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


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.

Type Journal
ISBN 2041-1723 (Electronic) 2041-1723 (Linking)
Authors Chen, W.; Wu, Y.; Zheng, Z.; Qi, T.; Visscher, P. M.; Zhu, Z.; Yang, J.
Publisher Name Nature Communications
Published Date 2021-12-31
Published Volume 12
Published Issue 1
Published Pages 7117
Status Published in-print
DOI 10.1038/s41467-021-27438-7
URL link to publisher's version