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Practical 2 Fst

Using the tools from the first practical (Simcoal2 and Arlequin) we will explore the effects of population subdivision and determine Fst.




Instructions for WinXP


Follow the same instructions as in Practical 1, only this time

  1. Revisit those parts of the documentation that deal with population structure and Fst. Make sure you understand the Simcoal2 input file below.
  2. Use the SimCoal2 file "Fst_V1.par" below instead of the "MitoEvol_V1"
  3. Before running the Arlequin3 analysis, make sure the following boxes are ticked:
    • In the tab "Settings" > "Population comparisons" check "Compute pairwise Fst", "Slatkin's distance", "Reynold's distance", "Compute pairwise differences (pi)" and leave the rest as it is.
    • In the tab "Batch File" select "User interface settings" and make sure that the following boxes are not dimmed out: "Gene diversity", "Nucleotide composition", "Molecular diversity", "Theta vales" and "Population comparisons". (Dimmed boxes are as good as unticked here, even if they appear to be ticked).
  4. Now start the analysis. You can "pause" and continue again ("resume") if you are in the middle of a big batch run (however, you will have to start again, if you click "stop"and if you restart Arlequin3).
  5. Your pairwise Fst results are in the file "FSTdist.sum" that can be imported in Excel as before. To work out the (missing) headers of the columns, open the analysis of e.g. the first sample (in folder "Fst_V1_0.res"; click on "...main.html") and locate the Fst values in there ("Genetic structure" > "Pairwise distances" > "Population pairwise FSTs"). The values there should allow you to work out which column belongs to which pairwise comparison.
  6. Plot a histogram of Fst values at least for 500 comparisons between two different population pairs. Are these significantly different from 0 ?
  7. Decrease the migration rate by a factor of 10 (change every value in the corresponding migration matrix). Are these significantly different from 0?

Now again you can play around, i.e. do a parameter sensitivity study. Feel free to explore your own ideas... but whatever you do, before you claim that you found anything interesting, you HAVE TO run 10 000 samples!


SimCoal2 - input file ("Fst_V1.par")


//Parameters for the coalescence simulation program : simcoal.exe
4 samples to simulate : 4 populations of mitochondrial DNA tentatively resembling some human parameters
//Population effective sizes (number of genes 2*diploids)
1000
1000
1000
1000
//Samples sizes (number of genes 2*diploids)
10
10
10
10
//Growth rates    : negative growth implies population expansion
0
0
0
0
//Number of migration matrices : 0 implies no migration between demes
2
// Migration rates matrix 0
0.000 0.001 0.001 0.001
0.001 0.000 0.001 0.001
0.001 0.001 0.000 0.001
0.001 0.001 0.001 0.000
// Migration matrix 1
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
//historical event: time, source, sink, migrants, new deme size, new growth rate, migration matrix index
4 historical events 
100000 0 1 1 1 0 1
100000 2 1 1 1 0 1
100000 3 1 1 1 0 1
100000 4 1 1 1 0 1
//Number of independent loci [chromosome] (Number of sequences of 300 bp per gamete)
1 1
// Chromosome structure 1 begins with number of loci
1
//per block: data type, number of loci, per generation recombination and mutation rates and optional parameters
DNA    1000        0.000      0.00002    0.33


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