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Mining the Genome for Susceptibilityto Diabetic Nephropathy: The Role of

Large-Scale Studies and Consortia

Sudha K. Iyengar, PhD,* Barry I. Freedman, MD,† and John R. Sedor, MD‡

Summary: Approximately 30% of individuals with type 1 and type 2 diabetes developpersistent albuminuria, lose renal function, and are at increased risk for cardiovascular andother microvascular complications. Diabetes and kidney diseases rank within the top 10causes of death in Westernized countries and cause significant morbidity. Given theseobservations, genetic, genomic, and proteomic investigations have been initiated to betterdefine basic mechanisms for disease initiation and progression, to identify individuals at riskfor diabetic complications, and to develop more efficacious therapies. In this review we havefocused on linkage analyses of candidate genes or chromosomal regions, or coarse genome-wide scans, which have mapped either categorical (chronic kidney disease or end-stage renaldisease) or quantitative kidney traits (albuminuria/proteinuria or glomerular filtration rate).Most loci identified to date have not been replicated, however, several linked chromosomalregions are concordant between independent samples, suggesting the presence of a diabeticnephropathy gene. Two genes, carnosinase (CNDP1) on 18q, and engulfment and cellmotility 1 (ELMO1) on 7p14, have been identified as diabetic nephropathy susceptibilitygenes, but these results require authentication. The availability of patient data sets with largesample sizes, improvements in informatics, genotyping technology, and statistical methodol-ogies should accelerate the discovery of valid diabetic nephropathy susceptibility genes.Semin Nephrol 27:208-222 © 2007 Elsevier Inc. All rights reserved.Keywords: Genetics, albuminuria, linkage, association, candidate genes

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n the past 15 years, studies with differentanalytic strategies have established that dia-betic nephropathy (DN) has a genetic pre-

isposition. DN does not segregate according toendelian rules, but rather is a multifactorial

rait that results from interplay between envi-onmental factors and multiple genes.1-8 Givenhis, strategies that were used successfully to

Departments of Epidemiology and Biostatistics, Ophthalmology and Genetics,Case Western Reserve University, Cleveland, OH.

Department of Internal Medicine/Nephrology, Wake Forest University Schoolof Medicine, Winston-Salem, NC.

Departments of Medicine and Physiology and Biophysics, Case School ofMedicine and Kidney Disease Research Center, Rammelkamp Center forResearch and Education, MetroHealth System Campus, Cleveland, OH.

upported by research grants U01DK57292, U01DK57293, UO1DK57298,RO1DK070942, and R01DK064719 from the National Institute of Diabetesand Digestive and Kidney Diseases and the Kidney Foundation of Ohio.

ddress reprint requests to Dr. Sudha K. Iyengar, Case Western ReserveUniversity, Wolstein Research Building, 1315, 2103 Cornell Rd, Cleveland,OH 44106-7281. E-mail: [email protected]

270-9295/07/$ - see front matter
s2007 Elsevier Inc. All rights reserved. doi:10.1016/j.semnephrol.2007.01.004
Seminars08

ap the causal mutations for monogenic disor-ers could not be used for DN and other com-lex traits and new analytic plans were devel-ped. Initially modest-sized study collectionsanging from 100 to 200 nuclear families withffected siblings were assembled and analyzedith model-free methods in the hopes of iden-

ifying genes for DN.9-11 This was a time ofncertainty when the genome sequencing wasot finished, and it was unclear which studyesigns and sample sizes would best facilitatehe identification of genes for DN. Sample sizesenerally were based on the number of subjectsho could be enrolled at a single center within2- to 5-year time line, extrapolating successful

trategies for mapping single gene disorders.hus, these sample sizes would comprise abouteveral hundred meiotic products (individuals)nd allow a map resolution of approximately 1egabase (1 megabase � 1 centiMorgan), as-

uming a single locus genome-wide. However,

in Nephrology, Vol 27, No 2, March 2007, pp 208-222

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Diabetic nephropathy genes—genome-wide tools 209

either the sample sizes nor the map resolu-ions were adequate for identifying DN suscep-ibility variants.

