CLI Guide | Core API Guide | Zea Eureka
JanusX (Joint Association and Novel Utility for Selection) is a GWAS and genomic selection toolkit that combines:
jx) for runtime/toolchain management and pipeline orchestration _ __ __
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\____/ \__,_|_| |_|\__,_|___/_/ \_\ Tools for GWAS and GS
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Main capabilities:
LM, LMM, FastLMM, FarmCPUGBLUP, adBLUP, rrBLUP, BayesA/B/Cpi, and ML models (RF/ET/GBDT/XGB/SVM/ENET)postgwas, postgarfield, postbsagrm, pca, gformat, gmerge, hybrid, fastpop, webui, sim, simulationfastq2vcf, fastq2countcurl -fsSL https://raw.githubusercontent.com/FJingxian/JanusX/main/scripts/install.sh | sh
Set-ExecutionPolicy RemoteSigned -scope CurrentUser
irm https://raw.githubusercontent.com/FJingxian/JanusX/main/scripts/install.ps1 | iex
pip install janusx==1.0.25
Recommended Bioconda channel order:
conda create -n janusx \
--channel conda-forge \
--channel bioconda \
janusx
# 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
jx postgwas -i test/mouse_hs1940.test0.lmm.tsv -manh -qq -thr 1e-6 -o testpost

# 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
* 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]
...
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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
...
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jx -h
jx <module> -h
See full usages in CLI Guide.
Genome-wide Association Studies (GWAS):
grmpcagwaspostgwas (Visualization, manh qq ldblock)fastpop (population-structure analysis; jx adamixture kept as compatibility alias)Attribution note:
Genomic Selection (GS):
gspostgs (Visualization)reml (Estimation of broaden heritability and blup values)GARFIELD:
garfield (Based on https://github.com/heroalone/Garfield)postgarfieldUtility:
gformat (Conversion between genotype data formats, support fast splicing/filtering/prune)gmerge (Merge genotype between samples)gstats (State freq/het/missing/ldscore of genotype)@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}
}
This project is licensed under GNU Affero General Public License v3.0 (AGPL-3.0-or-later). See LICENSE.
$ claude mcp add JanusX \
-- python -m otcore.mcp_server <graph>