JanusX: an integrated and high-performance platform for scalable genome-wide association studies and genomic selection

Abstract

As genomic datasets expand in both sample size and marker density, genome-wide association studies (GWAS) and genomic selection (GS) require workflows that remain statistically rigorous, computationally efficient, and reproducible across the full analysis path, from genotype matrix to decision-relevant outputs. Here we present JanusX, an integrated high-performance framework that provides a streamlined, user-oriented workflow for GWAS and GS by unifying data handling, model execution, and visualization. Across simulated and real datasets, JanusX maintained high concordance with established baselines while substantially reducing runtime and memory usage. In GWAS, JanusX achieved up to a 19-fold speedup over GEMMA in linear mixed model (LMM) inference, and implemented additional LMM inference based on a sparse genomic relationship matrix with GRAMMAR-Gamma calibration, alleviating computational and memory bottlenecks in large-scale cohorts. JanusX also provides a FarmCPU implementation within its GWAS module, achieving a median 11.4-fold runtime improvement and reducing peak memory usage by 84.9% relative to rMVP. In GS, JanusX integrates an optimized best linear unbiased prediction (BLUP) backend that adaptively selects sample- and SNP-space solvers and incorporates a Preconditioned Conjugate Gradient (PCG) solver. This implementation efficiently completes five-fold cross-validation of 500k individuals x 500k single-nucleotide polymorphisms (SNPs) in 35.1 minutes with only 14.3 gibibyte (GiB) of peak memory. Beyond BLUP, JanusX integrates Bayesian and machine-learning predictors under a single interface with compact automatic tuning to ensure robust cross-model performance. JanusX therefore enables efficient locus discovery and genomic prediction under consistent analytical assumptions, even in large-scale cohorts.