RHE

Randomized Haseman–Elston regression for Multi-variance Components: Python wrappers for Gene-ENviroment Interaction Estimator

Installation

pip install rhe

This package provides basic Python wrappers for the GENIE executables. Full documentation for the GENIE project, as well as sample data necessary to run the toy examples below, is available here: https://github.com/sriramlab/GENIE

Interface

This package provides the following functions:

• rhe.run_genie
• rhe.run_genie_mem
• rhe.run_genie_multi_pheno

Run GENIE with a list of command line arguments

from rhe import run_genie

gen = 'path/to/test'
phen = 'path/to/test.pheno'
covar = 'path/to/test.cov'
annot = 'path/to/single.annot'
env = 'path/to/test.env'

run_genie(
    ['-g', gen, '-p', phen, '-c', covar, '-e', env, '-m', 'G+GxE+NxE', '-k', '10', '-jn', '10', '-o',
     'path/to/analysis.out', '-annot', annot, '-t', '6']
)

Run GENIE with a single string of command line arguments

from rhe import run_genie

gen = 'path/to/test'
phen = 'path/to/test.pheno'
covar = 'path/to/test.cov'
annot = 'path/to/single.annot'
env = 'path/to/test.env'

run_genie(
    f'-g {gen} -p {phen} -c {covar} -e {env} -m G+GxE+NxE -k 10 -jn 10 -o path/to/analysis.out -annot {annot} -t 6'
)

Run GENIE with using a config file

from rhe import run_genie_mem

with open('config.txt', 'w') as f:
    f.write('genotype=path/to/test\n')
    f.write('phenotype=path/to/test.pheno\n')
    f.write('covariate=path/to/test.cov\n')
    f.write('environment=path/to/test.env\n')
    f.write('annotation=path/to/single.annot\n')
    f.write('output=path/to/test.py.list.3.out\n')
    f.write('nthreads=6\n')
    f.write('num_vec=20\n')
    f.write('num_jack=10\n')
    f.write('trace=1\n')
    f.write('model=G\n')
    f.write('verbose=1\n')

run_genie_mem(
    ['--config', 'config.list.txt']
)

Citation

1. Ali Pazokitoroudi, Andrew Dahl, Noah Zaitlen, Saharon Rosset, Sriram Sankararaman.
bioRxiv 2023.12.12.571316; doi: https://doi.org/10.1101/2023.12.12.571316
2. Ali Pazokitoroudi, Yue Wu, Kathryn S. Burch, Kangcheng Hou, Aaron Zhou, 
Bogdan Pasaniuc, Sriram Sankararaman. Nature Communications (2020); doi: https://doi.org/10.1101/522003

Version

v1.0.0