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Nephrol Dial Transplant. 2015 Aug;30 Suppl 4:iv26-34. doi: 10.1093/ndt/gfv087.

Genome-wide studies to identify risk factors for kidney disease with a focus on patients with diabetes.

Author information

1
Division of Internal Medicine 3, Department of Nephrology and Dialysis, Medical University Vienna, Vienna, Austria.
2
Quest Diagnostics, Alameda, CA, USA.
3
Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada.
4
Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada Department of Nephrology and Hypertension, Friedrich Alexander University, Erlangen, Germany.

Abstract

Chronic kidney disease (CKD) affects 10-13% of the general population and diabetic nephropathy (DN) is the leading cause of end-stage renal disease (ESRD). In addition to known demographic, biochemical and lifestyle risk factors, genetics is also contributing to CKD risk. In recent years, genome-wide association studies (GWAS) have provided a hypothesis-free approach to identify common genetic variants that could account for the genetic risk component of common diseases such as CKD. The identification of these variants might reveal the biological processes underlying renal impairment and could aid in improving risk estimates for CKD. This review aims to describe the methods as well as strengths and limitations of GWAS in CKD and to summarize the findings of recent GWAS in DN. Several loci and SNPs have been found to be associated with distinct CKD traits such as eGFR and albuminuria. For diabetic kidney disease, several loci were identified in different populations. Subsequent functional studies provided insights into the mechanism of action of some of these variants, such as UMOD or CERS2. However, overall, the results were ambiguous, and a few of the variants were not consistently replicated. In addition, the slow progression from albuminuria to ESRD could limit the power of longitudinal studies. The typically small effect size associated with genetic variants as well as the small portion of the variability of the phenotype explained by these variants limits the utility of genetic variants in improving risk prediction. Nevertheless, identifying these variants could give a deeper understanding of the molecular pathways underlying CKD, which in turn, could potentially lead to the development of new diagnostic and therapeutic tools.

KEYWORDS:

chronic kidney disease; diabetes mellitus; genome-wide association studies

PMID:
26209735
DOI:
10.1093/ndt/gfv087
[Indexed for MEDLINE]

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