The prevalence of end-stage renal disease (ESRD) is steadily increasing, contributing to growth in the provision of renal replacement therapy (dialysis and transplantation) for over one million individuals worldwide (Collins et al., 2003). Living with ESRD is challenging for individuals, and the economic costs of renal replacement therapy for society are enormous (Meguid El Nahas and Bello, 2005).

Figure 24.1 shows the relative prevalence of different primary renal diseases in the English and Welsh population on renal replacement therapy (RRT) in 2010 and in the American population in 2009.  Diabetic nephropathy is the most common cause of ESRD in the United States and is increasing in prevalence worldwide. There is increasing evidence for genetic (polygenic) susceptibility for diabetic nephropathy. Autosomal dominant polycystic kidney disease (ADPKD) accounts for 5–10% of prevalent ESRD and is the single most common monogenic cause of ESRD worldwide. In this chapter, we review the evidence of how genetic factors can modify the phenotypical expression of these two common diseases, and illustrate the different experimental approaches that are being used to identify modifying genes that are important in chronic kidney disease.

Figure 24.1 Primary diagnosis in prevalent patients on renal replacement therapy (RRT) in England and Wales (E&W) in 2010 and in the United States in 2009. Diabetes was the second most common cause in the U.K. (A) and the most common cause of ESRD in prevalent RRT patients in the U.S. (B). In the U.K., the major change in the over-65 age group was an increase in renovascular disease (from 1.1% to 7.8%). The male:female ratio was greater than unity for all groups except for PKD. Source: UK Renal Registry data, 2010, US Renal Data System 2011.



Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common human monogenic diseases, with an incidence of 1:400–1:1000. It is characterized by the progressive development and enlargement of focal cysts in both kidneys, typically resulting in end-stage renal disease (ESRD) by the fifth decade.

Two Genes Cause ADPKD

Mutation to PKD1 (chromosome region 16p13.3) is the most common cause of ADPKD (~86% cases), with most of the remainder due to changes to PKD2 (4q22). PKD1 and PKD2 patients have indistinguishable renal and extra-renal phenotypes: they were only recognized as distinct diseases by genetic linkage analysis in the late 1980s (Parfrey et al., 1990). PKD1 is a complex gene with 46 exons that generates a large transcript (~14kb) containing a long open-reading frame predicted to encode a 4302aa protein named polycystin-1 (Hughes et al., 1995). PKD2 has 15 exons, generates a ~5kb transcript, and encodes polycystin-2, a protein of 968aa (Mochizuki et al., 1996) (Figure 24.2).

Figure 24.2 Predicted structure and topology of the ADPKD proteins, polycystin-1 and polycystin-2. Several functional protein, carbohydrate, and lipid-binding functional domains of both proteins are depicted (Ong and Harris, 2005). Both proteins interact via a coiled coil domain in their C-termini. A G-protein coupled receptor proteolytic site (GPS) may allow polycystin-1 to be cloven into two halves that remain tethered by non-covalent  bonds.

Gene Locus Effect

PKD2 is a significantly milder disease in terms of the mean age at diagnosis, a lower prevalence of hypertension, and a later age at onset of ESRD (PKD1,54.3 yrs; PKD2, 74.0 yrs) (Hateboer et al., 1999). Consistent with this milder phenotype, there appears to be an enrichment of PKD2 patients among ADPKD patients who present late (after 63 yrs) with ESRD (Torra et al., 2000). Conversely the vast majority of very early onset (VEO) ADPKD manifesting in utero or infancy is related to mutations in PKD1 (Peral et   al.,1996).

The Effect of Gender

While there is no clear gender difference in PKD1, PKD2 females have a significantly better prognosis (age at onset of ESRD: males, 68.1  yrs; females, 76.0 yrs): the reason for this is unclear (Magistroni et al., 2003; Rossetti et al., 2002a).

Allelic Heterogeneity

Both PKD1 and PKD2 exhibit marked allelic heterogeneity, with over 400 different PKD1 and 100 different PKD2mutations reported to date (ADPKD mutation database, Unlike PKD2, PKD1 is highly polymorphic, with on average 10–13 changes described per individual (Garcia-Gonzalez et al., 2007; Rossetti et al., 2007). The majority of mutations are private (i.e., unique to a single family) and indicate that a significant level of new mutation is occurring (Rossetti et al., 2001). For PKD1, mutations 5′ to the median are associated with more severe disease (average age at onset of ESRD: 5′, 53 yrs; 3′, 56 yrs) and a significantly greater risk of developing intracranial aneurysms (Rossetti et al., 2002a; Rossetti et al., 2003).This association is not related to mutation type and may be due to the proposed cleavage of polycystin-1 via a G-protein coupled receptor proteolytic site (GPS) into two different proteins (Figure 24.2) with mutations to each half having potentially different  phenotypical consequences (Qian et al., 2002; Rossetti et al., 2003).As yet, no clear phenotype–genotype correlations have been reported for PKD2  (Magistroni et al., 2003).

Gene Syndromes

Patients with a contiguous gene deletion of both PKD1 and the neighboring tuberous sclerosis gene (TSC2) allele have more severe early-onset PKD than those with TSC2 mutations alone (Brook-Carter et al., 1994). Most probably this indicates a synergistic role between polycystin-1 and tuberin (the TSC2 protein)  in  cyst  development:  tuberin  may  play  a  role  in  trafficking polycystin-1 to the lateral cell membrane in kidney epithelial cells (Kleymenova et al., 2001; Ong et al., 1999).

A family with bilineal inheritance of germline PKD1 and PKD2 mutations has been described and recently further characterized (Pei et al., 2012; Pei et al., 2001). The PKD1 mutation is predicted to be a hypomorphic change (Y528C), while the PKD2 mutation is a truncating mutation (L736X). Although these patients have more severe disease than cases with either mutation alone, the difference is not dramatic (i.e., not every renal tubular cell gives rise to a cyst). The effect of a trans-heterozygous mutation in either gene appears to act as a modifying factor for the other in terms of the risk of cyst development or hastening its progression, rather than an effect on cyst initiation itself (Wu et al., 2002).


Polycystin-1 and polycystin-2 form a receptor-ion complex that regulates tubular morphogenesis (Ong and Harris, 2005). However, both proteins have been localized in multiple subcellular compartments that may differ between cell types and developmental status (Ong, 2000). Several models of polycystin function have thus been proposed (Giamarchi et al., 2010). It is likely that they form mechano-sensitive and ligand-gated channel complexes in primary cilia and at the basolateral surface of epithelial cells. Plasma membrane polycystin-1 may couple to activate endoplasmic reticulum (ER) located polycystin-2 to release intracellular Ca2+. Finally, both proteins have been found in urinary exosomes and may represent a form of urocrine signaling (Hogan et al., 2009).


Somatic Mutations

A “two-hit” mechanism of cyst formation has been proposed for ADPKD (consisting of a germline mutation to one allele and a somatic mutation to the other). There is evidence for epithelial cell clonality and a high rate  of somatic mutations in cells isolated from individual kidney and liver cysts (Brasier and Henske, 1997; Qian et al., 1996; Watnick et al., 1998). Experimentally, a unique Pkd2 knockout mouse  (Pkd2WS25  mutant)    which has an unstable allele (prone to inactivation by recombination) develops progressive cystic disease in a manner consistent with a two-hit model (Lu et al., 1997; Wu et al., 1998a). Certainly, a two-hit model could help explain both the focal nature of cyst development and the striking intrafamilial phenotypical variability seen in most families. However, there remain questions as to whether a two-hit mechanism is the only means to generate a cyst, and indeed, whether these somatic events may be later events more important for cyst expansion and progression rather than initiation (Ong and Harris, 1997).

