The increasing global prevalence and high morbidity and mortality associated with progressive CKD are well established (1,2). However, despite varied efforts, we often frustratingly remain unable to adequately account for who develops CKD or does not, given similar exposure to kidney stressors. Similarly, our ability to delineate specific pathways and develop needed novel treatments has not progressed adequately.
These factors, along with improved genomics technologies, have provided the impetus for increasing efforts to define genetic factors that would account for variable outcomes. Although initial studies focused primarily on candidate genes, most findings were not validated, demonstrating a limited ability to identify, a priori, pathophysiologic genes pertaining to CKD. As our ability to perform rapid and economical genotyping progressed, a milestone was achieved in 2005 when the first genome-wide association study (GWAS) was published (3) (see Figure 1). In this agnostic approach, single nucleotide polymorphisms (SNPs) that are interspersed through the genome are genotyped and tested for association with measure of physiologic or pathophysiologic processes, providing a broad scan of genetic contributions to such traits. Since then, DNA genotyping technologies have continued to progress, both in terms of density of coverage and diminished cost, with recent large-scale GWAS including up to 10 million SNPs, whereas full genome sequencing in thousands of individuals is rapidly becoming a practical reality. These agnostic genomic scans have, in turn, led to a wide number of novel discoveries, many of which were unexpected (4). Regarding CKD genomics, although some of these findings such as APOL1 and UMOD are paving their way into the clinical domain, most findings have not yet been sufficiently studied to have any immediate clinical applications.
Several reviews have already discussed different aspects of kidney genetics in detail, including the excellent recent article by Wuttke and Köttgen (5). Here, we summarize the main findings from kidney function and CKD-related GWAS studies (Figure 2), and discuss some of the lessons learned (Table 1) and challenges the nephrology community has to face to accelerate the translation of these new discoveries to the clinical domain.
Table 1. -
Summary of selected main GWAS category based findings and lessons learned
||Selected Top SNP
|eGFR (general population)
||Also associated with CKD
||Close to 100 loci identified with small effect size
Reasonable gene-level consistency across ancestral background
Numerous loci were also associated with CKD, but not ESRD (e.g., SHROOM3,NAT8, DAB2, and WDR37)
Most gene variants identified in general population cohorts have minimal prognostic power, but are able to identify many novel pathways, delineate underlying pathophysiology, and identify potential therapeutic treatments (e.g., UMOD)
||rs13146355; rs17319721; rs5020545
||Asian, European, Transethnic
||Also associated with CKD
||Also associated with CKD
||Also associated with CKD
||rs17730436; rs491567; rs1031755
||Asian, European, Transethnic
||Also associated with CKD, ESRD, and hypertension. Would lead to kidney lesions through activation of the NKCC2 transporter, which can be blocked by furosemide (potential translation to clinical care)
|Progression rate of CKD
||Also associated with ESRD and proteinuria. Similar trend in European Americans. Minimal overlap noted with eGFR-associated SNPs
||Context-specificity of genotype-phenotype associations (established CKD versus general population)
||Similar trend in blacks. Also associated with ESRD in Europeans and ESRD in diabetic blacks. Also associated with kidney graft failure in Europeans
||Context-specificity of genotype-phenotype associations (CKD versus general population versus diabetic population)
||Type 1 diabetes–associated ESRD
||None of these signals were consistently replicated demonstrating a complex genetic landscape
Possible role for epigenetic mechanisms and/or multiple interactions
||Type 1 diabetes–associated ESRD in women
||Type 2 diabetic nephropathy
||Both type 1 and type 2 diabetic nephropathy
||Type 1 diabetes–associated ESRD
||Correlated with anti-PLA2R1 antibodies in patients with MN
Antibody dosing used for diagnosis, response prediction to immunosuppressive therapy, long-term outcomes prediction (translation to clinical care)
||Focus on homogeneous disease phenotype even in face of small sample sizes can reveal strong associations
Major epistatic effect involved (OR, 78 CI, 34.6–178.2)
||Also associated with lupus nephritis, type 1 diabetic nephropathy, and FSGS in adults. Associated with steroid-sensitive nephrotic syndrome
||Numerous variants identified. High overlap with diverse autoimmune genes
||Complex, multilocus model may be needed to better capture overall genotypic effect. Importance of pathogen-driven selective pressure to shape beneficial versus pathogenic immune responses
||rs2523946; rs1883414; rs1794275; rs660895
|FSGS-HIVAN, Hypertension-attributed ESRD
||Strong recessive pattern Associated with CKD progression rate from diverse etiologies. Modest association with proteinuria. Associated with faster kidney allograft failure (potential translation to clinical care)
||Can have common variant with strong effect in face of strong selection factors. Importance of well characterized homogeneous phenotypes (e.g., HIVAN)
Highly variable gene effect on the basis of studied population (e.g., minimal association with eGFR in general population)
Chr, chromosome; SNP, single nucleotide polymorphism; OR, odds ratio; HIVAN, HIV-associated nephropathy.
