JAMMA (Highly-Accelerated Multi-method Mixed-Model Association) -- a modern Python and C reimplementation of GEMMA for large-scale GWAS.
- Drop-in GEMMA replacement: Same CLI flags, same file formats, same results. Change one word in your pipeline.
- Numerical equivalence: Validated against GEMMA -- 100% significance agreement, 100% effect direction agreement
- Fast: Native C kernels accelerate association testing. See the measured backend comparison for timings against NumPy and GEMMA 0.98.5.
- Memory-safe: Pre-flight memory checks prevent OOM crashes before allocation
- Cross-platform: Runs on Linux, macOS, and Windows with NumPy and vendor BLAS
- Optimized for Intel: Best performance on Intel CPUs with MKL BLAS. Runs well on Apple Silicon (Accelerate BLAS). Other architectures (AMD, ARM Linux) work correctly but with less BLAS optimization
- Pure Python + C extensions (OpenMP SIMD): NumPy stack with vendor BLAS dispatch (MKL-ILP64, Accelerate-ILP64) via jlinalg C layer for eigendecomposition and OpenMP-parallel Wald tests
- Large-scale ready: Optional numpy-mkl ILP64 wheels (numpy 2.4.6+) for >46k sample eigendecomposition
Installation
macOS (13.3+)
pip install jamma
That's it. macOS Accelerate BLAS handles large matrices natively (Accelerate-ILP64).
Windows (10+), Windows Server (2016+) and Linux (Intel/AMD)
Install numpy-mkl first -- standard numpy uses 32-bit BLAS integers which overflow at ~46k samples. Pre-built ILP64 wheels are available for Python 3.11-3.14:
pip install psutil loguru threadpoolctl click progressbar2 bed-reader
pip install numpy \
--index-url https://michael-denyer.github.io/numpy-mkl \
--force-reinstall --upgrade
pip install jamma --no-deps
From Git (latest development version):
pip install psutil loguru threadpoolctl click progressbar2 bed-reader
pip install numpy \
--index-url https://michael-denyer.github.io/numpy-mkl \
--force-reinstall --upgrade
pip install git+https://github.com/michael-denyer/jamma.git --no-deps
Why
--no-deps? JAMMA depends onnumpy>=2.4.6, so a normalpip install jammawill pull in standard numpy and overwrite the ILP64 build.--no-depsprevents this; you install the runtime dependencies manually instead.
See the User Guide for ILP64 verification steps.
Platform Support
| Platform | BLAS | ILP64 | Notes |
|---|---|---|---|
| Linux x86_64 | MKL (optimal) | numpy-mkl | Best performance |
| ARM Linux | OpenBLAS | -- | Works correctly |
| ARM Mac (M1+) | Accelerate | native | Excellent performance |
| Intel Mac (macOS 13.3+) | Accelerate | native | Full support |
| Windows x86_64 (10+) | MKL (optimal) | numpy-mkl | Best performance |
| Windows Server x86_64 (2016+) | MKL (optimal) | numpy-mkl | Best performance |
See the User Guide for BLAS backend details.
Quick Start
# Compute kinship matrix (centered relatedness)
jamma -gk 1 -bfile data/my_study -o output
# Output: output/output.cXX.npy (binary, fast)
# Add --legacy-text for GEMMA-compatible text format
# Run LMM association (Wald test)
jamma -lmm 1 -bfile data/my_study -k output/output.cXX.npy -o results
# Multiple phenotypes (eigendecomp computed once, reused)
jamma -lmm 1 -bfile data/my_study -k output/output.cXX.npy -n "1 2 3" -o results
Output files:
output.cXX.npy-- Kinship matrix (binary NumPy format;.cXX.txtwith--legacy-text)results.assoc.txt-- Association results (chr, rs, ps, n_miss, allele1, allele0, af, beta, se, logl_H1, l_remle, p_wald)results.log.txt-- Run log
The reader auto-detects format, so existing .cXX.txt files still work as -k input.