Evidence for Gene Dosage

A decrease (loss of a single allele or haploinsufficiency) or increase (transgenic expression) in polycystin-1 dosage may itself result in cyst formation (Lantinga-van Leeuwen et al., 2004; Pritchard et al., 2000; Kurbegovic et al., 2010). In addition, haploinsufficient Pkd2 mice have reduced survival (not due to renal failure) indicating that a dosage reduction of polycystin-2 itself can exert a phenotypical load (Wu et al., 2000). Recent studies have shown that Pkd2+/- vascular smooth muscle cells (which express a lower level of polycystin-2) have altered intracellular Ca2+ homeostasis and are hyperproliferative (Qian et al., 2003). Similar findings have been shown for Pkd1+/- cyst epithelia (Yamaguchi et al., 2004) and mouse collecting duct cells (Ahrabi et al., 2007). Recent work has pinpointed the influence of hypomorphic alleles in PKD1 and PKD2 in very early onset PKD, proving the importance of gene dosage.

In summary, there appear to be multiple mechanisms that could underlie cyst formation. Cyst development requires a germline mutation, but beyond this, the likelihood of cyst formation is influenced by a number of different factors, which could include somatic genetic events at the other (normal) allele, mutations at the other ADPKD gene, and possibly a wide array of other genetic loci (e.g. TSC2). In effect, these loci act as modifiers of disease presentation in ADPKD. Environmental or genetic factors that modulate the rate of somatic mutation or DNA repair could modify the disease phenotype (Peters and Breuning, 2001). Beyond the genetic events, stochastic factors probably also influence whether a cell, haploinsufficient for an ADPKD mutation, is diverted into a cystogenic pathway. Another factor that could modify the cystic phenotype is the presence of hypomorphic alleles giving rise to partially functional (mutant) polycystin-1 protein, such as in Pkd1 del34 mice (Lu et al., 2001); alternatively, nonfunctional mutant protein could act in a dominant negative manner.


The wide range of intrafamilial phenotypical variability in large ADPKD families and more recently for PKD1 and PKD2 has been described (Magistroni et al., 2003; Milutinovic et al., 1992; Rossetti et al., 2003). The phenomenon of genetic anticipation in ADPKD families was first postulated in 1925 with the description of very early onset (VEO) cases, often born to relatively asymptomatic parents (Cairns, 1925). However, no evidence for dynamic unstable mutations in PKD1 or PKD2 has been found. Nevertheless, there is a high recurrence risk (45%) of a severely affected sibling in subsequent pregnancies (Zerres et al., 1993). For VEO cases, it is difficult to envisage (though impossible to exclude) that somatic mutations to PKD1 could be the underlying reason for increased disease severity in utero (Ong et al., 1999).


The genetic characterization of very early onset families has identified hypomorphic alleles in PKD1 that are incompletely penetrant but, if co- inherited with a truncating or hypomorphic PKD1 mutation or another cystic gene (HNF-1b), give rise to a severe presentation (Rossetti et al., 2009). The combination of a hypomorphic PKD2 mutation and frameshifting PKHD1 mutation was reported in a single early-onset family (Bergmann et al., 2011). These findings raise several important points. First is the question as to how many unclassified variants (UCV) reported for PKD1 could represent hypomorphic alleles. Second, mutations in other cystic genes can potentially determine phenotype in an additive and dosage-dependent manner. Third, they imply that there may be a critical polycystin threshold for cyst formation and raise questions about the role of somatic mutation in cyst initiation. At present, we do not know the relative contribution that each makes to the phenotype in either very early onset disease or more typical adult-onset PKD.


A family with germline and somatic mosaicism in PKD1 has been reported (Connor et al., 2008). This could be a rare cause of intrafamilial variability.

Non-Allelic Factors

Other non-allelic causes of variability include modifying genes, environmental factors, or stochastic events. Many studies have attempted to define the possible role of modifying genes (heritability) in determining renal outcome. An informative study of 74 parent–offspring pairs where the age of renal death of both was known showed a wide Gaussian distribution of values around zero (Geberth et al., 1995). Furthermore, there was no difference in the median age of renal death between two decades (1950–1971, 1975–1985) thus excluding the effect of secular trend as a major confounding factor. A second study utilizing sib-pairs compared the age of ESRD between 56 sibships and 9 pairs of monozygotic twins and calculated the intraclass correlation coefficient (ICC) within both groups (Persu et al., 2004). The ICC, which compares the similarity within sibships to that between sibships, was significantly higher in the twins (0.92) than in other sibs (0.49). Discordance in the renal phenotype between a pair of dizygotic  twins carrying the same PKD1 mutation has been reported in one family (Peral et al., 1996). Later reanalysis of this family revealed that the more severely affected twin had inherited both the germline PKD1 mutation and a potential hypomorphic PKD1 mutation (from the clinically unaffected parent) (Rossetti et al., 2009).

The possible influence of modifying genes (heritability) in PKD1 families has also been addressed in two large studies. Heritability (h2) for the age of ESRD was estimated at between 43–78% (Fain et al., 2005; Paterson et al., 2005). However, the number of potential modifiers, their allele frequency, and their relative effects in relation to determining loss of renal function are not clear. Paterson and colleagues calculated that a genome-wide scan to look for a single modifier locus will require very large cohorts to be powered adequately and thus necessitate international collaboration (Paterson et al., 2005).

These studies also point to the likely significance of (unknown) environmental factors as contributing to the ESRD phenotype. These   factors could include urinary tract infections in men and the number of pregnancies in women (Gabow et al., 1992), but require verification in larger scale populations. Figure 24.3 summarizes the potential causes of phenotypical variability in this disease.

Figure 24.3 Genetic and environmental factors determining the renal phenotype in ADPKD. There is a strong locus effect, but allelic-specific factors are weak. Co-inheritance of two hypomorphic mutations in PKD1 as well as mutations in other cystic genes (PKHD1, HNF1b) have been recently reported in some very early onset cases. An effect of gender has been reported in PKD2 (but not PKD1) patients. The precise contributions of different non-allelic factors (somatic mutation, genetic modifiers, and environmental agents) are uncertain.

