John, M.; Korte, A.; Grimm, D. G.

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.