unravelling the genetic basis of renal diseases; from single gene to multifactorial disorders

Journal of PathologyJ Pathol 2010; 220: 198–216Published online 30 October 2009 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/path.2639

Invited Review

Unravelling the genetic basis of renal diseases; from singlegene to multifactorial disordersAmy J McKnight, Diane Currie and Alexander P Maxwell*Nephrology Research Group, Queen’s University of Belfast, Northern Ireland, UK

*Correspondence to:Alexander P Maxwell,Nephrology Research Group,Queen’s University of Belfast, c/oRegional Nephrology Unit, Level11, Tower Block, Belfast CityHospital, Lisburn Road, BelfastBT9 7AB, Northern Ireland.E-mail: [email protected]

No conflicts of interest weredeclared.

Received: 21 September 2009Revised: 25 September 2009Accepted: 26 September 2009

AbstractChronic kidney disease is common with up to 5% of the adult population reported to have anestimated glomerular filtration rate of <60 ml/min/1.73 m2. A large number of pathogenicmutations have been identified that are responsible for ‘single gene’ renal disorders, suchas autosomal dominant polycystic kidney disease and X-linked Alport syndrome. Thesesingle gene disorders account for <15% of the burden of end-stage renal disease thatrequires dialysis or kidney transplantation. It has proved more difficult to identify thegenetic susceptibility underlying common, complex, multifactorial kidney conditions, suchas diabetic nephropathy and hypertensive nephrosclerosis. This review describes success todate and explores strategies currently employed in defining the genetic basis for a number ofrenal disorders. The complementary use of linkage studies, candidate gene and genome-wideassociation analyses are described and a collation of renal genetic resources highlighted.Copyright 2009 Pathological Society of Great Britain and Ireland. Published by JohnWiley & Sons, Ltd.

Keywords: angiotensin-converting enzyme; degeneration; polycystic kidney disease; copynumber variation; kidney; glomerular filtration rate; genome-wide association; microRNA;mitochondrial DNA; polycystic kidney disease; systemic lupus erythematosis; singlenucleotide polymorphisms; diabetes

Introduction

Chronic kidney disease (CKD), defined by an esti-mated glomerular filtration rate (GFR) <60 ml/min/1.73 m2, is common with a reported prevalenceof almost 5% in most populations studied [1–3].CKD is also associated with significantly increasedrates of hospitalization and cardiovascular events andthere is a strong inverse correlation between mor-tality and estimated GFR at levels of renal function<45 ml/min/1.73 m2 [4,5]. These recent observationshave increased awareness of the public health perspec-tive of renal disease and the escalating economic costsof managing end-stage renal disease (ESRD) with dial-ysis and/or renal transplantation [6].

The underlying pathological basis for ESRD hasshifted dramatically over the last four decades withthe emergence of diabetes and hypertension as theprimary aetiologies for kidney failure, which togetheraccount for up to 75% of the ESRD recorded on somenational renal registries [7,8]. This reflects improvedsurvival of hypertensive and diabetic patients andmore liberal access to renal replacement therapies [9].The more ‘classical’ pathological kidney disorders,such as chronic glomerulonephritis, polycystic kidneydisease and chronic pyelonephritis remain importantaetiologies for ESRD [7,8].

Whilst there have been spectacular successes inidentifying causative mutations for a wide variety of

less common Mendelian renal disorders there has beenmuch less progress defining the genetic contributionto common complex renal diseases, such as diabeticnephropathy and hypertensive nephrosclerosis [10,11].Reviews of published genetic studies for acute kidneyinjury [12] and kidney transplantation [13], respec-tively, do not provide definitive evidence identifyinggenetic risk factors for either of these clinical con-ditions. The small sample sizes, complex phenotypesand various clinical outcomes make interpretation ofcurrently available genetic data difficult. The geneticsof renal tumours have also been recently evaluated indetail [14,15].

This review explores both the methodologicalapproaches and current challenges faced by researcherstrying to establish the genetic basis for chronic kidneydisease.

Early progress from genetic linkage studies

Almost 25 years ago, autosomal dominant polycys-tic kidney disease (ADPKD) was first localized tothe short arm of chromosome 16 by linkage analysisusing highly polymorphic DNA markers [16]. A longperiod of sustained research using positional cloningtechniques refined the locus and led to cloning ofthe PKD1 gene [17] encoding polycystin-1 [18]. Theidentification of PKD1 was complicated by its long

Copyright 2009 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.www.pathsoc.org.uk

Genetics of renal disease 199

DNA sequence length, the presence of six pseudo-genes in a duplicated region adjacent to the original16p13.3 PKD1 locus, and the presence of multiple(>300) unique PKD1 pathogenic mutations in differ-ent families with the clinical and pathological diag-nosis of ADPKD ( [19]; database accessed 3 Septem-ber 2009). This early success has been followed bydetailed assessment of the biochemical function ofpolycystins encoded by PKD1 and PKD2 (chromo-some 4q21-q23) [20–23] that has helped elucidate thepathophysiology of cyst growth. An inherited autoso-mal dominant germline mutation is not sufficient forcyst growth and a second somatic mutation in the cellis necessary for cell cycle regulation to be disrupted,associated with alteration in the function of primarycilia on renal tubular epithelial cells [24]. These mech-anistic studies, both in vitro and in animal modelsof polycystic kidney disease, have led to the devel-opment of potential therapeutic options for patientswith ADPKD for whom clinical trials of tolvaptan,sirolimus and somatostatin are now in progress [25,26](Figure 1).