Coexistent Diseases—Cystic Fibrosis

A significant body of evidence suggests that ADPKD cyst epithelial cells undergo a phenotypical switch to cAMP responsiveness as measured by cell proliferation and fluid secretion (Grantham, 2003). The cystic fibrosis transmembrane conductance regulator (CFTR) is a cAMP-dependent chloride channel protein that is mutated in patients with the autosomal recessive disease cystic fibrosis (CF). CFTR is expressed in the kidney and in cyst lining cells. A family with two individuals with coexistent CF and ADPKD has been reported with normal kidney function, suggesting a possible protective effect of the absence of CFTR (O’Sullivan et al., 1998). However, this was not substantiated in another family where neither heterozygous nor homozygous CFTR mutations (DeltaF508) had a protective effect on renal outcome (Persu et al., 2000)


Numerous studies examining the association between candidate-gene polymorphisms (e.g., Angiotensin Converting Enzyme [ACE], Endothelial Nitric Oxide Synthase [ENOS]) and kidney function have been performed. Overall, these finding have proven to be inconclusive and contradictory (Persu et al., 2002; Walker et al., 2003). A recent single-nucleotide polymorphism (SNP) genotyping association study of 173 candidate genes in a cohort of 794 white patients initially identified 12 SNPs associated with Estimated Glomerular Filtration Rate (eGFR) or renal survival (time to ESRD). However, only three SNPs remained associated with eGFR in a second replicative cohort of 472 white patients (Liu et al., 2010): these were all located at the DKK3 locus, DKK3 being a secreted antagonist of Wnt signaling. It is estimated that GWAS studies of PKD1 disease variance will require 3000–4000 patients to detect multiple loci with similar effect size.



The apparent similarities in the process of cyst formation between different rodent models and human disease have led some investigators to exploit the approach of cross-breeding a PKD allele onto different genetic backgrounds to map modifier loci. In theory, this approach allows environmental factors to be controlled for, thus permitting the contribution of genetic factors to be paramount. In one of the most-studied murine PKD models, the cpk mouse, it is apparent that disease expression and the severity of both renal and extra-renal phenotypes can be modified by genetic background. For instance, the biliary abnormality in cpk is not penetrant in the original C57BL/6 (B6) background but is variably penetrant when expressed in other strain backgrounds, such as CAST/Ei, DBA/2J, BALB/c or CD1 (Guay-Woodford, 2003).

PKD due to the pcy allele is more severe in a DBA/2 than a C57BL/6 background. A good example of the approach of whole-genome quantitative trait loci (QTL) mapping is one that led to the identification of two major modifiers (MOP1, MOP2) regulating renal disease progression in the pcy mouse (Woo et al., 1997). The pcy gene was subsequently identified as the murine homologue of the nephronopthisis Type 3 (NPHP3) gene, which gives rise to adolescent-onset nephronopthisis, a phenotype that can include cysts (Olbrich et al., 2003). It would therefore be of interest to investigate whether MOP1 and MOP2 can also act as modifiers in the human disease. Similar approaches have been taken in other rodent strains, including the recessive cpk mouse and the dominant Han:SPRD (cy/+) rat, genes that have not yet been associated with human PKD (Bihoreau et al., 2002; Mrug et al., 2005). Table 24.1 summarizes the most common rodent PKD models that have been characterized and those in which QTL have been mapped. It is hoped that a comparative genome analysis will enable any modifying loci identified in a rodent model to be mapped back to a human susceptibility locus in ADPKD.




Numerous Pkd1 deficient mice have been reported, all of which  are associated with a cystic kidney phenotype. Almost all are embryonically lethal in the recessive state apart from a hypomorphic allele (Pkd1nl) (Lantinga-van Leeuwen et al., 2004). Of interest, some, but not all, mutants develop skeletal or cardiovascular defects, unrelated to the site of the gene disrupted. There is a high prevalence of aortic aneurysms in homozygous Pkd1nl mice that has not been described in other models (Lantinga-van Leeuwen et al., 2004). The expression of these phenotypes could therefore be related to genetic background; that is, other modifying genes. This  may reflect the situation in human ADPKD where not all patients (i.e., ~8–10%) develop vascular complications (aneurysms), and, more rarely, patients associated with a Marfanoid phenotype have been reported (Rossetti et al., 2003; Somlo et al., 1993). Several other models have been  reported, including (1) conditional (inducible) tissue-specific Cre-Lox deletion mutants; (2) hypomorphic alleles; and (3) knockdown alleles. These will be valuable as models to test new drugs in a preclinical setting.


Numerous polycystin-like homologues have been identified from whole- genomic and Expressed Sequence Tag (EST) databases. In essence, they divide into “PC1-like” and “PC2-like” proteins (Table 24.2). In theory, these homologues could modulate the PKD phenotype caused by mutations in PKD1 or PKD2. However, the restricted tissue distribution of each homologue (most are not expressed in the kidney) suggests that they are more likely to play a role in expression of the extra-renal phenotype. Three of these complexes have been shown to play functional roles in unexpected phenotypes: the PKD1L1-PKD2 complex in left-right axis determination; PKD1L3-PKD2L1 in sour taste sensation; PKDREJ-PKD2 in the acrosome reaction (Hughes et al., 1999). It is likely that other combinations of PC1 and PC2 homologues could fulfill other yet undiscovered biological functions.



Our discussion so far has focused largely on factors that could modulate the rate of cyst initiation. However, factors that could regulate the rate of cyst growth (proliferation, apoptosis, fluid secretion) may be more appropriate therapeutic  targets  than  attempts  to  replace  the  gene  (Qian  et  al.,  2001) (Figure 24.4). Also, the diseased ADPKD kidney is characterized by non- cystic features such as interstitial fibrosis, inflammation, and vessel wall thickening (leading to ischemia) (Ong and Harris, 2005). Although it is unclear whether these are the direct downstream consequences of cystic transformation, alternative pharmacological strategies targeting these pathways might be effective ways to prevent or delay progression to ESRD. A large number of published studies have tested potential drugs, treatments, or diets in rodent PKD models, especially in the Han:SPRD rat (Guay- Woodford, 2003). However, it should be noted that this model displays some unusual features, such as gender dimorphism, which could limit its applicability to human ADPKD. Moreover, it is clear that what works in one model may not always be reproduced in another (Guay-Woodford, 2003). Therefore, future studies should ideally concentrate on orthologous ADPKD models.

Figure 24.4 Pathophysiology of disease progression in the cystic kidney. The rate of cyst formation as well as downstream phenotypical changes in the cystic cell (cell turnover, fluid secretion, matrix accumulation) could be modified by non-allelic and environmental factors (Ong and Harris, 2005). This may account for the phenotypical variability arising between individuals carrying the same germline mutation. Potential triggers to a cystic cell phenotype could include somatic mutations or stochastic factors.

Figure 24.5 Mechanism-based therapeutics in ADPKD. The major signaling pathways involved in the pathogenesis of ADPKD and the targets of candidate drugs (Chang and Ong, 2011). (A) The  “secretory” pathways; (B) the “proliferative” pathways. Abbreviations: AMPK, AMP-activated protein kinase; CaSR, calcium-sensing receptor; CFTR, cystic fibrosis transmembrane regulator; ER, endoplasmic reticulum; GlcCer, glucosylceramide; HDAC, histone deacetylase; KCa3.1, endothelial Ca2+-activated K+-channel; MAPK, mitogen-activated protein kinase; mTOR, the mammalian target of rapamycin; NKCC1, Na-K-Cl cotransporter; PC, polycystin; SR, somatostatin receptor; TSC, tuberous sclerosis;V2R, vasopressin V2 receptor.

Figure 24.5 summarizes the major signaling pathways in  ADPKD that have been identified and targeted based on a mechanism-based therapeutic strategy (Chang and Ong, 2011). The main approaches can be divided into those that target cell proliferation, fluid secretion, or cAMP turnover. Agents that block cAMP production, such as the vasopressin type 2 receptor (VP2R) antagonists, are likely to affect both proliferation and fluid secretion (Torres et al., 2004). Genetic variants in key components of these pathways could act as modifiers of the disease phenotype or determine response to treatment.


