Identify runs of homozygosity (ROH) in low coverage ancient human DNA data (1240K SNPs) using modern reference panel
This package identifies runs of homozygosity (ROH) in ancient and present-day DNA by using a panel of reference haplotypes. This package contains functions and wrappers to call ROH and functions for downstream analysis and visualization.
For downward compatibility, the package uses
hapsburg as module name. After installation you can import Python functions via
from hapsburg.XX import YY
Scope of the Method
Standard parameters are tuned for human 1240K capture data (ca. 1.2 million SNPs used widely in human aDNA analysis) and using 1000 Genome haplotypes as reference. The software is tested on a wide range of test applications, both 1240K data and also whole genome sequencing data downsampled to 1240K SNPs. Successful cases include 45k year old Ust Ishim man, and a wide range of American, Eurasian and Oceanian ancient DNA, showing that the method generally works for split times of reference panel and target up to a few 10k years, which includes all out-of-Africa populations (Attention: Neanderthals and Denisovans do not fall into that range, additionally some Subsaharan hunter gatherer test cases did not give satisfactory results).
Currently, hapROH works on on data for 1240K SNPs and in unpacked or packed
eigenstrat format (which is widely used in human ancient DNA). The software assumes pseudo-haploid or diploid genotype data (the mode can be set, by default it is pseudo-haploid). The recommended coverage range is 400,000 or more 1240K SNPs covered at least once (i.e. at least ca. 0.3x coverage).
If you have whole genome data available, you have to downsample an create eigenstrat files for biallelic 1240k SNPs first.
In case you are planning applications to other kind of SNP or bigger SNP sets, or even other organisms, the method parameters have to be adjusted (the default parameters are specifically optimized for human 1240K data). You can mirror our procedure to find good parameters (described in the publication), and if you contact me for assistance - I am happy to share my own experience.
You can install the package using the Package manager pip:
python3 -m pip install hapROH
python3 -m makes sure you use your python installation)
If you have it already installed and want to upgrade to a newer hapROH version you can use:
python3 -m pip install hapROH --upgrade
The package distributes source code. The setup.py contains information that should automatically install the package.
For customized installations, find more info in the section below (
To get started, please find vignette jupyter notebooks: https://www.dropbox.com/sh/eq4drs62tu6wuob/AABM41qAErmI2S3iypAV-j2da?dl=0
These are a ressource to do show example usecases, that you can use as template for your own applications.
These notebooks walk you through examples for
- how to use the core functions to call ROH from eigenstrat files, and generate ROH tables from results of multiple individuals ('callROH_vignette')
- how to use functions for visualizing ROH results ('plotting_vignette' - warning: Some of these are experimental and require additional packages. You might want to consider creating your own plotting functions for visualizing the results in the way that works best for you)
- how to call IBD on the X chromosome between two male X chromosomes ('callIBD_maleX_vignette', warning: experimental)
Download reference Data
hapROH currently uses global 1000 Genome data (n=5008 haplotypes), filtered down to bi-allelic 1240K SNPs. We use .hdf5 format for the reference panel - which includes a genetic map.
You can download the prepared reference data (including a necessary metadata .csv) from:
and unpack it using
tar -xvf FILE.tar.gz
You then have to link the paths in the hapROH run parameters (see vignette notebook)
Example Use Case: Vignettes
Please find example notebooks, walking you through a typical application to an eigenstrat file at https://www.dropbox.com/sh/eq4drs62tu6wuob/AABM41qAErmI2S3iypAV-j2da?dl=0
All you need is a Eigenstrat file, and the reference genome data (see link above), and you are good to go to run your own ROH calling!
There is a vignette notebook for...
- walking you through the calling of ROH (callROH)
- producing various figures from the output (plotROH)
- describing the experimental functionality to identify IBD segements between pairs of male X chromosomes (callIBD_maleX)
- estimating population sizes from inferred ROH, using a likelihood framework (estimateNe)
The basic requirements for calling ROH are kept minimal and only sufficient for the core ROH calling ('numpy', 'pandas', 'scipy' & 'h5py'). If you want to use extended analysis and plotting functionality: There are extra Python packages that you need to install (e.g. via
- If you want to use the advanced plotting functionality, you need
- For plotting of maps, you will need
basemap(warning: installing can be tricky on some architectures).