Subsequently, seminal studies in genetic ep-demiology literature have suggested that indi-iduals with long-standing diabetes who wereiscordant for nephropathy were needed to dis-inguish between genes responsible for diabe-es and those regulating nephropathy.12 Theationale for this strategy was that individualsithout diabetes did not have the hyperglyce-ic exposure necessary to develop chronic kid-

ey disease (CKD), even if they did possessenes for DN susceptibility. Clearly, individualsho did not develop nephropathy after a num-er of years of exposure to chronic hypergly-emia possessed inherent (potentially genetic)echanisms of protection; contrasting the ge-

etic profile of diabetic individuals with andithout DN would distil out the common dia-etes genes, allowing identification of DNenes. The mechanisms by which persistentyperglycemia sensitizes the kidney in individ-als predisposed to develop DN is not under-tood completely, although tight control of hy-erglycemia certainly offers renoprotection.13

Concomitant with evolving approaches toene mapping, outpatient screens for kidneyisease (phenotypes) were validated. Albumin-ria or proteinuria could be quantitated on apot sample and identified individuals witharly CKD initiation and progression.14 Simplestimating equations, based on the serum cre-tinine level and demographic data, allowed anstimation of the glomerular filtration rate with-ut the need to collect urine over 24 hours.15-17

hus, the renal community was well positionedo implement gene mapping studies with largeumbers of enrolled patients with easily quan-ifiable kidney disease traits.

The best prospect for the successful identifi-ation of CKD susceptibility alleles would beultiple families with diabetes with multipleembers with CKD and/or end-stage renal dis-

ase (ESRD). Considering the genetic load, fam-lies with more than 2 individuals on dialysisecause of DN were ideal because even for aomplex trait this would imply that nephropa-hy genes were segregating within the family.
owever, these types of families were difficult c
o locate because of early mortality from mac-ovascular disease. Albuminuria (microalbumin-ria) may be a better predictor for cardiovascu-

ar disease than for ESRD,18,19 explaining theifficulty in identifying multiplex DN families.o relieve some of the onus of collecting fami-

ies with 2 or more DN-ESRD sibling pairs, thetudy designs were expanded further to accom-odate family members with less advanced dis-

ase. Quantitative measures of albuminuria andalculated glomerular filtration rate (glomerularltration rate calculated based on the Modifica-ion of Diet in Renal Disease equation or theockcroft-Gault equation) were used as endo-henotypes. Both these traits were shown to beeritable in at least some populations,20,21 andre routine measures of cardiovascular and re-al function in clinical settings.

The impetus to map genes for nephropathy,specially DN, was driven by the modeling ofN inheritance in several multigenerational,

amily data sets or by looking at concordance inhenotypes between family members. Segrega-ion and comingling analyses are the only estab-ished methods for determining if a phenotypeclinical measure or trait) fits a particular ge-etic model. Two segregation analyses haveuggested that a major locus controls the albu-in excretion rate.20,22 In one study, protein-

ria was analyzed as a continuous variable, withhe conclusion that proteinuria was influencedy multiple genes with variable effects. Theeport by Imperatore et al22 in diabetic Pimaamilies considered overt proteinuria as a dis-rete variable, and determined that this traitas regulated by a major gene effect. Two stud-

es of type 2 DN partitioned the genetic andnvironmental influences in albumin excretionate and estimated heritability, a measure ofenetic predisposition.23,24 Both studies esti-ated the heritability for urinary albumin ex-

retion to be approximately 30%. The estimatesf heritability for urine albumin excretion weretatistically significant, even after adjusting forotential confounding covariables such as age,ex, body weight, diabetes duration, and envi-onment, suggesting a major genetic effect forroteinuria.23 Finally, a renal biopsy study inype 1 diabetic siblings showed high degrees of
orrelation between severity and patterns of

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210 S.K. Iyengar, B.I. Freedman, and J.R. Sedor

lomerular injury, despite the frequent lack ofoncordance of glycemic control between sib-ings.25

In constructing the studies for detecting ge-etic susceptibility to DN, consideration had toe given to several important issues. First, is DN

n type 1 diabetes a different entity from that inype 2 diabetes, such that genes for type 1 andype 2 DN differ? This remains an unanswereduestion, but one that hopefully will be re-olved by comparing the results from severalarge-scale gene mapping studies discussedater. However, current evidence suggests thatidney disease in type 1 and type 2 diabeticatients may result from common genes. Bothype 1 and type 2 DN cluster in the same fam-lies26 and show a similar disease tempo withephropathy onset approximately 10 to 15ears after diabetes onset.27 If hyperglycemicxposure can be considered an environmentalondition that primes the glomerulus regardlessf the cause, then the similar patterns of re-ponse under the influence of either type 1 orype 2 diabetes may have an explanation.

