jamma 3.3.2

High-performance Multi-method Mixed-Model Association for large-scale GWAS

JAMMA (High-performance Multi-method Mixed-Model Association) — a modern Python and C reimplementation of GEMMA for large-scale GWAS.

• GEMMA-compatible: Drop-in replacement with identical CLI flags and output formats
• Numerical equivalence: Validated against GEMMA — 100% significance agreement, 100% effect direction agreement
• Fast: Up to 14x faster than GEMMA 0.98.5 at scale
• Memory-safe: Pre-flight memory checks prevent OOM crashes before allocation
• Cross-platform: Runs on Linux, macOS, and Windows — NumPy backend works everywhere, JAX adds batch acceleration on Linux and ARM Mac
• 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 + optional C extensions: NumPy + optional JAX stack; C extensions for DSYEVR eigendecomposition and OpenMP-parallel Wald tests, JAX for batch MLE optimization
• Large-scale ready: Optional numpy-mkl ILP64 wheels (numpy 2.4.2) for >46k sample eigendecomposition

Installation

macOS (Intel or ARM)

pip install jamma          # NumPy backend
pip install 'jamma[jax]'   # + JAX acceleration (ARM Mac only)

That's it. macOS Accelerate BLAS handles large matrices natively.

Linux / Windows / Intel Mac

For small datasets (<46k samples), the standard install works:

pip install jamma          # NumPy backend
pip install 'jamma[jax]'   # + JAX acceleration

For large-scale GWAS (>46k samples) on x86_64 (Linux or Intel Mac), install numpy-mkl first — standard numpy uses 32-bit BLAS integers which overflow at ~46k samples. MKL is x86_64-only; ARM Mac and Windows users are limited to <46k samples. Pre-built ILP64 wheels are available for Python 3.11–3.14:

NumPy backend only:

pip install numpy \
  --extra-index-url https://michael-denyer.github.io/numpy-mkl \
  --force-reinstall --upgrade
pip install jamma --no-deps
pip install psutil loguru threadpoolctl click progressbar2 bed-reader

With JAX acceleration:

pip install numpy \
  --extra-index-url https://michael-denyer.github.io/numpy-mkl \
  --force-reinstall --upgrade
pip install 'jamma[jax]' --no-deps
pip install psutil loguru threadpoolctl click progressbar2 bed-reader \
  jax jaxlib jaxtyping

From Git (latest development version):

pip install numpy \
  --extra-index-url https://michael-denyer.github.io/numpy-mkl \
  --force-reinstall --upgrade
pip install git+https://github.com/michael-denyer/jamma.git --no-deps
pip install psutil loguru threadpoolctl click progressbar2 bed-reader

Why --no-deps? JAMMA depends on numpy>=2.0.0, so a normal pip install jamma will pull in standard numpy and overwrite the ILP64 build. --no-deps prevents this; you install the runtime dependencies manually instead.

See the User Guide for ILP64 verification steps.

Platform Support

Platform	pip install jamma	pip install jamma[jax]	BLAS	Notes
Linux x86_64 (Intel)	JAX (auto-included)	—	MKL (optimal)	Best performance; ILP64 for >46k samples
Linux x86_64 (AMD)	JAX (auto-included)	—	OpenBLAS	Works well; MKL also works on AMD but less optimized
ARM Mac (M1+)	JAX (auto-included)	—	Accelerate	Excellent performance via Apple's BLAS
ARM Linux	NumPy only	JAX manual install	OpenBLAS	Works correctly; less BLAS optimization
Intel Mac	NumPy only	Not available	MKL / Accelerate	JAX dropped Intel Mac; ILP64 for >46k samples
Windows	NumPy only	Not available	OpenBLAS	JAX dropped Windows support

JAMMA's heavy computation (eigendecomposition, matrix multiplication, REML optimization) is BLAS-bound. Intel MKL delivers the best throughput, particularly at scale. Apple Accelerate is a close second on Apple Silicon. OpenBLAS works correctly everywhere but is less tuned for these workloads.

JAX is auto-included on Linux and ARM Mac via platform markers. Force a specific backend with --backend numpy or --backend jax.

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.txt with --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.