Studies of CKD-Defining Traits
Kidney Function in the General Population
Several large GWAS have been completed in European ancestry general population cohorts to explore eGFR genetic determinants (6–12). With sample size increasing over time (from 41,000 in 2009 to 175,000 individuals in 2016), up to 90 associations with cross-sectional measures of eGFR were progressively unveiled (see blue in Figure 2). Several studies also explored the genetic determinants of kidney function in non-European ancestry background (one East Asian GWAS  and two transethnic studies [12,14]) and showed the consistency of loci associations across ancestry groups despite variability in allele distribution (Figure 2).
One of the early lessons learned from these very large studies is that common variants are generally associated with small effect sizes, especially for non–disease specific and multifactorial traits such as eGFR (see Figure 1, Table 1). Indeed, all noted eGFR signals were estimated to account for <4% of eGFR phenotypic variance in the general population. However, it should be pointed out that although these signals are not necessarily well suited to predict individual eGFR level, they are of high scientific value because they are helping delineate novel pathways related to kidney physiology and CKD, and are identifying potential therapeutic targets (12,15).
Kidney Function Decline and CKD
The following studies aimed at determining the genetic contribution to kidney function decline in general, CKD, and ESRD populations. In one European population-based meta-GWAS, UMOD variants, that were previously associated with eGFR, were associated with kidney function decline over time (16). Additional studies determined that most eGFR-associated loci (including SHROOM3 and NAT8) were affecting CKD (6–8,11–13,17,18), but were only modestly affecting progression to ESRD (17). Most significantly, promoter polymorphisms in UMOD have been associated with eGFR, CKD, ESRD (6,7,9,17,19), and hypertension (20). Specifically, the rs4293393 risk variant was associated with higher uromodulin expression in human kidney samples, which has presumably been selected secondary to providing increased protection from urinary tract infections (21,22). Interestingly, in transgenic mice, overexpression of uromodulin led to salt-sensitive hypertension and age-dependent kidney lesions via activation of the NKCC2 kidney sodium transporter. These results could translate to clinical care, because the pharmacologic inhibition of NKCC2 (furosemide) was more effective in lowering BP in hypertensive patients carrying UMOD promoter risk variants (21), and hypothetically in preserving kidney function (22).
More recently, we looked for variants associated with the loss of kidney function in both African ancestry and European ancestry individuals with established CKD who were enrolled in the prospective Chronic Renal Insufficiency Cohort study (18). In this study, LINC00923 variants were associated with nondiabetic CKD progression rate, time to ESRD, and proteinuria in blacks, with a similar trend in whites (see green in Figure 2).
The overall minimal overlap between identified loci associated with kidney function decline in general population and CKD-specific cohorts highlights the context-specific nature of genotype-phenotype associations.
Albuminuria remains one of the strongest predictors of CKD outcomes; however, to date, the only reliable association with albuminuria remains the CUBN locus, which was identified in a European ancestry general population (23,24) (see red in Figure 2), with a similar trend in blacks (23). Moreover, CUBN variants were modestly associated with an increased risk for ESRD and a trend for kidney graft failure in whites (25), and with ESRD in black patients with diabetes (26).
Interestingly, variants associated with cross-sectional measure of eGFR in the population-based cohorts were not found to be associated with albuminuria, which would suggest a distinct genetic contribution to these factors (24). Conversely, in individuals with established CKD, we noted nearly a third of loci associated with eGFR decline to be associated with proteinuria, suggesting a potential shared genetic contribution for progressive CKD and proteinuria (18). Lastly, several variants were associated with albuminuria in type 2 diabetic populations (24,27), demonstrating again the context specificity of genotype-phenotype associations and the importance of replication in similar populations.
