JAMMA
JAMMA stands for Highly-Accelerated Multi-method Mixed-Model Association. It is a Python and C reimplementation of GEMMA for genome-wide association studies (GWAS), using linear mixed models to account for relatedness between samples.
JAMMA reads PLINK binary or BGEN v1.2 data and supports GEMMA's core univariate LMM commands. Native C kernels accelerate association testing, while memory checks and chunked processing help fit analyses to available RAM. Use it from the command line or through a single Python function.
Install · First run · Python API · Performance · Documentation
Installation
Requires Python 3.11+ and NumPy 2.4.6+. PLINK itself is not required to read
.bed, .bim, and .fam files.
macOS 13.3+ or smaller Linux and Windows analyses
python -m pip install jamma
jamma --help
On macOS 13.3+, JAMMA can use Accelerate's native 64-bit BLAS integer interface on Apple Silicon and Intel Macs. On Linux and Windows, the standard NumPy build is suitable for smaller datasets; use the installation below for analyses above roughly 46,000 samples.
Large analyses on Linux and Windows x86_64
Large eigendecompositions need ILP64, a BLAS interface with 64-bit integers. The usual 32-bit interface can overflow around 46,000 samples. Install the runtime dependencies first, then NumPy with MKL ILP64, then JAMMA:
python -m pip install psutil loguru threadpoolctl click progressbar2 bed-reader
python -m pip install numpy --index-url https://michael-denyer.github.io/numpy-mkl --force-reinstall --upgrade
python -m pip install jamma --no-deps
# zstd-compressed BGEN on Python < 3.14 also needs: pip install 'backports-zstd>=1.7.0'
--no-deps preserves the chosen NumPy build during JAMMA installation. Installing
other packages later can replace it, so check the backend before a large run.
See the installation and backend verification guide
for details, and Deployment for Docker setup.
Quick start
Run with your data
For a PLINK dataset named data/study.bed, data/study.bim, and
data/study.fam, pass the prefix data/study. The default phenotype is column 6
of the .fam file.
# Compute centered kinship and save it for reuse.
jamma -gk 1 -bfile data/study -o kinship -outdir output
# Run a Wald association test using that kinship.
jamma -lmm 1 -bfile data/study -k output/kinship.cXX.npy -o results -outdir output
The commands create:
| File | Contents |
|---|---|
output/kinship.cXX.npy |
Centered kinship matrix in NumPy binary format |
output/results.assoc.txt |
Association results, including effect estimates and Wald p-values |
output/results.log.txt |
Run log |
An association command needs a kinship source: -k, the saved eigen files
-d and -u, or -loco, which computes kinship internally. Kinship and eigen
files default to binary .npy; add --legacy-text when you need GEMMA text
output. Existing text kinship files work as -k input.
BGEN imputed dosages
JAMMA reads BGEN v1.2 files (layout 2, biallelic, unphased diploid, bit depth
1 to 16) with their .sample file and a bgenix .bgi index
(bgenix -g data/imputed.bgen -index). A BGEN file carries no phenotypes, so
pass them with -p: a whitespace-separated file with no header and one row per
sample in .sample order, where NA and -9 mark a missing value. -n
selects the column, starting at 1.
jamma -gk 1 -bgen data/imputed.bgen -p pheno.txt -info 0.8 -o kinship -outdir output
jamma -lmm 1 -bgen data/imputed.bgen -p pheno.txt -info 0.8 -k output/kinship.cXX.npy -o results -outdir output
-sampledefaults to the.bgenpath with.samplein place of.bgen, and-bgidefaults to the.bgenpath plus.bgi.- The counted allele is the first allele of each variant, so the dosage is
2·P(11) + P(12).
allele1andafin.assoc.txtrefer to that allele. -infokeeps SNPs whose imputation INFO is at least the threshold, for kinship and association alike. INFO is GCTA's--info, recomputed over the analysed samples rather than read from an imputation summary. It applies only to BGEN input.-hweis rejected with-bgen, because fractional dosages fall in no HWE genotype class.--backend numpywithout-locois rejected too: the batch runner holds hard calls in memory, so BGEN input always streams.- zstd-compressed files need the
zstdextra below Python 3.14:python -m pip install "jamma[zstd]". -palso works with-bfile, in place of the.famphenotype columns.
