Workflow for creating artificial GWAS data sets based on real data
Several separate projects
This was our first version -- good thing is that it keeps structure very well but the resulting data is relatively identifiable and also we land up with smaller data sets than our original
This is C program that takes a source plink file, anonymises and does some randomisation. Here is an example.
./gensimul src 200 src.clt mutes new
Here
srcis the base name for the source PLINK files- 200 is the number of people you want in the new data set
src.cltis a cluster file (see below)mutesis a list of mutations (see below)new: the base name of the new PLINK files
The cluster file contains a list of clusters. Each cluster is on a line by itself with space separated integers. e.g.
1 15 39
2 7
3
4 8 124
20 11 5
There should be no overlap in clusters. In the current version, there should be a maximum of 3 members of each cluster. We hope/plan/sort of promise to generalise. The numbers are the 0-indexed entries in the FAM file.
The idea is that each cluster consists of closely related individuals. In our new data set we have a randomised amalgam from thse individuals. See below for how to create this.
The file consists of a list of mutations, each mutation on a line by itself, withree entries per line:
- The 0-indexed entry in the BIM file of the mutation
- The desired MAF of cases (between 0 and 1)
- The desired MAF of controls
The cluster file can be created in two steps.
Use PLINK to make a PLINK-style cluster file
plink --bfile data --cluster --ppc 0.01 --mc 3 --out data
Then call our auxiliary script to produce in the right format
python3 munge_cluster.py data data.clt
This is version 2 -- takes a largish input data set and produces a larger output randomised data set that preserves some LD but collapses population structure
This is a Nextflow script that uses hapsimul to get some population structure back in the data