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23andMe/phasedibd

templated positional Burrows-Wheeler transform

build

phasedibd is a Python package developed by the 23andMe Ancestry Research team to compute phase aware identity-by-descent (IBD) using the templated positional Burrows-Wheeler transform (TPBWT). See here for our manuscript describing the algorithm, its parameter options, and its performance in detail.

contact: Will Freyman willf@23andme.com

Dependencies

phasedibd requires the Python packages Cython, numpy, and pandas to be installed.

To build, install, and run tests:

After cloning the repo you'll need to compile it. First cd phasedibd and then

make
python setup.py install 
python tests/unit_tests.py

Now in Python you can import the module:

import phasedibd as ibd

Parameter options:

Adjust sensitivity to error (and the speed of the analysis) with the template argument in the TPBWTAnalysis constructor. template must be a two dimensional python list; by default it is:

[[1, 0, 1, 0],
 [0, 1, 0, 1],
 [1, 1, 0, 0],
 [0, 0, 1, 1],
 [1, 0, 0, 1],
 [0, 1, 1, 0]]

This arrangement of templates guarantees all matches will be found as long as no more than two errors are in any four SNP-window.

When template is set to [[1]] the TPBWT collapses down to Durbin’s original PBWT. This is fast but highly sensitive to error.

When template is set to [[1,0],[0,1]] the TPBWT will find all matches as long as no more than one error is found within any two SNP window.

When template is set to [[1,0,0],[0,1,0],[0,0,1]] the TPBWT will find all matches as long as no more than two errors are found within any three SNP window.

Other parameters are set in the compute_ibd method in TPBWTAnalysis:

Parameter Description
chromosome Default value ‘all’; valid values ‘1’, ‘2’, ..., ‘22’, ‘X’. This is useful for setting up computes that parallelize by chromosome.
segments_out_path By default the IBD segments are output as a pandas DataFrame. Large analyses will use up all available memory and throw a MemoryError. By setting this parameter the IBD segments will instead be written to a file.
use_phase_correction Default value is True. Turns on/off the phase correction heuristic for the entire analysis.
L_m Default value is 300. The minimum number of SNPs a matching subsegment must span to be included in an IBD segment.
L_f Default value is 3.0. The minimum genetic length (cM) of an IBD segment.
missing_site_threshold Default value is 10. The maximum number of consecutive missing SNPs a match will be extended over.

IBD output format:

The output is either a pandas DataFrame or a .csv file with the following columns.

chromosome    start    end    start_cm    end_cm    id1    id2    id1_haplotype    id2_haplotype
1             143      2500   2.375       9.8427    243    634    0                1 
1             3470     7037   12.679      26.348    84     591    1                1                             

The start and end columns are the start and end SNP, not the physical (bp) positions. So the number of SNPs a segment spans is end - start. The columns id1_haplotype and id2_haplotype must always be 0 or 1.

Any self IBD is included in the output like this:

chromosome    start    end    start_cm    end_cm    id1    id2    id1_haplotype    id2_haplotype
1             7467     9940   27.236      50.621    586    586    1                0   

For in-sample analyses id1 < id2. For out-of-sample analyses id1 is always from the first haplotype alignment and id2 is from the second haplotype alignment.

To run a basic in-sample IBD analysis on VCF files:

First import the module:

import phasedibd as ibd

Now create an object to hold the haplotype alignment. This object parses the VCF file; it expects a VCF file with a diploid GT field. In case of haploid data, the GT field must be transformed to a pseudo-diploid field (such as 0 -> 0|0). Additionally, the sites in the VCF file must be sorted by physical position. There should be one VCF file per chromosome.

haplotypes = ibd.VcfHaplotypeAlignment('chr22_sorted.vcf')

Next we perform the IBD analysis -- instantiate an object of class TPBWTAnalysis and call its method compute_ibd(). This method has many options (described above) for performing different types of analyses. The default output is a pandas DataFrame with all the IBD segments.

tpbwt = ibd.TPBWTAnalysis()
ibd_results = tpbwt.compute_ibd(haplotypes)

Optionally you can specify a genetic map:

haplotypes = ibd.VcfHaplotypeAlignment('chr22_sorted.vcf', 'chr22_genetic_map.map')

If used, it should be a PLINK format genetic map file and contain the same SNPs found in the VCF file. The map file format is:

22 . 0.9 16888577
22 . 1.0 16900001
22 . 1.5 17007138
22 . 1.7 19107656

If no genetic map is used then the physical positions in the VCF file will be converted to cM assuming 1e6 bp = 1 cM.

