A genome scan for all-cause end-stage renal disease in African Americans

Barry I. Freedman1, Donald W. Bowden1,2,3, Stephen S. Rich4, Christopher J. Valis4, Michèle M. Sale1,2, Pamela J. Hicks2,3 and Carl D. Langefeld4

1 Department of Internal Medicine, 2 Center for Human Genomics, 3 Department of Biochemistry and 4 Department of Public Health Sciences, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC, USA

Correspondence and offprint requests to: Barry I. Freedman, MD, Department of Internal Medicine/Section on Nephrology, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157-1053, USA. Email: bfreedma{at}wfubmc.edu



   Abstract
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Background. In an attempt to map the genes predisposing to the common, complex aetiologies of end-stage renal disease (ESRD), we performed a genome-wide scan in 1023 individuals with chronic kidney disease (946 dialysis dependent and 77 with advanced chronic renal failure) from 483 African American families.

Methods. The study sample comprised 563 ESRD-affected sibling pairs, with nephropathy attributed to diabetes mellitus, chronic glomerular disease or hypertension. Multipoint non-parametric linkage (NPL) analysis methods were employed.

Results. NPL regression provided modest evidence of linkage to 13q33.3 near D13S796 [log of the odds (LOD) = 1.72], 9q34.3 near D9S1826 (LOD = 1.22), 4p15.32 near D4S2639 (LOD = 1.11) and 1q25.1 near D1S1589 (LOD = 1.01). Adjusting for the evidence of linkage at the other loci using NPL regression analysis provided evidence for linkage to 4p15.32, 9q34.3 and 13q33.3. NPL regression interaction and ordered subset analysis (OSA) suggested that the evidence for linkage to ESRD significantly increased with higher body mass index (BMI) at 13q33.3 (LOD = 4.94 in 61% of families with the highest BMI). Additionally, OSA suggested that linkage significantly improved in the 13% of families with earliest age at ESRD onset (LOD = 3.05 at 2q32.1) and in the 16% of families with latest age at ESRD onset (LOD = 2.47 at 10q26.3).

Conclusions. Multipoint single-locus linkage analysis provided modest evidence of linkage to all-cause ESRD in African Americans on 13q33.3, and NPL regression and OSA suggested that evidence for linkage in this region markedly increased in obese families. This region, as well as 9q34.3, 4p15.32 and 1q25.1, should receive priority in the search for loci contributing to ESRD susceptibility in African Americans.

Keywords: African American; diabetic nephropathy; end-stage renal disease; genome scan; hypertensive nephrosclerosis; linkage analysis



   Introduction
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Hypertension, diabetes and chronic glomerular disorders are the leading causes of end-stage renal disease (ESRD) in African Americans, together accounting for >85% of incident ESRD cases requiring renal replacement therapy [1]. Minority populations are disproportionately affected by all major aetiologies of ESRD. African Americans have a 4-fold or greater risk of being diagnosed with hypertensive and diabetic ESRD, relative to European Americans [1]. This disparity is even greater in the southeastern USA.

ESRD is widely held to be a complex genetic trait with significant genetic heterogeneity, and gene–gene and gene–environment interactions. This hypothesis is supported by extensive evidence that ESRD in the African American population has a strong genetic component [2]. African Americans with close relatives having ESRD are at markedly increased risk for developing future ESRD [3–6]. Nearly 25% of African Americans with hypertension-, chronic glomerulonephritis- or diabetes-associated ESRD have first- or second-degree relatives on dialysis [6], and far more have relatives with undiagnosed nephropathy. Risk factors for familial aggregation include African American race, younger age at ESRD onset, level of education, increasing obesity and aetiology of ESRD [6].

African Americans with nephropathy typically present to nephrologists with more advanced disease than whites, making determination of a diagnosis less accurate. Physician bias in ESRD diagnosis is also present [7], and multiple aetiologies of ESRD often co-exist within African American families [4]. Therefore, the search for genes that predispose African Americans to the common aetiologies of ESRD required that we use methods to account for the characteristics of complex genetic traits (e.g. genetic heterogeneity, gene–gene and gene–environment interactions). Herein, we report the results of the largest genome scan to date in African American sibling pairs with ESRD attributed to diabetes, hypertension or chronic glomerular disease (renal limited and systemic), with an emphasis on the potential influences of body mass index (BMI) and age at ESRD onset. Increased BMI [8] and earlier age at ESRD onset [6] are independently associated with an increased familial clustering of ESRD. Therefore, subsetting based upon these phenotypes may identify a unique cohort of families that have a more homogeneous aetiology of renal failure. The analyses identified several priority regions for further study in an effort to map and clone the genes that predispose African Americans to ESRD.



