Summary statistics are available for download from the GWAS Catalog.
Publications
A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes.
van Zuydam NR, et al.
Diabetes. 2018 Jul;67(7):1414-1427. doi: 10.2337/db17-0914
The Genetic Landscape of Renal Complications in Type 1 Diabetes.
Sandholm N, et al.
J Am Soc Nephrol. 2017 Feb;28(2):557-574. doi: 10.1681/ASN.2016020231
Phenotypes
- chronic kidney disease
- chronic kidney disease and diabetic kidney disease
- all diabetic kidney disease
- late diabetic kidney disease
- end-stage renal disease vs. no ESRD
- eGFR-creat (serum creatinine)
- microalbuminuria
Dataset subjects
| All DKD cases | All DKD controls | Cohort | Ancestry |
|---|---|---|---|
| T2D discovery cohorts | |||
| 1,250 | 580 | Scannia Diabetes Registry (SDR) | European |
| 188 | 165 | Bergamo Nephrologic Diabetes Complications Trial phase A and B (BENEDICT) | European |
| 163 | 131 | STENO | European |
| 885 | 816 | Genetics of Diabetes Audit Research Tayside Scotland (GoDARTS 1) | European |
| 859 | 680 | Genetics of Diabetes Audit Research Tayside Scotland (GoDARTS 2) | European |
| T2D replication cohorts | |||
| 655 | 1,433 | FIND GWAS/4D/LURIC/Joslin | European |
| 362 | 435 | FIND GWAS/1000 Genomes | European |
| 253 | 861 | Diabetes register Vasa (DIREVA) | European |
Project
SUMMIT is a pan-European research consortium that receives support from the Innovative Medicines Initiative (IMI). It aims at identifying markers that predict the risks of developing diabetes chronic micro- and macro-vascular complications with focus on diabetic nephropathy, diabetic retinopathy, and cardiovascular disease.
Experiment summary
SUMMIT Diabetic Kidney Disease GWAS is a genome-wide meta-analysis of diabetic kidney disease analyzed in subjects with type 1 or type 2 diabetes. Several different renal phenotypes were analyzed separately in type 1 and type 2 diabetics, and a combined analysis was also performed. The 1000G phase 1 March 2012 b37 reference panel was used for imputation.
DKD phenotypes were assessed for association with each SNP using a logistic regression for binary phenotypes and a linear regression for eGFR against genotype using an additive genetic model corrected for age, sex and duration of diabetes. P values were derived from a linear mixed model that took relatedness into account as well as age, sex and duration of diabetes.