Diabetic nephropathy is the most common identified cause of chronic kidney disease in the United States and the second most common cause of ESRD in the United Kingdom (U.S. Renal Data System 2010; U.K. Renal Registry 2010; Levey and Coresh, 2012). The prevalence of diabetic kidney disease continues to rise, reflecting the epidemic of type 2 diabetes, the improving survival  of  persons  with  diabetes,  and  the  increased  number  of  diabetic patients with end-stage renal disease (ESRD) accepted for renal replacement therapy (chronic dialysis or renal transplantation) (Jones et al., 2005).

The clinical diagnosis of diabetic nephropathy is based on the presence of persistent proteinuria (albuminuria) in an individual with diabetes in the absence of other renal diseases, urinary tract infection, or heart failure. It is usually associated with rising blood pressure and eventual decline in glomerular filtration rate (GFR). The fall in GFR is normally identified once the serum creatinine rises outside the normal range.

The early changes in kidney function and structure include development of glomerular capillary hypertension, epithelial cell (podocyte) dysfunction, and glomerular basement membrane thickening with associated mesangial matrix expansion (Adler, 2004; Fioretto and Mauer, 2007; Fioretto et al., 1992). These pathological changes are associated with the loss of the normal barrier function of the glomerular basement membrane; consequently, albumin can pass from the glomerular capillaries through “leaky” glomerular basement membranes and disrupted slit diaphragms into the urinary space (Gross et al., 2005a) (Figures 24.6a and 24.6b). Initially, this abnormal urinary albumin excretion is low in quantity and not detected on dipstick urinalysis. Microalbuminuria, a term that describes this small amount of urinary albumin excretion, is important since it is often the first detectable clinical sign of diabetic kidney disease (Shields and Maxwell, 2010). Persistent glomerular injury is associated with renal tubular dysfunction, clinical proteinuria (dipstick urinalysis positive), and progressive renal failure (Figure 24.6c).

Figure 24.6a Normal glomerular filtration. Normal glomerular filtration depends on glomerular capillary pressure (P) providing a hydraulic force for filtration across the semipermeable glomerular basement membrane. Constant glomerular capillary pressure is maintained by autoregulation of glomerular blood flow by adjustment of the vascular resistance provided by the afferent (pre- glomerular) and efferent (post-glomerular)  arterioles.

Figure 24.6b Early structural and functional changes in the pathogenesis of diabetic nephropathy. Glomerular capillary pressure (P) is elevated due to vasoconstriction of the efferent arteriole mediated by higher renal tissue levels of angiotensin II. The glomerular basement membrane is widened but has decreased charge and size selectivity permitting an increased flux of albumin from blood to urinary space.

Figure 24.6c Progressive glomerular sclerosis and tubulointerstitial fibrosis are associated with loss of renal function. An inflammatory interstitial infiltrate is seen in association with tubular atrophy.

Diabetic nephropathy is arguably the most important microvascular complication of diabetes in terms of healthcare costs, morbidity, and premature mortality (Alicic and Tuttle, 2010; Groop et al., 2009; Hex et al., 2012). Individuals with diabetes have an increased mortality rate due to cardiovascular disease, such as coronary heart disease and stroke (Morgan et al., 2000; Williams and Airey, 2002; Ninomiya et al., 2009; Drury et al., 2011). The development of diabetic nephropathy further amplifies this risk of macrovascular disease which is increased two- to four-fold in early renal disease (microalbuminuria), nine times with established nephropathy (proteinuria) and up to 50-fold in persons with ESRD (Deckert et al., 1996;Dinneen and Gerstein, 1997; Tuomilehto et al., 1998; Fuller et al., 2001). Diabetic nephropathy is associated with prolonged duration of diabetes, poor glycemic control, and raised blood pressure, and is more common in diabetic individuals of Asian or African ancestry (The Diabetes Control and Complications Trial (DCCT) Research Group, 1993; UK Prospective Diabetes Study [UKPDS] Group 1998; Nelson et al., 1997).

Primary prevention of diabetic nephropathy requires excellent glycemic control that is often difficult to achieve in practice (The Diabetes Control and Complications Trial (DCCT) Research Group,1993; UK  Prospective Diabetes Study (UKPDS) Group 1998; Barnett, 2004). Intensive control of diabetes reduces the incidence of diabetic nephropathy in type 1 diabetes (de Boer et al., 2011) and type 2 diabetes (Stratton et al., 2000). Nevertheless, efforts to intensively manage glycemic control in patients with both type 2 diabetes and cardiovascular disease have been reported recently to be associated with an increased overall patient mortality despite improvements in renal outcomes (Dluhy and McMahon, 2008). Progression of renal disease can be modified by inhibition of the renin-angiotensin system with angiotensin-converting enzyme inhibitors and/or angiotensin II receptor blockers (Brenner et al., 2001; Lewis et al., 1993; Lewis et al., 2001). These agents have beneficial effects beyond blood pressure lowering, including lowering glomerular capillary pressure and reducing proteinuria (Chiurchiu et al., 2005). Smoking cessation, aspirin therapy, and lipid-lowering drugs are also part of the multiple-risk-factor management to reduce cardiovascular events in persons with nephropathy (Fioretto and Solini, 2005; Gaede et al., 2003; Gaede et al., 2008). Dietary protein restriction (to 1g/kg body weight) can also slow progression in proteinuric diabetic patients, although the clinical utility of this strategy is limited (Pedrini et al., 1996; Robertson et al., 2007).

Unfortunately, despite these interventions, kidney disease still develops in up to 30% of individuals with type 1 diabetes and up to 50% of persons with type 2 diabetes, and progresses in approximately 30% of diabetics (Krolewski et al., 1996; Pambianco et al., 2006; Parving et al., 2006). Additional novel strategies are therefore urgently required to fight the epidemic of diabetic renal disease (Wolf and Ritz, 2003). Of interest, diabetic nephropathy is not an inevitable complication of a prolonged duration of diabetes (Bain et al., 2003). Also, whilst the cumulative incidence of nephropathy is greatest in those persons with the worst glycemic control, the majority of persons with poorly controlled diabetes still do not develop clinically apparent renal disease (Krolewski et al., 1996). This illustrates that hyperglycemia is necessary but not sufficient for the development of nephropathy. These clinical paradoxes have led to renewed interest in determining patients’ genetic susceptibility to diabetic nephropathy.


Mesangial expansion and tubulointerstitial fibrosis are two important renal structural changes associated with the progression of diabetic nephropathy. The degree of tubulointerstitial fibrosis also correlates with prognosis (Gilbert and Cooper, 1999). There is clinical and experimental evidence for a wide range of mechanisms mediating the effects of hyperglycemia and hypertension on the diabetic kidney (Conway et al., 2012).

Upregulation of the renin-angiotensin system (RAS) plays a prominent role in the pathophysiology of nephropathy, and experimental evidence demonstrates that treatment with angiotensin-converting enzyme (ACE) inhibitors can reduce proteinuria, tubulointerstitial injury, and glomerulosclerosis (Gilbert and Cooper, 1999; Zatz et al., 1986). Local activation of intra-renal RAS leads to high levels of angiotensin II in diabetic kidneys (Anderson et al., 1993). Whilst angiotensin II mediates its effects in part through alteration in systemic blood pressure and glomerular hemodynamics, angiotensin II is also recognized to be a potent inducer of transforming growth factor-β(TGF-β). Overexpression of TGF-β is associated with tissue fibrosis (Ziyadeh and Wolf, 2008).

