isweep 1.0.2

Methods for selective sweep inference

Statistical inference using long IBD segments

There is a major methodological update for multiple-testing corrections.

Please read misc/multiple-testing.md. You should read our citation below for more details.

See workflow/scan-case-control if you are here for IBD mapping, not selection.

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.

See on GitHub "Issues/Closed" for some comments about the pipeline.

Contact [e-mail] or Github issues for troubleshooting.

Citation

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Please cite if you use this package.

Methods to model selection:

Temple, S.D., Waples, R.K., Browning, S.R. (2024). Modeling recent positive selection using identity-by-descent segments. The American Journal of Human Genetics. https://doi.org/10.1016/j.ajhg.2024.08.023.

IBD central limit theorems:

Temple, S.D., Thompson, E.A. (2024). Identity-by-descent segments in large samples. Preprint at bioRxiv, 2024.06.05.597656. https://www.biorxiv.org/content/10.1101/2024.06.05.597656v2.

Genome-wide significance thresholds in the selection scan:

Temple, S.D., Browning, S.B. (2024). "Multiple testing corrections in selection studies using identity-by-descent segments. Draft in progress.

Genome-wide significance thresholds in the IBD case-control mapping:

Temple, S.D., ..., Wijsman, E., and Blue, E. (2024-25). "Multiple testing corrections in case-control studies using identity-by-descent segments." Draft in progress.

Simulating IBD around a locus:

Temple, S.D., Browning, S.B., and Thompson, E.A. (2024). "Fast simulation of identity-by-descent segments." bioRxiv. https://www.biorxiv.org/content/10.1101/2024.12.13.628449v2

Unifying framework of the selection scan and sweep modeling:

Temple, S.D. (2024). "Statistical Inference using Identity-by-Descent Segments: Perspectives on Recent Positive Selection." PhD thesis (University of Washington). https://www.proquest.com/docview/3105584569?sourcetype=Dissertations%20&%20Theses.

Methodology

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Acronym: incomplete Selective sweep With Extended haplotypes Estimation Procedure

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

• 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%).

In modeling a sweep, we assume 1 selected allele at a locus.

Automated analysis pipeline(s):

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1. A genome-wide selection scan for anomalously large IBD rates

• With multiple testing correction

2. Inferring anomalously large IBD clusters
3. Ranking alleles based on evidence for selection
4. Computing a measure of cluster agglomeration (Gini impurity index)
5. Estimating frequency and location of unknown sweeping allele
6. Estimating a selection coefficient
7. Estimating a confidence interval

Step 1 may be standalone, depending on the analysis. (You may not care to model putative sweeps (Steps 2-7).)

The input data is:

---

See misc/usage.md.

• Whole genome sequences
  • Probably at least > 500 diploids
  • Phased vcf data 0|1
  • No apparent population structure
  • No apparent close relatedness
  • Tab-separated genetic map (bp ---> cM)
    • Without headers!
    • Columns are chromosome, rsID, cM, bp
  • Recombining diploid autosomes
    • For haploids, see issue 5 "Not designed for ploidy != 2"
• Access to cluster computing
  • Not extended to cloud computing

Chromosome numbers in genetic maps should match chromosome numbers in VCFs.

Repository overview

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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.

Installation

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

1. Clone the repository

git clone https://github.com/sdtemple/isweep.git 

2. Get the Python package

mamba env create -f isweep-environment.yml

mamba activate isweep

python -c 'import site; print(site.getsitepackages())'

3. Download software.

bash get-software.sh 

• Requires wget.
• You need to cite these software.

Pre-processing

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Phase data w/ Beagle or Shapeit beforehand. Subset data in light of global ancestry and close relatedness. Example scripts are in scripts/pre-processing/.

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

Main analysis

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You will see more details for each step in workflow/some-pipeline/README.md files.

For all workflows

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1. Make pointers to large (phased) vcf files.
2. Edit YAML files in the different workflow directories.

Detecting recent selection

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Run the selection scan (workflow/scan-selection).

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 *.log files from ibd-ends. If it recommends an estimated err, change error rate in YAML file.
• Then, run with all your chromosomes.

Make the IBD rates plot customized if you want: workflow/scan-selection/scripts/plotting/plot-scan.py.

Outputs:

• scan.modified.ibd.tsv should have all the data for the scanning statistics and thresholds.
  • 'Z' variables are standardized/normalized.
  • 'RAW' are counts.
  • p values assume that IBD rates are (asymptotically) normally distributed.
• roi.tsv are your significant regions.
• autocovariance.png is autocovariance by cM distance. The black line is a fitted exponential curve.
• zhistogram.png is a default histogram for the IBD rates standardized. It should "look Gaussian".
• scan.png is a default plot for the selection scan.
• fwer.analytical.txt gives parameters and estimates for multiple-testing selection scan.

Modeling putative sweeps

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1. Estimate recent effective sizes :workflow/scan-selection/scripts/run-ibdne.sh.
2. Checkout the roi.tsv file.

• Edit with locus names if you want.
• Edit to change defaults: additive model and 95% confidence intervals.

3. Run the region of interest analysis (workflow/model-selection).

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

The script to estimate recent Ne can be replaced with any method to estimate recent Ne, as it happens before the snakemake command. This method HapNe is one such option.

Outputs:

• summary.hap.norm.tsv are estimated selection coefficients, and other estimates, for regions of interest.
  • Read Temple, Waples, and Browning (AJHG, 2024) to learn about the estimates.
  • Confidence intervals assume IBD rates are (asymptotically) normally distributed.
  • Frequency estimate is based on the best differentiated SNP subset.
  • Models are 'a' additive, 'm' multiplicative, 'd' dominance, and 'r' recessive.
• Other types of confidence intervals.
  • 'perc' wildcard means percentile-based confidence intervals.
  • 'snp' wildcard means that frequency estimate is based on best differentiated SNP.

Other considerations

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These Markdown files are in the folder misc/.

See telomeres-centromeres.md for cautionary comments on interpreting results near these genomic regions.

See small-chromosomes.md for comments on modified analyses when some chromosomes measure <= 10 cM.

See different-chromosome-rates.md for comments on modified analyses when chromosome subsets have vastly different mean/median IBD rates.

For species-specific conversions very different from 1.0 cM $\approx$ 1 Mb (humans), see recombination-rates.md

Picture of selection scan workflow

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The flow chart below shows the steps ("rules") in the selection scan pipeline.

Diverting paths "mle" versus "scan" refer to different detection thresholds (3.0 and 2.0 cM).

See dag-roi.png for the steps in the sweep modeling pipeline.