ibdpainting 0.2.0

Identify parents of a crossed individual by comparing identity in windows across their genomes

ibdpainting

ibdpainting is a Python tool to visually validate the identity of crossed individuals from genetic data.

Contents

• Premise
• Installation
• Input data files
• Usage
• Author information
• Contributing

Premise

ibdpainting addresses the situation where you have multiple individuals derived from a crosses between individuals in a reference panel, and you want to verify that the crosses really are the genotype you think they are. Taking the simple example of a biparental cross, you would expect an offspring of the F2 generation or later to be a mosaic of regions homozygous for either parent, potentially interspersed with heterozygous regions, depending on the generation. ibdpainting is a tool to visualise this mosaic pattern.

Installation

Install with pip:

pip install ibdpainting

Input data files

The program requires two HDF5 files created from VCF files:

• Input panel: An HDF5 file containing SNPs for the crossed individual(s). This can contain multiple individuals, but the program will only work on one at a time.
• Reference panel: An HDF5 file conataining SNP information for a panel of reference candidate parents.

The reason for using HDF5 is that it allows for loading data in chunks, which is much quicker than loading an entire VCF file into memory every time you want to check a single sample. I recommend creating this using vcf_to_hdf5 from scikit-allel. For example:

import allel
allel.vcf_to_hdf5('example.vcf', 'example.h5', fields='*', overwrite=True)

Tips for preparing the data:

• ibdpainting will only compare SNPs that intersect the input and reference files. One one hand, this means that it does not matter if the offspring and reference files contain SNPs that do not match exactly. On the other, this may cause problems if you are comparing samples with loads of structural variation.
• It is better to have a smaller number of reliable SNPs than a larger number of dubious SNPs. For example, in Arabopidopsis thaliana that means only using common SNPs located in genes.
• ibdpainting creates a subplot for every contig label in the input/reference panel. If you work on an organism with many chromosomes or incompletely assembled contigs, this could get messy. There is currently no way to subset which contigs are shown, so it is probably easiest to supply input data based on only a subset of contigs. The longest contigs are likely to be most informative because you are more likely to be able to spot recombination break points.

Usage

After installing, ibdpainting can be run as a command line tool as follows

ibdpainting \
    --input input_file.hdf5 \
    --reference reference_panel.hdf5 \
    --window_size 500000 \
    --sample_name "my_cross" \
    --expected_match "mother" "father" \
    --outdir path/to/output/directory

Explanation of the parameters (see also ibdpainting --help):

• --input: HDF5 file containing the crossed individuals. See above.
• --reference: HDF5 file containing the reference panel. See above.
• --window_size: Window size in base pairs.
• --sample_name: Name of the crossed individual to compare to the reference panel. This must be present in the input file - you can check the original VCF file with something like bcftools query -l $input_vcf.vcf.gz | grep "my_cross".
• --expected_match: List of one or more expected parents of the test individual. These names should be among the samples in the reference panel. Names should be separated by spaces.
• --outdir: Path to the directory to save the output.

Additional optional parameters:

• --keep_ibd_table: Write an intermediate text file giving genetic distance between the crossed individual and each candidate at each window in the genome. Defaults to False, because these can be quite large.
• --max_to_plot: Integer number of candidates to plot. ibdpainting makes an intial ranking of candidates based on genome-wide similarity to the test individual, and plots only the top candidates.

Author information

Tom Ellis

Contributing

I will repeat the following from the documentation for scikit-allel:

This is academic software, written in the cracks of free time between other commitments, by people who are often learning as we code. We greatly appreciate bug reports, pull requests, and any other feedback or advice. If you do find a bug, we’ll do our best to fix it, but apologies in advance if we are not able to respond quickly. If you are doing any serious work with this package, please do not expect everything to work perfectly first time or be 100% correct. Treat everything with a healthy dose of suspicion, and don’t be afraid to dive into the source code if you have to. Pull requests are always welcome.