The diversity of genetic mechanismscontributing to renal disease

Differences in DNA range from a single nucleotidebase change to large-scale chromosome rearrange-ments. Pathogenic gene mutations have been identifiedfor many kidney disorders that tend to be inherited ina Mendelian manner (Table 1); however, these typ-ically account for <15% of individuals with ESRDin national registries [7,8]. More than 300 inheriteddisorders have been described with kidney involve-ment and further studies of common renal diseases

Figure 1. The route from linkage studies of autosomaldominant polycystic kidney disease (ADPKD) to clinical trials ofnovel therapies for this renal disorder

have shown that patterns of inheritance are complex[11]. In addition to traditional Mendelian inheritancepatterns (autosomal dominant, autosomal recessiveand X-linked), renal diseases may also be influencedby epistasis [27], variable gene expression or pene-trance [28] and mosaicism [29]. Differential allelicexpression, due to epigenetic mechanisms modifyinggene function or the presence of polymorphisms inregulatory elements, affects the expression of morethan 20% of genes [30]. Allelic heterogeneity mayalso be evident; for example, different mutations ofthe collagen COL4A5 gene reflect phenotypic differ-ences in X-linked Alport syndrome [31] and indeedsome types of mutation are associated with a higherrisk of ESRD [32]. Deletions, duplications and sin-gle nucleotide polymorphisms (SNPs) in the comple-ment factor H (CFH ) gene have been associated withatypical haemolytic uraemic syndrome and glomeru-lonephritis [33]. Common genetic variants increasesusceptibility to chronic kidney disease and progres-sion to ESRD [34,35]. A coding variant in the nitricoxide synthase (NOS3 ) gene [36] and a haplotype inthe vascular endothelial growth factor (VEGF ) gene[37] are reported to act as genetic modifiers of therate of progression to ESRD in ADPKD patients.A combination of genetic and environmental modi-fiers partly explain why there is clinical heterogeneityin a ‘single-gene’ disorder such as ADPKD, wherethere can be wide variation in the age of onset ofESRD in individuals with the same pathogenic PKD1gene mutation [38]. Renal disease phenotypes are theresult of dynamic interactions between genetic factorsand environmental influences modifying the ‘risk’ ofdeveloping kidney disease (Figure 2).

To date there has been more limited success inidentifying definitive genetic risks for renal disorderswith a complex, multifactorial aetiology. The rateof progression of a ‘genetic’ chronic kidney diseasemay be influenced by environmental factors, suchas whether the individual is a smoker or receivestreatment for hypertension. These external factorswill have an impact on whether a particular renalphenotype, such as ESRD, is reached. Furthermore,even an individual’s response to medication, such asangiotensin-converting enzyme (ACE) inhibitors, maybe partly genetically determined [39,40].

Genome-wide linkage studies for kidneydiseases and traits

Historically, familial aggregation of a particular phe-notype was observed and linkage mapping, usinghighly polymorphic microsatellite markers, conductedto investigate correlations between phenotype andpatterns of allele segregation within families. Con-siderable efforts have been expended conductinggenome-wide linkage scans for common renal disease;however, few studies have achieved the levels ofstatistical evidence proposed for significance [41]

J Pathol 2010; 220: 198–216 DOI: 10.1002/pathCopyright 2009 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

200 AJ McKnight et al

Table 1. Genes identified for glomerular, tubular and ciliary disorders

Disease Locus Gene symbol Gene name Reference

Alport syndrome 2q35-37 COL4A3 COL4A4 Collagen, type IV, α3 (Goodpasture antigen);Collagen, type IV, α4

[193,194]

Xq22 COL4A5 Collagen, type IV, α5 (Alport syndrome) [195]Thin basement membranenephropathy

2q35-37 COL4A3 COL4A4 Collagen, type IV, α3 (Goodpasture antigen);Collagen, type IV, α4

[196,197]

1q25-q31 NPHS2 Nephrosis 2, idiopathic, steroid-resistant (podocin) [198]Xq22 COL4A5 Collagen, type IV, α5 ( [199], sourced

from abstract)Benign familial haematuria 2q35-37 COL4A3 COL4A4 Collagen, type IV, α3 (Goodpasture antigen);

Collagen, type IV, α4[200]

Congenital nephroticsyndrome of Finnish type

19q12-q13.1 NPHS1 Nephrosis 1, congenital, Finnish type (nephrin) [201]

Pierson’s syndrome 3p21.3-p21.2 LAMB2 Laminin, β2 (laminin S) [202]Bartter’s syndrome 15q15-q21 SLC12A1 Solute carrier family 12 (sodium/potassium/chloride

transporters), member 1[203]

11q24 KCNJ1 Potassium inwardly-rectifying channel, subfamily J,member 1

[204]

1p32.3 1p36 BSND, CLCNKA,CLCNKB

Bartter syndrome, infantile, with sensorineuraldeafness (Barttin); chloride channels Ka and Kb

[205–207]

Distal renal tubular acidosis 17q12-21 SLC4A1 Solute carrier family 4, anion exchanger, member 1(erythrocyte membrane protein band 3, Diegoblood group)

[208]

8q23 SLC26A7 Solute carrier family 26, member 7 [209]7q34 ATP6V0A4 ATPase, H+ transporting, lysosomal V0 subunit a4 [210]2p13 ATP6V1B1 ATPase, H+ transporting, lysosomal 56/58 kDa, V1

subunit B1[211]

Gitelman’s syndrome 16q13 SLC12A3 Solute carrier family 12 (sodium/chloridetransporters), member 3

[212]

Liddle’s syndrome 16p12 16p12 SCNN1B SCNN1G Sodium channel, non-voltage-gated 1, β (Liddle’ssyndrome); sodium channel, non-voltage-gated 1, γ

[213,214]

Nephrogenic diabetesinsipidus

Xq28 AVPR2 Arginine vasopressin receptor 2 (nephrogenicdiabetes insipidus)

