Documentation

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 are:

• Selection scan: detects selected loci with rigorous multiple testing thresholds

• Modeling hard sweeps: estimates location, allele frequency, and selection coefficient of sweep

• Case-control scan: detects loci where IBD rates differ between binary cases and controls

• Phasing and ancestry: supports haplotype phasing, local ancestry, and kinship inference

Each automated workflow has a dedicated page under Usage. The general way to run these methods is:

1. Navigate to the appropriate workflow directory

2. Modify parameters in YAML configuration files

3. Send the jobs to a cluster with nohup snakemake [...] &

I made a Zenodo repository with some simulated data to test the workflows. See Testing workflows.

The source code is here

Installation

git clone https://github.com/sdtemple/isweep.git
mamba env create -f isweep-environment.yml
bash get-software.sh
pip install isweep

Note

This project is in a stable state. I am commited to providing quick support via GitHub Issues at least into 2026.

Data Requirements

The main requirements are a tab-separated genetic recombination map and enough samples to detect more than 0 IBD segments at all positions.

• Phased haplotypes (VCF files)

• Samples with a similar ancestry

• No close relatives

• Recombining autosomes

If you don’t already have your data phased or cohort selected, we support Phasing and ancestry with another workflow.

In humans, more than 1000 is enough samples, but more than 3000 samples is recommended.

At some point, there are no gains in statistical power with more samples. I do not recommend analyzing more than 100k samples in a biobank. See our Temple and Browning (2025+) publication.

The tree of life is messy. Email or make a GitHub Issue for analysis advice about the nuances of your sample population.

Vignette

Outside of the running the workflows, the main functions are:

• isweep.coalescent.simulate_ibd_isweep: generate long IBD segments around a locus (w/ selection)

• isweep.coalescent.chi2_isweep: use a uniroot finder with this to estimate the selection coefficient

• isweep.utilities.read_Ne: load in recent effective population sizes

For instance:

from isweep import *
# parameter settings
s = 0.03
p=float(0.5) # allele freq
Ne=read_Ne('constant-10k.ne') # demo history
model='m'
long_ibd=3.0
ab=[long_ibd,np.inf]
nsamples=200
# calculate denominator
ploidy=2
msamples=ploidy*nsamples
N=msamples*(msamples-1)/2-nsamples
# simulate data
out=simulate_ibd_isweep(
 nsamples,
 s,
 p,
 Ne,
 long_ibd,
 long_ibd,
 one_step_model=model,
 ploidy=ploidy,
)
ibd=out[0][0]
# estimating the selection coefficient
se = minimize_scalar(
 chi2_isweep,
 args=(p,Ne,N,(ibd,),ab,model,0,-0.01,ploidy),
 bounds=(0,0.5),
 method='bounded'
).x
print('true selection coefficient')
print(s)
print('estimate selection coefficient')
print(se)

There are also some IPython notebooks as examples in vignettes/.

Citations

This software and its methods are the basis of six publications.

• Modeling hard sweeps

• Simulating IBD segments

• Central limit theorems

• Thesis on recent positive selection

• Multiple testing in selection scan

• Multiple testing in case-control scan

The software Beagle, ibd-ends, hap-ibd, and flare are also used and should be cited.

• workflow/scan-selection: hap-ibd and ibd-ends

• workflow/scan-case-control: hap-ibd and ibd-ends

• workflow/model-selection: hap-ibd

• workflow/phasing-ancestry: Beagle, flare, and hap-ibd

Contents

• Usage
  • Selection scan
  • Modeling hard sweeps
  • Case-control scan
  • Phasing and ancestry
  • Publication-ready Figures
• Visualizing workflows
  • Selection scan
  • Phasing and local ancestry
• Miscellaneous
  • Installing a fast package manager
  • Snakemake options
  • Slurm options
  • Testing workflows
  • Ploidy extension
  • Other considerations
  • Potential errors
  • Reproducing paper results

API

• API
  • empty_function()
  • read_ibd_file()
  • bin_ibd_segments()
  • read_Ne()
  • chi2_isweep()
  • chi2_labeled_isweep()
  • simulate_ibd()
  • simulate_ibd_constant()
  • simulate_ibd_isweep()
  • simulate_ibd_isweep_tv()
  • simulate_ibd_split()
  • basic_coalescent()
  • varying_Ne_coalescent()
  • walk_variant_backward()
  • walk_variant_forward()
  • walk_variant_backward_tv()
  • bootstrap_standard()
  • bootstrap_standard_bc()
  • bootstrap_percentile()
  • when_freq()
  • bootstrap_freq()
  • make_ibd_graph()
  • diameter_communities()
  • write_Ne()
  • make_constant_Ne()
  • make_exponential_Ne()
  • probability_ibd()
  • probability_ibd_isweep()
  • big_format_distribution()

Contact

Seth Temple (sethtem@umich.edu) or GitHub Issues