Accumulation of extracellular matrix is a prominent histological feature of diabetic nephropathy. This extracellular matrix expansion is associated with elevated TGF-β mRNA and protein levels in both renal glomeruli and the tubulointerstitium in diabetic patients and animal models of diabetes (Rocco et al., 1992; Yamamoto et al., 1993; Yamamoto et al., 1996). ACE inhibitors can reduce renal TGF-β production in diabetic rodent models (Benigni et al., 2006).

Persistent hyperglycemia can induce formation of polyol compounds, as glucose is reduced to sorbitol by the nicotinamide adenine dinucleotide phosphate (NADPH)-dependent enzyme, aldose reductase (Dunlop, 2000). The role of this pathway in development of microvascular diabetic complications is supported by the efficacy of aldose reductase inhibitors such as tolrestat and sorbinol in animal models (Greene et al., 1999; Oates and Mylari, 1999). However, toxicity of these agents has largely prevented their therapeutic use in diabetic nephropathy.

Advanced glycosylation end products (AGEs) are non-enzymatic modifications of amino acids and proteins formed in states of chronic hyperglycemia (Brownlee, 2001). Serum levels of AGEs are increased in diabetic nephropathy and are also found in glomeruli and tubules in diabetic animals (Chen et al., 2000). Receptors for AGEs (RAGE) are upregulated in diabetic nephropathy and may play a role in the epithelial to mesenchymal transdifferentiation of renal tubular cells in diabetes. Infusion of AGEs causes albuminuria and glomerulosclerosis (Vlassara et al., 1994).

Hyperglycemia may also play a direct role in upregulating the protein kinase C (PKC) family of serine threonine kinases involved in multiple cell signaling pathways that regulate gene transcription (Murphy et al., 1998). The PKC enzymes are implicated in regulation of vascular permeability, blood flow, smooth muscle contractility, angiogenesis, and fibrinolysis (Koya et al., 1997). Thus hyperglycemia, by stimulation of  PKC-regulated pathways, can induce increased production of cytokines and growth factors critical to the progression of nephropathy. Experimental models of diabetes have shown that pharmacological inhibitors of PKC can reduce severity of renal injury (Koya et al., 2000).


Several environmental risk factors are next described that contribute to the pathogenesis of diabetic nephropathy.


Raised blood pressure is arguably the most important modifiable risk factor affecting progression of nephropathy (Cooper, 1998; Marshall, 2004; Ritz et al., 2001; James et al., 2010). Patients with type 1 diabetes usually develop a rise in blood pressure after the onset of microalbuminuria, whereas hypertension often predates the onset of clinical renal disease in persons with type 2 diabetes (Remuzzi et al., 2002). Familial clustering of hypertension has been reported where there are diabetic offspring affected by nephropathy (De Cosmo et al., 1997; Roglic et al., 1998).


The risk of developing diabetic nephropathy is increased by poor glycemic control (Chase et al., 1989; Molitch et al., 1993). The Diabetes Control and Complications Trial (DCCT) conclusively demonstrated that intensive glycemic control in patients with type 1 diabetes reduced the risk of developing nephropathy (microalbuminuria) and the progression to early nephropathy (proteinuria) (de Boer et al., 2011). The effect of improved glycemic control remained after four years of follow-up, despite the fact that the difference in glycemic control between the intensive and conventional groups had begun to converge (The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications [DCCT/EDIC] Research Group, 2000). Recently the DCCT/EDIC Research Group reported that the long-term risk of an impaired GFR in individuals with type 1 diabetes was significantly lower with intensive diabetes therapy early in the course of the disease, compared to those treated with conventional diabetes therapy (The DCCT/EDIC Research  Group, 2011). The evidence for a beneficial effect of intensive glycemic control in retarding progression in patients who already have established nephropathy is less convincing. Reversal of diabetic nephropathy pathology has been reported following successful pancreatic transplantation for type 1 diabetes (Fioretto and Mauer, 2012; Fioretto et al., 1998).

Multifactorial intervention, including improved glycemic control, can also achieve impressive reductions in the risk of nephropathy in patients with type 2 diabetes. In the Steno study, the intensively treated group had a target HbA1c of <6.5% and a target blood pressure of <130/80 mmHg (Gaede et al., 1999; Gaede et al., 2008). In addition, the intensively treated group received low-dose aspirin, ACE inhibition, and lipid-lowering therapy. During eight years of follow up, the intensive treatment achieved a 61% reduction in nephropathy and 58% reduction in retinopathy compared to the conventionally treated group (Gaede et al., 2003).

Birth Weight

Low birth weight is associated with an increased risk of cardiovascular disease and type 2 diabetes (Barker and Bagby, 2005; Barker et al., 1993; Hales and Barker, 2001; Phillips et al., 1994). It has been suggested that intrauterine growth retardation is associated with a reduction in nephronnumber in man, a hypothesis supported by animal models (Brenner and Chertow, 1994; Luyckx and Brenner, 2005, 2010; Schreuder et al., 2005). Some clinical studies have challenged this concept, reporting no association between birth weight and progression of diabetic nephropathy (Jacobsen et al., 2003). Careful autopsy studies have correlated the reduced nephron number with the presence of essential hypertension prior to death (Gross et al., 2005b; Keller et al., 2003). It has also been reported that fetal exposure to maternal type 1 diabetes is associated with renal dysfunction in adulthood, possibly as a result of reduced nephron numbers in offspring of diabetic mothers (Abi Khalil et al., 2010).


Modification of dietary protein intake has been studied in an effort to retard the progression of renal failure (Klahr et al., 1994; Levey et al., 1999). Animal studies have shown dietary protein-restriction reduces glomerular hyperfiltration, intraglomerular capillary hypertension, and the progression of renal disease (Molitch et al., 2003). In clinical studies, protein-restricted diets have been associated with a fall in proteinuria and reduction in the rate of decline of GFR (Hansen et al., 2002; Pedrini et al., 1996). In routine practice, however, the effects of dietary protein restriction have been difficult to replicate. Interest has also been focused on the influence of dietary fat intake on the development of nephropathy and atherosclerosis. Hypercholesterolemia has been reported to be associated with more rapid fall in GFR (Breyer et al., 1996). Treatment with statin therapy reduced cardiovascular events in type 2 diabetics with microalbuminuria and proteinuria (Colhoun et al., 2004), but paradoxically, statin therapy did not reduce cardiovascular mortality in dialysis-dependent diabetic patients (Wanner et al., 2005). Potentially atherogenic lipoprotein profiles are associated with renal dysfunction in type 1 diabetes (Jenkins et al., 2003). Individuals with type 2 diabetes, chronic kidney disease, and the most “atherogenic” lipid profiles appear to derive clinical benefits (in terms of fewer cardiovascular events, less retinopathy, and reduction in albuminuria) from combined statin and fibrate treatment (Rosenblit, 2012).