Second, how long should the diabetic rela-ives of probands with DN remain free of ne-hropathy after the initiation of diabetes to beonsidered truly unaffected (and contribute todiscordant relative pair)? Absent a more sen-

itive marker of DN progression, a lack of albu-inuria (�30 mg/g albumin-to-creatinine ratio

n repeated measurements) after 10 years ofiabetes was considered strong evidence of re-istance to DN. The figure of 10 years is notased on an arbitrary threshold but is based onpidemiologic studies that established that theeak of nephropathy initiation is within 10 to0 years after the initiation of diabetes,28,29 andhe risk for nephropathy plateaus approxi-ately 25 to 30 years after disease onset.8 In

ontrast, retinopathy risk increases with diabe-es duration. Intrinsic within this simple idea iscomplex model—individuals can be nephrop-thy-free because they do not carry a specificusceptibility allele at a particular locus, or theyan be nephropathy-free because they haverotective alleles elsewhere in the genome thatuppress the outcome of a deleterious allele, or,nally, their glomerular cells are resistant to the
ffects of the environmental insult, such that h
hey can withstand hyperglycemic exposure foronger periods without consequence. The latterlso would be the result of the action of modi-er genes, just not genes directly controlling anbvious avenue to DN. It may not be possible toisentangle all these possibilities in a singlexperiment, and the genetic epidemiologytudy designs chosen may influence which ofhese outcomes are detectable. The strengthsnd weaknesses of the study designs pertinento gene identification are discussed later afteronsideration of some of these problems.

ENETIC COMPLEXITY: A DILEMMA OFODEST SAMPLE SIZES?

ene mapping studies run the gamut from col-ection of unrelated cases and controls, to fam-ly-based studies, to cohort studies (Fig. 1, Table). All of these study designs have been appliedo identifying genes for DN, and nested withinhese basic designs specific molecular (genetic)ontrasts have been devised with the goal ofest using the study population at hand. Severalarly studies attempted to identify genes for DNith small to modest sample sizes, but subse-uent investigations (both linkage and associa-ion) were unable to replicate findings in otheramples or attempts at replication were noteported. Few of these investigations have pro-uced results that meet modern guidelines fortatistical significance in the context of a mul-ifactorial trait.30 Primed by the successes inonogenic disorders (eg, polycystic kidney dis-

ase) in which the gene effect size is large, theack of speedy identification of DN genes ledhe general scientific audience to conclude thatenetic complexity precluded the identificationf genes for DN susceptibility. However, aseviewed later the sample sizes were modest inhe majority of investigations and ascertainmentriteria were not standardized between studies,or were the chromosome locations or candi-ate genes that were investigated similar be-ween the studies. Thus, direct comparison be-ween the studies was difficult and noturprisingly the results seldom were concor-ant. Some of these early reports reflected ei-her false-positives findings (type 1 error) orample-specific signals whose effect was en-
anced by the modest size of the sample. Fail-

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re to replicate gene mapping results in inde-endent samples, a key requirement forroving that a gene variant causes a disease,ay be the result of small sample sizes, geneticeterogeneity between samples from varioustudies, ascertainment bias (also leading to het-rogeneity), phenocopies, unaccounted envi-onmental correlates, and other epigeneticechanisms. Replication studies face some

nique design issues. The exact hypothesis be-ng examined in replication studies is not theame as the null hypothesis in the originaltudy. As an example, if in an initial scan therere 10 genes in total genome-wide that can

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igure 1. Study designs used in gene mapping experimesigns test for association whereas family based designsre more powerful for association testing in the contextower for rare variants. Some rare variants are difficult toypes of mutations. (A) Potential uses for the illustrated stc) gene � environment interaction; (d) genome-wideandidate genes; (b) gene � gene interaction; (c) gearent-of-origin effects; (f) sex-influenced traits. (C) Poteb) gene � gene interaction; (c) gene � environment inf) sex-influenced traits.

ause nephropathy, finding sufficient evidence o

or linkage to any 1 of these 10 is considered auccess. In a replication study, genetic evi-ence at a specific locus identified in the initialcan must be reproduced. If patient pheno-ypes in the second data set are not identical tohose characteristics in the first set, gene map-ing results may be at variance. This does notecessarily imply that the results of the secondcan or the first scan are false, merely that morevidence is needed to advance the experimentorward.

Each method for gene mapping has its owndvantages and disadvantages, and these mayhange with advances in technology. All meth-

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or complex traits. Case control and trio (or TDT)-basedst for linkage or association. The first 2 designs generallymmon variants that cause disease. The latter has morect and only direct sequencing may be able to find thesesigns: (a) candidate genes; (b) gene � gene interaction;(B) Potential uses for the illustrated study designs: (a)environment interaction; (d) genome-wide scans; (e)ses for the illustrated study designs: (a) candidate genes;on; (d) genome-wide scans; (e) parent-of-origin effects;

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xtent of the bias depends on the study design.ohort studies that adequately represent theeneral population have the least bias, whereasoth family-based designs and case-control de-igns may have significant bias. The advantageor the latter is of course the increase in poweror gene finding, by recruiting subjects withxtreme phenotypes. Although identifying sus-eptibility genes is often within the means ofhe study, determining population-attributableisk (the fraction of risk that would be elimi-ated if the risk factor was removed) is difficultith common gene mapping designs.31-35

