Progressive Loss of Renal Function Is an Age-Dependent Heritable Trait in Type 1 Autosomal Dominant Polycystic Kidney Disease
Andrew D. Paterson*,
Riccardo Magistroni,,
Ning He,
Kairong Wang,
Ann Johnson,
Pamela R. Fain,
Elizabeth Dicks||,
Patrick Parfrey||,
Peter St. George-Hyslop and
York Pei
* Program in Genetics and Genomic Biology, Hospital for Sick Children and Department of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada; Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada; Division of Nephrology, University of Modena and Reggio Emilia, Modena, Italy; Division of Renal Diseases and Hypertension, University of Colorado Health Sciences Center, Denver, Colorado; and || Division of Nephrology, Memorial University, St. John’s, Newfoundland, Canada
Address correspondence to: Dr. York Pei, Division of Nephrology, University Health Network, 13 EN-228, 200 Elizabeth Street, Toronto, Ontario, Canada M5G 2C4. Phone: 416-340-4257; Fax: 416-340-4999; E-mail: york.pei{at}uhn.on.ca
Significant intrafamilial phenotypic variability is well documentedin autosomal dominant polycystic kidney disease (ADPKD) andsuggests a modifier effect. In this study, variance componentsanalysis was performed to estimate the contribution of geneticfactors for within-family renal disease variability in 406 patientsfrom 66 type 1 ADPKD families. Overall, 39% of the study patientshad ESRD at their last follow-up, and their renal survival didnot differ by gender (P = 0.35, log-rank test). Because theirfrequency plot of creatinine clearance (Ccr) assumed a bimodaldistribution with a marked kurtosis that was not improved bytransformations, the study cohort was decomposed into two separategroups (non-ESRD [n = 247] and ESRD [n = 159]) in which theCcr plots were normally distributed. The heritability (h2) ofCcr and age at ESRD (ageESRD) and the genetic correlations betweenthese measures and their covariates were estimated. In patientswithout ESRD, a significant heritability was found for Ccr (h2= 0.42; P = 0.0015) after adjusting for age (P = 0.0001), systolicBP (P = 0.0006), and treatment with angiotensin-converting enzymeinhibitor/angiotensin receptor blocker (P = 0.00001). Birthyear, gender, BMI, diastolic and mean BP, and pack-years ofcigarette smoking did not significantly influence the heritabilityof this trait. In patients with ESRD, ageESRD provides a bettermeasure than Ccr, which was very narrowly distributed. A significantheritability was found for ageESRD (h2 = 0.78; P = 0.00009)in these latter patients. None of the above covariates influencedthe heritability of this trait. It is concluded that a significantmodifier gene effect influences the progression of renal diseasein type 1 ADPKD.
Autosomal dominant polycystic kidney disease (ADPKD) (MIM 173900)is the most common hereditary kidney disorder with an incidenceof approximately 1/1000 live births and accounts for approximately5 to 8% of ESRD (1,2). It is characterized by progressive formationand enlargement of renal cysts, typically leading to chronicrenal failure by late middle age. Other manifestations of thisdisorder, such as cyst formation in nonrenal organs, cardiacvalvular defects, colonic diverticulosis, and intracranial arterialaneurysms, accompany the renal disease variably. Two genes (PKD1[MIM 601313] and PKD2 [MIM 173910]) have been identified andrespectively account for the disease in approximately 80 to85% and approximately 10 to 15% of families in the white population(3,4). Polycystin 1 and 2, the gene products of PKD1 and PKD2,are transmembrane proteins that are thought to be componentsof a novel signaling pathway that regulates intracellular calcium(2,4). Polycystin 1 is predicted to have a receptor-like structureand may be involved in cell–cell and/or cell–matrixinteraction (4). By contrast, polycystin 2 is thought to functionas a cation ion channel subunit with nonselective permeability(4). Both proteins have been shown to interact in vitro throughtheir cytoplasmic region (2,4) and transmit fluid flow–mediatedmechanosensation by the primary cilium in renal epithelium (4,5).Disruption of the function of polycystin 1 or 2 may cause ADPKDowing to the inability of the tubular epithelial cells to sensemechanical cues that normally regulate tissue morphogenesis(4,5).