Diabetic nephropathy remains the leading cause for ESRD and has a high heritability of approximately 35% (28). Nevertheless, despite numerous efforts, the identification of gene variants robustly associated with type 1 and type 2 diabetic nephropathy has been limited. Interestingly, a transethnic meta-analysis revealed that the MYO16/IRS2 locus would be associated with susceptibility to both type 1 and 2 diabetic nephropathies (29), and a SCAF8 variant was consistently associated with type 2 diabetic nephropathy across populations of different ancestry (30) (see dark gray in Figure 2). In addition, GWAS carried out in European populations highlighted several associations with type 1 diabetes–associated ESRD (31,32), and other studies revealed candidates for both type 1– and type 2–associated ESRD (26,33–37). However, a subsequent comprehensive, large, meta-GWAS effort was unable to identify clear loci consistently associated with diabetic nephropathy and many of the previous identified candidate signals were not validated (28). However, given the general modest effect size for the associated loci in each of the discovery cohorts, it is challenging to discern between lack of reproducibility due to difference in populations, study design, outcome ascertainment, and/or false positives. Overall, these studies demonstrate the genetic landscape of diabetic nephropathy to be more complex than anticipated and bring forth the possibility that epigenetic factors, as well as potential higher order interactions, may play a prominent role.
Epigenetic modifications consist of dynamic acquired and at times heritable changes on our genome, which, although not modifying the DNA nucleotide sequence, can strongly affect gene regulation. Epigenetic profiles can adjust in response to the environment, including nutrition, glycemic levels, drug exposures, inflammation, and oxidative stress. Even though the study of epigenetic contribution to diabetic nephropathy is in its early stage (most reports were performed in vitro and in animal models—see review ), it is believed that epigenetic modifications could mediate the “metabolic memory” phenomenon. This was highlighted in large studies where long-term protection from diabetes complications, including nephropathy, was observed for >10 years after a tight control of glycemia (39,40). This could potentially also account, in part, for why SNP-based studies have failed to identify more consistent associations with diabetic nephropathy.
Membranous nephropathy (MN) is a common cause of GN and nephrotic syndrome leading to progressive CKD. A European GWAS revealed two loci strongly associated with MN risk using a discovery cohort of only 146 cases and further extending the results in 556 cases (see gray in Figure 2): PLA2R1 (odds ratio [OR], 2.3 CI, 2–2.6; P=8.6×10−29) and HLA-DQA1 (OR, 4.3 CI, 3.5–5; P=8.0×10−93) (41). Remarkably, the effect size of these loci was even stronger when analyzed jointly with a striking OR of 78 CI, 34.6–178.2, indicating an epistatic effect between PLA2R1 and HLA-DQA1 variants. Interestingly, it was shown that PLA2R1 associations are specific to MN (42), whereas HLA-DQA1 variants also associated with lupus nephritis, type 1 diabetic nephropathy, and FSGS in adults (42), as well as with steroid-sensitive nephrotic syndrome in children (43). Of note, the exploration of acquired nephrotic syndrome in Japanese did not identify HLA-DQA1 associations, but revealed GPC5 as another susceptibility factor (44).
The PLA2R1 specificity for MN is consistent with the presence of anti-PLA2R1 antibodies in approximately 70% of patients with MN (45,46), some of the remaining variability being explained by THSD7A autoantibodies (47). This has enabled a rapid translation to clinical practice for improved diagnosis and for predicting response to immunosuppressive therapy (48) and long-term outcomes (49) for patients with MN (see  for a review).
Lessons from these studies do not only pertain to the importance of and interaction between the production of autoantigens and heightened immune response in MN, but even more so demonstrate the possibility of very strong single gene effects that can be uncovered in small-sized cohorts of individuals with homogenous disease etiologies (Figure 1, Table 1).
IgA nephropathy (IgAN), the most common form of primary GN (51), perfectly exemplifies how multiple gene interactions can affect kidney injury. The increasing number of larger GWAS in European and Asian populations has progressively unveiled numerous loci associated with IgAN, including HLA, DEFA, ITGAM, and VAV3 (see pink in Figure 2) (52–55). The distribution of the combined risk alleles strongly correlates with the worldwide geographic prevalence gradient (high prevalence in Asians, intermediate in Europeans, and rare in Africans), and suggests that the selective pressure exerted on the host by intestinal pathogens, particularly by helminths, during evolution would have shaped the susceptibility to IgAN (56,57). In addition to these common genetic variations, two recent studies involving exon sequencing suggested a potential role for rare variants in IgAN susceptibility (58,59).
Overall, these studies highlight a more complex multilocus model for IgAN compared with MN, and also reveal the extent of pathogen selective pressures to shape strong protective immune responses versus detrimental increased autoimmune disease risk (including IgAN, lupus nephritis, and inflammatory bowel diseases [57,60,61]).
