GWAS summary statistics
Regensburg GEM Platform - Development of genetic-epidemiologic methods (GEM) und their realization in software (GWAS data quality control, interaction analyses, stratified approaches, Imputation)
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Prof. Dr. Iris Heid, Dr. Thomas Winkler, Dr. Mathias Gorski, Kira Stanzick M.Sc.
Here you can download genome-wide summary statistics (e.g., genetic effect estimates and association P-Values for millions of genetic variants) that resulted from various genome-wide association (GWAS) meta-analysis projects.?
Kidney function decline (Gorski et al. 2022)
Download summary statistics (Gorski et al., Kidney Int. 2022):?
CKDGen_eGFR-decline_overall_adjDM.txt.gz?
(md5sum:?ea27368f59e7dc65cffdfb7d904e1d32)
These GWAS summary statistics for eGFR-decline are based on 343,339 individuals and adjusted for age, sex and diabetes-status. These can be considered equivalent to GWAS summary statistics on eGFR-decline adjusted for age and sex (not adjusted for diabetes-status): when comparing these summary statistics to summary statistics for eGFR-decline adjusted for age and sex (not adjusted for diabetes-status) in a subgroup, we found no difference in terms of beta-estimates, standard errors, P-values (Gorski et al., Kidney Int. 2022).?
CKDGen_eGFR-decline_overall_adjBL.txt.gz?
(md5sum:?1a64e96fb5945803642c8f6f38a9429b)
These GWAS summary statistics for eGFR-decline are based on 320,737 individuals and adjusted for age, sex and eGFR-baseline. The adjusting for eGFR-baseline here, and in general, for GWAS on eGFR-decline can induce a collider bias for genetic variants that are associated with eGFR-baseline; these summary statistics should thus be used with an understanding of such a collider bias.
(md5sum:?ea427d8c34cd4db798492c0377cffe86)
These GWAS summary statistics for eGFR-decline are based on 37,375