Try the included example
After installing JAMMA, clone the repository to obtain the synthetic dataset:
git clone https://github.com/michael-denyer/jamma.git
cd jamma
jamma -gk 1 -bfile tests/fixtures/gemma_synthetic/test -o kinship -outdir output/example
jamma -lmm 1 -bfile tests/fixtures/gemma_synthetic/test -k output/example/kinship.cXX.npy -o results -outdir output/example
Open output/example/results.assoc.txt to inspect the results. The fixture is
included in the repository; installing the package alone does not provide it.
Supported analyses
| Analysis | Option |
|---|---|
| PLINK or BGEN genotypes, phenotype file | -bfile, -bgen, -p |
| Centered or standardized kinship | -gk 1 or -gk 2 |
| Wald, likelihood ratio, or Score test | -lmm 1, -lmm 2, or -lmm 3 |
| All three association tests | -lmm 4 |
| Leave-one-chromosome-out analysis (LOCO) | -loco |
| Covariates, including categorical columns | -c, -cat |
| Multiple phenotypes with eigendecomposition reuse | -n "1 2 3" |
| SNP subsets and quality filters | -snps, -ksnps, -maf, -miss, -hwe, -info |
| Saved eigendecomposition and LOCO caches | -eigen, -d, -u, --eigen-dir |
For example, run all tests with covariates, or compute a separate kinship for each chromosome's LOCO analysis:
jamma -lmm 4 -bfile data/study -k output/kinship.cXX.npy -c covars.txt -o adjusted
jamma -lmm 1 -bfile data/study -loco -o loco
See the User Guide for input formats and examples, and Configuration for every flag and default.
GEMMA CLI parity
For supported univariate LMM workflows, replace gemma with jamma while
keeping the core flags and PLINK inputs. Association output uses GEMMA's
mode-dependent .assoc.txt format.
Compatibility has limits. JAMMA does not implement multivariate LMM, BSLMM, plain linear regression, or BIMBAM input. Binary kinship output is the default, and floating-point results are compared within documented tolerances rather than required to match bit for bit.
Read the numerical equivalence analysis, validation coverage and remaining scope, and known differences from GEMMA when migrating a pipeline.
Python API
gwas() loads the data, computes or reads kinship, runs the association tests,
and writes results:
from jamma import gwas
result = gwas("data/study", output_dir="output", output_prefix="results")
print(f"Tested {result.n_snps_tested} SNPs in {result.timing.total_s:.1f}s")
print(result.assoc_path) # output/results.assoc.txt
Supply kinship_file="output/kinship.cXX.npy" to reuse a matrix, lmm_mode=4
to run all tests, or loco=True for LOCO. For BGEN input, pass
gwas(bgen="data/imputed.bgen", phenotype_file="pheno.txt", info=0.8) in
place of the PLINK prefix. Use phenotype_columns=[1, 2, 3] to
share one eigendecomposition across phenotypes; this is separate from a
multivariate LMM.
Results stream to disk. result.associations is empty for this pipeline;
result.assoc_path identifies the output, and result.assoc_paths lists the
files for multiple phenotypes. See the Python API guide
for more examples and lower-level components.
Memory safety
JAMMA checks memory before major allocations, chooses batch or streaming execution, and writes association results incrementally. These checks reduce allocation failures; they cannot guarantee that the operating system will never run out of memory.
Streaming reduces genotype memory, but kinship and eigenvectors still require dense matrices whose storage grows with the square of the sample count. ILP64 removes the BLAS integer limit; it does not remove the RAM requirement. At 100,000 samples, the documented eigendecomposition estimates are roughly 240 GB with DSYEVD or 160 GB with the lower-workspace DSYEVR path. The estimator adds a safety margin (10%, capped at 10 GB) for the process's own memory: a 100,000-sample Wald run measured 250 GB peak resident memory on 2026-09-23.
See memory planning before scaling up.
Performance
JAMMA on mouse_hs1940 (1,940 samples x 12,226 SNPs; 1,410 samples and 10,768
SNPs retained for association), Apple M5 Pro (18 cores), Accelerate-ILP64,
GEMMA 0.98.5, measured 2026-09-24 at revision bea53eec with a load average
between 1.1 and 2.8 on 18 cores. Every row times a fresh process from PLINK
input to written output, best of three with backend order rotated. Association
rows read the same precomputed kinship file in both tools.
