TorchLIMIX: GPU-accelerated multivariate genome-wide association studies
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Summary
We introduce T orch LIMIX, a GPU-accelerated PyTorch implementation of the LIMIX multivariate genome-wide association study pipeline. By leveraging batched GPU linear algebra, T orch LIMIX achieves speedups of up to two orders of magnitude over the original CPU-based implementation while maintaining numerically equivalent results and full concordance of significantly associated loci. In simulation studies, replacing the default initialization of the genetic covariance factor with a QR-based strategy reduces genomic inflation factors to near-unity values under the common and interaction effect null hypotheses, ensuring well-calibrated type I error control. Applying T orch LIMIX to metabolic traits of Arabidopsis thaliana measured in two experiments uncovered 37 additional associated SNPs at the same significance threshold used in the original univariate GWAS.
Availability
The T orch LIMIX pipeline is openly available on GitHub at https://github.com/bi-horn/torchLIMIX . An adapted version of the multivariate association testing part of the original LIMIX pipeline is available at https://github.com/bi-horn/LIMIX_modified .
Contact
bibiana.horn@hpi.de
Supplementary information
Supplementary materials are included with this submission.
