isweep is a Python package and a series of automated workflows to study natural selection with identity-by-descent (IBD) segments. The Python package simulates IBD segments around a locus and estimates selection coefficients. The automated workflows perform selection scans, selection coefficient estimation, IBD case-control mapping, haplotype phasing, and local ancestry inference. Scripts in the workflows can be run individually in scripts/, with argparse documentation and inputs.
These methods are suitable for analyses for recent genetic/evolution events. For example,
- By recent, we mean within the last 500 generations.
- By strong, we mean selection coefficient s >= 0.015 (1.5%).
- Scan may have moderate power for s >= 0.01 (1%).
Please review the Readthedocs for detailed support, including which relevant publications to cite if you use this software.
Please file an Issue on GitHub for troubleshooting.
Contact [email protected] for support specific to your analysis, e.g., analyses of non-human genetic data.
- Whole genome sequences
- Probably at least > 500 diploids
- Phased vcf data 0|1 of recombining chromosomes
- Tab-separated genetic map (bp ---> cM)
- Without headers!
- Columns are chromosome, rsID, cM, bp
- Access to cluster computing
workflow/phasing-ancestry provides support for phasing and selecting an ancestry cohort.
The main workflows, workflow/scan-selection and workflow/model-selection do:
- Scan genome for extreme IBD rates
- Detect anomalously large IBD clusters
- Rank alleles based on evidence for selection
- Compute a cluster agglomeration measure
- Estimate frequency, location of unknown sweeping allele
- Estimate a selection coefficient (w/ CIs)
In general, you run workflows with
nohup snakemake -s Snakefile-*.smk -c1 --cluster "sbatch [options]" [options] --jobs XX --configfile *.yaml &
You modify the relevant YAML files, which define the method parameters. You should run the pipelines in the mamba activate isweep environment.
Step 1 may be standalone, depending on the analysis. (You may not care to model putative sweeps (Steps 2-6), which also requires demographic Ne estimation.)
To install the dependencies and our package:
- Clone the repository
git clone https://github.com/sdtemple/isweep.git
- Get the Python package
mamba env create -f isweep-environment.yml
- Download some Java software.
bash get-software.sh
You can test the workflows with our small Zenodo repository.
The flow chart below shows the steps ("rules") in the selection scan pipeline.