We have summarized large-scale studies thatre in the process of gene identification for DNusceptibility and perhaps also for nephroprotec-ion (Table 2). Most studies have a population-ased collection strategy, the exceptions beinghe Family Investigation of Nephropathy and Di-betes (FIND)36 and the Genetics of Kidneys iniabetes (GoKinD) studies.37 The FIND study hadmixed design with approximately half the sam-le comprising families and the other half com-

Table 1. Study Designs for Gene Mapping Stu

Characteristic Case-Control Design

Phenotypecontrast

Predefined choice of diseasecontrol status

Sample sizerequired

Small to modest

Collection process Can be clinic or center base

Type of genetichypothesisexamined

Only association analysis witgenes, environment or ge� environment

Ascertainment bias Yes; can lead to biased oddratio to estimate disease ri

Study oflongitudinaloutcomes

No

rising an admixture mapping approach in Mex- i

can Americans and African Americans. TheoKinD study focused on trios of an affected typeparticipant and their 2 parents. Although the

pidemiology of Diabetes Interventions and Com-lications/Diabetes Control and Complicationsrial (EDIC/DCCT) has a clinical trial base, effortsere made to assess the family history of compli-

ations among participants.26 Table 2 shows thathe outcomes under investigation and the studyesigns are fairly distinct, although each generallyxamines some DN trait. Further, the major focuss on type 1 and not the more common type 2iabetes; all of the studies but FIND and Theetherlands Cooperative Study on the Adequacyf Dialysis examined type 1 diabetes. Other inde-endent type 2 diabetes studies in specific ethnicroups also have been published in the literatureeg, the DN genome scan in African Americans,38

he DN case control single-nucleotide polymor-hism [SNP] scan in the Japanese39), but the sam-le sizes for the majority of these investigationsere modest. These cohorts can be used for rep-

ication when genes are found, but the contrast-

Retrospective/ProspectiveCohorts

LinkageStudies

an examine multiple diseasestages

Predefinedchoice ofdisease

arge Small tomodest

opulation-based and requiresepidemiologic principles offollow-up evaluation

Can be clinic orcentercontrolled

f data on familial relationshipscollected, either associationor linkage can be examined

Eitherassociation orlinkage testscan beperformed

o Yes; maymagnifyeffects of rarealleles

es No

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ize of the gene quite difficult and the compari-ons should be accepted with caution. Thus, theajority of the studies have some bias, even ones

hat represent larger populations because of theampling scheme. Several analyses were initiateds cross-sectional studies and later expanded toohorts. The benefit of these studies is that theata are being collected prospectively and theyniformly are using strict phenotyping protocols,hich will allow for computation of attributable

isk.Recently the use of standardized criteria for

ecruitment and large sample sizes have al-owed the emergence of unifying results acrosstudies. This should increase confidence in fu-ure trials to identify DN genes. The prospectsf success already have been realized with the

Table 2. Large-Scale Investigations in Diabetic

Consortium Name Study Design Diab

EDIC/DCCT26,69 Prospective cohortlinked with tightglucose control trial

Type

EURODIAB70,71 Prospective cohort Type

FIND36 Cross-sectional familybased; mapping byadmixturedisequilibrium

Predotyp

FINNDIANE72 Prospective cohort Type

GENDIAN73 Case control;prospective

Type

GoKinD37,74,75 Cross-sectional trios(family based)

Type

NECOSAD76,77 Prospective cohort Both

EURODIAB, European Diabetes IDDM Complications Study GGenetic and Clinical Predictors of Morbidity, Mortality, anMellitus Type 2; NECOSAD, The Netherlands Cooperative

dentification of specific DN genes, for exam- a

le, carnosinase (CNDP1)40 and engulfmentnd cell motility 1 (ELMO1),39 although theseenes need to be validated in other popula-ions. Similarly, linkage and/or association sig-als have been replicated at several chromo-omal regions (eg, 10p, 18q), suggesting thathese regions harbor important susceptibilityoci (discussed later).

INKAGE STUDIES

e have compiled a list of linkage studies for DNnd all-cause nephropathy (Table 3).9-11,38,41-51

he early linkage studies considered DN as a di-hotomous outcome, but the later studies havencluded albuminuria as a quantitative trait (Table). Other studies with glomerular filtration rate as

hropathy

ype Outcome Locale

Multiplecomplications ofdiabetes

Multicenter UnitedStates/Canada

Multiplecomplications ofdiabetes

Multiple Europeancenters; UnitedStates

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Severely affected anddiscordant relativepairs with DN;cases and controlswith and withoutDN

Multicenter UnitedStates

Follow-up evaluationof DN in 25% ofadult type 1diabetics

Finland

Follow-up evaluationof DN and othercomplications oftype 2 diabetes

Germany

Probands with CKDand parents

Multicenter UnitedStates

New ESRD casefollow-upevaluation

The Netherlands

INNDIANE, Finnish Diabetic Nephropathy Study; GENDIAN,etic Nephropathy with End Stage Renal Disease in Diabeteson the Adequacy of Dialysis.

