Genome-wide association studies of chronic kidney disease: what have we learned?

Abstract

The past 3 years have witnessed a dramatic expansion in our knowledge of the genetic determinants of estimated glomerular filtration rate (eGFR) and chronic kidney disease (CKD). However, heritability estimates of eGFR indicate that we have only identified a small proportion of the total heritable contribution to the phenotypic variation. The majority of associations reported from genome-wide association studies identify genomic regions of interest and further work will be required to identify the causal variants responsible for a specific phenotype. Progress in this area is likely to stem from the identification of novel risk genotypes, which will offer insight into the pathogenesis of disease and potential novel therapeutic targets. Follow-up studies stimulated by findings from genome-wide association studies of kidney disease are already yielding promising results, such as the identification of an association between urinary uromodulin levels and incident CKD. Although this work is at an early stage, prospects for progress in our understanding of CKD and its treatment look more promising now than at any point in the past.

Figure 1

An additive genetic model for GFR. In this additive model, each copy of the G allele is associated with an increase in GFR. The β-coefficient (β) indicates the unit increase in GFR (a continuous trait) per copy of the G allele. The blue circles represent individual GFR estimates for individuals with the genotype indicated on the x-axis. The red line indicates the regression slope of GFR on genotype. Abbreviation: GFR, glomerular filtration rate.

Figure 2

Graphical presentation of results from a GWAS of eGFR. a | A Manhattan plot showing significance level for each of the SNPs tested. SNP locations on the plot reflect their position across the 23 human chromosomes. SNPs with P values below 5.0 × 10⁻⁸ are colored orange. Regions with multiple significant SNPs are labeled by the likely disease-related gene. b | A quantile–quantile (Q–Q) plot detects systematic bias in a GWAS. Observed and expected P values are plotted for each SNP, ordered from the lowest to the highest level of significance. Under the null distribution, where there are no significant associations, the Q–Q plot will lie along the 45° line. Extremely small P values deviate from this line at the right-hand tail of the distribution. c | A regional association plot shows a close-up of one association peak from the Manhattan plot. The lead SNP is labeled, with other SNPs in the region color-coded based on the degree of linkage disequilibrium with the lead SNP. The blue lines illustrate the inferred local rate of recombination across the region. Recombination hotspots are indicated by peaks in the blue lines, with SNPs lying between these hotspots being strongly correlated with each other in several haplotypes. Abbreviations: eGFR, estimated glomerular filtration rate; GWAS, genome-wide association study; SNP, single nucleotide polymorphism. Permission obtained from Nature Publishing Group © Köttgen, A. et al. Nat. Genet. 42, 376–384 (2010).

Figure 3

Magnitude of association between GFR estimated from creatinine and cystatin C and SNPs identified in a GWAS of GFR and CKD. Loci lying along the red line are equally associated with both measures of kidney function (creatinine and cystatin C), which suggests that they are related to true GFR. Loci that lie along the x-axis or y-axis are predominantly associated with GFR estimated from creatinine (eGFR_crea) or cystatin C (eGFR_cys), respectively. These loci are more likely to be related to genetic variability in the production of creatinine or cystatin C than to true GFR. Abbreviations: CKD, chronic kidney disease; eGFR, estimated GFR; GFR, glomerular filtration rate; GWAS, genome-wide association study; SNP, single nucleotide polymorphism. Permission obtained from Nature Publishing Group © Köttgen, A. et al. Nat. Genet. 42, 376–384 (2010).