Diversity reduction due to linked deleterious mutation

This is bgshr, a package for computing the expected reduction in diversity due to linked deleterious mutations.

Installation

Dependencies are listed in requirements.txt. bgshr can be installed directly from github using pip. At the moment, the dev branch has the most complete suite of features. In this sample we create a Python virtual environment and install the dev branch:

python -m venv ~/path/to/venv
source ~/path/to/venv/bin/activate
pip install git+https://github.com/apragsdale/bgshr.git@dev

Alternatively, we can clone and build the package locally:

git clone https://github.com/apragsdale/bgshr.git

Then cd into the cloned directory and perform a local installation. If we wish, we can switch to the dev branch with git checkout dev before installing.

cd bgshr
pip install .

Dependencies

If bgshr was cloned locally, we can install dependencies listed in requirements.txt using:

pip install -r requirements.txt

Minimal dependencies are the ubiquitous numpy, scipy and pandas. It is highly useful to also have ipython installed. jupyter is necessary to run the example notebooks in examples/.

Usage

Calculation of diversity reduction (B values) uses a lookup table of two-locus predictions for relative reduction in diversity, and combines the effects across many loci multiplicatively. A first-order correction for interference can be applied by adjusting parameters based on the local rescaling of effective population sizes. We generate lookup tables using the moments++ mode,l available here: https://github.com/gvbarroso/momentspp/tree/main.

In addition to the lookup table, we need one or more arrays of constrained genomic elements that experience selected mutations, an array indicating estimates of the per-base deleterious mutation rate, and a recombination map or per-base recombination rate. Along with these data we require parameters for the relevant gamma-distributed DFEs, which may include a point mass of neutral mutations.

Example usage is shown in human-chr22-example.ipynb.

Current features:

• Discretized gamma DFEs
• Handling of mutation maps
• Allows multiple element types (with their own DFEs)
• Likelihood function
• Inference of drift-effective Ne