Cigarette smoking is associated with development of nephropathy in patients with either type 1 or type 2 diabetes (Nilsson et al., 2004; Olivarius Nde et al., 1993; Telmer et al., 1984). Smoking increases the risk of cardiovascular death, particularly in diabetic patients with proteinuria (Borch-Johnsen et al., 1987; Moy et al., 1990). There is also evidence that smoking is associated with more rapid progression of renal disease (Orth et al., 1997; Ritz et al., 2000). This may be induced by smoking-related changes to glomerular structure and function (Baggio et al., 2002). Smoking-cessation is still an important strategy in reducing the risk of developing nephropathy in persons with diabetes (Tonstad, 2009).

The Impact of Diabetes Duration on the Risk of  Nephropathy

If renal disease were directly due to persistent hyperglycemia, then a linear relationship between cumulative incidence and duration of diabetes would be expected. Background diabetic retinopathy is present in almost all diabetic individuals after 50 years’ duration, but in contrast, the cumulative incidence of nephropathy in persons of European ancestry plateaus at 30% after approximately 25 years’ duration (Doria et al., 1995).


The prevalence of diabetic nephropathy varies among ethnic groups, being highest amongst Asian, African American, and Native American populations (Cowie et al., 1989; Satko et al., 2002). After 25 years’ diabetes duration, the cumulative risk for diabetic nephropathy in persons of European ancestry is approximately 30%, compared to 80% in Pima Indians (Ballard et al., 1988; Nelson et al., 1993).


Familial aggregation of renal disease is not explained by the known environmental risk factors, and this finding provides strong support for an inherited genetic susceptibility to nephropathy. The study of familial clustering can be achieved by comparing the incidence of renal disease in families where a proband has both diabetes and renal disease, to the incidence of renal disease in families with diabetic probands without obvious renal disease. Such families, with multiple diabetic offspring, are more difficult to recruit than individual diabetic patients, with or without nephropathy. The likelihood of developing diabetic nephropathy is higher when the individualwith diabetes has a parental history of cardiovascular disease (De Cosmo et al., 1997). In family-based studies, estimates of the heritability  of albuminuria and glomerular filtration rate (GFR) have ranged from 35–75% (Placha et al., 2005; O’Seaghdha and Fox, 2011).

Familial clustering of nephropathy in white individuals with type  1 diabetes was first reported by Seaquist and colleagues (Seaquist et al., 1989) (Figure 24.7). They found that renal disease was present in 83% of the diabetic siblings of probands who had received kidney transplants. In contrast, renal disease affected only 17% of diabetic siblings of probands without nephropathy (p < 0.001). Both groups had similar glycemic control. Confirmation of this familial clustering phenomenon in type 1 diabetes mellitus in other studies demonstrates that, despite similar glycemic control, the prevalence of nephropathy was greater in diabetic siblings of a proband with diabetic renal disease (Borch-Johnsen et al., 1992; Quinn et al., 1996; Harjutsalo et al., 2004).

Figure 24.7 Familial clustering of diabetic nephropathy in type 1 diabetes. Data are from Minnesota, USA, (Seaquist et al., 1989); Steno Clinic, Copenhagen, Denmark (Borch-Johnsen et al., 1992); and Joslin Diabetes Center, Boston, USA (Quinn et al.,  1996).

These observations on familial clustering of diabetic nephropathy have been extended to type 2 diabetes mellitus and are evident in different ethnic groups, including Native Americans, African Americans, and Asians (Pettitt et al., 1990; Satko and Freedman, 2005). Of interest, the risk of developing renal failure is also increased in first-degree relatives of probands with ESRD due to etiologies other than diabetes, suggesting common genetic risk factors for progressive kidney disease (Freedman et al., 2005; O’Dea et al., 1998).

Careful analysis of renal biopsy material has demonstrated apparent inherited differences in diabetes-induced glomerular pathology (Fioretto et al., 1999). These insights into inherited differences in glomerular structure are reinforced by assessments of the heritability of albuminuria and determinants of renal function such as GFR (Forsblom et al., 1999; Hunter et al., 2002; Langefeld et al., 2004). An inherited predisposition to   progressive renal disease may exist; for example, in individuals with reduced nephron number or subtle defects in glomerular function resulting in albuminuria. The phenotype of progressive renal failure only becomes apparent if the susceptible individual is exposed to additional injury from hypertension or hyperglycemia.


The ultimate goal of genetic studies of diabetic nephropathy is the identification and characterization of the gene variants conferring susceptibility to progressive renal failure. Investigators need to bear in mind the multifactorial etiology of nephropathy and the likelihood that multiple common gene variants, each conferring a small individual relative risk of this complication, may be responsible (Rich, 2006). Alternatively, multiple rare variants (minor allele frequency 1–5%) with large effect sizes may be important in individual susceptibility to diabetic nephropathy (Bodmer and Bonilla, 2008; Schork et al., 2009; Marian, 2012).

The molecular methods for detecting variants associated with complex disease are candidate-gene analyses and whole-genome scans, and both approaches have been utilized for diabetic nephropathy. The studies are designed to test either association between the frequency of a specific genetic marker in different populations, or linkage to assess the inheritance of a particular genetic locus within families.


In candidate-gene analysis, candidate genes for diabetic nephropathy are selected on the basis of an a priori hypothesis that the protein products of such candidate genes are involved in the pathogenesis of the disease (Conway and Maxwell, 2009; McKnight et al., 2010). It is possible to prioritize the search for candidate genes in a rational manner based on knowledge of the physiological and biochemical pathways implicated in diabetic nephropathy. For instance, genes involved in the renin-angiotensin system regulating blood pressure or enzymes regulating glucose metabolism are studied. Problems with this approach include the relatively the small number of genes studied to date (<1% of the known genome) and the fact that candidates can only be selected based on current, and therefore incomplete, understanding of disease pathogenesis (Thomas et al., 2012).

Rational selection of candidate genes can also be augmented by data derived from subtractive hybridization experiments and DNA microarrays that may detect upregulated or downregulated genes that had not been previously implicated in disease pathogenesis (Clarkson et al., 2002; Connolly et al., 2003; Holmes et al., 1997; Murphy et al., 1999). These gene- expression profiles may be taken from direct analysis of mRNA derived from microdissected renal tissue from patients with diabetic nephropathy (Baelde et al., 2004). Once a candidate gene has been selected, the sequence variation within the gene can be tested for association with the disease, using case- control or family-based study designs.

Case-Control Studies

Case-control studies are arguably the simplest design and a powerful method to detect association between a gene variant and disease. The frequency of a marker (sequence variant) in a population with a disease like nephropathy (cases) is compared with that of a matched population without disease (controls). A 2 x 2 contingency table and an X2 test is used to determine if there is a statistical difference between the observed and expected marker frequency.