[215]

12q12-13 AQP2 Aquaporin 2 (collecting duct) [216]Autosomal dominantpolycystic kidney disease

16p13.3 PKD1 Polycystic kidney disease 1 (autosomal dominant) [17]

4q21-23 PKD2 Polycystic kidney disease 2 (autosomal dominant) [217]2p PKD3 Polycystic kidney disease 3 (autosomal dominant) [218,219]

Autosomal recessivepolycystic kidney disease

6p21.1-p12 PKDH1 Polyductin [220]

Meckel–Gruber syndrome 17q21-q24 MKS1 Meckel syndrome, type 1 [221]11q13 MKS2 Meckel syndrome, type 2 [222]8q22.1 TMEM67 Transmembrane protein 67 [223]

Medullary cystic kidneydisease

1q21 MCKD1 Medullary cystic kidney disease 1 (autosomaldominant)

[224]

16p12.3 UMOD Uromodulin (uromucoid, Tamm–Horsfallglycoprotein)

[97]

Nephronophthisis 2q13 NPHP1 Nephronophthisis 1 (juvenile) [225]9q31 INVS Inversin [226]3q22 NPHP3 Nephronophthisis 3 (adolescent) [183]1p36 NPHP4 Nephronophthisis 4 [227]

3q21.1 IQCB1 IQ motif containing B1 [182]12q21.33 CEP290 Centrosomal protein 290 kDa [228]16p13.3 GLIS2 GLIS family zinc finger 2 [229]

Xp11.23-23 CLCN5 Chloride channel 5 (nephrolithiasis 2, X-linked,Dent’s disease)

[230]

17q11.1 NEK8 NIMA (never in mitosis gene a)-related kinase 8 [231]16q12.2 RPGRIP1L RPGRIP1-like [232]8q22.1 TMEM67 Transmembrane protein 67 [233]

6q23.2 AHI1 Abelson helper integration site 1 [234]

and identifying disease-causing mutations by posi-tional cloning methods has proved difficult [42].Genetic linkage to renal diseases and quantitativetraits such as GFR [43] has been reported for all

chromosomes [11] (Table 2; see also Supporting infor-mation, Table S1). Many studies have demonstratedlinkage to chromosome 3q with further associationstudies refining loci and identifying several biological



Figure 2. The interactions between genes and environment governing the development of renal disease phenotypes

and positional candidate genes for renal phenotypes(Figure 3).

Relatively few single nucleotide polymorphism(SNP)-based genome-wide linkage scans have beenconducted for renal disorders. The first identified anovel, disease-causing mutation in PAX2 for autoso-mal dominant renal dysplasia [44]. Examining 100discordant sib pairs using >5000 SNPs highlightedlinkage to chromosome 19 (maximum likelihood score= 3.1) for nephropathy in type 1 diabetes [45]. Mostrecently, SNP-based linkage for renal function (mea-sured by serum creatinine) was established with chro-mosomes 7p14, 9p21, 11p15, 15q15-21, 16p13 and18p11 and a novel locus on chromosome 10p11 [46].

Several methods for the meta-analysis of linkagedata have been proposed. The multiple scan probabil-ity approach combines reported p values and appearsrobust to heterogeneity [47]. A modified genome scanmeta-analysis method has been proposed to adjustfor heterogeneity between studies (HEGESMA) [48].A novel, kernel-based estimation procedure has beenapplied to rheumatoid arthritis datasets [49] and col-laborative analyses on raw data would be ideal for thepurposes of meta-analysis [41,50,51]. Linkage analysishas proved successful for many ‘single-gene’ disor-ders, however association studies are believed to bemore powerful in detecting susceptibility genes forcomplex diseases such as CKD [52,53].

Candidate gene associations for renal diseases

Robust replication of initial candidate gene associa-tions has proven difficult, largely due to differences inbiological exposures or study designs [54]. The powerto identify statistically significant genetic associationswith disease is crucially important in designing suchstudies. Genetic power calculations typically consider

model parameters such as disease prevalence, diseaseallele frequency, disease genotype relative risks, miss-ing or erroneous data, marker allele frequencies, corre-lations between markers and disease, significance lev-els and case : control ratios before estimating requiredsample sizes [55,56]. Despite the apparent lack of con-sistent replication in the majority of candidate genestudies, collaborative efforts have led to the robustreplication of several loci. For example, convincingevidence was reported for linkage to 18q22.3–23 (log-arithm of odds = 6.1) in Turkish individuals withdiabetic nephropathy [57] and subsequently supportedby data from African-American individuals [58] andin the Family Investigation of Nephropathy and Dia-betes (FIND) collection [59]. Carnosine (CNDP1 ) isa candidate gene located in this region that has beenassociated with diabetic nephropathy [60–62]. Withinthe last year, four papers have described an associ-ation between variants in the MYH9 gene and CKDin African-American individuals [63–66]. A linkagestudy for serum creatinine has also recently reportedassociation with MYH9 in European populations [46].

Frequently studied genes for common, multifactorialrenal disease include ACE, AGT, AGTIR, AKR1B1,APOE, FCGR, GNB3, NOS3 and MTHFR. Combiningpublished results by meta-analysis is now practicalfor several genes (Table 3). The majority of studiesfor renal disease, which is inherited in a complexmanner, are based on the common variant–commondisease hypothesis [67]. This hypothesis suggests thatpredisposition to chronic kidney disease is based onnumerous genetic variants that are relatively commonin the population. It is possible that rare alleles with aminor allele frequency <5%, or multiple variants withsmall individual effects, contribute to chronic kidneydisease, but many thousands of individuals would berequired to identify these markers.



