - this is an application for displaying and printing molecular phylogenies, in particular those obtained using BEAST. At the time of writing, the current version is v1.4.3. It is available for download from http://beast.community/figtree.
• BEAST SSM (standard substitution models) package - containing the following standard time-reversible substitution models: JC, F81, K80, HKY, TrNf, TrN, TPM1, TPM1f, TPM2, TPM2f, TPM3, TPM3f, TIM1, TIM1f, TIM2, TIM2f, TIM3 , TIM3f, TVMf, TVM, SYM, GTR.

## Data

The number of replicates, L is the number of characters of alignment, D. The alignment, D is read from the Nexus file with a file name of "data/hcv.nexus". numGroups = 4 The n, w is calculated by taxa.length()-1. The taxa is the list of taxa of alignment, D.

## Model

The alignment, D is assumed to have evolved under a phylogenetic continuous time Markov process (Felsenstein; 1981) on phylogenetic time tree, ψ, with a molecular clock rate of 7.9E-4, instantaneous rate matrix, Q and siteRates, r. The instantaneous rate matrix, Q is the general time-reversible rate matrix (Rodriguez et al; 1990) with relative rates, rates and base frequencies, π. The base frequencies, π have a Dirichlet distribution prior with a concentration of [3.0, 3.0, 3.0, 3.0]. The relative rates, rates have a Dirichlet distribution prior with a concentration of [1.0, 2.0, 1.0, 1.0, 2.0, 1.0]. The double, ri is assumed to come from a DiscretizeGamma with shape, γ and a ncat of 4, for i in 0 to L - 1. The shape, γ has a log-normal prior with a mean in log space of 0.0 and a standard deviation in log space of 2.0. The phylogenetic time tree, ψ has a skyline coalescent prior (Drummond et al; 2005) with population sizes, Θ, group sizes, A and taxa. The group sizes, A is assumed to come from a RandomComposition with n, w and k, numGroups of 4. The taxa.length() is the .length of taxa. The population sizes, Θ have a smoothing prior in which each element has an exponential prior with a mean of the previous element in the chain with firstValue, θ1 and number of steps, numGroups of 4. The firstValue, θ1 has a log-normal prior with a mean in log space of 9.0 and a standard deviation in log space of 2.0.

## Posterior

$$\begin{split} P(\boldsymbol{\pi}, \boldsymbol{\textbf{rates}}, \boldsymbol{r}, \gamma, \boldsymbol{\psi}, \boldsymbol{A}, \boldsymbol{\Theta}, \theta1 | \boldsymbol{D}) \propto &P(\boldsymbol{D} | \boldsymbol{\psi}, \boldsymbol{Q}, \boldsymbol{r})P(\boldsymbol{\pi})\\& P(\boldsymbol{\textbf{rates}})\prod_{i=0}^{L - 1}P(\textrm{r}_i | \gamma)P(\gamma)\\& P(\boldsymbol{\psi} | \boldsymbol{\Theta}, \boldsymbol{A})P(\boldsymbol{A})P(\boldsymbol{\Theta} | \theta1)\\& P(\theta1)\end{split}$$

## References

• Drummond, A. J., Rambaut, A., Shapiro, B., & Pybus, O. G. (2005). Bayesian coalescent inference of past population dynamics from molecular sequences. Molecular Biology and Evolution, 22(5), 1185–1192. https://doi.org/10.1093/molbev/msi103
• Bouckaert, R., Heled, J., Kühnert, D., Vaughan, T., Wu, C.-H., Xie, D., … Drummond, A. J. (2014). BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Computational Biology, 10(4), e1003537. https://doi.org/10.1371/journal.pcbi.1003537
• Bouckaert, R., Vaughan, T. G., Barido-Sottani, J., Duchêne, S., Fourment, M., Gavryushkina, A., … Drummond, A. J. (2019). BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLOS Computational Biology, 15(4).
• Ray, S. Ê. C., Arthur, R. Ê. R., Carella, A., Bukh, J., & Thomas, D. Ê. L. (2000). Genetic Epidemiology of Hepatitis C Virus throughout Egypt. The Journal of Infectious Diseases, 182(3), 698–707. https://doi.org/10.1086/315786
• Pybus, O. G., Drummond, A. J., Nakano, T., Robertson, B. H., & Rambaut, A. (2003). The Epidemiology and Iatrogenic Transmission of Hepatitis C Virus in Egypt: A Bayesian Coalescent Approach. Molecular Biology and Evolution, 20(3), 381–387. https://doi.org/10.1093/molbev/msg043
• Pybus, O. G., Charleston, M. A., Gupta, S., Rambaut, A., Holmes, E. C., Harvey, P. H., … Felsenstein, J. (2001). The epidemic behavior of the hepatitis C virus. Science (New York, N.Y.), 292(5525), 2323–2325. https://doi.org/10.1126/science.1058321
• Rosenberg, N. A., & Nordborg, M. (2002). Genealogical trees, coalescent theory and the analysis of genetic polymorphisms. Nature Reviews Genetics, 3(5).
• Pybus, O. G., Rambaut, A., & Harvey, P. H. (2000). An Integrated Framework for the Inference of Viral Population History From Reconstructed Genealogies. Genetics, 155(3).
• Heled, J., & Drummond, A. J. (2008). Bayesian inference of population size history from multiple loci. BMC Evolutionary Biology, 8(1), 289. https://doi.org/10.1186/1471-2148-8-289
• Minin, V. N., Bloomquist, E. W., & Suchard, M. A. (2008). Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics. Molecular Biology and Evolution, 25(7), 1459–1471. https://doi.org/10.1093/molbev/msn090
• Heller, R., Chikhi, L., & Siegismund, H. R. (2013). The confounding effect of population structure on Bayesian skyline plot inferences of demographic history. PloS One, 8(5), e62992. https://doi.org/10.1371/journal.pone.0062992
• Drummond, A. J., & Bouckaert, R. R. (2014). Bayesian evolutionary analysis with BEAST 2. Cambridge University Press.