APOL1-Related Kidney Disease
The disparity in ESRD in individuals of African ancestry compared with European ancestry has long been noted (62). The identification of APOL1 risk variants as the genetic determinant explaining most of the disparity for FSGS-HIV-associated nephropathy (HIVAN) (OR, 17 CI, 11–26 for FSGS; and OR, 29 CI, 13–68.5 for HIVAN) and hypertensive-associated nephropathy represents one of the most significant GWAS discoveries within the medical field (see yellow in Figure 2) (63–65). The APOL1 high-risk genotypes only occur in African-derived chromosomes (66). These variants are believed to have reached such high frequencies due to a recent selection event in West Africa that is primarily attributed to providing protection from Trypanosoma brucei rhodesiense, the pathogen causing acute African sleeping sickness (65,67).
The APOL1 high-risk signal was extended to numerous kidney conditions, including hypertension-attributed ESRD (65), SLE-associated glomerulopathy (68), proteinuria (69), and CKD progression rate (70). Interestingly, whereas cross-sectional prevalent ESRD studies did not demonstrate a significant association between APOL1 high-risk genotype and nephropathy in patients with diabetes, we identified a clear and consistent association with faster CKD progression in APOL1 high-risk individuals regardless of diabetes status. We notably found that CKD individuals with diabetes and APOL1 high-risk genotype progressed much faster than any other groups, including the APOL1 risk individuals with no diabetes (rate of eGFR loss of −4.3 versus −2.9 ml/min per year), demonstrating that APOL1 risk effect is additive to diabetic nephropathy. Our observation therefore suggests that although APOL1 high-risk genotype does not appear to cause diabetic nephropathy (hyperglycemic milieu does), it causes a more rapid progression of established diabetic nephropathy (70).
APOL1 genotyping could soon affect kidney transplantation practice. Indeed, a strong association between the kidney donor APOL1 risk genotype and halving of time to kidney allograft failure has been shown (71,72), whereas the allograft recipient genotype does not seem to affect graft survival (73). These findings raise some important issues: should kidneys from APOL1 risk genotype donors be used? Given the ongoing shortage of organs and that most risk genotype kidneys are still viable, they should arguably be used. However, the approximately two-fold increased risk for failure warrants further discussion regarding potential reclassification of such kidneys as higher-risk organs. Another critical question pertains to the risk assessment for living donors with APOL1 risk genotype. Because APOL1 risk genotypes appear to affect the progression of all CKD causes and because the rate of postdonation CKD is relatively high in blacks, one should presume that donors carrying APOL1 risk genotypes are at increased risk for progressive CKD. Although at this time no definitive risk estimates can be provided, given the lack of long-term outcome data for such donors, it is encouraging to see the new APOL1 Long-term Kidney Transplantation Outcomes Network (APOLLO) initiative by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) that aims at initiating a large, observational, multicenter cohort of APOL1 risk donors and kidney recipients across the United States to more precisely quantify the risk to both donors and recipients. In the meantime, a conservative approach, notably in young potential donors in whom long-term CKD risk estimation is challenging, is fitting in our quest to “first do no harm.”
Sickle Cell Trait
Sickle cell trait (SCT) is present in 7%–9% of blacks and had typically been described as a relatively benign carrier state. However, this assumption was recently proven wrong in two large seminal analyses of nearly 10,000 and 16,000 individuals, where a near two-fold increase in incident CKD and albuminuria (74), as well as a two-fold increase in incident ESRD (75), were noted in individuals with SCT. Although the role of SCT in CKD was not realized by standard GWAS analyses, it is fitting and important enough to mention in this review, notably because it is common, and the noted effect sizes for incident CKD and ESRD are similar to that conferred by APOL1 high-risk genotype in the general population. SCT may indeed account for a significant portion of the remaining racial disparity in CKD that is unaccounted for by APOL1 genotype. Accordingly, efforts to better define the phenotype of SCT-associated CKD are sorely needed to shed further light on this longstanding underappreciated genetic and potentially modifiable CKD risk factor. This also further demonstrates the counter-selective pressures for gene variants’ beneficial effects in protecting from infectious diseases versus inducing kidney injury (e.g., UMOD, SCT, APOL1, and IgAN variants), and demonstrates that many of these variants are not simply random disease variants, but the end result of balancing multiple environmental/evolutionary pressures.
The rate of discovery and identification of novel gene loci related to kidney function and CKD over the last 10 years has greatly exceeded our ability to synthesize the information. Despite the nice genomic success stories which are moving into the clinical domain (e.g., APOL1, PLA2R1, and UMOD), the nephrology field has also faced similar challenges and, for some, disappointments to those experienced by every single discipline that has dived into the genomics waters: (1) finding the balance between exploring large population cohorts with increased statistical power or smaller specific, homogeneous, and even extreme, well defined phenotypes; (2) most findings only account for a very small amount of the trait variance; and (3) translating the genetic association–based findings to the clinical domain. However, it is essential to bear in mind that the strength of a single gene variant may convey little of the overall functional relevance of the gene. For example, although common SNPs in CUBN and UMOD showed weak-to-modest association with albuminuria and CKD, respectively, rare UMOD mutations have been associated with tubular dysfunction, microcysts, and progressive CKD (76), whereas a CUBN mutation has been strongly associated with overt proteinuria (77), which better illustrates the overall effect of the gene on outcomes, and the potential for pharmacologic intervention.
