Deriving LD-adjusted GWAS summary statistics through linkage disequilibrium deconvolution

Abstract

Genome-wide association studies (GWAS) have identified numerous genetic variants associated with complex traits. However, linkage disequilibrium (LD) confounds these associations, leading to false positives where non-causal variants appear associated because they are correlated with nearby causal variants. This is particularly the case in highly polygenic traits where the genome can be saturated in causal variants. To address this issue, we propose LDeconv a method based on truncated singular value decomposition (SVD) that adjust GWAS summary statistics without requiring individual-level genotype data. This approach accounts for LD structure, isolates causal variants in high-LD regions, and improve the reliability of effect size estimates. We assessed the performance of LDeconv through simulations across various LD scenarios, conducted extensive sensitivity analyses, and applied the method to real GWAS data from the UK Biobank. Our results demonstrate that LDeconv effectively reduces false discoveries while preserving true associations, offering a robust framework for post-GWAS analyses, such as Mendelian Randomization.