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n endophenotype also are forthcoming. The first

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enome scan for DN was performed in the Pimand included 98 affected pairs,9 but sample sizesave increased significantly since these types of

nvestigations were begun in the late 1990s.mong family based studies, the largest samplesizes will come from FIND and GoKinD for type 2nd type 1 DN, respectively. For example, FIND

Table 3. Summary of Linkage Studies for DN

PhenotypeDiabetes

Type Population

All-cause ESRD41 AfricanAmerican


DN9 Type 2 Pima IndianAll-cause ESRD11 African

AmericanDN10 Type 1 CaucasianAll-cause ESRD43,44 African

AmericanDN45 Type 2 Turkish

Albumin-to-creatinine ratio46

Multipleethnicgroups

DN47 Type 2 CaucasianandAfricanAmerican

DN38 Type 2 AfricanAmerican


Albumin-to-creatinine ratio49

Type 2 Multipleethnicgroups

DN50 Type 1 Finnish

DN and albumin-to-creatinine ratio51

Both, mostlytype 2

Multipleethnicgroups

ASP, concordantly affected sibling pair; DSP, discordant sibli*Genome scan not performed.†Does not comprise the full FIND sample.

as collected approximately 1,200 families from 4 e

ifferent ethnic groups, Caucasian, African Amer-can, Native American, and Mexican American,cross the United States. Similarly, the GoKinDollection encompasses 71 cases, 623 controls,72 case trios with a type 1 diabetic probandaving nephropathy, and 323 control trios ascer-ained by a diabetic proband without renal dis-

KD in the Literature

udy Design Sample Size Linkage

65 families Renin-angiotensin-aldosterone*

65 families? Cytokinegenes*

98 ASPs 3q, 7q, 9, 20142 ASPs Kallikrein

genes*66 DSPs 3q*129 ASPs and

356 ASPs10p and 10q

18 extendedfamilies

18q22

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805 families 19, 12q

, DSP, USP 27 Caucasianand 38 AfricanAmericanfamilies

10p*

166 families 3q, 7p, 18q

483 extendedfamilies

13q33.3,9q34.3,4p15.32,1q25.1

antitativeait analysis

63 extendedfamilies

22q, 7q, 5q

P 83 DSPs 3q, 4p, 9q,22p

, DSP,SP,uantitativeaits

378 families† 7q, 10p, 14q,18q

r; USP, concordantly unaffected sibling pair.

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ssociation mapping than for linkage. Other co-orts described in Table 2, although not familyased, also have the potential to discover ne-hropathy genes through association analyses asescribed later.

In total, only 5 genome scans performed forype 2 DN and 1 for type 1 DN have beeneported in the literature. The definitions of DNave evolved over time, with microalbuminuricatients being considered affected in sometudies but not in others. The establishment ofiabetes before nephropathy initiation and the

ength of diabetes duration may have been vari-ble between studies as well, especially in typeDN. These subtle features have not been wellocumented and it is possible that undetectedeterogeneity has crept into the analyses as aesult of differences in the clinical characteris-ics of recruited patients.

In comparing the genome scans, most pub-ished studies have discovered at least onenique locus (Table 3). The best evidencerom the genome scans abstracted from the

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hromosome. Either the log of the likelihood ratio (LODhe height of the bars is proportional to the significancen Table 3 and references 9, 38, 39, 49, and 51.

riginal articles shows little replication be- m

ween studies except at 10p and 18q. Within0p the marker D10S1435 shows evidence of

inkage in at least 3 studies for type 2 DN,ncluding FIND,40,42,43,52 and is validated fur-her in an association study of type 1 DN.53

n chromosome 18q, the carnosinase geneas been identified after a linkage scan of 18urkish families multiplex for DN45 and 2 of

he larger published linkage scans showedvidence in this region.38,40 Fine mapping toetermine if genetic variation in carnosinaseest explains the linkage signal in these 2tudies still is needed. Although chromosome

consistently has been reported to showinkage and association for DN, careful exam-nation of these loci does not show overlapFig. 2). Evidence against linkage at specificoci has not been compared rigorously acrosstudies, and loci that fail to meet genome-ide significance criteria in replication may

how signal corroboration at more modestignificance levels. One mechanism to com-are genetic evidence across studies is a

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2 DN on chromosome 7, placed on the microsatelliteeach study are circumscribed along the length of theor the P value are marked along the Y-axis. Therefore,

