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.?
Genetic-by-age interaction (Winkler et al. 2024)
Genetic-by-age interaction summary statistics based on UK Biobank for obesity (bmi, weight), lipid (LDL-cholesterol, HDL-cholesterol and triglycerides) and blood pressure (systolic, diastolic, pulse pressure) traits are available from zenodo:
https://doi.org/10.5281/zenodo.14141226
Details on the analyses can be found in Winkler et al. Genome Biol. 2024. Please cite this paper if you use this data.?
Kidney function (eGFR, Stanzick et al. 2021; UPDATE: 2023)
Stanzick et al. BMC Bioinformatics 2023:
Here you can download summary statistics from GWAS meta-analysis for eGFR based on serum creatinine including European-only data from CKDGen and UK Biobank (N=1,004,040; same data used as below in Stanzick et al. NatComm 2021; but with higher precision in the EUR-only meta-analysis yielding improved secondary signals and fine mapping analyses):?
The GWAS summary statistics for eGFR based on creatinine, EUR-only (updated compared to Stanzick et al. NatComm 2021):?
- metal_eGFR_meta_ea_6_7_NK_20230328_1.TBL.map.annot.gc.gz
- Find here a short readme.txt?with a description of the columns included.
The 594 independent signal index variants for eGFR based on creatinine:
The web tool to perform gene/variant/region searches and gene prioritization on these updated results can be found here:?https://kidneygps.ur.de/gps/??
Further details on the comparison with the 634 independent eGFR signals from Stanzick et al. Nat Commun 2021 can be found in the BMC Bioinformatics paper.? If you use the updated data, please cite:
- Stanzick, K.J., ... Heid I.M.,?Winkler T.W.?KidneyGPS: a user-friendly web application to help prioritize kidney function genes and variants based on evidence from genome-wide association studies.?BMC Bioinformatics?24, 355 (2023). https://doi.org/10.1186/s12859-023-05472-0??
Stanzick et al. Nat Commun 2021:
Here you can download summary statistics from our GWAS meta-analysis for kidney function traits including data from CKDGen and UK Biobank (Stanzick et al. NatComm 2021):?
The genome-wide association summary statistics for
- eGFR based on creatinine, ALL anc:?metal_eGFR_meta1.TBL.map.annot.gc.gz
- eGFR based on creatinine, EUR only:?metal_eGFR_meta_ea1.TBL.map.annot.gc.gz
- eGFR based on cystatin:?metal_eGFRcys_meta1.TBL.map.annot.gc.gz
- BUN:?metal_bun_meta_all1.TBL.map.annot.gc.gz?
- Find here a short readme.txt?with a description of the columns included.?
The?Gene PrioritiSation (GPS) table for the 424 identified eGFR loci:
- GPS_Stanzick_SuppData14?
- This is a direct link to Supplementary Data 14a from the Stanzick et al paper.?
Further details on the phenotype transformation or sample inclusions can be found in the paper (Stanzick et al. NatComm 2021).?
If you use this data, please cite:?
- Stanzick, K. J., … Heid I.M., Winkler, T. W. (2021). Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals.?Nature communications,?12(1), 4350. https://doi.org/10.1038/s41467-021-24491-0?
Please contact thomas.winkler@ukr.de?if you have questions.
Diabetes-stratified kidney function (eGFR, Winkler et al. 2022)
Here you can download summary statistics from our diabetes-stratified GWAS meta-analysis on estimated glomerular filtration rate (eGFR, Winkler et al. Commun Biol 2022):?
- eGFR stratified by diabetes-status (mostly European; n_DM=178,691 vs. n_NoDM=1,296,113):?meta_egfr_dmstrat_stage1plus2.txt.gz?
- eGFR stratified by diabetes-status (EUR-only;?n_DM=136,824 vs. n_NoDM=1,142,422):?meta_egfr_dmstrat_stage1plus2_EURonly.txt.gz?
- Find here a short readme.txt?with a description of the columns included.?
The genome-wide association summary statistics contain genetic effects on log(eGFR) in individuals with diabetes, in individuals without diabetes, a difference test P-Value and a 2-degree-of-freedom chi-squared joint (main+interaction) test P-Value.
The results are derived from the all ancestry Stage 1+2 meta-analysis (up to 178,691 individuals with diabetes and 1,296,113 individuals without diabetes, mostly European ancestry) or from the European-only Stage 1+2 meta-analysis (up to 136,824 individuals with diabetes and 1,142,422 individuals without diabetes, all European ancestry) . Further details on the phenotype transformation or sample inclusions can be found in the paper (Winkler et al. Commun Biol 2022).?
If you use this data, please cite:?
Winkler TW, Rasheed H, Teumer A, et al. Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals.?Commun Biol. 2022;5(1):580. Published 2022 Jun 13. doi:10.1038/s42003-022-03448-z
Please contact thomas.winkler@ukr.de?if you have questions.
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 individuals with diabetes at baseline adjusted for age and sex.?
CKDGen_eGFR-decline_CKD.txt.gz?
(md5sum: d040d352a2a60f27909fc109d9e1292c)
These GWAS summary statistics for eGFR-decline are based on 26,653 individuals with Chronic Kidney Disease (CKD) at baseline and can be considered genetic effects on CKD-progression. Similar to the adjusting for eGFR-baseline, the restriction to individuals with CKD at baseline can induce a bias in beta-estimates for genetic variants that are associated with CKD at baseline.?
If you use this data, please cite:?
Gorski M, et al. Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies. Kidney Int. 2022 Kidney Int. Jun 15:S0085-2538(22)00454-9. doi:10.1016/j.kint.2022.05.021. PMID: 35716955
Format:?
Chr: Chromosome
Pos_b37: Base position (b37)
RSID: rs identifier
Allele1: Code