janusx 1.0.10

A Suite of Genotyping Tools for Genome-Wide Association Study and Genomic Selection

JanusX

简体中文(推荐) | English

Project Overview

JanusX is a high-performance, ALL-in-ONE suite for quantitative genetics that unifies genome-wide association studies (GWAS) and genomic selection (GS). It incorporates well-established GWAS methods (LM, LMM, and FarmCPU) and a flexible GS toolkit including GBLUP and various machine learning models. It also combines routine genomic analyses, from data processing to publication-ready visualisation.

It provides significant performance improvements over tools like GEMMA, GCTA, and rMVP, especially in multi-threaded computation.

Installation

PyPI

pip install janusx

From Source

git clone https://github.com/FJingxian/JanusX.git
cd JanusX
pip install .

Building from source requires a Rust toolchain (maturin will compile the native core).

Pre-compiled Releases

We provide pre-compiled binaries on the GitHub Releases page for Windows, Linux, and macOS. Download and extract the archive, then run the executable directly.

Running the CLI

jx -h
jx <module> [options]

Note that running jx -h might take a while at first! This is because the Python interpreter is compiling source code into the pycache directory. Subsequent runs will use the pre-compiled code and load much faster!

Available Modules

Module	Description
gwas	Unified GWAS wrapper (LM/LMM/fastLMM/FarmCPU)
gs	Genomic Selection (GBLUP, rrBLUP, BayesA/B/Cpi)
postGWAS	Visualization and annotation
grm	Genetic relationship matrix calculation
pca	Principal component analysis
sim	Genotype and phenotype simulation

Quick Start Examples

GWAS Analysis

# Using unified gwas module (select one or more models)
jx gwas --vcf data.vcf.gz --pheno pheno.txt --lmm -o results

# Run multiple models at once
jx gwas --vcf data.vcf.gz --pheno pheno.txt --lm --lmm --fastlmm --farmcpu -o results

# With PLINK format
jx gwas --bfile genotypes --pheno phenotypes.txt --grm 1 --qcov 3 --thread 8 -o results

# With diagnostic plots (SVG)
jx gwas --vcf data.vcf.gz --pheno pheno.txt --lmm --plot -o results

Genomic Selection

# Run both GS models
jx gs --vcf data.vcf.gz --pheno pheno.txt --GBLUP --rrBLUP -o results

# Specific models
jx gs --vcf data.vcf.gz --pheno pheno.txt --GBLUP -o results

# Bayesian GS models
jx gs --vcf data.vcf.gz --pheno pheno.txt --BayesA --BayesB --BayesCpi -o results

# With PCA-based dimensionality reduction
jx gs --vcf data.vcf.gz --pheno pheno.txt --GBLUP --pcd -o results

Visualization

# Generate Manhattan and QQ plots
jx postGWAS -f results/*.lmm.tsv --threshold 1e-6

# With SNP annotation
jx postGWAS -f results/*.lmm.tsv --threshold 1e-6 -a annotation.gff --annobroaden 50

Test data in example is from genetics-statistics/GEMMA, published in Parker et al, Nature Genetics, 2016

Population Structure

# Compute GRM
jx grm --vcf data.vcf.gz -o results

# PCA analysis
jx pca --vcf data.vcf.gz --dim 5 --plot --plot3D -o results

Input File Formats

Phenotype File

Tab-delimited, first column is sample ID, subsequent columns are phenotypes:

samples	trait1	trait2
indv1	10.5	0.85
indv2	12.3	0.92

Genotype Files

• VCF: .vcf or .vcf.gz
• PLINK: .bed/.bim/.fam (use prefix)

Architecture

Core Libraries

• python/janusx/pyBLUP - Core statistical engine

  • GWAS implementations (LM, LMM, FarmCPU)
  • QK matrix calculation with memory-optimized chunking
  • PCA computation with randomized SVD
  • Cross-validation utilities

• python/janusx/gfreader - Genotype file I/O

  • VCF reader
  • PLINK binary reader (.bed/.bim/.fam)
  • NumPy format support

• python/janusx/bioplotkit - Visualization

  • Manhattan and QQ plots
  • PCA visualization (2D and 3D GIF)
  • LD block visualization

Native Core (src/)

Rust kernels for fast linear algebra and association testing.

CLI Entry Points (python/janusx/script/)

Each module corresponds to a CLI command. The launcher script (jx) dispatches to script/<name>.py.

Key Features

• Two Core Functions: Unified GWAS and GS workflows in one tool
• Easy to Use: Simple CLI interface, minimal configuration required
• High Performance: Optimized LMM computation with multi-threading

Key Algorithms

GWAS Methods

Method	Description	Best For
Linear Model (LM)	Standard GLM for association testing	Large datasets without population structure
Linear Mixed Model (LMM)	Incorporates kinship matrix to control population structure	Most GWAS scenarios
fastLMM	Fixed-lambda mixed model for speed	Fast approximate screening
FarmCPU	Iterative fixed/random effect alternation	High power with strict false positive control

GS Methods

Method	Description	Best For
GBLUP	Genomic Best Linear Unbiased Prediction	Baseline prediction
rrBLUP	Ridge Regression BLUP	Additive genetic value estimation
BayesA	Marker effects with scaled-t prior	Polygenic traits with heavier tails
BayesB	Variable selection with marker-specific variance	Sparse genetic architecture
BayesCpi	Variable selection with shared variance	Sparse architecture with shared variance

Kinship Methods

• Method 1 (VanRaden): Centered GRM (default)
• Method 2 (Yang): Standardized/weighted GRM

Python Version

Requires Python 3.10+

Test Data

Example data in example/ directory from Parker et al, Nature Genetics, 2016 (via GEMMA project)

Citation

@software{JanusX,
  title = {JanusX: High-performance GWAS and Genomic Selection Suite},
  author = {Jingxian FU},
  url = {https://github.com/FJingxian/JanusX}
}