Population-aware permutation-based significance thresholds for genome-wide association studies.

ABSTRACT:

Motivation

Permutation-based significance thresholds have been shown to be a robust alternative to classical Bonferroni significance thresholds in genome-wide association studies (GWAS) for skewed phenotype distributions. The recently published method permGWAS introduced a batch-wise approach to efficiently compute permutation-based GWAS. However, running multiple univariate tests in parallel leads to many repetitive computations and increased computational resources. More importantly, traditional permutation methods that permute only the phenotype break the underlying population structure.

Results

We propose permGWAS2, an improved method that does not break the population structure during permutations and uses an elegant block matrix decomposition to optimize computations,

SUBMITTER: John M 

PROVIDER: S-EPMC11639184 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature