SUMMIT Diabetic Kidney Disease GWAS: subjects with T2D

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
4,109 5,234 RIKEN1N07 East Asian
704 2,903 RIKEN2Univ08 East Asian
465 154 Singapore Study of Macro-angiopathy and Micro-vascular Reactivity in Type 2 Diabetes (SMART2D) East Asian
358 87 Singapore Study of Macro-angiopathy and Micro-vascular Reactivity in Type 2 Diabetes (SMART2D) South Asian
2,459 638 Hong Kong Diabetes Registry (HDKR) East Asian

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.

Dataset ID
GWAS_SUMMITDKD-T1DT2D_T2D