Table 2. Genes for common forms of renal disease identified from genome-wide studies

Phenotype Study Chromosomal region Gene(s) identified References

CKD Association. 70 987 SNPs 15q24.3, p = 0.001, rs6495446 MTHFS [235]CKD Association. Up to 900 000 SNPs 16p12.3, p = 2.8 × 10−09, rs12917707 UMOD [85]T1DN Linkage. 5382 SNPs in DSPs 20p12.1, max LOD = 2.8, rs466243 MACROD2 [45]

19q1, max LOD = 3.1, rs306450 RGS1010q25, max LOD = 2.4, rs14678135q, max LOD = 2.7, rs27342

T1DN Association. Affymetrix 10 K inCC and trios

2p22, p = 2.12 × 10−5, rs1368086;p = 8.96 × 10−4, rs725238

PLEKHH2 [236]

2p21, p = 1.86 × 10−4, rs11886047DN Linkage. 404 markers, sibs 18q22-23, max LOD = 2.1,

D18S1371–D18S1390CNDP1, CNDP2 [57,60–62,237]

DN Association. 81 315 SNPs in CC 7p14.1, p = 0.000008, Intron18A9170G

ELMO1 [114,117,118,238]

DN Association. 360 000 SNPs in CC 9q21.33, p = 5.0 × 10−7, rs1888747 FRMD3 [82]11p15.5, p = 3.1 × 10−6, rs451041 CARS

DN Association. 6000 markers in CC 10p15, pcorrected = 0.005, D10S558 andD10S1435

GTPBP4 [77,239]

DN Association. ∼80 000 SNPs inCC

1615 loci where p < 0.01. ACE [113,115,238,240]

p = 0.00005, (ACE I/D)/M (AGTM235T)/A (AGTR1

AGT

A1166C) AGTR116q13, rs2289116 ELMO17p14.1, rs741301 SLC12A3

ESRD Association. 1536 SNPs in CC 22q13.1, max LOD = 4.55 (non DNmax LOD = 8.56)

MYH9 [63,63,66,241]

ESRD Association. 115 352 SNPs in CC 8q24, rs2648875, p = 2 × 10−6 PVT1 [83]FSGS Association. 1272 SNPs in CC 22q13.1, max LOD = 12.4,

p = 1 × 10−7, rs5756152MYH9 [64]

IgA nephropathy Association. 80 000 SNPs in CC 1q31-41, p = 0.0003, A580V PIGR [81]Lupus nephritis Association. HumanHap550 2q32.2-q32.3, p < 10−11, rs7574865 STAT4 [79]Renal dysplasia Linkage. 10 000 markers in MF 10q23.31-q25.1, 55 SNPs, G874A PAX2 [44]GFR Association. 100 000 SNPs 21q22.3, p = 1.6 × 10−05, rs2839235 PCNT [242]eGFR—creatinine Linkage. 387 markers in sibs 3q27, LOD = 3.61 [243,244]

6q15-q21, LOD = 2.97eGFR—creatinine Association. Up to 900 000 SNPs 16p12.3, p = 3.0 × 10−11, rs12917707 UMOD [85]

4q21.1, p = 9.7 × 10−08, rs17319721 SHROOM315q15.1, p = 3.4 × 10−07, rs2467853 GATM–SPATA5L1

eGFR—creatinine Linkage. 747–6008 markers 22q13.1, p = 0.0089, rs11089788 MYH9 [46]eGFR—cystatin C Association. Up to 900 000 SNPs 16p12.3, rs12917707, p = 2.0E–07 UMOD [85]

8p22-p12, rs1731274, p = 4.6E–08 STC120p11.2, rs13038305, p = 2.2E–88 CST3–CST9

eGFR—cystatin C Association. 70 897 SNPs 20p11.21, p = 8.5 × 10−9, rs1158167 CST9L, CST9, CST3 [242]UAE Association. 70 897 SNPs 11q23.2, p = 1.9 × 10−6, rs1712790 FAM55B [242]Serum uric acidconcentration andgout

Association. 503 551 SNPs 4p16.1, p = 7.0 × 10−168, rs16890979 SLC2A9 [102,245]

4q22.1, p = 2.5 × 10−60, rs2231142 ABCG2 [246,247]6p22.2, p = 3.3 × 10−26, rs1165205 SLC17A3 [248,249]

ACR, urinary albumin excretion; MF, multigenerational family; ASPs, affected sib pairs; DSPs, discordant sib pairs; CC, cases vs. controls; UAE,urinary albumin excretion.

MicroRNAs (miRNAs) are small, non-protein cod-ing RNAs that function by post-transcriptional repres-sion of target coding RNAs. The role of miRNAsin the kidney is not yet clearly defined; however,miRNA function appears critical for maintenance ofthe glomerular filtration barrier [68]. Recent studieshave revealed genetic variants affecting miRNAs asso-ciated with renal disease. Horikawa and colleagues[69] observed SNPs involved with miRNA machinery(Asn929Asp, Cys1033Arg in the GEMIN4 gene) asso-ciated with the risk of renal cell carcinoma. The PVT1locus encodes several miRNAs [70] and variants in

this gene have been associated with diabetic nephropa-thy [71]. A1166C in the Ang II type 1 receptor gene(AGTR1 ) has been associated with renal disease; thisvariant attenuates binding of a specific miRNA (miR-155) [72].

