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JanusX

★ 35 Open GitHub ↗

A high-performance, ALL-in-ONE suite for quantitative genetics that unifies genome-wide association studies (GWAS) and genomic selection (GS).

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# JanusX

[CLI Guide](./doc/JanusXcli.md) | [Core API Guide](./doc/JanusXcore.md) | [Zea Eureka](https://mp.weixin.qq.com/s/jl3h2DPRC21l8QJ0WxzXDA)

![Python](https://img.shields.io/badge/Python-3.10+-blue.svg) ![License](https://img.shields.io/badge/License-AGPLv3-blue.svg)

## Overview

JanusX (Joint Association and Novel Utility for Selection) is a GWAS and genomic selection toolkit that combines:

- Rust-accelerated kernels (PyO3 extension)
- Python analysis modules
- A Rust launcher (`jx`) for runtime/toolchain management and pipeline orchestration

```text
 _ __ __
 | | \ \ / /
 | | __ _ _ __ _ _ ___ \ V /
 _ | |/ _` | '_ \| | | / __| > <
 | |__| | (_| | | | | |_| \__ \/ . \
 \____/ \__,_|_| |_|\__,_|___/_/ \_\ Tools for GWAS and GS
 ---------------------------------------------------------
```

**Main capabilities**:

- Genome-Wide Association Study (GWAS): `lm`, `lmm`, `fvlmm`, `farmcpu`
- Genomic Selection (GS): `BLUP`, `BayesA/B/Cpi`, and ML models (`RF/ET/GBDT/XGB/SVM/ENET`)
- Streaming genotype IO for VCF/HMP/PLINK
- Post-analysis workflows: `postgwas`, `postgs`
- Utility workflows: `grm`, `pca`, `gformat`, `gmerge`, `fastpop`

---

## Installation

### Quick installation: Python with uv (recommend)

* Linux | MacOS
```Bash
curl -fsSL https://raw.githubusercontent.com/FJingxian/JanusX/main/scripts/install.sh | sh
```

* Windows
```PowerShell
powershell -ExecutionPolicy ByPass -c "irm https://raw.githubusercontent.com/FJingxian/JanusX/main/scripts/install.ps1 | iex"
```

### Option A: Python package install

```Bash
pip install janusx
```

### Option B: Conda / Bioconda

```Bash
conda create -n janusx \
  --channel conda-forge \
  --channel bioconda \
  janusx
```

---

## Quick start

### 1) GWAS

```Bash
# Estimate variance for every snp, similar with GEMMA. (Exact, recommand)
jx gwas -vcf example/mouse_hs1940.vcf.gz -p example/mouse_hs1940.pheno -lmm -o test
# Estimate variance for every snp, similar with GEMMA. (Exact, wald and LR test, recommand)
jx gwas -vcf example/mouse_hs1940.vcf.gz -p example/mouse_hs1940.pheno -lmm2 -o test
# Estimate variance once in NULL model, similar with EMMAX. (Fast)
jx gwas -vcf example/mouse_hs1940.vcf.gz -p example/mouse_hs1940.pheno -fvlmm -o test
# Linear mixed model with sparse GRM, fastGWA-compatible sparse REML null + approximate GRAMMAR-gamma scan.
jx gwas -vcf example/mouse_hs1940.vcf.gz -p example/mouse_hs1940.pheno -splmm-approx -o test
# Linear mixed model with sparse GRM, fastGWA-compatible sparse REML null + exact g'Pg scan.
jx gwas -vcf example/mouse_hs1940.vcf.gz -p example/mouse_hs1940.pheno -splmm -o test
# FarmCPU (Fast, and more sites, prepared for biobank cohorts)
jx gwas -vcf example/mouse_hs1940.vcf.gz -p example/mouse_hs1940.pheno -farmcpu -o test
```

### 2) Post-GWAS

```Bash
jx postgwas -i test/mouse_hs1940.test0.lmm.tsv -manh -qq -thr 1e-6 -o testpost
jx postgwas -i test/mouse_hs1940.test0.lmm.tsv a.tsv -manh-merge -qq-merge -fontsize 11 -fontstyle arial -o testpost
jx postgwas -i a.tsv b.tsv c.tsv -manh-merge -qq-merge -scatter-size 4 10 -alpha 0.2 0.5 -ylim 2 10 -o testpost
```

### 3) Genomic selection

```Bash
# BLUP method, prepared for biobank cohorts
# n≤15,000 GBLUP
# n>15,000 & m≤15,000 rrBLUP
# n>15,000 & m>15,000 rrBLUP with PCG (Jacobi)
jx gs -vcf example/mouse_hs1940.vcf.gz -p example/mouse_hs1940.pheno -BLUP -o test -cv 5
# Bayesian methods
jx gs -vcf example/mouse_hs1940.vcf.gz -p example/mouse_hs1940.pheno -BayesA -BayesB -BayesCpi -o test -cv 5
```

```text
* Genomic Selection for trait: test0
Train size: 1410, Test size: 530, EffSNPs: 8960
** BLUP
✔︎ Cross-validation ...Finished [0.8s]
✔︎ Fitting ...Finished [0.3s]
✔︎ Predicting ...Finished [0.0s]
** BayesA
✔︎ Cross-validation ...Finished [17.9s]
✔︎ Fitting ...Finished [4.1s]
✔︎ Predicting ...Finished [0.0s]
...
------------------------------------------------------------
Fold Method     Pearsonr Spearmanr R2     time(s)  Best
1    BLUP       0.704    0.675     0.493  0.198    
1    BayesA     0.709    0.680     0.493  3.507    
...
------------------------------------------------------------
```

### 4) Get module help

```Bash
jx -h
jx <module> -h
```

**See full usages in [CLI Guide](./doc/JanusXcli.md).**

---

## Module map

**Genome-wide Association Studies (GWAS)**:

- `grm`
- `pca`
- `gwas`
- `postgwas` (Visualization, `manh` `qq` `ldblock`)
- `fastpop` (population-structure analysis)

Attribution note:

- FastPop is JanusX's own population-structure workflow and public name for this module.
- Historical JanusX releases referenced ADAMIXTURE as a related implementation; the BSD-3-Clause attribution notice is recorded in [THIRD_PARTY_NOTICES.md](./doc/THIRD_PARTY_NOTICES.md).

**Genomic Selection (GS)**:

- `gs`
- `postgs` (Visualization)

**GARFIELD**:

- `garfield` (Based on https://github.com/heroalone/Garfield)
- `postgarfield`

**Utility**:

- `gformat` (Conversion between genotype data formats, support fast splicing/filtering/prune)
- `gmerge` (Merge genotype between samples)
- `gstats` (State freq/het/missing/ldscore of genotype)

---

## Citation

```bibtex
@article {FuJanusX,
  title = {JanusX: an integrated and high-performance platform for scalable genome-wide association studies and genomic selection},
  author = {Fu, Jingxian and Jia, Anqiang and Wang, Haiyang and Liu, Hai-Jun},
  year = {2026},
  doi = {10.64898/2026.01.20.700366},
  publisher = {Cold Spring Harbor Laboratory},
  URL = {https://www.biorxiv.org/content/early/2026/01/23/2026.01.20.700366},
  journal = {bioRxiv}
}
```

---

## License

This project is licensed under **GNU Affero General Public License v3.0** (AGPL-3.0-or-later). See [LICENSE](./LICENSE).

JanusX

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