At this point, there is no doubt that kidney genomics has led to numerous novel discoveries and elucidated new pathways, and that it will continue to do so, notably within disease-specific cohorts, further diversification of studied populations, and with next-generation sequencing technologies. The question at hand now is, whether further refinement of established methods will soon reach a point of diminishing returns and whether new approaches will be required, if we are to efficiently translate genomics-based findings to improving outcomes for individuals with, or at risk for, kidney disease.
We have long taken a reductionist approach to medical research. Breaking down complex interactive processes into individual components has undoubtedly worked in greatly advancing science, but is not necessarily suitable to predict complex interactions nor to capture multifaceted processes leading to kidney disease (78,79). At this point, genomics can no longer be viewed as single SNPs working independently to meaningfully affect clinical outcomes; it must be viewed in the context of its transcriptional and translational regulation and dynamic interactions. Correspondingly, we must also appreciate that the effect of a genetic variant is often not fixed, but highly variable and situational. For example, APOL1 risk genotype has a range of no significant association with eGFR in the general population, to approximately two-fold increase in CKD, seven-fold increase in hypertensive-associated ESRD, 17-fold increased odds for FSGS, and near 30-fold increased odds of HIVAN. Moreover, given the sizeable gap related to the number of steps and potential pathways leading from any given gene variation to clinical outcomes, it stands that the association and integration of gene variants with intermediate processes (e.g., transcriptome, proteome, epigenome, and metabolome) using systems-based analytic approaches (e.g., Bayesian and neural network based) will be necessary. This will allow both leveraging multiomics outputs and capturing complex biologic processes to help bridge the gap. Accordingly, we feel that maximizing efforts to bring forth systems-based analytic approaches is now, more than ever, necessary.
It had long been hypothesized that, irrespective of the cause of kidney injury, progression of CKD converged into a common pathway of local inflammation followed by injury, fibrosis, and decline in GFR; but this did not necessarily bear out. Moreover, this view inadvertently supported the often nonspecific staging of CKD based primarily on GFR and levels of proteinuria, with insufficient attention to metrics of tubular function and disease heterogeneity (80). Indeed, this grouping of diverse processes such as “hypertensive nephropathy” could account, in part, for the contrast in single gene variant effect size between broad eGFR population-based cohorts and disease-specific homogenous cohorts (e.g., MN and HIVAN), and perhaps for the lack of success with most CKD clinical trials targeted at heterogeneous groups of individuals with CKD.
Contrasting with the nephrology field, cancer biologists have been able to obtain various tissue samples pertaining to clinical cases and unveil many of the molecular pathophysiology mechanisms underlying cancer diversity (81,82). This strategy has allowed the delineation of numerous disease subtypes, increased accuracy of prognostic models, and tailoring of clinical trials. In order to help overcome this serious limitation within the nephrology domain, the NIDDK has recently launched the Kidney Precision Medicine Project (KPMP) initiative to prospectively collect kidney biopsy tissue from individuals with primarily non–immune mediated CKD and AKI. The goal will then be to apply state-of-the-art agnostic “omics” techniques to develop a detailed molecular atlas of healthy and diseased kidney tissues. This should, in turn, allow the critically needed detailed phenotypic classification of new homogenous disease subgroups on the basis of the underlying molecular pathophysiology and, secondarily, enhance our ability to identify genetic and nongenetic processes leading to them. Indeed, even FSGS, which is a kidney biopsy–based diagnosis, refers to a pattern of injuries and is likely the end result of a wide variety of pathophysiologic processes. Importantly, such endeavors should significantly improve our ability to provide more precise diagnoses and prognoses and identify specific targets needed for treatment.
Achieving the promise of genomics-based tailored treatment modalities will undoubtedly require a conscious effort in moving from a longstanding culture of reductionism to true integrative systems-based approaches, as well as redefining many of the existing heterogeneous clinical-based kidney disease classifications to those on the basis of specific molecular pathways. Fortunately, key steps toward these achievable paradigm shifts are being put in place. So, although the patience needed to translate, validate, and implement the vast amount of novel genomics-related data may be more than some had hoped for, the harvest is as promising as ever.
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