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tudy, an approach that has not yet beenndertaken by the kidney community but haseen used with success to evaluate suscepti-ility alleles for type 2 diabetes and otheromplex genetic traits.54-56 Meta-analyses alsoould enable comparisons in the phenotypeefinitions between studies.Successful, systematic fine mapping of DN

inkage signals to find the causative genes orariants has been reported. Janssen et al40 iden-ified carnosinase (CNDP1) as the causativeene under the 18q linkage peak in the Turkishample and confirmed this finding in a sampleet obtained from Pima Indians. Other groupsave confirmed that the trinucleotide repeat inxon 2 of the CNDP1 gene, with homozygosityf a 5-leucine repeat in the leader peptide, pro-ects diabetes mellitus patients against ne-hropathy, particularly in Caucasian samplesB. I. Freedman, personal communication). In-erestingly, Zschocke et al57 were unable to seeny association between CNDP1 and coronaryisease or survival/longevity, suggesting thathis gene may be specific for renal failure sus-eptibility. It is unclear if a common set ofenes regulates both DN initiation and progres-ion. The distinction between initiation genesnd progression genes has been difficult to ad-ress. Individuals with renal insufficiency un-ergo a survival bottleneck as a result of comor-id cardiovascular disease, which frequently

eads to early mortality. Some investigators havepeculated that death of DN patients as a resultf cardiovascular disease is not random but isriven by a common gene set for kidney andardiovascular disease. Therefore, survivorsho progress to proteinuria and ESRD may re-

ain only a fraction of DN susceptibility alleles,iasing findings toward survival alleles or less-obust disease susceptibility variants. In prac-ice, using very strict trait definitions in case-ontrol studies may bias which genes areocated as a result of this phenomenon. How-ver, in family based studies the use of theuantitative data in all available siblings shouldrovide some safeguards against such a trend; a

inkage peak that retains a good proportion ofts signal after the exclusion of microalbumin-ric individuals should argue for a lack of bias.
he investigation by Zschocke et al57 addressed s
he issue of common genes for coronary diseasend survival bias in ESRD. Many more studiesocusing on this gene are underway.

In addition to fine mapping efforts followingp linkage signals, another study58 used theransmission disequilibrium test (TDT)59,60 tovaluate candidate type 1 DN genes in trios, aesign that requires inclusion of a child affectedith DN and both parents (ie, a trio). Neuropi-

in 1 (NRP1) is located under the 10p linkageeak identified in several genome-wide scansdiscussed previously) and marginal evidencef association was reported for 2 NRP1 SNPs. Inhe same report, the B-cell leukemia/lymphoma

proto-oncogene (BCL2), which is located onhromosome 18q21 near the carnosinaseCNDP1) locus, was reported to be associatedith type 1 DN. The BCL2 association signalay indicate the presence of a second DN gene

n the 18q region or may reflect extended link-ge disequilibrium (correlation between mark-rs near each other on the same chromosome)n this region. The linkage scans that reportedhe 10p and 18q signals are based predomi-antly on type 2 diabetes, and the investigationy Ewens et al58 and by McKnight et al52 may behe first evidence for commonality of geneticeterminants between type 1 and type 2 DN,ut the results, although intriguing, remain toe confirmed in larger-scale studies.

ANDIDATE GENE STUDIES ANDYSTEMATIC GENOME-WIDESSOCIATION SCREENS

andidate gene analysis has been conducted forN and ESRD in hundreds of genes because it isore feasible for a single investigator to assem-

le a modest number of cases and controls. Theoncern with this approach is that the geneselected are driven solely by previously knowniology and therefore are limited in spectrum.e have not attempted to review the many

ssociation studies published in the literatureor single genes, but have focused this reviewn the newer studies that systematically havexamined more than 100 genes or have per-ormed genome-wide association scans. Our ra-ionale for this approach is that many singleandidate gene reports were based on a small
ample size and minimal coverage (one or only

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few markers) within or near the gene. There-ore, results are hard to interpret because mosttudies lack adequate power to detect an asso-iation. In recent years, standards for reportingssociations have changed,61,62 and even singleene investigations need to genotype multipleNPs in a sufficiently large sample.