Genome-wide association studies for renalphenotypes

The nature of linkage analysis data means thatgenomic regions of interest are typically large and may



Figure 3. Chromosome 3, demonstrating the distribution of microsatellite and SNP markers related to kidney disease and thehighlighted genes that are associated with renal phenotypes

contain many genes. Genome-wide association (GWA)may be used as a complementary approach to linkagestudies or where robust linkage analysis is not feasible.GWA studies have been especially successful recentlyin identifying genes implicated in autoimmune dis-orders [73], including type 1 diabetes [74], rheuma-toid arthritis [75] and inflammatory bowel disease[76]. Since complex polygenic disorders have provedtractable to GWA analyses, it is hoped that employingsimilar strategies can determine genetic contributionsto autoimmune renal disease, such as glomerulonephri-tis. Technical innovations have reduced the cost ofSNP-based genotyping and, together with improvedbioinformatic and genetic statistical tools, have madelarge-scale GWA screens possible. Genome-wide case-control association studies for renal disease have beenconducted using microsatellites [77], gene-based SNPs[78], SNPs indirectly distributed across the genome[79] or putatively functional SNPs [80]. Genome-wide screens are advantageous in that they requireno a priori hypothesis, neither do they limit analy-sis to previously examined genes. Several renal dis-eases and traits have been, or are currently, the sub-ject of GWA studies, including IgA nephropathy [81],diabetic nephropathy [78,82], ESRD [83], gout [84],glomerular filtration rate [85] and blood pressure [86].

Genome-wide association studies are designed toestablish the relationships between genotype and phe-notype. These steps include:

1. Accurate phenotyping (clinical ascertainment) toincrease the likelihood of observing true differencesat the level of individual genomes.

2. Extraction and normalization of DNA from bloodor tissues.

3. Genotyping of DNA samples with appropriatequality controls.

4. Statistical tests of association with stringent pvalues to correct for multiple testing.

5. Replication of the results in another similar cohort.

Arguably the main objective of GWA studies is toidentify biological pathways implicated in the diseaseor trait of interest, rather than the identification ofa causative mutation per se that is then employedin population screening or risk prediction [87]. Suchnew insights will not translate quickly into changes inclinical practice until the gap between genotype andphenotype is bridged [88]. The necessary translationof genetic mapping data to clinical applications willrequire connections to be made with gene expressionprofiles in cells and tissues under resting and stimu-lated states [89,90], eg renal mesangial cells exposedto normal and high extracellular glucose to mimicdiabetic kidney injury [91,92]. Such clinical connec-tions are increasingly being made between genotypesand human physiology, as recently exemplified by thefinding that an interleukin-2 receptor α (IL2RA) locusis directly related to cell surface expression of theIL2RA protein (or CD25) on T cells [93]. In this study,higher CD25 expression was associated with IL2RAhaplotypes that confer protection from type 1 diabetes.There is now a real impetus to undertake the detailedclinical studies of biological variation in geneticallycharacterized populations to fully understand the linksbetween gene(s) and phenotype(s).

Chronic kidney disease and genome-wideassociation analyses

Chronic kidney disease (CKD) is one of the broadclinical phenotypes that can be easily ascertained in anumber of existing GWA datasets that included mea-surements of kidney function (serum creatinine ± cys-tatin C) when individuals were recruited. CKD is asso-ciated with a number of loci in a recent meta-analysisof GWA studies performed on >40 000 individuals ofEuropean ancestry [85]. The meta-analysis identifiedSNPs in the UMOD gene locus (UMOD ; chromosome16p12.3) to be associated with CKD in 2388 cases, andthese findings were then replicated in a further 1932



Table 3. Meta-analysis of candidate genes for kidney disease

Gene Investigation DNAs Outcome Ref

Lupus nephritisTNFAIP3 rs5029939 from two studies n = 6973 (cases, 1453;

controls, 3381; trios, 713)Associated with SLE, particularly inpatients with renal manifestations.(p = 1.67 × 10−14; OR = 2.09; 95% CI,1.68–2.60)

[250]

FCGR2A R/H131 from 29 studies n = 7960 (cases, 1685;controls, 1892; 4383non-SLE controls)

RR is a risk factor (OR = 1.24; 95% CI,1.04–1.48

[251]

FCGR2A R/H131 from 17 studies n = 5694 (cases, 1405;controls, 1709; 2580non-SLE controls)

No association [252]

FCGR3A V/F158 from 11 studies n = 3870 (cases, 1154;controls, 1261; 1455non-SLE controls)


Renal transplantation

TGFβ 1, TNFA, IL-10 TGFβ1, cd10, cd25; TNFA,−308; IL-10, −1082 G/A in amaximum of five studies

n = 1087 Possible association between TGFβ1cd10 and IL-10 with poor outcomes inrenal transplantation

[254]

ADPKD

ACE Indel in 13 studies n = 1940 (cases, 398;controls, 1022)


IgA nephropathy

SCGB1A1 G38A in six studies n = (cases, 930; controls,768)


ACE Indel in five studies n = 901 (cases, 346;controls, 555)

DD associated with progression(p < 0.05)

[257]

ACE Indel in 11 studies ? DD associated [258]ACE Indel n = 1838 (cases, 981;

controls, 857)No association [259]

Diabetic nephropathy

AKR1B1 (CA)n in eight T1Ds and nineT2Ds

n = 5302 (cases, 2751;controls, 2551)

Z−2 allele risk (OR 1.40, 1.07, 1.84).Z+2 protective with OR 0.77. Noassociation with T2D

[260]

AKR1B1 (CA)n and 106C > T in 11studies

n = 2391 (cases, 1165;controls, 1226)

No association with (CA)n Associationwith −106T

[261]

MTHFR C677T in 15 studies n = 4027 (cases, 1877;controls, 2150)

Pooled random effects: OR = 1.30; 95%CI, 1.03–1.64; no association whendHWE studies removed. More rigorousstudies are required

[262]

SLC2A1 Xbal in six studies n = 1460 (cases, 710;controls, 750)

Large studies failed to show associationwhile small studies claimed an association

[263]

ACE Indel in 53 studies n = 17 791 II protective (pooled OR = 0.78; 95%CI, 0.70–0.87; p < 0.001)

[264]

ACE Indel in 47 studies n = 14 727 (cases, 8663;controls, 6064)

II associated with lower risk of DN [265]

ACE Indel in 18 studies ? Homozygous deletion associated withpoorer renal outcome

[266]


Trend towards protective effect ofinsertion on renal function

[267]


Deletion allele associated with DN in adominant model

[268]

ACE Indel in 11 studies ? Deletion allele is not associated withhypertension, but is a marker for DN

[269]


D allele is associated with progression,not initiation of DN

[270]

dHWE, deviation from Hardy–Weinberg equilibrium; T1D, type 1 diabetes mellitus; T2D, type 1 diabetes mellitus; DN, diabetic nephropathy;SLE, systemic lupus erythematosis.