Disease progression of ADPKD is highly variable, with the ageat onset of ESRD ranging from childhood to old age (1,2). Genelocus effect is a major determinant for interfamilial diseasevariability: patients from PKD1-linked families have a muchearlier onset of ESRD than patients from PKD2-linked families(median age, 53 [95% confidence interval (CI), 51.2 to 54.8]vs. 69 [95% CI, 66.9 to 71.3] years) (6,7). A gender effecton renal survival (i.e., absence of ESRD) favoring the femalepatients is also evident in type 2 but in not type 1 ADPKD (7–9).In addition, allelic heterogeneity may have a weak effect onrenal disease progression in type 1 (8) but not type 2 ADPKD(9). However, significant intrafamilial renal disease variabilityhas been well documented in both type 1 and 2 ADPKD (9–11).These latter findings suggest that renal disease progressionin ADPKD may be modified by genetic, environmental, and stochasticfactors independent of the germline PKD mutations. Nevertheless,the magnitude of the genetic effects for within-family renaldisease variability in ADPKD is not currently known. In thepresent study, we performed variance components analysis toquantify the contribution of genetic factors for within-familyrenal disease variability in type 1 ADPKD. In addition, we estimatedthe sample size required for both candidate gene associationand genome-wide linkage studies as a first step toward the mappingof genetic modifiers for type 1 ADPKD.
Study Patients
We studied 66 multiplex type 1 ADPKD families from Toronto,Newfoundland, and Denver consisting of 554 affected and 317unaffected family members. In each family, we had detailed informationon the pedigree structure over at least three generations. Inaddition, we genotyped these families with at least three polymorphicsimple-sequence repeat markers at each of the PKD1 and PKD2loci. An autosomal dominant model from a single locus with adisease allele frequency of 0.01 was assumed. Two-point linkageanalysis was performed using the MLINK program of the FASTLINKpackage. Most of these families were found to have significantevidence for PKD1 linkage (Table 1). In all of the smaller families,inspection of the PKD1 and PKD2 haplotypes showed that the diseasewas inconsistent with PKD2 linkage. The assignment of diseasestatus in the at-risk individuals was based on both genotypedata and ultrasonographic findings using age-dependent criteria(12). The study cohort used for the quantitative genetic analysisconsisted of 406 affected individuals in whom demographic, clinical,and laboratory data were available. Eighty-one percent (120of 148) of the affected individuals who were excluded were bornbefore 1930. The research protocols for the study were approvedby the Institutional Review Boards of the participating centers.
Table 1. Genetic linkage data for the study familiesa
Clinical Assessment
We collected in the study patients their age, gender, height,weight, systolic (SBP) and diastolic BP (DBP), and antihypertensivetreatment including treatment with an angiotensin-convertingenzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB).In addition, we assessed their cumulative exposure (pack-years)to cigarette smoking. Serum creatinine was measured using standardTechnicon methods on a SMAC II analyzer (Technicon, Tarrytown,NY) with similar reference ranges in all three centers. Twoprimary renal disease outcomes were used in this study. Creatinineclearance (Ccr) was calculated on the basis of age, gender,body weight, and serum creatinine using the formula of Cockroftand Gault (13) and normalized to 1.73 m2 body surface area.Age of onset at ESRD (ageESRD) was defined as the age at initiationof renal replacement therapy (chronic dialysis or renal transplantation).Any patient with concomitant renal disease (e.g., diabetic nephropathy)was excluded from this study.
Statistical Analyses
Continuous variables are expressed as mean ± SD, andcategorical data are expressed as proportions. Time from birthto ESRD (renal death) was computed by the product-limit methodof survival analysis using the SAS statistical package v8 (Carey,NC). To assess the effects of gender on renal survival (freedomfrom ESRD), we used a two-sided log-rank test (14). Heritability(h2) in this study is broadly defined as the proportion of totalphenotypic variability of a trait that is attributable to geneticeffects (15). We estimated the heritability of Ccr and ageESRDusing a pedigree-based variance components approach (15–17).This novel approach of quantitative genetic analysis allowsfor the integration of phenotype and genotype data from multipleaffected relative pairs from large extended pedigrees and ismore powerful than the traditional sibling pair approach (15,17).With this approach, a quantitative trait is assumed to havea multivariate normal distribution and the relative componentsof the variance are estimated using maximum-likelihood analysis.The total phenotypic variability of the study trait is partitionedinto three components: (1) an additive genetic variance, asa result of the sum of the average effects of all of the genesthat influence the trait variance; (2) a shared environmentalvariance, caused by the effects of environmental factors commonto the households; and (3) a random environmental variance thatis specific for each individual. The random environmental variancealso absorbs nonadditive genetic effects, such as interactionsbetween alleles within loci (dominance effects), interactionsbetween alleles at different loci (epistatic effects), and effectscaused by gene–environment interactions. Thus, in general,the above models provide an underestimation of the genetic contributionto the trait variance (16,17). For ageESRD, we also fitted aCox proportional hazard model and derived Martingale residualsfrom the Cox regression to be used as a quantitative trait inthe variance components analysis. All of the affected individualswithin a pedigree were connected through their common ancestors,and the phenotypes of any affected and unaffected individualswho were not analyzed in the study were coded as unknown. Wealso corrected for ascertainment bias by conditioning on thelikelihood of observing the trait value of the proband in eachfamily. All of the covariates screened with a P < 0.1 wereforced in the model. The statistical genetic analyses were performedusing the computer software SOLAR v2.1.2 (15).
