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Population model assumptions
The population in each generation evolves following standard Wright Fisher model without mutation and selection. That is, randomly sample two individuals from the population, and randomly choose one of the chromosomes in each individual, pair them and recombine to form a new chromosome pair for next generation. Repeat this process until sampled N chromosome pair. Here N denotes population size in specific generation.
Recombination is modeled as a Poisson Process along the chromosome with rate 1.
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What the admixture model simulator can do.
Here implemented a very flexible admixture model simulator, in which:
1). Can take arbitrary number of parental populations;
2). Can take arbitrary wave of population admixture;
3). Population size can be changed generation by generation;
4). And admixture proportions can also be changed generation by generation.
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Requirements
To run the simulator, java 1.6 or upper is required.
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Obtain AdmixSim
AdmixSim can be either downloaded directly from the release from the URL: https://github.com/Shuhua-Group/AdmixSim/releases or cloned from github repository by
git clone https://github.com/Shuhua-Group/AdmixSim.git -
Get command line help
java -jar AdmixSim.jar -h or java -jar AdmixSim.jar --help -
Run it with toy data
java -jar AdmixSim.jar --gen 10 --nanc 2 --leng 1.0 --file toy.par --samp 20 --input test --output test_out
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Input files
1). model description file
firstly, in model description file, set up initial number of haplotypes to be sampled for parental populations
secondly, set up the population size and parental proportions for each generation in one line
Note: anything follow "#" was treated as comments
Here is a complete example:
#set up number of haplotypes in each parental population to be sampled from 10 10 // #indicate start of population size and parental proportions for each generation 100 0.5 0.5 #init population, two parental population, each contribute 50% 100 0 0 100 0.1 0 #second wave admixture, with 10% gene flow from parental population 1 100 0 0 100 0 0 100 0 0.2 #third wave admixture, with 20% gene flow from parental population 2 100 0 0 150 0 0 #population increase to 150 100 0 0 #population size decrease to 100 100 0 0It's quite simple to implemented HI, GA or CGFR or CGFD models as described in Jin Wenfei et al (2012) AJHG.
HI model:
10 10 // 100 0.7 0.3 #init population, two parental population, contribute 70% and 30% 100 0 0 ...... 100 0 0GA model:
10 10 // 100 0.7 0.3 #init population, two parental population, contribute 70% and 30% 100 0.07 0.01 ...... 100 0.07 0.01CGFD(First parental population as gene flow donor) model:
10 10 // 100 0.1 0.9 #init population, two parental population, contribute 10% and 90% 100 0.1 0 ...... 100 0.1 0CGFR(First parental population as gene flow recipient) model:
10 10 // 100 0.9 0.1 #init population, two parental population, contribute 90% and 10% 100 0 0.1 ...... 100 0 0.12). Map file
The genetic positions for each marker are given in Morgan, one line per marker.
Here is an example:
0.00097100 0.00238066 0.00367538 ......3). Haplotype file
The haplotypes of parental populations to be sampled from are combined in one file, one haplotype per line. The first n1 lines correspond to haplotypes for first parental population, second n2 lines correspond to haplotypes for second parental population and so on. In which the number of parental populations and the number of haplotypes for each parental population ((n1, n2 and so on) are given in model description file.
Here is an example:
1011000000100100001000000010110100010101000011010111000000000010000000011101100000000101000010010010 0010000010000100001000000010110101010011000001011111001000000010000000011001100000010101000010110010 0010000010000100001000000010110101010001010001010111000000100011000000011001110010000101000010010010 0010000010000100001010000010110101010001000001010111000000100010000000011001110010000101000010010010 ...... 0100000000000110100000010000000100010000001000000001010001000010000101001011000100001100000111000100 -
Output file
1). Admixed haplotypes
Record the haplotypes from the individuals sampled from the admixed population, format is the same as input haplotype file
2). Segment file
In which record the start, end and which parental that segment comes from. Each line corresponds to one chromosomal segment.
Here is an example:
0.00000000 0.07785695 2 0.07785695 0.30178126 1 ...... 0.30178126 0.41594482 2
-h/--help print help message [optional]
-f/--file model description file [required]
-i/--input prefix of input files [required]
-g/--gen generations since admixture [optional, default: 1]
-k/--nanc number of parental populations [optional, default: 2]
-l/--leng length of chromosome to be simulated [optional, default: 1.0]
-n/--samp number of individual(s) to be sampled [optional, default: 10]
-o/--output prefix of output files [optional, default: output]
-s/--seed seed of random generator [optional, default: current time]
It's very easy to couple with a coalescent simulator MS. For example, use MS to simulate two parental populations, whose Ne remains constant, i.e. Ne=5000, split 4000 generations ago, command as below:
ms 200 1 -t 2000 -r 2000 10000000 -I 2 100 100 -ej 0.2 2 1 -p 8 > simAnc.txt
Then just simply convert into the files needed in the admixture model simulator:
python convert.py simAnc.txt simAnc 200
It will produce the map file and the parental haplotype file:
simAnc.map simAnc.hap
Afterwards, the simulated parental haplotypes could be used in the admixture model simulator, for example:
java -Xmx2g -jar AdmixSim.jar -g 20 -k 2 -l 2 -f sim1.par -n 100 -i simAnc -o sim1
Notes: the software is free and open source, users are at their own risk and without any guarantee.
Details can be also found in wiki page : https://github.com/Shuhua-Group/AdmixSim/wiki
When using AdmixSim, please cite
Yang X, Yuan K, Ni X, Zhou Y, Guo W, Xu S. AdmixSim: A Forward-Time Simulator for Various Complex Scenarios of Population Admixture. Front Genet. 2020 Dec 3;11:601439. doi: 10.3389/fgene.2020.601439. PMID: 33343638; PMCID: PMC7744625.
(https://www.frontiersin.org/articles/10.3389/fgene.2020.601439/full)