CKD cases. This is an intriguing finding, since UMODencodes uromodulin (Tamm–Horsfall protein) whichis the most abundant protein in normal urine [94].

Uromodulin has antimicrobial properties providingdefence against uropathogens responsible for urinarytract infection [95] and may also play a role in



preventing crystallization of calcium and uric acidin kidneys and urine [96]. Although defects in theuromodulin gene have been associated with rareMendelian kidney disorders, such as autosomal dom-inant medullary cystic disease-2 and familial hyper-uricemic nephropathy [97,98], it is possible that subtlerdefects in uromodulin function contribute to progres-sive CKD. It is known that immunization of animalmodels with Tamm–Horsfall protein is associated witha chronic tubulointerstitial nephritis [99,100] and uro-modulin gene-deficient mice have a >60% reductionin GFR (as measured by creatinine clearance) com-pared to wild-type mice [101].

Renal function and GWA studies

The GWA meta-analysis by Kottgen and co-workersalso highlighted an association between the UMODlocus and the continuous renal trait of GFR (mea-sured by estimated GFR incorporating serum creati-nine) [85]. In addition, estimated GFR was associ-ated with intronic SNPs in genes encoding Jagged 1(JAG1 ; chromosome 20p12.1-p11.23), shroom fam-ily member 3 (SHROOM3 ; chromosome 4q21.1and spermatogenesis-associated 5-like 1 (SPATA5L1/GATM ; chromosome 15q21.1) (Table 2; see also Sup-porting information, Table S1). Using an alternativemethod of estimating GFR, incorporating cystatin Cassays, the UMOD locus was again found to be asso-ciated with renal function [85].

Gout and GWA analyses

GWA analyses have proved to be spectacularly suc-cessful in determining the links between plasma uratelevels and kidney function, with multiple novel locireported [102,103] (Table 2; see also Supporting infor-mation, Table S1). These novel insights into renalphysiology have been extended recently with the iden-tification of a urate transporter, human ATP-bindingcassette, subfamily G, 2 (ABCG2 ), which harbours acommon functional polymorphism causing gout [84].The native ABCG2 protein is located in the brush bor-der membrane of kidney proximal tubule cells, whereit mediates renal urate secretion. The common SNPrs2231142 (NP 004 818.2:p.Gln141Lys) reduces uratetransport rates by 53% compared to the wild-typeABCG2. This finding is an excellent example of thecommon variant–common disease hypothesis, wherea genetic association with ABCG2 provides physio-logical insights to explain the mechanism for hyper-uricaemia and gout.

IgA nephropathy and GWA studies

A major challenge now for investigators is to estab-lish large enough collections of DNA from care-fully phenotyped individuals, with and without renaldisease, for GWA studies to be sufficiently pow-erful to detect significant associations. This usu-ally requires national or international collaboration

to agree both on the definition of phenotype andto recruit patients for study. IgA nephropathy [104]is the most common glomerulonephritis identifiedin renal biopsy registries [105–107]. Several largeconsortia have been assembled to try and identifythe genetic susceptibility to IgA nephropathy includ-ing the European IgA nephropathy Biobank [108],MRC Kidney Research UK National DNA Bankfor Glomerulonephritis (www.renal.org/Research/GN-DNAbank.html) and an alliance of researchers fromCanada, Finland and France [109]. A limited genome-wide analysis (in terms of SNP coverage) was under-taken in 2003 in Japanese patients with and withoutIgA nephropathy [81]. This study, employing approx-imately 80 000 SNPs, identified a significant associa-tion between IgA nephropathy and six SNPs locatedin the polymeric immunoglobulin receptor gene atchromosome 1q31-q41. The larger groups represent-ing the IgA nephropathy consortia are expected toreport more extensive GWA data within the nextyear.

Diabetic nephropathy and GWA approaches

Diabetic nephropathy is the commonest cause ofESRD and there is evidence for a genetic sus-ceptibility to diabetic kidney disease [110]. Sus-tained efforts have been made to assemble suf-ficiently large case-control collections to achieveadequate statistical power in GWA analyses anda number of multicentre collaborations have beenforged into consortia, such as Genetics of Kid-neys in Diabetes (GoKinD) groups in the USAand UK (www.niddkrepository.org/niddk/jsp/public/GOKIND/publications.jsp), the Warren 3 DNA Col-lection [111], the EURAGEDIC study [112], FinnDi-ane, (www.finndiane.fi) and the Family Investigation of Nephropa-thy and Diabetes (darwin.cwru.edu/FIND). The ben-efits of such collaboration are agreement on rigor-ous phenotypic criteria for recruitment of cases andcontrols, pooling of DNA resources, shared costsof large-scale GWA studies and combined expertisein analysis and replication. In 2009 a SNP-basedGWA study for nephropathy in type 1 diabetes wasreported by Pezzolesi and colleagues, highlightingfour loci (FERMD3, CARS, CHN2 and CPVL) withmoderately strong association with kidney disease[82].

