GWAS summary statistics

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.?

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:

• 594_signal_leads.xlsx

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.

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.

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: Coded (effect) allele
Allele2: Noncoded allele
Freq1: Frequency of Allele1
Effect: Genetic effect of allele1
StdErr: Standard error, 2nd GC corrected
P-value: Association P value, 2nd GC corrected
N_total_sum: Number of individuals in the analysis

Contact:?

mathias.gorski@ukr.de, iris.heid@ukr.de

Rapid kidney function decline - Summary Statistics

From the annual eGFRcrea decline we derived two case-control-definitions: (1) “Rapid3” cases defined as eGFRcrea decline of >3 mL/min/1.73m? per year compared to “no decline” (1 to +1 mL/min/1.73m? per year), (2) ”CKDi25” cases defined as ≥25% eGFRcrea decline during follow-up together with a movement from eGFRcrea≥60 mL/min/1.73m? at baseline to eGFRcrea<60 mL/min/1.73m? at follow-up compared to “CKDi25” controls defined as eGFRcrea≥60 mL/min/1.73m? at baseline and follow-up. Both binary phenotype were adjusted for sex, age and baseline eGFRcrea.

Download summary statistics:?

CKDGen_ckdi25_overall.txt.gz?

? - 19,901 cases and 175,244 controls

? - md5sum: 0baf7d8e7dccbf1a22bd2b945101e5f4

CKDGen_rapid3_overall.txt.gz?

? - 34,874 cases and 107,090 controls

? - md5sum:?b1a0d8ebbdb37478dc70be04af3b2c84

If you use this data, please cite:?

Gorski M, et al. Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline. Kidney Int. 2020 Oct 30:S0085-2538(20)31239-4. doi: 10.1016/j.kint.2020.09.030. Epub ahead of print. PMID: 33137338.

Format:?

Chr: Chromosome

Pos_b37: Base position (b37)

RSID: rs identifier

Allele1: Coded (effect) allele

Allele2: Noncoded allele

Freq1: Frequency of Allele1

OR: Odds ratio of allele1

StdErr: Standard error, 2nd GC corrected

P-value: Association P value, 2nd GC corrected

N_total_sum: Number of individuals in the analysis

Contact:?

mathias.gorski@ukr.de, iris.heid@ukr.de

Early AMD - Summary Statistics

This site provides a download link for the summary statistics from our genome-wide association meta-analysis for early AMD (14,034 early AMD?cases; 91,214 controls). The meta-analysis included summary statistics from 11 sources, including data from the International AMD?Genomics Consortium (IAMDGC) and the UK Biobank. An EasyQC cleaning script for the individual study GWAS results can be found at www.genepi-regensburg.de/easyqc.?

Download summary statistics:?

winkler_et_al_earlyamd_meta.gz

The data is also available from the GWAS catalogue.

If you use this data, please cite:?

Winkler, T.W., Grassmann, F., Brandl, C. et al. Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease. BMC Med Genomics 13, 120 (2020). https://doi.org/10.1186/s12920-020-00760-7

Format:?

rsid: rs identifier

chr: Chromosom

pos_b37: Base position (b37)

ea: Effect allele

oa: Other allele

eaf: Effect allele frequency

beta: Genetic effect (per ea)

se: Standard error of beta (single study-specific GC correction)

se_2gc:?Standard error of beta (double GC correction)

p: Association P value?(single study-specific GC correction)

p_2gc:?Association P value?(double GC correction)?

n_cases: Number of early AMD?cases

n_ctrls: Number of controls

Contact:?

thomas.winkler@ukr.de, iris.heid@ukr.de