posseleff_simulations

The posseleff_simulations repository is designed to assess the impact of positive selection on Identity by Descent (IBD)-based inferences, leveraging population genetic simulation and true IBD methodologies. The pipeline begins by running simulations to generate tree sequences, allowing for both single and multiple population models for various analyses. The selection simulation can be tailored with parameters such as the selection coefficient, number of origins of the favored mutation, selection starting time, and high-relatedness simulation options. From there, the pipeline involves calling true IBD segments from the tree sequence, followed by IBD processing for generating input files, selection correction, and calling IBDNe and Infomap for Ne and population structure inference. The pipeline is highly configurable, accommodating different scenarios and requirements, and produces detailed output for further analysis. Installation and execution instructions are provided, as well as options for customization, making it a versatile tool for many IBD-related analyses.

Outline of the pipeline

• Run simulation and generate tree sequence
  • Two demographic models
    • Single population model for IBD distribution and Ne analysis, and
    • Multiple population model for population structure analysis
  • Selection simulation:
    • Each of the above models allows for positive selection simulation
    • Tunable Simulation parameters include
      1. selection coefficient
      2. number of origins of the favored mutation
      3. selection starting time (generations)
  • High-relatedness/inbreeding simulations
    • support inbreeding modeling via three strategies:
      • shrinking population size
      • positive assortative mating
      • selfing
    • see simulations/Readme.md for examples.
• Call true IBD segment from tree sequence
• IBD processing and selection correction
  • IBD processing for generating input files for IBDNe (Ne estimation)
  • IBD processing for generating IBD for calling Infomap (population structure)
  • Identify and validate IBD peaks (due to selection)
  • Remove IBD within validated IBD peak region and generate a selection-corrected version of IBD for calling IBDNe and Infomap
• Call IBDNe and Infomap for Ne and population structure inference

System requirements and Software environment

The pipeline has been tested on Linux Operation system and can be easily adapted to MacOS with simple changes. Software dependencies and the version numbers are specified in the './env.yaml' Conda recipe. Additional depencies that are not available from Conda are specified in the installation instruction below. The overall installation time is about 5-15 minutes.

To create the software environment:

1. Install nextflow. See nextflow documentation
2. Install conda from here if you have not
3. Install software: python3 ./init.py, this will
   • Detect and Install simulation Conda environment
     • including ibdutils
   • Download and compile tskibd for true IBD inference from tree sequence
   • Download IBDNe

How to run the pipeline

1. Activate the simulation environment: conda activate simulation
2. Run the pipeline: nextflow ./main.nf -profile sge --num_reps 30 -resume.
3. For large datasets, using a cluster such as SGE is recommended. An example sge profile is provided in the nextflow.config file and should be adjusted to fit your cluster system. If run on a local computer, please remove the -profile sge option from the above command.
4. The pipeline can be reconfigured in the following files
   • Pipeline parameters can be found top lines in main.nf
   • More simulation parameters can be found in the definition of sp_defaults,mp_defaults, sp_sets and mp_sets dictionaries within the main.nf file
   • For more complicated or large scale simulations, the --sp_sets_json, --mp_sets_json arguments and -entry option are recommended. see simulations/Readme.md for examples.

Input, Test Data and Output

1. No input or test data is needed. If desired, pipeline can be reconfigured as mentioned above. The pipeline can be tested by commenting all but one entry in the sp_sets or mp_sets.
2. Output files/folders:
   • each subfolder of resdir represents simulations for a set of chromosomes
   • within each subfolder:
     • ifm_output contains infomap results
     • ne_output contains IBDNe estimates

Citations

If you find this repository useful, please cite our preprint:

Guo, B., Borda, V., Laboulaye, R., Spring, M. D., Wojnarski, M., Vesely, B. A., Silva, J. C., Waters, N. C., O'Connor, T. D., & Takala-Harrison, S. (2023). Strong Positive Selection Biases Identity-By-Descent-Based Inferences of Recent Demography and Population Structure in Plasmodium falciparum. bioRxiv : the preprint server for biology, 2023.07.14.549114. https://doi.org/10.1101/2023.07.14.549114

Other citations:

• iHS statistics and calculation:

iHS calculation via scikit-allel:

Miles, A. et al. cggh/scikit-allel: v1.3.7. (2023) doi:10.5281/ZENODO.8326460.

iHS statistics:

Voight, B. F., Kudaravalli, S., Wen, X. & Pritchard, J. K. A map of recent positive selection in the human genome. PLoS Biol. 4, e72 (2006).

• IBDNe

Browning, S. R., & Browning, B. L. (2015). Accurate Non-parametric Estimation of Recent Effective Population Size from Segments of Identity by Descent. American journal of human genetics, 97(3), 404–418. https://doi.org/10.1016/j.ajhg.2015.07.012

• Infomap algorithm

Rosvall, M., & Bergstrom, C. T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences of the United States of America, 105(4), 1118–1123. https://doi.org/10.1073/pnas.0706851105

Related Repository:

tskibd: https://github.com/bguo068/tskibd