Case-control studies are relatively easy to undertake but are bedeviled by numerous potential sources of errors. Replication of studies where a positive association has been found has been notoriously difficult (Hirschhorn et al., 2002; Conway and Maxwell, 2009; Currie et al., 2008). One review of 166 candidate-gene association studies that had been replicated at least three times found only six associations that remained significant (Hirschhorn et al., 2002). Nevertheless, those that did remain positive after multiple replication studies were clinically relevant and included the associations of variants of the ApoE gene with Alzheimer’s disease, CTLA4 with Grave’s disease, and factor V Leiden with deep-venous thrombosis (Hirschhorn et al., 2002). Further examination of genetic association studies in a series of meta- analyses has provided grounds for optimism that positive associations can be replicated if efforts are made to avoid smaller, underpowered studies (Ioannidis et al., 2003; Lohmueller et al., 2003).  A  recent  meta-analysis   of 671 genetic association studies for diabetic nephropathy identified 21 replicated genetic variants that remained significant (Mooyaart et al., 2011). These genetic variants were in or near the following genes: ACE, AKR1B1, APOC1, APOE, EPO, NOS3, HSPG2, VEGFA, FRMDS, CARS, UNC13B, CPVL and CHN2, and GREM1, plus four variants not near genes. The odds ratios of associated genetic variants ranged from 0.48 to 1.70.

Potential pitfalls in the interpretation of case-control studies performed to date include incorrect phenotype definition, small sample size, population stratification from ethnic admixture, over-interpretation of results including subgroup analysis, multiple testing, the effect of duration of exposure to risk factors, and publication bias. Sufficient cases and controls must be recruited in order to minimize the risk of identifying false associations that are due to chance alone (type 1 error); or conversely, of failing to detect a true association between a variant and a disease (type 2 error). It is also important to remember that statistically significant genetic association does not prove causation.

Family-Based Investigations of Genetic Susceptibility

Family-based studies, where family members are used as controls, have been employed to avoid the problem of population stratification in case-control studies. The relatives used may be siblings or parents and are ethnically matched, thereby eliminating the risk of stratification. In the transmission disequilibrium test (TDT), first pioneered by Spielman and colleagues to study the genetics of type 1 diabetes, affected offspring and their parents are genotyped (Spielman et al., 1993). The test compares the actual number of alleles transmitted from heterozygous parents to offspring to the number expected to be transmitted (using the MacNemar X2 test). If the allele is transmitted more frequently than the expected 50:50 ratio, then it is likely to be associated with development of disease. The power of these studies is reduced, however, since parents may be homozygous for the marker allele, and in that case, no information regarding transmission can be determined.

Unfortunately, family-based studies using a TDT analysis have been of very limited value in later-onset disease such as nephropathy in type 2 diabetics (where parents are likely to be deceased), and this design has also been difficult to use for nephropathy in type 1 diabetics, as there is familial clustering of cardiovascular disease (and premature death) in the parents of type 1 diabetic offspring with nephropathy. One further option, avoiding the requirement to recruit parents, is the use of sibling pairs in a modification known as the sib TDT analysis (Spielman and Ewens, 1998).

The criteria for ideal genetic association studies include large sample sizes, small p values, associations that make biological sense, alleles (sequence variants) that alter the gene product in a physiological way, an initial study that is independently replicated in a separate population and (where possible) the association is seen in both case-control and family-based studies (Anonymous, 1999). Efforts have been made to improve the consistency and quality of published genetic association studies by encouraging authors to address issues such as population stratification, genotyping errors, haplotype variation, Hardy-Weinberg equilibrium, replication, rationale for choice of genes and variants, and phenotyping of recruited individuals (Little et al., 2009). Several publicly accessible databases now collate hundreds of genetic epidemiology studies relevant to diabetic nephropathy, such as the HuGEnavigator (Yu et al., 2010) and the Centralized Online Renal Genetics Initiative (Currie et al., 2008).


A complementary approach to identify candidate genes for complex disease such as diabetic nephropathy is the use of genome-wide scans. A major advantage of this approach is that no detailed understanding of the pathophysiology of the disease of interest is required, and there is no prior hypothesis as to which gene or genes are implicated in causation. Previously, these screens were based on the principle of linkage disequilibrium; that is, where a genetic marker and a disease susceptibility gene are in close proximity, the marker will segregate with the disease more often than expected by chance alone. Further detailed mapping of the chromosomal segment adjacent to the linked marker should in theory reveal the identity  of a disease susceptibility gene. The linkage method employs several hundred genetic markers (microsatellites consisting of variable sequence lengths of repeated CA base pairs). In practice, this approach has been much more successful in identifying monogenic renal diseases such as X-linked Alport syndrome (Barker et al., 1990) or ADPKD (Reeders et al., 1985) where the pattern of inheritance is known.

There have been relatively few linkage studies in diabetic nephropathy, reflecting the practical difficulty in recruiting large families with multiple affected individual members. For instance, in a study of familial clustering of nephropathy in siblings with type 1 diabetes, Seaquist and colleagues identified a cohort of 696 patients with diabetic nephropathy who had received a renal transplant. Of these 696 patients, only 113 had a type 1 diabetic sibling, and only 26 of these siblings could be enrolled in the study (Seaquist et al., 1989).

Genome-wide linkage studies have been performed for nephropathy in persons with type 2 diabetes. The earliest study, performed in Pima Indians, employed 98 sib-pairs concordant for nephropathy and reported four chromosomal regions linked to nephropathy: 3q26.9, 7q35, 9q, and 20q (Imperatore et al., 1998). The strongest linkage was with chromosome 7q35. One issue in using sib-pairs concordant for both type 2 diabetes and nephropathy is unravelling whether the linkage is with diabetes or with renal disease. To resolve this problem, a further genome-wide scan in Pima Indians showed no evidence of linkage to type 2 diabetes on chromosome 7q35, suggesting that this region may indeed harbor a susceptibility gene for nephropathy (Hanson et al., 1998). The Family Investigation of Nephropathy and Diabetes (FIND) study recruited diabetic sibling pairs concordant and discordant for diabetic nephropathy (Iyengar et al., 2007). The strongest evidence of linkage to diabetic nephropathy was on chromosomes 7q21.3, 10p15.3, 14q23.1, and 18q22.3. True linkage has proven hard to find in genome-wide scans of common complex disease (Altmüller et al., 2001). Overall, most linkage data for diabetic nephropathy does not reach genome- wide significance, and only a few regions (3q, 7q, 18q) have been identified and replicated in multiple studies.

An alternative approach to determining the genetic susceptibility to diabetic nephropathy is to study an intermediate trait, such as urine protein excretion. Segregation analysis has been employed in Caucasian families to model inheritance patterns of proteinuria (urinary albumin/creatinine ratio or ACR) in type 2 diabetes (Fogarty et al., 2000b). Sib-pairs, discordant for type 2 diabetes, had urinary ACR tests performed, and this quantitative trait was modelled with age, gender, and duration of diabetes as covariables. Urinary ACR was heritable and genetically correlated to blood pressure (Fogarty et al., 2000a). A similar segregation analysis for urine protein excretion has been performed in Pima Indians with type 2 diabetes (Imperatore  et al., 2000).  These  studies   are  both  consistent  with   a  Mendelian   model     of inheritance, albeit that the heritability was 27% and proteinuria will be influenced by multiple gene variants and environmental factors (Fogarty et al., 2000b).

Previously, investigators had only these two broad options (candidate-gene studies or linkage analysis) to determine genetic susceptibility to common disease such as diabetic nephropathy. Direct-association studies testing candidate-gene variants for association with disease were undertaken based on an a priori hypothesis. The limitations of this approach included the limited number of candidate genes that have been systematically examined for any disease. The alternative approach of genome scans to identify chromosomal regions linked to common disease has proved less successful than for monogenic disorders, with some notable exceptions such as type 1 diabetes (Nistico et al., 1996) and inflammatory bowel disease (Hugot et al., 2001).

