TorchLIMIX: GPU-accelerated multivariate genome-wideassociation studies

SummaryWe introduce TO_SCPLOWORCHC_SCPLOWLIMIX, a GPU-accelerated PyTorch implementation of the LIMIX multivariate genome-wide association study pipeline. By leveraging batched GPU linear algebra, TO_SCPLOWORCHC_SCPLOWLIMIX 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 TO_SCPLOWORCHC_SCPLOWLIMIX 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. AvailabilityThe TO_SCPLOWORCHC_SCPLOWLIMIX 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. Contactbibiana.horn@hpi.de Supplementary informationSupplementary materials are included with this submission.