Improved analyses of GWAS summary statistics by reducing data heterogeneity and errors

Analyses of summary statistics from GWAS are subject to biases due to errors in the discovery GWAS or linkage disequilibrium reference data set or heterogeneity between data sets. Here, the authors propose a quality control method to be added to analysis of GWAS summary data that can reduce such biases. Summary statistics from genome-wide association studies (GWAS) have facilitated the development of various summary data-based methods, which typically require a reference sample for linkage disequilibrium (LD) estimation. Analyses using these methods may be biased by errors in GWAS summary data or LD reference or heterogeneity between GWAS and LD reference. Here we propose a quality control method, DENTIST, that leverages LD among genetic variants to detect and eliminate errors in GWAS or LD reference and heterogeneity between the two. Through simulations, we demonstrate that DENTIST substantially reduces false-positive rate in detecting secondary signals in the summary-data-based conditional and joint association analysis, especially for imputed rare variants (false-positive rate reduced from >28% to <2% in the presence of heterogeneity between GWAS and LD reference). We further show that DENTIST can improve other summary-data-based analyses such as fine-mapping analysis.

Automated summary

Summary statistics from GWAS are prone to biases due to errors in discovery GWAS or LD reference data set or heterogeneity between data sets. The authors propose a quality control method, DENTIST, leveraging LD to detect and eliminate errors in GWAS or LD reference. DENTIST substantially reduces false-positive rate in detecting secondary signals and improves other summary-data-based analyses.