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Haplotype-based inference of recent effective population size in modern and ancient DNA samples

Project description

HapNe

Haplotype-based inference of recent effective population size in modern and ancient DNA samples

Summary

  1. Prerequisites
  2. HapNe-LD
  3. HapNe-IBD
  4. Analyses of ancient samples
  5. How to cite

1. Prerequisites

Some pre-processing features require plink1.9 and plink2 to be installed. HapNe assumes that the commands plink and plink2 work in the terminal.

All functionalities have been tested on macOS and Linux within the following conda environment:

name: HapNe
channels:
  - conda-forge
  - bioconda
  - defaults
dependencies:
  - python
  - pytest
  - numpy
  - pandas
  - plink
  - plink2
  - flake8
  - numba

We strongly encourage to install HapNe within this environment by running: conda env create --file conda_environment.yml

2. HapNe-LD

HapNe-LD can be run by adapting the following config file:

[CONFIG]
vcf_file=data
keep=data.keep
map=genetic_map_chr@_combined_b37.txt
pseudo_diploid=False
output_folder=HapNe/data
population_name=POP
  • vcf_file: path to the vcf file (without the .vcf.gz extension)
  • keep (facultative): samples to keep, useful to filter out relatives
  • map: path to the genetic maps
  • pseudo_diploid: False for modern data, true for ancient ones
  • output_folder: folder where the results will be saved
  • population_name: name of the analysis

The analysis can be run using a script like this one:

from configparser import ConfigParser
import argparse

from hapne.convert.tools import split_convert_vcf
from hapne.ld import compute_ld, compute_ccld
from hapne import hapne_ld


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='HapNe-LD pipeline')
    parser.add_argument('--config_file',
                    help='configfile')
    args = parser.parse_args()

    config = ConfigParser()
    config.read(args.config_file)
    print("Starting stage 1")
    split_convert_vcf(config)
    print("Starting stage 2")
    compute_ld(config)
    compute_ccld(config)
    print("Starting stage 3")
    hapne_ld(config)

3. Running HapNe-IBD

Starting from a vcf file, HapNe starts by splitting the file into different genomic regions and convert them into FastSMC's input format:

from configparser import ConfigParser
import pandas as pd
import argparse

from hapne.convert.tools import vcf2fastsmc

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='HapNe-IBD preprocessing pipeline')
    parser.add_argument('--config_file',
                    help='configfile')
    args = parser.parse_args()

    config = ConfigParser()
    config.read(args.config_file)
    print("Starting stage 1")
    vcf2fastsmc(config)

FastSMC can then be run on the 39 .haps files generated by the previous command.

It is then required to add the location of the FastSMC output in the config file

[CONFIG]
vcf_file=data
keep=data.keep
map=genetic_map_chr@_combined_b37.txt
pseudo_diploid=False
output_folder=HapNe/data
population_name=POP
ibd_files=FASTSMC_OUTPUT_FOLDER

Using this config file, HapNe-IBD can be run using the following script:

from configparser import ConfigParser
from hapne.ibd import build_hist

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='HapNe-IBD preprocessing pipeline')
    parser.add_argument('--config_file',
                    help='configfile')
    args = parser.parse_args()

    config = ConfigParser()
    config.read(args.config_file)
    build_hist(config)
    hapne_ibd(config)

4. aDNA analyses

HapNe provides a pipeline to easily study samples from the "Allen Ancient DNA Resource" data set. After downloading the data, HapNe can take a file with the indices of samples to study as input (Caribbean_Ceramic_recent.keep in the following example).

Note that it is assumed that samples present in the keep file are unrelated. Kinship information is usually present in the anno file.

To perform the analysis, create the following configuration file:

[CONFIG]
eigen_root=DATA/v50.0_1240k_public
anno_file=DATA/v50.0_1240k_public.anno
keep=CONFIG/Caribbean_Ceramic_recent.keep
pseudo_diploid=True
output_folder=RESULTS/Caribbean_Ceramic_recent
population_name=Caribbean_Ceramic_recent

eigen_root describes the location to the main data set, anno_file points to the annotation file, keep refers to as a file containing the indices of the individuals to study (one index per row).

The output will be written in a new output_folder folder. pseudo_diploid must be set to true when studying ancient data. Finally, population_name will be used to name the output files.

Next, the following pipeline.py script can be run using python pipeline.py --config_file config.ini

from configparser import ConfigParser
import pandas as pd
import argparse

from hapne.convert.eigenstrat2vcf import eigenstrat2vcf
from hapne.convert.eigenstrat2vcf import split_convert_vcf
from hapne.ld import compute_ld, compute_ccld, create_cc_file
from hapne.utils import get_age_from_anno
from hapne import hapne_ld


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='HapNe-LD pipeline')
    parser.add_argument('--config_file',
                    help='configfile')
    args = parser.parse_args()

    config = ConfigParser()
    config.read(args.config_file)
    print("Starting stage 1")
    eigenstrat2vcf(config)
    print("Starting stage 2")
    split_convert_vcf(config)
    print("Starting stage 3")
    compute_ld(config)
    compute_ccld(config)
    print("Starting stage 4")
    get_age_from_anno(config)
    hapne_ld(config)

5. How to cite?

If you use this software, please cite:

R. Fournier, D. Reich, P. Palamara. Haplotype-based inference of recent effective population size in modern and ancient DNA samples. (preprint) bioRxiv, 2022.

Acknowledgments

Two scripts of the convert module were downloaded from the following repositories and edited to fit into this package:

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