BRANCHING AND MAKING UPDATES IN JULY 2024 NEW FEATURES AS A PART OF THIRD THESIS PROJECT

See misc/announcements.md for high-level updates on this repo.

See misc/fixes.md for any major bug fixes.

See misc/usage.md to evaluate if this methodology fits your study.

See misc/cluster-options.md for some suggested cluster options to use in pipelines.

Please cite if you use this package.

Temple, S.D., Waples, R.K., Browning, S.R. (2023) "Modeling recent positive selection in Americans of European ancestry" https://www.biorxiv.org/content/10.1101/2023.11.13.566947v1

Temple, S.D., Thompson, E.A. (2024) "Identity-by-descent in large samples" https://www.biorxiv.org/content/10.1101/2024.06.05.597656v1

PhD dissertation at University of Washington

Temple, S.D. (2024) "Statistical inference using identity-by-descent segments"

Acronym: Incomplete Selective sweep With Extended haplotypes Estimation Procedure

This software presents statistical methods to study very recent, very strong positive selection.

• By very recent, we mean within the last 500 generations
• By very strong, we mean selection coefficient s >= 0.015 (1.5%)

The methods relate the lengths of IBD tracts to a coalescent model under selection.

We assume 1 selected allele at a biallelic locus.

There are ~ seven major ideas.

1. A genome-wide selection scan that looks for anomalously large IBD rates
2. Inferring anomalously large IBD clusters
3. Ranking alleles based on evidence for selection
4. Computing a measure of cluster agglomeration (an IBD information entropy)
5. Estimating allele frequency of unknown sweeping allele
6. Estimating selection coefficients (w/ nice statistical properties)
7. Estimating a confidence interval (w/ nice statistical properties)

These steps are implemented automatically in a snakemake pipeline.

See misc/installing-mamba.md to get a Python package manager.

1. git clone https://github.com/sdtemple/isweep.git
2. mamba env create -f isweep-environment.yml

• mamba activate isweep
• python -c 'import site; print(site.getsitepackages())'

3. Download software

• You need to cite these software
• bash get-software.sh software
  • Puts these in a folder called software/
  • Requires wget
• For simulation study, download SLiM yourself
  • Put in software/
  • https://messerlab.org/slim/

See workflow/other-methods/ folder for how we run methods we compare to.

This repository contains a Python package and some Snakemake bioinformatics pipelines.

• The package ---> src/
• The pipelines ---> workflow/

You should run all snakemake pipelines in their workflow/some-pipeline/.

You should be in the mamba activate isweep environment for analyses.

You should run the analyses using cluster jobs.

We have made README.md files in most subfolders.

• Whole genome sequences
  • Probably at least > 500 diploids
  • Phased vcf data 0|1
  • No apparent population structure
  • No apparent close relatedness
  • A genetic map (bp ---> cM)
    • If not available, create genetic maps w/ uniform rate
  • Recombining diploid chromosomes
    • Not extended to human X chromosome
• Access to cluster computing
  • For human-scale data, you should have at least 25 Gb of RAM and 6 CPUs on a node.
    • More for TOPMed or UKBB-like sequence datasets
  • Not extended to cloud computing

This is the overall procedure. You will see more details for each step in workflow/some-pipeline/README.md files.

Phase data w/ Beagle or Shapeit beforehand. Subset data in light of global ancestry and close relatedness.

• Here is a pipeline we built for this purpose: https://github.com/sdtemple/flare-pipeline
• You can use IBDkin to detect close relatedness: https://github.com/YingZhou001/IBDkin
• You could also use PCA or ADMIXTURE to determine global ancestry.

1. Make pointers to large (phased) vcf files
2. Edit yaml files in the different workflow directories
3. Run the selection scan (workflow/scan)

• nohup snakemake -s Snakefile-scan.smk -c1 --cluster "[options]" --jobs X --configfile *.yaml &
  • See the file misc/cluster-options.md for support
• Recommendation: Do a test run with your 2 smallest chromosomes.
• Check the *.log files from ibd-ends. If it recommends an estimated err, change the error rate in yaml.
• Then, run with all your chromosomes.

4. Estimate recent effective sizes (workflow/scan)

• workflow/scan/scripts/run-ibdne.sh

5. Make the Manhattan plot (workflow/scan)

• workflow/scan/scripts/manhattan.py

6. Checkout the roi.tsv file

• cat roi.tsv
• Edit it with locus names if you want

7. Run the region of interest analysis (workflow/roi)

• nohup snakemake -s Snakefile-roi.smk -c1 --cluster "[options]" --jobs X --configfile *.yaml &

Tip: define variables for file, folder names, e.g., variable1=1224 then echo $variable1

• Provide some scripts to summarize analyses !!!
• Provide option for model selection, standing variation, etc. in roi.tsv
• Test performance in array data, less dense sequence data
• Test performance in dominance selection (sequence data)
• Not designed for ploidy != 2 (yet)