To run a basic out-of-sample IBD analysis:

Runs an IBD analysis between two sets of haplotypes:

import phasedibd as ibd
haplotypes_a = ibd.VcfHaplotypeAlignment('a_chr1.vcf', 'chr1_genetic_map.map')
haplotypes_b = ibd.VcfHaplotypeAlignment('b_chr1.vcf', 'chr1_genetic_map.map')
tpbwt = ibd.TPBWTAnalysis()
ibd_results = tpbwt.compute_ibd(haplotypes_a, haplotypes_b)

The output is a pandas DataFrame with only the IBD segments shared between the two sets of haplotypes. It does not include any IBD shared within the same alignment object.

Generating TPBWT-compressed haplotypes:

TPBWT-compressed haplotypes are stored in the .tpbwt binary file format. TPBWT-compressed haplotypes are useful for fast and efficient out-of-sample IBD computes against very large cohort panels.

To TPBWT-compress the haplotypes, set up the haplotypes just like above but now pass them into the compress_alignment() method:

haplotypes = ibd.VcfHaplotypeAlignment('chr1_sorted.vcf', 'chr1_genetic_map.map')
tpbwt.compress_alignment('compressed_haplotypes/', haplotypes)

This will generate files for each chromosome (1.tpbwt, 2.tpbwt, …, X.tpbwt) in the directory compressed_haplotypes/.

To combine two haplotype alignments into a larger TPBWT-compressed haplotype:

tpbwt.compress_alignment('combined_compressed_haplotypes/', 
    haplotypes_1, haplotypes_2)

Generating TPBWT-compressed haplotypes while simultaneously computing IBD:

The compress_alignment() method described above is the most memory efficient way to TPBWT-compress haplotypes. However, it is also possible to simultaneously TPBWT-compress haplotypes and compute IBD. Set up an IBD compute just like above but use the compressed_out_path argument:

haplotypes = ibd.VcfHaplotypeAlignment('chr1_sorted.vcf', 'chr1_genetic_map.map')
ibd_segs = tpbwt.compute_ibd(haplotypes, compressed_out_path=’compressed_haplotypes/’)

During the IBD compute this will generate files for each chromosome (1.tpbwt, 2.tpbwt, …, X.tpbwt) in the directory compressed_haplotypes/.

The IBD compute will be slightly slower due to writing the binary .tpbwt files.

To combine two haplotype alignments into a larger TPBWT-compressed haplotype:

ibd_segs = tpbwt.compute_ibd(haplotypes_1, haplotypes_2, 
    compressed_out_path=’combined_compressed_haplos/’)

Using TPBWT-compressed haplotypes:

Create CompressedHaplotypeAlignment objects with the path to the directory that contains all the TPBWT-compressed files. A simple out-of-sample analysis:

haplotypes_1 = ibd.CompressedHaplotypeAlignment(‘compressed_haplotypes_1/’)
haplotypes_2 = ibd.CompressedHaplotypeAlignment(‘compressed_haplotypes_2/’)
tpbwt = ibd.TPBWTAnalysis()
ibd_segs = tpbwt.compute_ibd(haplotypes_1, haplotypes_2)

Note that any CompressedHaplotypeAlignment objects you want to analyze together must be compressed with the same template and the same set of SNPs. CompressedHaplotypeAlignment objects can be analyzed together with VcfHaplotypeAlignment objects as long as they share the same set of SNPs.

Other examples:

See the test file for more examples on how to set up different types of analyses: tests/unit_tests.py

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Identity-by-descent inference using the templated positional Burrows-Wheeler transform (TPBWT)

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