Three published studies have canvassed theenome for DN variants using association orave genotyped a large number of genes at areater depth. These articles include a genomecan using 81,315 SNPs of 87 patients with typeDN and 92 controls without DN in a Japaneseopulation,39 scanning of 200 Irish type 1 dia-etic patients with overt nephropathy and 200ype 1 diabetic patients with normal albuminxcretion using 6,000 microsatellite markers,52

nd the evaluation of 115 candidate genes usinghe TDT approach for type 1 DN.58 The Japa-ese association scan lead to the discovery ofLMO1, a novel gene on chromosome 7, thatay be associated with DN. In the scan, 1,615

NP loci had significant P values of less than .01etween DN and control patients. Patients andontrols were ascertained at clinics and wereesignated according to their renal functionaltatus. Thus, patients with DN, that is, patientsith diabetic retinopathy and overt nephropa-

hy, were individuals with urinary albumin ex-retion rates of 200 �g/min or greater or uri-ary albumin-to-creatinine ratios of 300 mg/g orore of creatinine, or patients receiving

hronic renal-replacement therapy. Controlubjects included patients with diabetic reti-opathy but showing no evidence of renal dys-unction (ie, urinary albumin excretion rates20 �g/min or urinary albumin-to-creatinine

atios �30 mg/g creatinine). The SNPs chosenor genotyping were selected randomly fromhe gene-based Japanese SNP database. Ofhese, the best evidence was at SNP rs741301n 7p14. Two other nearby SNPs, rs7799004nd rs1558688, also showed good associationignals in a haplotype analysis. The evidenceutside of those 3 SNPs was weaker but stilldequate. The association of the rs741301 SNPith DN was confirmed in a second sample of

59 patients and 242 controls. However, theaplotype around this SNP shows very weak
ssociation, probably owing to very weak link- m
ge disequilibrium in the sample. The risk alleleG; reference allele is A) is rare in the Chinese,apanese, and Caucasian populations, but is theore common allele in the Yoruba population.he SNP itself is intronic—in intron 18—and isot functional, and falls within a conserved hap-

otype block between exons 16 and 22, suggest-ng that the sentinel SNP may not be the causativeLMO1 variant. Because of the reduced linkageisequilibrium (lack of correlation betweeneighboring markers) within ELMO1, it may beifficult to replicate this report until a causal vari-nt is identified. This hypothesis is supported byhe haplotype analyses reported in this article.he G risk allele is found in haplotypes bothssociated and not associated with DN, suggest-ng that the causative variant has not yet beenocated. Therefore, typing only the 3 most signif-cant SNPs from the Japanese sample, rs741301,s7799004, and rs1558688, may or may not showvidence of association in a different sample. Byomparing the haplotype blocks and examiningagging SNPs from several different populations inAPMAP63,64 we have estimated that in most eth-ic groups it will be necessary to genotype ap-roximately 90 to 100 SNPs to obtain full cover-ge of this gene, showing the need to planeplication studies carefully.

The genome scan by McKnight et al52 used aNA pooling paradigm. In this study, DNA from

he affected (patients) and control enrolleesas pooled separately and genotyped for ap-roximately 6,000 markers. The allele fre-uency profiles at each marker were comparedetween case and control pools, and the result-

ng profiles were ranked in order of the greatesto the least difference between pools. Markersn 10p (described previously) showed associa-ion with DN. Although the best evidence forssociation was on 10p, other loci including6S281, D4S2937, D2S291, and D17S515,

anked next in order of priority. Participants inhis study were type 1 diabetic patients andontrols from Northern Ireland and the Repub-ic of Ireland. Phenotype criteria for a case defi-ition required an onset of persistent proteinuria0.5 g protein/24 h) at least 10 years after theiabetes diagnosis, the presence of hypertensionblood pressure � 135/85 mm Hg and/or treat-
ent with antihypertensive agents), and the pres-

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nce of diabetic retinopathy. Control subjects hadiabetes for at least 15 years, normal urinary albu-in excretion, and normal blood pressures with-

ut treatment. DNA pooling, although not a com-only used disease gene mapping design, can

educe genotyping costs significantly, and whenntegrated into a 2-stage analytic plan it has beenn efficient approach for the association of ge-etic variants with other disease phenotypes.65

ith only 6,000 microsatellite markers, thecreen reported by McKnight et al52 has a lowesolution.

These 2 whole-genome association screensllustrate 2 approaches to gene mapping design.he phenotype definition in the Japanesecreen was much less stringent whereas theenotyping design was more comprehensive.n contrast, the ascertainment scheme in therish scan was much more restrictive, but theenotyping screen was less comprehensive.urrently, there is a move to very dense ge-ome-wide association screens with genotyp-

ng of 500,000 to 1,000,000 markers to ensuredequate coverage,66 and neither of these 2nvestigations meet that criterion. Their effortss landmark studies in the DN arena should beegarded as the initiation points for more de-ailed studies.

