PT  - JOURNAL ARTICLE
AU  - John, Maura
AU  - Korte, Arthur
AU  - Grimm, Dominik G.
TI  - permGWAS2: Enhanced and Accelerated Permutation-based Genome-Wide Association Studies
AID  - 10.1101/2023.11.28.569016
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2023.11.28.569016
4099  - http://biorxiv.org/content/early/2023/11/29/2023.11.28.569016.short
4100  - http://biorxiv.org/content/early/2023/11/29/2023.11.28.569016.full
AB  - Motivation Permutation-based significance thresholds have been shown to be a robust alternative to Bonferroni-based significance thresholds in genome-wide association studies (GWAS). However, the implementation of permutation-based thresholds is computationally demanding. The recently published method permGWAS introduced a batch-wise approach using 4D tensors to efficiently compute permutation-based GWAS. However, running multiple univariate tests in parallel leads to many repetitive computations and increased computational resources. More importantly, the previous version of permGWAS does not take into account the population structure when permuting the phenotype.Results We propose permGWAS2, an improved and accelerated version that uses a block matrix decomposition to optimize computations, thereby reducing redundant computations. It also introduces an alternative permutation strategy that takes into account the population structure during permutation. We show that this improved framework provides a more streamlined approach to performing permutation-based GWAS with a lower false discovery rate compared to the previous version and the traditional Bonferroni correction.Availability permGWAS2 is open-source and publicly available on GitHub for download: https://github.com/grimmlab/permGWAS.Competing Interest StatementThe authors have declared no competing interest.