Python API

One-call GWAS (recommended)

The gwas() function is the recommended way to run JAMMA from Python. It 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 (skip eigendecomp on subsequent runs)
result = gwas("data/my_study", loco=True, write_eigen=True, eigen_dir="output/eigen")
result = gwas("data/my_study", loco=True, eigen_dir="output/eigen")  # reuses cache

# Multi-phenotype with eigendecomp reuse (Python API)
result = gwas("data/my_study", write_eigen=True, phenotype_column=1)
result = gwas("data/my_study", eigenvalue_file="output/result.eigenD.npy",
              eigenvector_file="output/result.eigenU.npy", phenotype_column=2)
# Or use the CLI for automatic multi-phenotype: jamma -lmm 1 ... -n "1 2 3"

# SNP filtering
result = gwas("data/my_study", kinship_file="k.txt", snps_file="snps.txt", hwe=0.001)

Low-level API (JAX backend)

import numpy as np

from jamma.io import load_plink_binary
from jamma.kinship import compute_centered_kinship
from jamma.lmm import run_lmm_association_streaming
from jamma.lmm.eigen import eigendecompose_kinship

# Load PLINK data and phenotypes
data = load_plink_binary("data/my_study")
phenotypes = np.loadtxt("data/my_study.pheno")  # loaded separately from .fam or phenotype file

# Compute kinship and eigendecompose (treat kinship as consumed after this)
kinship = compute_centered_kinship(data.genotypes)
eigenvalues, eigenvectors = eigendecompose_kinship(kinship)

# Run association (streaming from disk)
results, n_tested = run_lmm_association_streaming(
    bed_path="data/my_study",
    phenotypes=phenotypes,
    eigenvalues=eigenvalues,
    eigenvectors=eigenvectors,
    chunk_size=5000,
)

Low-level API (NumPy backend)

import numpy as np

from jamma.io import load_plink_binary
from jamma.kinship import compute_centered_kinship
from jamma.lmm import run_lmm_association_numpy
from jamma.lmm.eigen import eigendecompose_kinship

data = load_plink_binary("data/my_study")
phenotypes = np.loadtxt("data/my_study.pheno")
kinship = compute_centered_kinship(data.genotypes)
eigenvalues, eigenvectors = eigendecompose_kinship(kinship)

snp_info = [
    {"chr": str(data.chromosome[i]), "rs": data.sid[i],
     "pos": int(data.bp_position[i]), "a1": data.allele_1[i], "a0": data.allele_2[i]}
    for i in range(data.n_snps)
]

# Returns LmmRunResult — .associations for list[AssocResult], .pve for heritability, .pve_se for SE
run_result = run_lmm_association_numpy(
    genotypes=data.genotypes,
    phenotypes=phenotypes,
    kinship=None,  # Not needed when eigenvalues/eigenvectors provided
    snp_info=snp_info,
    eigenvalues=eigenvalues,
    eigenvectors=eigenvectors,
    lmm_mode=1,
)
results = run_result.associations

Memory Safety

Unlike GEMMA, JAMMA includes pre-flight memory checks that prevent out-of-memory crashes:

from jamma.core.memory import estimate_workflow_memory

# Check memory requirements BEFORE loading data
estimate = estimate_workflow_memory(n_samples=200_000, n_snps=95_000)
print(f"Peak memory: {estimate.total_gb:.1f}GB")
print(f"Available: {estimate.available_gb:.1f}GB")
print(f"Sufficient: {estimate.sufficient}")

Key features:

• 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.

Performance

Benchmark on mouse_hs1940 (1,940 samples × 12,226 SNPs), Apple M2 (AC power), GEMMA 0.98.5. Best-of runs, end-to-end wall clock:

Operation	GEMMA 0.98.5	JAMMA NumPy	JAMMA NumPy+C	JAMMA JAX (batch)	JAMMA JAX (streaming)	C speedup	vs GEMMA
Kinship (-gk 1)	2.2s	262ms	262ms	—	—	1.0x	8.5x
LMM Wald (-lmm 1)	11.3s	4.2s	1.2s	2.1s	2.6s	3.4x	9.2x
LMM All (-lmm 4)	20.7s	6.0s	1.4s	2.8s	4.2s	4.2x	14.3x
LMM Wald+4cov (-lmm 1 -c)	41.7s	12.0s	4.6s	4.7s	7.4s	2.6x	9.0x

NumPy+C uses a C extension with OpenMP for Wald (-lmm 1) — REML optimization is compute-bound and parallelizes well across SNPs. The C speedup grows with covariates (2.6x with 4 covariates) because the Pab table recursion is more expensive. NumPy+C is now the fastest backend at all modes including all-tests (-lmm 4) at mouse scale (14.3x vs GEMMA). JAX (batch) uses jax.vmap batching for MLE optimization and is competitive on -lmm 4. JAX (streaming) reads genotypes from disk in chunks and is the production code path for large datasets that don't fit in memory. Kinship is always pure NumPy/BLAS regardless of backend.

