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https://github.com/transgirlcodes/supertypes_redqueen_tse


https://github.com/transgirlcodes/supertypes_redqueen_tse

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# Evolutionary genetics of immunological supertypes reveals two faces of the Red Queen



This repository houses work done as part of the project and publication:



[**"Evolutionary genetics of immunological supertypes reveals two faces of the Red Queen"**](https://www.nature.com/articles/s41467-017-01183-2)



by



- Jackie Lighten

- Alexander S.T. Papadopulos

- Ben J. Ward

- Ian Paterson

- Lyndsey Baillie

- Ian R. Bradbury

- Andrew P. Hendry

- Paul Bentzen

- Cock van Oosterhout



[**DOI:** doi:10.1038/s41467-017-01183-2](doi:10.1038/s41467-017-01183-2)



Two parts of the project are included in this repository:



**1). Computation of Jost D on Super-Type data, and bootstrapping simulations.**



This repository houses scripts used for the computation of a measure of

differentiation called JostD, on the sample data.

In addition, random bootstrap simulations were performed, with the aim of

randomizing supertype category allocations to sequenced alleles. To see if JostD

estimated from the actual data differed significantly from the distribution of

estimates of JostD computed for randomized simulations.



Much of the computation of JostD and the randomized simulations is handled by a

package used in these scripts called SuperTypeR, so as other users wanting to

use similar calculations in the future can include

[SuperTypeR](https://github.com/Ward9250/SuperTypeR) in their own scripts.



[SuperTypeR](https://github.com/Ward9250/SuperTypeR),

and the scripts in this Repo were written by co-author

Ben J. Ward (Github: Ward9250).



**2). Simulations of immune gene evolution in a host-parasite system.**



The code provided as .mac files in the src folder is for a Minitab 12.1 macro

to study the evolution of immune genes in a host-parasite system, examining

whether trans-species polymorphisms of STs can evolve in a Red Queen's arms race.



In particular, it analyses the adaptive evolutionary change in epitope

recognition of immune alleles and STs (i.e. their paratope) during host

parasite co-evolution using an agent-based model.



The Minitab 12.1 macro is for the most basic model that (1) would result in

antagonistic host-parasite co-evolution, and (2) in which we could quantify the

resulting adaptive evolutionary change in phenotype over time.



Hence, rather than using a strict population genetic model, it models the

paratope of immune alleles and the epitope of parasites in a 2-D grid with

size 1000 x 1000.



The fitness effect of immune alleles was determined by the position of immune

alleles and parasites in the epitope/paratope space.

The adaptive evolutionary change in phenotype of alleles and STs was quantified

by tracking changes in their position within this space over time.

Analysing the phenotypic change enabled us to study trans species evolution.

Furthermore, by analysing fluctuations in immune allele frequencies, we were

able to study the population genetic characteristics of the model.  



Hosts were diploid with one immune locus. Parasites were haploid. Each host was

infected by one parasite every generation. The minimum Euclidean distance was

calculated between an individual’s immune alleles and one randomly drawn

parasite representing the infection. Depending on this distance, the parasite

was either recognized (in a resistant host) or not (in a susceptible host).

Fitness was relative so that 50% of all parasites died (on resistant hosts).

The other 50% of parasites (on susceptible hosts) reproduced clonally one

individual offspring. Parasite offspring mutated, causing them to change their

X or Y coordinates by one unit within the grid.

Parasite infection on the susceptible hosts reduced host fitness by 0.25, and

host with zero fitness died. Resistant host gained 0.25 fitness units, and

individuals with one fitness units reproduced offspring that all started with

0.25 fitness units.



Reproduction of hosts was sexual, and hosts produced gametes, each containing

one parental immune allele. This immune allele could mutate with probability µ,

which caused it to change its X or Y coordinates by one unit within the grid.

Note that by simulating a high mutation rate, we effectively accelerated

evolutionary time in the model.



For example, simulations with µ= 0.1 equate to 3.1 x 106 generations in real

time, assuming a base mutation rate of 10-9 per base per generation, and 16

PBR codons with a total of 32 replacement sites (i.e. the 1st and 2nd codon

positions of the PBR).  Gametes of reproducing hosts united randomly with one

another to produce the next generation of diploid offspring. This resulted in a

Poisson distribution of offspring per parent (mean=variance=unity).

Output is displayed in the Minitab 12.1 Data Window.



**3). Plotting scripts**



We also tried plotting Supertype evolution using R with different colour schemes

for publication. Some of these plots, data used to make them, and the scripts

are included in this repository.



## Repository Details:



### Folders

* data - Raw data given to me at start of project or computational experiments.

* results - The result files of computational experiments.

* src - Source code of binary executable files used in this project and / or scripts.



### Execution environment(s)

1. R with the [SuperTypeR](https://github.com/Ward9250/SuperTypeR) package installed.



### Computational Experiments/Tasks.



1. _analysis_: Calculate JostD of the data, and do bootstrap simulations of JostD.



## More details:



### Data Files:



**File:** *SuperType_Designations.csv*



This contains the original supertype designation for each allele.

The first column is the supertype, the second is genotype, and the third is the

sequence of the genotype.



**File:** *Samples.csv*



This contains the genotypes per individual per population and the corresponding

STs. Each row is one sample.



Columns are (left to right):



1. Sample ID

2. Population

3. Drainage

4-12. Alleles in sample  

14-22. Supertypes corresponding to alleles in columns 4-12.

24. Number of alleles in sample.

25. Number of supertypes in sample.



**Files:** *The .xls files, and ST_RQ_Dynamics.csv*



These contain the data used for plotting the plots in results/plotting/



### Analysis



The script in `src/DataAnalysis.R` scripts the the analysis for this project.

It pre-processes the input data file `data/Samples.csv`, before using the

[SuperTypeR](https://github.com/Ward9250/SuperTypeR) package to compute JostD

on the data in Samples.csv, before doing bootstrap simulations to estimate the

mean and standard error of the JostD estimates.



### Supertype counting and computation of JostD



Supertype counting, computation of of JostD and Pairwise JostD is done by the

[SuperTypeR](https://github.com/Ward9250/SuperTypeR) package.



#### Random Bootstrap Simulations



##### Aim

The aim is to randomly assign the alleles to the supertypes in file

*SuperType_Designations.csv*, generating a set of artificial supertypes composed

of a random set of alleles. The analysis can then be re-run on these null

datasets to assess whether the results from the actual data are significantly

different.



##### Simulation Steps



**FOR EACH IN N ITERATIONS:**



1. Wipe ST designations across all samples in data from *Samples.csv*.



2. For the data in *SuperType_Designations.csv*, randomly reallocate the alleles

in the total gene pool (~539) to the supertypes (the supertypes still get the

same number of alleles allocated to them). Allocation is done without

replacement.



3. For each population in *Samples.csv*, count the number of each supertype

present in the population. This results in a matrix of supertype counts per

population, where each row is a supertype, and each column is a population.



4. Calculate the pairwise Jost D for the matrix output from step 3.

Also calculate the average of each column i.e. the mean Jost D for each

population relative to all others.



### Plotting

The julia and R scripts called *RQ_Evo_Plotting.R*, and *RE_Evo_Plotting.jl*

in the src/ directory are were used to process the data in *ST_RQ_Dynamics.csv*

(which was saved from the .xls files), to make the plots in results/plotting.