Clinical Characteristics of Study Patients
The clinical characteristics of our study patients are shownin Table 2. Of the entire patient cohort, 47% (190 of 406) weremale and 68% (276 of 406) had hypertension at the last follow-up.Among the hypertensive patients, 50% (138 of 276) of them weretreated with an ACEI or ARB. The mean BP at the last follow-updid not differ significantly between our male and female patients.Overall, approximately 36% (144 of 406) of our patients were(current or ex-) cigarette smokers, and there was a trend formore male smokers (two-tailed P = 0.098 by Fisher exact test).The cumulative dose of cigarette smoking for the entire cohortwas 6.6 ± 13 pack-years and for the smokers was 18 ±6 pack-years.
Table 2. Clinical characteristics of study patientsa
At the last follow-up, the Ccr of the study patients was 55± 43 ml/min per 1.73 m2, and 39% (159 of 406) of themhad ESRD. The patients who developed ESRD on average were 12yr older than those without ESRD. The median age at onset ofESRD in the male and female patients were 54 (95% CI, 52 to56) and 53 (95% CI, 51 to 57) years, respectively. We foundthat the probability of renal survival did not differ by genderin the study patients (2 = 0.88, P = 0.35 by log-rank test;Figure 1). These findings are consistent with the results oftwo large recent studies showing an absence of gender effecton the renal survival of patients with type 1 ADPKD (7,8). Wealso found that the frequency plot of Ccr values in our studypatients fit a bimodal distribution, with the two modes reflectingpatients with and without ESRD (Figure 2).
Figure 1. Renal survival (absence of ESRD) analysis of study cohort. The probability of renal survival did not differ between male (black line; n = 190) and female (gray line; n = 216) patients in our PKD1 study cohort (2 = 0.88; P = 0.35 by log-rank test). The median age at onset of ESRD in our male and female patients were 54 years (95% confidence interval [CI], 52 to 56) and 53 years (95% CI, 51 to 57), respectively. Each circle denotes a censored event.
Figure 2. Frequency plot of PKD1 study patients (n = 406) showing a bimodal distribution of their creatinine clearance (Ccr).
Heritability Estimates for Renal Disease Progression
We found a high heritability for Ccr (age- and gender-adjustedh2 = 0.69, SE = 0.13; P = 8.1 x 109) in our study cohort (n= 406). However, as we noted above, the frequency plot of Ccrdeviated significantly from the expected normality (Figure 2).In an alternative approach, we derived Martingale residualsfrom a Cox regression model for ageESRD. Although we also founda significant heritability for the Martingale residuals of ageESRD(age- and gender-adjusted h2 = 0.38, SE = 0.08; P = 1.1 x 10–8),there was marked kurtosis in this outcome as well. We attemptedvarious transformations on these data sets, which generallyimproved the skewness but not kurtosis. By decomposing our studypatients into two nonoverlapping groups with (n = 159) and withoutESRD (n = 247), we noted that the frequency plot for Ccr wasnormally distributed within these patient groups (Figure 3).We therefore performed the heritability analysis separatelyin these patient groups.
Figure 3. Frequency plots of Ccr in the PKD1 study patients with ESRD (right; n = 159) and without ESRD (left; n = 247). Both plots now assume a normal distribution. However, the Ccr values of patients with ESRD are distributed over a very narrow range (90% of the Ccr are between 6 and 14 ml/min per 1.73 m2).
Heritability Analysis in Patients without ESRD
We examined Ccr as a quantitative trait and performed univariateanalysis to test for statistical significance of the followingcovariates: age, birth year, gender, BMI, SBP, DBP, mean BP,ACEI/ARB treatment, and pack-years of cigarette smoking. Wefound that age (P = 0.0001), SBP (P = 0.0006), and ACEI/ARBtreatment (P = 0.00001) were significant covariates for thistrait. Birth year, gender, BMI, DBP, mean BP, and pack-yearsof cigarette smoking were nonsignificant covariates for thistrait. The best model that is maximized on these covariatesyields a heritability estimate for Ccr of 0.42 (SE = 0.16; P= 0.0015). Among the significant covariates, age seemed to bemost important and accounts for 41% of the variance. Age andSBP in this analysis are collinear such that when both variableswere included in the same model, the effect of SBP became nonsignificant.In addition, SBP in this patient cohort is a heritable trait(h2 = 0.25, SE = 0.10; P = 0.0019), with age being the mostsignificant covariate accounting for 12% of the variance (P= 1.2 x 10–8).
