pytwosamplemr 0.1.0

Pure-Python port of two-sample Mendelian randomization — the MendelianRandomization estimator suite plus the TwoSampleMR harmonisation / diagnostics workflow.

py-twosamplemr

Pure-Python two-sample Mendelian randomization — a faithful port of the statistical-methods core of the R/CRAN package MendelianRandomization (Yavorska & Burgess, Int. J. Epidemiol. 2017) together with the harmonisation / diagnostics workflow of TwoSampleMR (Hemani et al., eLife 2018).

• PyPI dist name: pytwosamplemr  |  import name: pytwosamplemr
• Pure Python — numpy / scipy / pandas / matplotlib. No rpy2.
• Numerical parity with R is the design goal. Closed-form estimators reproduce MendelianRandomization 0.10.0 bit-for-bit (relative difference < 1e-5); bootstrap-based estimators agree to within Monte-Carlo error.

Installation

pip install pytwosamplemr
# or, from a checkout:
pip install -e .

Quick start

import pytwosamplemr as mr
from pytwosamplemr.datasets import ldl_chd_input

# the MendelianRandomization bundled lipid GWAS example (28 SNPs)
obj = ldl_chd_input()

mr.mr_ivw(obj)        # inverse-variance weighted
mr.mr_egger(obj)      # MR-Egger regression (+ pleiotropy intercept)
mr.mr_maxlik(obj)     # maximum likelihood
mr.mr_divw(obj)       # debiased IVW
mr.mr_conmix(obj)     # contamination mixture
mr.mr_lasso(obj)      # MR-Lasso post-selection
mr.mr_cml(obj, n=17723)   # constrained maximum likelihood

# panel + diagnostics
mr.mr_allmethods(obj, method="main")
mr.mr_heterogeneity(obj)
mr.mr_pleiotropy_test(obj)
mr.mr_singlesnp(obj)
mr.mr_leaveoneout(obj)

# plots
mr.mr_scatter(obj)    # SNP effects + fitted method lines
mr.mr_forest(obj)
mr.mr_funnel(obj)
mr.mr_loo(obj)

What is implemented

Input

Function	Description
mr_input / MRInput	harmonised SNP-exposure / SNP-outcome effect container

Estimators (full coverage)

Function	Method	R parity
mr_ivw	inverse-variance weighted (fixed / random, penalized, correlated SNPs, Cochran's Q)	bit-exact
mr_egger	MR-Egger regression: causal estimate + pleiotropy intercept + I²_GX	bit-exact
mr_median	simple / weighted / penalized weighted median	estimate exact, bootstrap SE ≈
mr_mbe	mode-based estimate	estimate exact, bootstrap SE ≈
mr_maxlik	maximum likelihood (R Nelder-Mead optimiser ported)	bit-exact
mr_divw	debiased IVW	bit-exact
mr_conmix	contamination mixture	bit-exact
mr_lasso	MR-Lasso post-selection IVW	post-estimate exact
mr_cml	constrained maximum likelihood (cML-MA / cML-BIC)	cML-BIC bit-exact; DP ≈
mr_allmethods	run the standard panel and tabulate	—

Diagnostics / workflow (TwoSampleMR side)

harmonise_data · mr_heterogeneity · mr_pleiotropy_test · mr_steiger / directionality_test · mr_singlesnp · mr_leaveoneout

Plots

mr_plot / mr_scatter · mr_forest · mr_funnel · mr_loo

R parity

py-twosamplemr was validated against MendelianRandomization 0.10.0 on the package's own bundled lipid GWAS example data. Closed-form estimators agree with R to machine precision:

Method	R estimate	Python estimate	rel. diff
IVW (random)	2.834214	2.834214	6e-16
IVW (fixed)	2.834214	2.834214	6e-16
MR-Egger	3.252890	3.252890	3e-16
Maximum likelihood	3.224944	3.224944	6e-12
Debiased IVW	2.939646	2.939646	1e-15
Contamination mix	2.730000	2.730000	2e-16
MR-Lasso	2.670896	2.670896	2e-15
cML-MA-BIC	2.894808	2.894808	1e-15

A standout detail: mr_maxlik calls R's optim(method="Nelder-Mead"), which on this rough 29-dimensional likelihood stops at a non-global local point — so reproducing R requires a line-for-line port of R's nmmin C routine (pytwosamplemr/_nmmin.py). With that port the maximum-likelihood estimate matches R to 12 significant figures.

Bootstrap-based estimators (mr_median, mr_mbe, mr_cml with data perturbation) cannot be bit-exact across RNGs: their point estimates still match R exactly (the median / mode location does not depend on the bootstrap), and the bootstrap SE agrees to within ~10%.

The R-parity test suite (tests/test_r_parity.py) runs the R reference driver live and asserts these tolerances; it skips gracefully when R is unavailable.

Testing

python -m pytest tests/ -q

License

GPL-3, matching the upstream MendelianRandomization package (licensed GPL-2 | GPL-3).

References

• Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734-1739.
• Hemani G, et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife. 2018;7:e34408.