LOCO (Leave-One-Chromosome-Out)

Backend	LOCO Wald	vs GEMMA
GEMMA 0.98.5	3m36s	1.0x
JAMMA NumPy+C	7.7s	28.2x
JAMMA JAX	13.0s	16.6x

The large speedup has two sources: (1) JAMMA computes per-chromosome LOCO kinship via streaming and tests only that chromosome's SNPs, while GEMMA -loco tests all SNPs against each LOCO kinship (19× redundant work on 19 chromosomes); (2) JAMMA runs all chromosomes in a single process, avoiding 19 cold-start overheads. On this dataset, NumPy+C is faster than JAX because the JIT compilation overhead per chromosome outweighs XLA's compute benefit at 1,940 samples.

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 .npy I/O — default for kinship and eigen files; --legacy-text for 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-dir saves/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 2.4.2)
• JAX acceleration (CPU) with automatic device sharding
• XLA profiling traces (--profile-dir) for TensorBoard/Perfetto
• 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
• Optional C extensions: DSYEVR eigendecomposition (O(n) workspace, enables >100k samples) and OpenMP-parallel Wald tests (auto-fallback to pure Python)

Planned

• Multivariate LMM (mvLMM)

Architecture

JAMMA uses NumPy for data loading and kinship. Eigendecomposition defaults to DSYEVD (via numpy) but falls back to DSYEVR (C extension, O(n) workspace) under memory pressure — critical for >100k samples. At LMM it splits into a JAX backend (JIT, vmap, sharding) or a NumPy backend with an optional C extension for OpenMP-parallel Wald tests.

flowchart TD
    CLI["CLI / gwas()"] --> PIPE["PipelineRunner"]
    PIPE --> LOAD["Load PLINK + Phenotypes<br>(NumPy)"]
    LOAD --> KIN["Kinship<br>(NumPy matmul)"]
    KIN --> EIGMEM{"DSYEVD fits<br>in memory?"}
    EIGMEM -->|yes| EIGD["Eigendecomposition<br>(LAPACK DSYEVD · O(n²) workspace)"]
    EIGMEM -->|no| EIGR["Eigendecomposition<br>(LAPACK DSYEVR · O(n) workspace)"]
    EIGD --> DET{"detect_backend()"}
    EIGR --> DET
    DET -->|"jax"| JAX["JAX Streaming Runner<br>JIT + vmap + sharding"]
    DET -->|"numpy"| NP["NumPy Batch Runner"]
    NP --> CEXT{"C LMM extension<br>available?"}
    CEXT -->|yes| C["C Extension<br>OpenMP + SIMD"]
    CEXT -->|no| PY["Pure Python<br>fallback"]
    JAX --> RES["AssocResult"]
    C --> RES
    PY --> RES

Both backends share the same core algorithms (likelihood.py, prepare_common.py) and produce identical results. Backend-specific files follow a naming convention: *_jax.py / *_numpy.py.

See Code Map for the full architecture diagram with source links.

Documentation

• Why JAMMA? — Key differentiators from GEMMA
• User Guide — Installation, usage examples, CLI reference
• Code Map — Architecture diagrams and source navigation
• Equivalence Proof — Mathematical proofs and empirical validation against GEMMA
• GEMMA Divergences — Known differences from GEMMA
• Performance — Bottleneck analysis, scale validation, configuration guide
• Contributing — Development setup, testing, and PR guidelines
• Changelog — Version history

Requirements

• Python 3.11+
• NumPy 2.0+
• JAX 0.5.0+ (auto-included on Linux/ARM Mac; explicit extra on other platforms: pip install 'jamma[jax]')

License

GPL-3.0 (same as GEMMA)