| Operation | GEMMA (OpenBLAS) | GEMMA (Accelerate) | JAMMA NumPy | JAMMA NumPy+C | JAMMA NumPy+C (stream) | C speedup | vs GEMMA (OB) | vs GEMMA (Accel) |
|---|---|---|---|---|---|---|---|---|
Kinship (-gk 1) |
981ms | 1.2s | 765ms | 407ms | n/a | 1.9x | 2.4x | 2.8x |
LMM Wald (-lmm 1) |
6.9s | 4.2s | 5.9s | 520ms | 547ms | 11.4x | 13.2x | 8.1x |
LMM All (-lmm 4) |
12.7s | 7.5s | 11.0s | 577ms | 571ms | 19.1x | 22.3x | 13.1x |
| Full GWAS Wald (compute kinship + association) | 7.9s | 5.4s | 6.1s | 652ms | 692ms | 9.4x | 12.1x | 8.3x |
LMM Wald+4cov (-lmm 1 -c) |
26.1s | 12.4s | 15.1s | 1.1s | 1.1s | 14.2x | 24.6x | 11.7x |
| Backend | LOCO Wald | vs fastest GEMMA |
|---|---|---|
| GEMMA (OpenBLAS) | 34.4s | 1.0x |
| GEMMA (Accelerate) | 33.3s | 1.0x |
| JAMMA NumPy+C | 3.2s | 10.6x |
LOCO computes each chromosome's excluded kinship and tests each SNP once in both tools. Every repetition's output was checked against the first within the validation tolerances before any time was recorded.
See Performance for the protocol, raw repetitions, run-to-run ranges and the large-scale (125k) results.
Architecture
The pipeline loads PLINK or BGEN data, computes or reads kinship, decomposes the kinship
matrix, and tests SNPs in batches. The jlinalg layer dispatches linear algebra
to vendor ILP64 BLAS/LAPACK, with a NumPy fallback. The association C extension
provides OpenMP-parallel kernels. Batch and streaming execution both run with or
without that extension; BGEN input needs it, since the decoder is C.
View the pipeline diagram
See Architecture for component responsibilities and Code Map for source navigation.
Documentation
| I want to... | Read |
|---|---|
| Install JAMMA or troubleshoot setup | Getting Started |
| Choose inputs, tests, and output formats | User Guide |
| Look up a flag or environment variable | Configuration |
| Understand a statistical or computing term | Glossary |
| Reproduce benchmarks or plan a large run | Performance |
| Assess numerical agreement with GEMMA | Equivalence and validation matrix |
| Build, test, or deploy JAMMA | Development, Testing, and Deployment |
| See release history | Changelog |
Contributing
Start with CONTRIBUTING.md for prerequisites, test conventions,
and pull request guidance. A development checkout uses uv and prek:
git clone https://github.com/michael-denyer/jamma.git
cd jamma
uv sync
uv run python -m jamma.lmm._compile_accel
uv run python -m jamma.jlinalg._compile_jlinalg
prek install
uv run pytest tests/ -x
prek run --all-files
Report bugs through GitHub issues. Include the command, JAMMA version, platform, BLAS backend, and relevant log output so the problem can be reproduced.
Citation and acknowledgments
Use the archived JAMMA release and citation metadata when citing the software. JAMMA builds on the methods and file conventions of GEMMA.
To support development, buy the maintainer a coffee.
License
JAMMA is licensed under GPL-3.0-or-later.
1 maintainer
Release files for jamma 8.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| jamma-8.2.0.tar.gz | 76.5 MB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| jamma-8.2.0-cp314-cp314-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl | CPython 3.14 | CPython 3.14 | Linux glibc 2.26+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| jamma-8.2.0-cp314-cp314-macosx_14_0_arm64.whl | CPython 3.14 | CPython 3.14 | macOS 14.0+ ARM64 | Details |
| jamma-8.2.0-cp313-cp313-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl | CPython 3.13 | CPython 3.13 | Linux glibc 2.26+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| jamma-8.2.0-cp313-cp313-macosx_14_0_arm64.whl | CPython 3.13 | CPython 3.13 | macOS 14.0+ ARM64 | Details |
| jamma-8.2.0-cp312-cp312-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.26+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| jamma-8.2.0-cp312-cp312-macosx_14_0_arm64.whl | CPython 3.12 | CPython 3.12 | macOS 14.0+ ARM64 | Details |
| jamma-8.2.0-cp311-cp311-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl | CPython 3.11 | CPython 3.11 | Linux glibc 2.26+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| jamma-8.2.0-cp311-cp311-macosx_14_0_arm64.whl | CPython 3.11 | CPython 3.11 | macOS 14.0+ ARM64 | Details |
Total release size: 81.1 MB
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