Heritability Analysis in Patients with ESRD
Although the Ccr value in this patient group was also a normallydistributed trait, its range (and hence variance) was distributedover a very narrow scale (Figure 3, right). In addition, theCcr estimations at this level of renal dysfunction are imprecise.These two factors, taken together, make the heritability estimateof this trait unreliable. By contrast, the frequency plot ofageESRD is normally distributed over a wide scale (Figure 4)and therefore is a better measure for the heritability analysis.We found a high heritability for ageESRD (h2 = 0.78, SE = 0.24;P = 0.00009) in this patient group. However, none of the abovecovariates was found to contribute significantly to this trait.
Figure 4. Frequency plot of PKD1 study patients showing a normal distribution of the age at ESRD (n = 159). Compared with the right panel of Figure 3, the age at ESRD displays a much wider range of variability than Ccr in the same patient group.
Using two measures of renal disease severity (Ccr and ageESRD)in patients without and with ESRD, we found heritability estimatesof 42 and 78%, respectively. Thus, the most definitive conclusionof our study is that there is a significant modifier gene effectthat influences the within-family renal disease severity intype 1 ADPKD. However, depending on the underlying architectureof the genetic modifiers for these traits, there are at leasttwo possible explanations for these results. First, it is possiblethat the heritability estimates for Ccr and ageESRD did notreally differ. Rather, their apparent difference was relatedto the large variances of the estimates because of the modestsample size of our study. In this case, increasing the patientsample size should improve both the precision and the accuracyof these estimates. The implicit assumption of this scenariois that the same genetic modifiers play a constant role at bothearly and late stages of renal disease progression. Alternatively,because our patients without ESRD are on average 12 yr youngerthan the patients with ESRD, the difference of these heritabilityestimates might be real, reflecting the effects of distinctgenetic modifiers at two different stages of the renal disease.
For example, it is possible that microcyst formation as a resultof biallelic PKD1 inactivation within individual renal epithelialcells (the "two-hit" model of ADPKD) may be the predominantpathogenetic event at the early stage of ADPKD, which is notassociated with significant renal structural and functionalalterations (18–21). By contrast, growth and expansionof macrocysts at a later stage of ADPKD can result in renaltubular compression and obstruction (21–24) and activationof genes for inflammation and apoptosis (23,25). These laterevents can lead to significant tubulointerstitial inflammation(21) and the formation of "atubular glomeruli" (26), mediatingthe loss of renal parenchyma and function.
It is interesting to note that SBP and ACEI/ARB treatment weresignificant covariates for the heritability estimate of Ccrbut not ageESRD. The high prevalence of hypertension (approximately80%) and ACEI/ARB treatment (approximately 60%) in our cohortof patients with ESRD might have confounded the detection ofa covariate effect in these qualitative clinical parameters.Alternatively, it is possible that these two clinical risk factorsmay not be important in the late stages of renal disease progression.Previous studies have shown that the renin-angiotensin system(RAS) is activated in the early stage of ADPKD and may modulatethe development of systemic hypertension (27,28). In addition,a recent study has documented both the existence and the activationof an intrarenal RAS within the cystic tissue of patients, suggestingthat angiotensin II may also modulate renal cyst growth in ADPKD(29). Taken together, these findings suggest that systemic orintrarenal activation of the RAS may modulate renal diseaseprogression in ADPKD. Although the clinical effect of antagonismof the RAS in human ADPKD is presently inconclusive (30), ourfindings suggest that genes encoding the RAS are strong biologiccandidates for genetic modifiers in ADPKD.