- If you want to use the effective population size fitting functionality from ROH output, you require the package
For performance reasons, the heavy lifting of the algorithm is coded into a c method (cfunc.c). This "extension" is built via cython from cfunc.pyx This should be done automatically via the package cython (as CYTHON=True in setup.py by default).
You can also set CYTHON=False, then the extension is compiled from cfunc.c directly (experimental, not tested on all platforms).
The code used to develop this package is deposited at the github repository: https://github.com/hringbauer/hapROH
The package is packed in the folder
./package/. In addition, there are a large number of notebooks used to test and extensively use the functionality in
Frequently Asked Questions (FAQ)
I have a .bam, what should I do?
For ancient DNA data,
hapROH takes pseudo-haploid eigenstrat files as input (https://reich.hms.harvard.edu/software/InputFileFormats). As of 2021, this data type is widely used in human ancient DNA data analysis (such as calculating PCA projections or f-statistics) due to its robustness with respect to genotyping assumptions. To produce such files, you will have to generate pseudo-haploid genotype calls from the raw
.bam file. A common tool used for this purpose is
pileupCaller distributed via the software
sequenceTools (described in detail at https://github.com/stschiff/sequenceTools). Ideally, this pseudo-haploid genotype calling is done in a way adjusted to the data generation process (e.g. clipping a certain amount of terminal base-pairs depending on the form of UDG treatment).
roh_full.csv tables, what is the difference between the
The columns without the
M report the index of SNPs covered in the target. One can use them for quality control (e.g. how many SNPs are covered within an identified ROH) and to quickly identify ROH regions by index in the posterior output (which also only outputs data for covered SNPs). The key result column are the ones with the
M, which is the length of each ROH, their start and their end position in Morgan.
Why is the value of
lengthM sometimes multiplied per 100?
Some of the visualizations and in the full individual table depict the data in centimorgan. Thus, the Morgan value is multiplied with 100 to transform from Morgan to centimorgan.
How can I implement hapROH for another set of SNPs, such as all 1000Genome SNPs?
HapROH requires hdf5 reference files for each chromosome. These hdf5 files have to include the phased genotype data as well as a genetic map for each SNP (as a matching array in the field
variants/MAP). One can get such files from a
.vcf file by using the scikit allele function
vcf_to_hdf5, and adding the genetic map field into the resulting .hdf5. For the latter, one can use the function
merge_in_ld_map in this package (
Example code for preparing the 1240K reference panel including cleaning SNPs and merging in the linkage map can be found at:
For 1000 Genome SNPs, a reference panel (which has not been extensively tested) is available upon request.
If you use the software for a scientific publication and want to cite it, you can use: https://www.biorxiv.org/content/10.1101/2020.05.31.126912v1
If you have bug reports, suggestions or general comments, please contact me. I am happy to hear from you. Bug reports and user suggestions will help me to improve this software - so please do not hesitate to reach out!
harald_ringbauer AT eva mpg de (fill in blanks with dots and AT with @)
Big thank you to my co-authors Matthias Steinrücken and John Novembre. The project profited immensely from Matthias' deep knowledge about HMMs and from John's extensive experience in developing population genetics software. Countless discussions with both have been key for moving forward this project. Another big thanks goes to Nick Patterson, who informed me about the benefits of working with rescaled HMMs - substantially improving the runtime of hapROH.
I want to acknowledge users who find and report software bugs (Mélanie Pruvost, Ke Wang, Ruoyun Hui) and all users who reached out with general questions and requests (Rosa Fregel, Federico Sanchez). This feedback has helped to remove errors in the program and to improve its usability. Many thanks!
Author: Harald Ringbauer, 2021
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