GEMMA CLI Parity
JAMMA supports GEMMA's core GWAS flags (-gk, -lmm, -bfile, -k, -c, -o, -n, -loco, -snps, -hwe) with identical names and semantics. Existing GEMMA commands work by changing gemma to jamma:
| GEMMA | JAMMA |
|---|---|
gemma -gk 1 -bfile study -o out |
jamma -gk 1 -bfile study -o out |
gemma -lmm 1 -bfile study -k kinship.cXX.txt -o results |
jamma -lmm 1 -bfile study -k kinship.cXX.txt -o results |
gemma -lmm 4 -bfile study -k k.txt -c covars.txt -o results |
jamma -lmm 4 -bfile study -k k.txt -c covars.txt -o results |
- Reads and writes GEMMA
.assoc.txtand.cXX.txtformats - Accepts PLINK binary
.bed/.bim/.famfiles (same as GEMMA) - Output columns match GEMMA (mode-dependent -- see User Guide)
- Also supports binary
.npyformat for kinship (faster I/O); use--legacy-textfor GEMMA text format
Python API
The gwas() function handles the full pipeline -- data loading, kinship computation, eigendecomposition, and LMM association -- in a single call. You don't need to compute a kinship matrix separately unless you want to reuse it across runs.
from jamma import gwas
# Simplest usage: computes kinship internally, no separate kinship step needed
result = gwas("data/my_study")
print(f"Tested {result.n_snps_tested} SNPs in {result.timing['total_s']:.1f}s")
# Or supply a pre-computed kinship matrix to skip recomputation
result = gwas("data/my_study", kinship_file="data/kinship.cXX.npy")
# Compute kinship from scratch and save it for reuse
result = gwas("data/my_study", save_kinship=True, output_dir="output")
# With covariates and LRT test
result = gwas("data/my_study", kinship_file="k.txt", covariate_file="covars.txt", lmm_mode=2)
# LOCO analysis (leave-one-chromosome-out)
result = gwas("data/my_study", loco=True)
# LOCO with eigen caching: writes a per-chromosome eigen cache to output_dir
result = gwas("data/my_study", loco=True, write_eigen=True, output_dir="output")
# Reusing a LOCO eigen cache on a later run is CLI-only — the cache is a set of
# per-chromosome files keyed by directory, so point --eigen-dir at the same dir:
# jamma -lmm 1 -bfile data/my_study -loco --eigen-dir output -o result
# Several phenotypes against one eigendecomposition (the CLI's -n "1 2 3")
result = gwas("data/my_study", phenotype_columns=[1, 2, 3])
# SNP filtering
result = gwas("data/my_study", kinship_file="k.txt", snps_file="snps.txt", hwe=0.001)
See the User Guide for the low-level component API (kinship, eigendecomposition, LMM runners).
Memory Safety
Unlike GEMMA, JAMMA includes pre-flight memory checks that prevent out-of-memory crashes:
- Pre-flight checks before large allocations (eigendecomposition, genotype loading)
- RSS memory logging at workflow boundaries
- Incremental result writing (no memory accumulation)
- Safe chunk size defaults with hard caps
GEMMA will silently OOM and get killed by the OS. JAMMA fails fast with clear error messages. See the User Guide for the programmatic memory estimation API.
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 2026-09-14 on an idle machine. 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) |
1.0s | 1.2s | 800ms | 747ms | — | 1.1x | 1.4x | 1.6x |
LMM Wald (-lmm 1) |
6.9s | 4.2s | 5.2s | 514ms | 558ms | 10.1x | 13.5x | 8.2x |
LMM All (-lmm 4) |
12.7s | 7.5s | 7.5s | 537ms | 569ms | 14.0x | 23.7x | 14.0x |
| Full GWAS Wald (compute kinship + association) | 7.9s | 5.5s | 5.5s | 714ms | 760ms | 7.6x | 11.1x | 7.6x |
LMM Wald+4cov (-lmm 1 -c) |
26.5s | 12.6s | 16.6s | 1.0s | 1.1s | 15.8x | 25.3x | 12.1x |
| Backend | LOCO Wald | vs fastest GEMMA |
|---|---|---|
| GEMMA (OpenBLAS) | 35.4s | 1.0x |
| GEMMA (Accelerate) | 34.0s | 1.0x |
| JAMMA NumPy+C | 3.3s | 10.4x |
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.
