arXiv:2604.21095v2 Announce Type: replace Abstract: Imaging, molecular, and machine-learning workflows can generate thousands of quantitative phenotypes in a single cohort, creating substantial computational and output bottlenecks when testing traits individually. TorchGWAS is a GPU-accelerated framework that uses batched operations for high-throughput, covariate-adjusted linear association testing across large panels of quantitative phenotypes. Across 500,036 allele-harmonized tests, TorchGWAS t statistics agreed with PLINK 2.0. On an NVIDIA H100 80-GB GPU with a 48-core Intel Xeon Gold 6442Y host and measured disk read and write rates of 5.98 and 1.49 GB/s, respectively, median end-to-end times for 4.57 billion associations (8,931,083 variants by 512 phenotypes in 35,365 samples) were 28.46 s for BED, 29.13 s for hard-call PGEN, 51.48 s for BGEN, and 58.95 s for dosage PGEN, including writing 36.7 GB of binary summary statistics. TorchGWAS provides an efficient Python-based framework for parallel fixed-effect association screening at biobank scale.TorchGWAS is implemented in Python and distributed as a documented source repository at https://github.com/ZhiGroup/TorchGWAS.