Diabetic nephropathy is arguably a more diffi-cult renal phenotype to ascertain in type 2 diabetes,since the patients recruited for genetic studies tendto be older than individuals with nephropathy andtype 1 diabetes. The renal phenotype in older type2 diabetes individuals may be confounded by coex-isting hypertensive kidney injury and/or atheroma-tous renal vascular disease. Confirmation of diabeticnephropathy by renal biopsy is usually not under-taken. GWA studies of nephropathy in type 2 diabeteshave been reported in Japanese populations. These



studies reported the association of several candidategenes with nephropathy, including the solute carrierfamily 12 (sodium/chloride transporters) member 3(SLC12A3 ) [113] and the engulfment and cell motility1 gene (ELMO1 ) [114]. Variants in SLC12A3 weresubsequently found to be associated in a relativelysmall collection (n = 177 cases) [115]; however, thisfinding was not replicated in a Caucasian popula-tion with type 2 diabetes [116]. Different SNPs inELMO1 have been associated with diabetic nephropa-thy in American individuals with diabetic nephropathy[117,118].

Improvements in genotype technologies and dataanalyses have enabled the extension of GWA studiesto identify copy number variants that are associatedwith susceptibility to common disease [119].

Copy number variation

Copy number variation (CNV) represents an intrigu-ing field for renal geneticists. Resequencing of thehuman genome has shown that fragments of DNAare deleted, duplicated or inverted and that thesegenetic rearrangements may alter the number of copiesfor a particular gene [120]. Copy number variationrepresents a substantial source of variation withinthe human genome and there is significant over-representation in genes involved with differentiationand development [121]. Structural rearrangements ofthe genome increase the risk of developing complexdiseases [122–124] and almost 15% of genes collatedin the OMIM morbid map overlapped with a first-generation map of copy number variation [120]. Lowcopy number of the FCGR3B gene is associated withglomerulonephritis in systemic lupus erythematosis inboth rat models and human disease [124]. Willcocksand colleagues demonstrated the functional effect ofCNV at the FCGR3B locus on protein expressionand function [125]. Copy number variation may alsoplay a role in the development of nephronophthisis[120].

SNPs in complement factor H (CFH ) genes areassociated with atypical haemolytic uraemic syn-drome, membranoproliferative glomerulonephritis,chronic kidney disease and age-related macular degen-eration (AMD) [126–128]. The relationship betweenrenal function and AMD [127,129] is not clearlydefined; however, complement genes are subject toCNV and complete duplication of complement factorH-related genes (CFHR1–CFHR4 ) has been observed[130]. A recent study suggests that CNV contributesto the development of AMD and may provide furtherinsights to renal disease mechanisms [131].

The mitochondrial genome

There is increasing awareness of the critical role ofmitochondrial defects in a diverse range of clinical

disorders. Mitochondrial DNA (mtDNA) content incells is lower in those individuals with renal fail-ure compared to healthy controls and, of interest,higher mtDNA copy number in patients with ESRD onhaemodialysis has been associated with improved sur-vival [132]. Mitochondrial mutations have been impli-cated in a range of complex diseases including dia-betes mellitus [133], age-related macular degeneration[134] and neurodegenerative disorders [135]. Alteredrenal phenotypes have been observed in several com-plex mitochondrial disorders, including Kearns–Sayre,Pearson and Leigh syndromes [136]. Increased preva-lence of mitochondrial deletions [137,138] and singlebase variants [139] have been reported in individu-als with ESRD compared to healthy controls. A 5778bp mitochondrial deletion [140] and the 3243A >G single base mutation [141–143] in the mitochon-drial genome have been suggested to contribute to thepathogenesis of diabetic nephropathy. Renal functionwas impaired in more than half of individuals withmaternally inherited diabetes and deafness associatedwith 3243A > G [144]. Renal tubular dysfunction isassociated with multiple overlapping deletions, dupli-cations and rearrangements [145–147]. Tubulointer-stitial nephropathy is associated with several geneticabnormalities, including two large-scale deletions andthe 608A > G SNP at the distal end of the tRNA(Phe)molecule [148]. Both the 7374 bp ( [149] deletionand 3243A > G variant have been investigated forglomerulosclerosis [150]. The 3243A > G mutationhas been observed in patients with focal segmen-tal glomerulosclerosis [151], with further individualswith this renal disease reported to have the 5843A> G mutation [152]. Several reports of progressiverenal disease are described with deletions of mito-chondrial DNA, along with 3243A > G [153] and11 778G > A [154]; three cases with 3243A > Ghave presented with a phenotype similar to Alportsyndrome [155–157]. Multiple SNPs have been inves-tigated for essential hypertension with the 10 086A >G variant found to be significantly associated withhypertension-associated ESRD in African-Americans[158].

Due to complex inheritance patterns, heteroplas-mic differences, copy number variance, existence ofnuclear–mitochondrial pseudogenes and high muta-tional rate of mitochondrial DNA, the importanceof accurate sequencing and efficient quality con-trol mechanisms cannot be underestimated for mito-chondrial DNA projects [159,160]. Determining therole of mitochondrial DNA variants and imple-menting effective therapeutic strategies for com-mon disease remain a major challenge for renalgeneticists.