Supported Features
Current
- Kinship matrix computation -- centered (
-gk 1) and standardized (-gk 2) - Univariate LMM Wald test (
-lmm 1) - Likelihood ratio test (
-lmm 2) - Score test (
-lmm 3) - All tests mode (
-lmm 4) - LOCO kinship -- leave-one-chromosome-out analysis (
-loco) - Binary
.npyI/O -- default for kinship and eigen files;--legacy-textfor GEMMA text format - Multi-phenotype support --
-n "1 2 3"with single eigendecomposition reuse - Eigendecomposition reuse -- manual via
-d/-u/-eigen, automatic in multi-phenotype mode - LOCO eigen caching --
--eigen-dirsaves/loads per-chromosome eigen files across runs - Phenotype column selection (
-n) - SNP subset selection for association and kinship (
-snps/-ksnps) - HWE QC filtering (
-hwe) - Pre-computed kinship input (
-k) - Covariate support (
-c) - PLINK binary format (
.bed/.bim/.fam) with input dimension validation - Large-scale streaming I/O (>100k samples via numpy-mkl ILP64 -- numpy 2.4.6+)
- Lambda optimization bounds (
-lmin/-lmax) - Individual weights for kinship (
-widv) - Categorical covariates with one-hot encoding (
-cat) - Pre-flight memory checks (fail-fast before OOM)
- RSS memory logging at workflow boundaries
- Incremental result writing
- In-place mean imputation for missing genotypes (per-chunk, zero-copy)
- Early sample filtering -- kinship accumulated at filtered size when phenotype missingness is present
- jlinalg C layer: vendor BLAS dispatch for eigendecomposition (DSYEVD default, DSYEVR O(n) workspace fallback under memory pressure), DSYRK, DGEMM
- Optional C extension: OpenMP-parallel Wald tests (auto-fallback to pure Python)
Planned
- Multivariate LMM (mvLMM)
Architecture
JAMMA uses NumPy for data loading and kinship. Eigendecomposition uses jlinalg.eigh which dispatches to vendor DSYEVD (default) or DSYEVR (O(n) workspace, under memory pressure) via the jlinalg C layer. LMM association uses a NumPy backend with an optional C extension for OpenMP-parallel Wald/Score/LRT tests. Mode is auto-selected based on available memory: batch runner when genotypes fit in RAM, streaming runner (two-pass disk I/O) for large datasets.
Core algorithms (likelihood.py, pab.py, prepare_common.py) are shared between batch and streaming runners. See jlinalg Architecture for the C vendor BLAS dispatch layer.
See Code Map for the full architecture diagram with source links.
Documentation
Start here
- Getting Started -- Install, first run, common setup problems
- Why JAMMA? -- Key differentiators from GEMMA
- User Guide -- Installation, usage examples, CLI reference
- Configuration -- Every CLI flag, environment variable, and tool setting
- Glossary -- Terms and abbreviations (ILP64, REML, LOCO, BLAS, etc.)
Internals
- Architecture -- Components, data flow, key abstractions
- Code Map -- Architecture diagrams and source navigation
- Performance -- Bottleneck analysis, scale validation, configuration guide
- jlinalg Architecture -- C vendor BLAS dispatch layer design
- jlinalg Algorithms -- Vendor DSYEVD algorithm notes
GEMMA parity
- Equivalence Proof -- Mathematical proofs and empirical validation against GEMMA
- Numerical Equivalence Bound -- End-to-end FP error bound vs GEMMA
- GEMMA Divergences -- Known differences from GEMMA
Contributing and operations
- Contributing -- Development setup, testing, and PR guidelines
- Development -- Build commands, code style, local gates
- Testing -- Tiers, mocking policy, suite map, sanitizer repro
- Deployment -- Docker image, PyPI publishing, release pipeline
- Changelog -- Version history
Requirements
- Python 3.11+
- NumPy 2.4.6+
License
GPL-3.0 (same as GEMMA).
1 maintainer
Release files for jamma 8.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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| jamma-8.1.0.tar.gz | 75.6 MB | Details |
Built distributions (wheels)
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| jamma-8.1.0-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl | CPython 3.14 | CPython 3.14 | Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64 | Details |
| jamma-8.1.0-cp314-cp314-macosx_14_0_arm64.whl | CPython 3.14 | CPython 3.14 | macOS 14.0+ ARM64 | Details |
| jamma-8.1.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl | CPython 3.13 | CPython 3.13 | Linux glibc 2.24+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| jamma-8.1.0-cp313-cp313-macosx_14_0_arm64.whl | CPython 3.13 | CPython 3.13 | macOS 14.0+ ARM64 | Details |
| jamma-8.1.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.24+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| jamma-8.1.0-cp312-cp312-macosx_14_0_arm64.whl | CPython 3.12 | CPython 3.12 | macOS 14.0+ ARM64 | Details |
| jamma-8.1.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl | CPython 3.11 | CPython 3.11 | Linux glibc 2.24+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| jamma-8.1.0-cp311-cp311-macosx_14_0_arm64.whl | CPython 3.11 | CPython 3.11 | macOS 14.0+ ARM64 | Details |
Total release size: 79.9 MB
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