Finally, a study by Ewens et al,58 an extensiveurvey of 115 known and novel candidateenes, deserves mention. The study by Ewenst al58 examined 72 families with type 1 diabe-es and nephropathy, defined as ESRD or 2 of 3andom urine albumin-to-creatinine ratiosreater than 300 �g/mg collected at least 6eeks apart, and used the TDT to test for asso-

iation. This family based association studyombined both linkage and association testingnd controlled for population substructureshat resulted in false-positive results in case-ontrol designs from gene mapping. Modestvidence was detected for association of DNith multiple candidate genes, including aqua-orin 1, B-cell leukemia/lymphoma 2 (BCL2)roto-oncogene, catalase, glutathione peroxi-ase 1, insulin-like growth factor (IGF)1, lami-in alpha 4, laminin gamma 1, mothers againstPP homolog (SMAD), SMAD 3, transformingrowth factor beta receptors II and III, tissue
nhibitor of metalloproteinase 3, and upstream b
ranscription factor 1. However, the sampleize is this study was small and the results haveet to be confirmed in other studies.

ONCLUSIONS AND FUTURE DIRECTIONS

ne third of all diabetic subjects remain undi-gnosed in the United States.67 The complica-ions of diabetes accrue in these individualsnd, as shown by Koopman et al,67 after adjust-ng for age and diagnosed or undiagnosed hy-ertension, the association between undiag-osed diabetes and nephropathy persists (oddsatio, 2.35; 95% confidence interval, 1.38-4.01).

ith an unprecedented increase in the world-ide incidence of diabetes looming in the ho-

izon, it is very important that multiple ap-roaches to identifying individuals at risk bendertaken to reduce the burden of diabeticomplications. In this review, we have shownhat an array of techniques has been used by theephrology community to map genes for DNusceptibility. Each of these techniques has spe-ific advantages and disadvantages, but the find-ngs require replication to be meaningful. Asescribed earlier, replication may not be per-eptible at first glance and careful secondarynalysis may be necessary to reconcile appar-ntly discrepant results.

Replication will be particularly hard if rareariants cause disease. The refocusing of geneapping efforts from large-scale linkage studies

o large-scale association studies has changedhe paradigm from finding both rare and com-on variants to finding common variants. Dis-

overy of the polycystic kidney disease genesPKD1 and PKD2) was successful through these of traditional linkage mapping and subse-uent fine mapping in families. The allelic spec-rum of mutations in PKD1 and PKD2 is size-ble,68 and it is unclear if association methodsould have been able to find these genes.herefore, the results of a single cohort (eg,IND, EDIC/DCCT, or GoKinD) should be note evaluated in isolation but rather put in con-ext of the type of study design and the powerf the design to find genes for specific elementsf type 1 or type 2 DN. For example, the kidneyraits emphasized in FIND are extreme type 2N phenotypes, with the majority of probands
eing on dialysis or having greater than 1 g

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lbuminuria. However, siblings of FIND pro-ands span the range of albuminuria from noenal disease (�30 �g/mg albumin-to-creatinineatio) to proteinuric patients (�1 g/24 h), withariable duration of diabetes. In contrast, thescertainment strategies of EDIC/DCCT ex-luded subjects with overt albuminuria and ne-hropathy complications have developed over

ollow-up periods of up to 23 years. GoKinDrobands included subjects with type 1 diabe-es and both microalbuminuria and normoalbu-inuria. The FIND design has the potential to

dentify both rare and common disease genes,hereas the power in EDIC/DCCT and GoKinD

s geared toward common disease genes. Con-idering the strengths of the other cohorts,DIC/DCCT and GoKinD have a better repre-entation from the population with less pro-ounced disease, features that are conducive tohe identification of initiation genes. EDIC/CCT also has longitudinal data and may beble to distinguish initiation genes from pro-ression genes using survival analyses–typeethods.With the advent of new SNP technology,

nabling thousands of markers to be genotypedimultaneously, new DN gene mapping initia-ives are in progress. FIND is in the process of

6,000� marker genome-wide SNP linkagecan. Admixture scans in African Americans andexican Americans are also on the agenda for

IND. GoKinD has been accepted for genotyp-ng by a new public-private initiative called Ge-etic Association Information Network (GAIN)http://www.fnih.org/GAIN/GAIN_home.shtml)nd will undergo the genotyping for 1,000,000NP markers. Similarly, the EDIC/DCCT cohortlso is expected to initiate a very dense genomecan in that sample. Therefore, we anticipatehat a vast amount of data will be generatedhrough these larger-scale initiatives in the fu-ure. A comparison of the variety of genetic andolecular data between studies such as FIND,oKinD, and EDIC/DCCT is anticipated to de-

ect genes for DN. The identification of ELMO1nd CNDP1 already has spawned investigationsnto the biology of these genes in an effort toranslate the basic science to clinical medicinend therapeutic approaches. In conclusion, the
rospects for DN genetics are very positive, and
he interplay between these larger initiativesnd other genome-scale projects should help tolucidate the pathobiology of DN.

cknowledgmentshe authors would like to thank Shannon Quade,aula Wedig, and Sarah Ialacci for technical assis-ance with the manuscript.

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