Towards a systems biology approach

Technological advances now facilitate the generationof large-scale datasets. The rapid accumulation of



genomic, transcriptomic and proteomic informationprovides renal researchers with the opportunity tointegrate these datasets to highlight biological path-ways for investigation. Systems biology employs amultidisciplinary approach, combining clinical insightwith basic research to investigate how molecules,cells and tissues interact in both healthy and diseasedstates. Several online resources have been created tostore biological information [161], including those thatdistribute results from GWA projects. For example,the Database of Genotypes and Phenotypes (dbGaP)presently contains information on 109 studies ( [162];accessed 3 September 2009) linking clinical charac-teristics with genetic profiles. A unique catalogue hasbeen developed to store the results from genome-wide association studies; analysis of > 150 studiesand 500 SNP-trait associations revealed that hundredsof common genetic variations have been reproduciblyidentified for more than 80 disorders [163]. An inte-grated genomics approach has recently suggested that> 35% of cis-acting SNPs associated with expressiontraits are located more than 100 kb distant to transcrip-tion start/stop sites, so that genes physically closestto SNPs highlighted in GWA studies may not be theappropriate gene(s) of interest [164]. GWA studieshave also revealed several genes in linkage disequi-librium; in silico network modelling relating genesto relevant pathways may further resolve causal loci[165].

Exploring integrated datasets has the potential toreveal novel pathophysiological mechanisms con-tributing to disease states. The integration of genomicand transcriptomic data from human and mousedatasets identified SORT1 and CELSR2 genes and

candidates at chromosome 1p13.3, influencing suscep-tibility to coronary artery disease and hyperlipidaemia[166]. An international effort led to the development ofa unique, coordinated systems biology-based approachfor type 1 diabetes (T1DBase [167]). T1DBase is aweb-based resource that presents a simplified, inte-grated view of complex datasets including annotatedgene data for linkage and association results, geneexpression data, visualization of combined molecularinteraction networks and results from animal models.Systems biology is being employed to provide novelinsights to understanding diverse disorders from pan-creatic β cell death in type 1 diabetes [164,168], tocardiovascular disease [169] and adverse drug reac-tions [170]. Such comprehensive projects are not yetdeveloped for kidney diseases; however, several onlineresources have been specifically developed to read-ily facilitate public access to renal-related informa-tion (Table 4). Stand-alone resources also exist suchas the catalogue of predicted protein–protein inter-actions that was developed from meta-analysis ofglomerular transcriptional profiles to improve under-standing of glomerular signalling networks (GlomNet,[171]).

Online resources are often individualized with theirown content, layout and dynamic capabilities [172],so that automated integration of diverse datasets andeffortless retrieval of relevant information will beessential to maximize our understanding of complexdisease processes [173]. Systems biology provides amechanism of combining knowledge from biologicalpathways and large-scale ‘omics’ data to inform per-sonalized clinical medicine. Computational systemsmedicine has been proposed to enable changes from

Table 4. Online resources for renal genetics

Autosomal dominant polycystic kidney disease http://pkdb.mayo.edu/cgi-bin/mutations.cgi[mutation database (PKD1/PKD2)]Autosomal recessive polycystic kidney disease http://www.humgen.rwth-aachen.de(mutation database (ARPKD/PKHD1)]CDDB http://cddb.nhlbi.nih.gov/cddb(Collecting Duct DataBase)CORGI http://www.qub.ac.uk/neph-res/CORGI(Centralized Online Renal Genetics Initiative)EuReGene http://www.euregene.org(European Renal Genome Project)GENECURE http://www.genecure.eu(GENomic stratEgies for treatment and prevention of Cardiovasculardeath in Uraemia and end-stage REnal disease)GUDMAP http://www.gudmap.org(GenitoUrinary Development Molecular Anatomy Project)KGDB http://www.urogene.org/kgdb(human Kidney Gene DataBase)Kidney development database http://golgi.ana.ed.ac.uk/kidhome.html(collation of studies relating to kidney development)Predictions http://www.rzuser.uni-heidelberg.de/∼jb5/aboutproject.htm(EU-funded PREvention of DIabetic ComplicaTIONS)ReGeNet http://www.regenet.eu(the REnal GEnome NETwork project)Renal genes http://www.renalgenes.org(aims to provide information on nephronophthisis, nephrotic syndromeand urinary tract malformations)



population-based to personal health care strategies[174].

Conclusions

Genetic studies reported now are very different fromthose published > 5–10 years ago, with standardsimproving as we learn more about the confound-ing factors for genetic analyses [11,175]. Guidelineshave been published for STrengthening the REport-ing of Genetic Association studies (STREGA) [176].Important study design issues for genetic investiga-tions include careful phenotyping, appropriate samplesizes, tests for cryptic relatedness, a sound rationalefor genetic marker selection, minimizing genotypingerrors, managing population stratification, avoidanceof bias, rigorous statistical methods, replication ofpositive results and the public availability of non-identifiable data.

Commercial organizations now offer to ‘explain’how an individual European’s genetic data will affecttheir odds of developing kidney disease (https://www.23andme.com/health/pre kidney disease). This is apremature application of the burgeoning area of‘renal genetics’ research. Nonetheless, there are real-istic prospects of enhancing our understanding ofmultiple genes and biological pathways involved inthe pathogenesis of CKD. With continued effortsto bridge the many knowledge gaps between geno-types and phenotypes, it should prove possible toenhance our ability to predict and identify individ-uals ‘at risk’ of renal disease and improve the effi-cacy of treatment by developing novel therapies orutilizing pharmacogenetic insights for existing medi-cations.

Acknowledgements

DC is supported by a Northern Ireland Kidney Research FundPhD studentship.

Teaching Materials

PowerPoint slides of the figures from this revieware supplied as supporting information in the onlineversion of this article.

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∗ References cited in supporting information online only.

SUPPORTING INFORMATION ON THE INTERNET

The following supporting information may be found in the online version of this article:

Table S1. Chromosomal regions and associated genes for common forms of renal disease identified fromgenome-wide studies.


unravelling the genetic basis of renal diseases; from single gene to multifactorial disorders

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