Recent studies suggest that the variability of a complex traitdisease is due to the effects of multiple genes acting in concertwith environmental factors. Moreover, common modifier gene variantswith modest effects contribute to the overall heritability ofthe disease trait (31–33). Using simulation, we performedpower calculations to estimate the patient resource requiredfor family-based association studies using a candidate geneapproach (testing = 0). For a candidate gene with a diseaseallele frequency of 20 to 40%, approximately 600 trios (parentsand affected offspring) are sufficient to detect a dominantlocus with moderately large effect (10 to 15% of the trait variance;Figure 5, top). For a recessive locus, the same sample sizewill be underpowered unless the disease allele frequency isin the 50% range (Figure 5, bottom). Otherwise, approximately1000 trios are required to detect a recessive locus with similareffect size (Figure 6, bottom). To detect a modifier locus usinga genome-wide linkage approach with an intermarker density ofapproximately 5 cM (i.e., with approximately 400 microsatellitemarkers), at least five times the above sample sizes are requiredto ensure proper power. These estimates therefore suggest thatthe mapping of modifier genes in ADPKD will be challenging andwill require international collaboration from multiple researchcenters. A corollary of these findings is that all of the geneticassociation studies in ADPKD published to date are grossly underpoweredto detect a modestly strong modifier locus (35–40).
Figure 5. Power calculations for candidate gene association studies using the statistical package for family-based association tests (PBAT) (34). The data were derived from simulations of 1000 replicate samples, each containing 600 trios (two parents and one affected offspring), under random mating. A quantitative trait was simulated with a polygenic effect, a random environmental effect, and a major gene (the trait locus) effect for each individual. Heterozygosity of parental genotypes was not assumed. This method of simulation is robust for nonnormality of data. h2 refers to the heritability of the trait accounted by the candidate gene. Two-tailed of 0.01 was assumed.
Figure 6. Power calculations for candidate gene association studies using PBAT (34). The data were derived from simulations of 1000 replicate samples, each containing 1000 trios, under random mating. A quantitative trait was simulated with a polygenic effect, a random environmental effect, and a major gene (the trait locus) effect for each individual. Heterozygosity of parental genotypes was not assumed. This method of simulation is robust for non-normality of data. h2 refers to the heritability of the trait accounted by the candidate gene. Two-tailed of 0.01 was assumed.
Finally, our study suggests that environmental factors may alsoplay an important role in modifying renal disease progressionin ADPKD. Recent studies suggest that somatic PKD1 mutationswithin individual renal epithelial cells constitute a majormechanism for cystogenesis (18–20). Thus, the burden ofsomatic mutations is expected to influence the total cyst numberwithin the kidney. In this regard, cigarette smoking may beparticularly relevant among the potential environmental modifiersfor ADPKD. Although we did not find that cigarette smoking wasa significant covariate in our heritability analyses, our studyis likely underpowered to detect such an effect given that only35% of our patients were smokers with light to moderate exposure.Future study with a larger patient sample size and heavier exposureto cigarette smoking is needed to resolve this issue.
Acknowledgments
This study was supported by a Canada Research Chair in Geneticsof Complex Diseases (A.D.P.); grants from the Polycystic KidneyResearch Foundation, Canadian Institutes of Health Research(MOP53324, and Kidney Foundation of Canada (Y.P.); Departmentof Health and Human Services, Public Health Service, and GeneralClinical Research Centers Program of the Division of ResearchResources, National Institutes of Health, (A.J. and P.F.); andCanadian Institutes of Health Research (R.P.P.) DistinguishedScientist Research Award (P.P.).
We are indebted to all of the participating members of the ADPKDfamilies.
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Received for publication September 11, 2004.
Accepted for publication December 9, 2004.
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Abstract
Introduction
2 = 0.88, P = 0.35 by log-rank test; Figure 1). These findings are consistent with the results of two large recent studies showing an absence of gender effect on the renal survival of patients with type 1 ADPKD (7,8). We also found that the frequency plot of Ccr values in our study patients fit a bimodal distribution, with the two modes reflecting patients with and without ESRD (Figure 2). 
= 0). For a candidate gene with a disease allele frequency of 20 to 40%, approximately 600 trios (parents and affected offspring) are sufficient to detect a dominant locus with moderately large effect (10 to 15% of the trait variance; Figure 5, top). For a recessive locus, the same sample size will be underpowered unless the disease allele frequency is in the 50% range (Figure 5, bottom). Otherwise, approximately 1000 trios are required to detect a recessive locus with similar effect size (Figure 6, bottom). To detect a modifier locus using a genome-wide linkage approach with an intermarker density of approximately 5 cM (i.e., with approximately 400 microsatellite markers), at least five times the above sample sizes are required to ensure proper power. These estimates therefore suggest that the mapping of modifier genes in ADPKD will be challenging and will require international collaboration from multiple research centers. A corollary of these findings is that all of the genetic association studies in ADPKD published to date are grossly underpowered to detect a modestly strong modifier locus (35–40). 
of 0.01 was assumed.