Several additional approaches are now technically feasible and potentially affordable to identify genetic susceptibility to diabetic nephropathy. These are indirect genome screens for association with nephropathy (using an extended panel of genetic markers) and direct genome screens employing panels of SNPs to directly assess thousands of candidate genes simultaneously.


There has been remarkable progress in the development of resources  to enable genome-wide association studies (GWAS), including  the identification of more than 40 million SNPs and the further genotyping of 3.8 million of these SNPs in individual DNA samples from different ethnic groups within the HapMap Project (Consortium, 2003). This allowed more efficient assessment of genetic architecture based on haplotype blocks with the human genome (Barrett et al., 2005). These initiatives, coupled with technological advances in genotyping and reduction in DNA-sequencing costs, have enabled more affordable GWAS approaches for discovery of genetic susceptibility to common complex diseases. Furthermore, sophisticated analytical tools have been designed to quality-control and mine large datasets (Marchini et al., 2005; Marchini et al., 2007; Purcell et al.,2007).

A number of individual research centers have collaborated to develop much larger collections of cases and controls with rigorous definitions of clinical phenotypes relevant to diabetic nephropathy. These larger multicenter collections include FIND, DCCT/EDIC, Genetics of Kidneys in Diabetes [GoKinD] US, Genetics of Kidneys in Diabetes [GoKinD] UK, and European rational approach for the genetics of diabetic complications [EuraGedic] (Iyengar et al., 2007; Group, 1999; Mueller et al., 2006; Currie et al., 2008; Tarnow et al., 2008).

To identify chromosomal regions that harbor possible candidate genes for diabetic nephropathy, it has been possible to perform a low-resolution, genome-wide, microsatellite association screen. This method employs several thousand fluorescently labelled microsatellite markers (compared to the usual 300–400 microsatellite markers for a linkage screen) and a pooling  strategy to minimize the costs of typing large numbers of samples with individual genetic markers. Careful analysis of separate pools of DNA from cases and controls generates a series of allele frequencies for the microsatellite markers. This strategy was successful in identifying regions of chromosome 10 significantly associated with diabetic nephropathy in a cohort of Irish patients with type 1 diabetes (McKnight et al., 2006). The data from these indirect screens allow investigators to select candidate genes, identified close to the associated microsatellite markers, for further study.

Significantly larger marker sets of SNPs have been used, as an alternative to limited microsatellite panels, in an indirect genome-wide association screen. Using information from the HapMap project (Altshuler et al., 2005; Phimister, 2005), it is possible to reduce the size of the marker set required by employing haplotype-tagged SNPs (htSNPs). Early proof of principle for this strategy was reported for type 1 diabetes (Johnson et al., 2001; Lowe et al., 2004). A non-synonymous SNP (nsSNP) scan is an alternative method of directly detecting gene variants associated with disease. An nsSNP may influence phenotype by altering gene expression or protein function (Savage et al., 2008).


Direct genome-wide association studies employ either a large panel of unselected SNPs randomly distributed throughout the genome, or a more focused   set   of   markers   utilizing  non-synonymous   SNPs,   which   may potentially alter gene expression or protein structure.

In any study using commercially available arrays, a very large number of SNPs will be genotyped, and it is therefore likely that many putative disease- susceptibility variants will be detected. Most of the direct associations between SNPs and disease will be by chance alone (for example, one would expect 50,000 SNPs in a scan employing 1,000,000 SNPs to be associated by chance alone at the 5% confidence level). Therefore, a major challenge is to rationalize the output from genome-wide association studies. One possibility is to apply a correction factor for the number of SNPs genotyped. For example, in a genome screen with 1,000,000 markers, adoption of a significance threshold of p = 5 x 10–8 for individual SNPs would result in a genome-wide false positive rate of 1 in 20, the accepted level of confidence for an individual genotype. However, this correction may be too rigorous and therefore miss some true associations that could be tested in a replication study.

One approach for a GWAS is to adopt a two-stage screening strategy. Markers showing association in an initial screen (the discovery cohort) could be reassessed in an independent, larger sample (the replication cohort). The threshold of significance for associated markers in the first screen would purposely be set low so that it remained sufficiently powerful to detect loci with a relatively modest effect on disease, despite employing only a fraction of the total samples available. The potential for detecting susceptibility variants in this initial screen may be enhanced by including only extreme phenotypes: cases with onset of nephropathy after a relatively short duration of diabetes or despite good glycemic control; and conversely, controls that have not developed nephropathy despite a long duration of diabetes or poor glycemic control. The majority of associations detected in the first screen will be false positives; therefore, a much more stringent significance threshold is required for the replication study. Hence a subset of patient samples would be screened using the complete marker set, with only a fraction of these markers being genotyped in a larger sample, thereby improving efficiency without a reduction in power, while reducing the risk of detecting false-positive associations.

A screen that employed a two-stage strategy similar to that described has been undertaken in a Japanese population with type 2 diabetes (Shimazaki et al., 2005).  Over  80,000  SNP  markers  were  genotyped,  and  SNPs  in two candidate genes were associated with diabetic nephropathy. The genes identified were a sodium-chloride co-transporter known to be mutated in Gitelman syndrome (Tanaka et al., 2003) and the engulfment and cell motility (ELMO 1) gene, which is upregulated in high extracellular glucose concentrations and promotes accumulation of matrix proteins (Shimazaki et al., 2005). A GWAS employing 100,000 SNPs in Pima Indians (105 cases and 102 controls) using pooled DNA samples highlighted  association between the plasmacytoma variant translocation gene (PVT1) and diabetic nephropathy (Hanson et al., 2007).

One of the largest published GWAS for diabetic nephropathy was published by the U.S. Genetics of Kidneys in Diabetes (GoKinD) group with attempted confirmation of associated SNPs in the DCCT/EDIC collection (Pezzolesi et al., 2009). The U.S. GoKinD study employed 820 cases with nephropathy and type 1 diabetes (284 with proteinuria and 536 with end- stage renal disease) and 885 controls. Despite its size, this GWAS only identified 13 SNPs associated with diabetic nephropathy with P < 1 x 10–5, and none reached the conventional threshold for genome-wide significance. The strongest association was at the FRMD3 (4.1 protein ezrin, radixin, moesin [FERM] domain containing 3) locus (odds ratio [OR] = 1.45, P = 5.0 x 10–7). Arguably, the sample size was simply too small to detect alleles with effect sizes now being reported in GWAS of common complex diseases. International efforts are now being made to integrate available case-control collections into much larger datasets to optimize the investigation of the genetics of diabetic nephropathy (Hirschhorn and Gajdos, 2011; Wheeler and Barroso, 2011).


The Human Genome Project and the HapMap initiative accelerated the technological advances necessary to allow dissection of the molecular  basis of disease. Well-powered genome-wide association studies with high-density SNP arrays have recently identified new signals for renal phenotypes such as IgA nephropathy and membranous nephropathy (Feehally et al., 2010; Stanescu et al., 2011). It has proven more challenging to determine  the genetic architecture of diabetic nephropathy. The current focus on rare gene variants and epigenetic modifications of the genome may discover new insights into the genetic susceptibility to this common kidney disease.


We thank Pauline Whittaker for secretarial assistance.


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