TorchGWAS 1.0: GPU-accelerated GWAS at scale

Clicks: 17
ID: 332332
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #111 of 172 articles by views in Bioinformatics advances

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 172 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Motivation Imaging, molecular and machine-learning workflows can generate thousands of quantitative phenotypes in one cohort. Repeating genome-wide scans trait by trait creates a computational and output bottleneck. Results 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, its test statistics agreed with PLINK 2.0, with a Pearson correlation difference from one of 1.3 × 10⁻¹². On an NVIDIA H100 with 80 gigabytes of memory and a 48-core Intel Xeon Gold 6442Y host, median end-to-end runtimes for 4.57 billion associations spanning 8,931,083 variants, 512 phenotypes and 35,365 samples ranged from 28.46 to 58.95 seconds across four genotype formats. These measurements included writing 36.7 gigabytes of binary summary statistics. TorchGWAS supports efficient fixed-effect association screening at scale. Availability and implementation TorchGWAS is implemented in Python and distributed as a documented source repository at https://github.com/ZhiGroup/TorchGWAS.
Reference Key
openalex_W7221758742 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xingzhong Zhao, Ziqian Xie, Sheikh Muhammad Saiful Islam, Tian Xia, Cheng Chen, Degui Zhi
Are you one of these authors? Claim your profile
Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag310
URL
https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/10.1093/bioadv/vbag310/71920933/vbag310.pdf
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.