   Methods
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
Subjects
This study was approved by the Institutional Review Board at the Wake Forest University School of Medicine and all participants provided written, informed consent. DNA samples were collected from African American families having multiple siblings concordant for the common aetiologies of ESRD (or advanced chronic renal failure in one sibling). The vast majority of families were identified through the ESRD Network 6 ‘Family History of ESRD’ study (North Carolina, South Carolina and Georgia) [6]. The study population was later broadened to include small numbers of families from the midwestern, northeastern and southern USA. Probands were dialysis patients who self-reported their race as African American and had living siblings with ESRD. The nephrologists caring for these individuals were contacted and medical information was provided in order to determine the aetiology of ESRD. Recruitment strategies and criteria for diagnosis have been reported previously [9,10]. Families with a member having ESRD attributed to polycystic kidney disease, Alport's syndrome, urological disease or surgical nephrectomy were excluded.

The current genome scan contained 1023 African American individuals with advanced chronic renal failure (946 with ESRD and 77 with chronic diabetic nephropathy manifested by a serum creatinine concentration ≥3 mg/dl) from 483 families (563 ESRD concordant sibling pairs). This scan combined families from two prior published genome scans [9,10] and added 53 newly genotyped families. The 53 newly genotyped families contained 110 members with type 2 diabetes-associated ESRD, comprising 61 ESRD sibling pairs. The previous genome scans were performed in 278 type 2 diabetic ESRD and 77 chronic diabetic nephropathy members in 166 families (206 ESRD sibling pairs) [9], and 558 ESRD members in 264 families, each family having an index case with a non-diabetic aetiology of ESRD (296 ESRD sibling pairs) [10].

Genotyping
DNA extraction was performed using the PureGene system (Gentra Systems, Minneapolis, MN). Through the National Institute of Diabetes and Digestive and Kidney Diseases-funded ‘Family Investigation of Nephropathy and Diabetes Study’ (FIND), and the International Type 2 Diabetes Linkage Analysis Consortium, genome-wide scans were completed by the Center for Inherited Disease Research (CIDR). The marker set used was based on combined sets from the two previous genome scans [9,10]. Map distances were based on the Marshfield genetic map. Both scans largely consisted of tetra- and tri-nucleotide repeats, and included 392 primer pairs at an average spacing of 8.8 cm and a maximal inter-marker gap of 18 cm. Since the two scans were completed at different times, the calls for the alleles were not consistent. In order to resolve this apparent inconsistency and combine the marker sets, markers that were common to both scans were placed next to each other with a 0.005 cm gap between them. Each individual was genotyped on one of these ‘two’ markers. Markers that were only in one set as defined by the Marshfield genetic map were placed at their original position.

Each pedigree was examined for consistency of familial relationships using PREST (Pedigree Relationship Statistical Test) [9,10]. When the self-reported familial relationships were inconsistent with that determined from the observed genotypic data for that pedigree, then (i) the pedigree was modified when the identity by descent (IBD) statistics suggested a clear alternative or (ii) a minimal set of genotypic data was converted to missing. A total of 87 pedigrees (18%) exhibited probable incorrect familial relationships and were modified as above, with 98% (85 of 87 families) of these changes being from a full-sibling to half-sibling relationship. Each genetic marker was also examined for Mendelian inconsistencies using PedCheck, and sporadic problem genotypes converted to missing. Allele frequency estimates were computed using the maximum likelihood methods implemented in the software Recode (http://watson.hgen.pitt.edu/register/docs/recode.html).

Linkage analyses
Multipoint linkage analyses were carried out, as previously described in detail [9,10]. In brief, non-parametric linkage (NPL) regression analyses were based on the NPLpairs statistics computed in Genehunter. The NPL regression approach is a conditional logistic regression in which the family-specific NPL statistic (e.g. NPLpairs) at one or more loci is the predictor variable. We also explored the effects of age at ESRD onset and BMI on the evidence for linkage using NPL regression interaction analyses. We limited the interaction and subsetting analyses to age at ESRD onset and BMI since they could be precisely quantified in all members of our families. In contrast, it is notoriously difficult to collect accurate quantitative data on albuminuria or renal biopsy material in the African American population due to their late referral to nephrologists.

If a subset of pedigrees that are phenotypically more homogeneous can be identified, it should be possible to improve the power of linkage analysis. Ordered subset analysis (OSA) is a methodology developed to address this possibility. OSAs were computed to investigate the influence of a pedigree's mean age at diagnosis of ESRD and mean BMI (similar to the NPL regression analysis above). NPL regression, NPL regression interaction and OSA have been applied previously to related studies for linkage to type 2 diabetes mellitus and ESRD [9,10].



   Results
 Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
The genome scan was conducted on 483 African American pedigrees containing 563 ESRD-affected sibling pairs (460 full-sibs and 103 half-sibs, total 1023 affected individuals). Four hundred and forty-eight families contained two affected siblings, 31 families contained three affected siblings, and four families contained four affected siblings. Family data consisted primarily of individuals from a single generation, with both parents available in none of the families and one ESRD-affected parent available in 18 families. Based on the phenotypic information received, a single investigator (B.I.F.) coded 290 participants with ESRD as likely to have hypertensive ESRD, 125 with glomerular disease-associated ESRD and 608 with diabetes-associated ESRD. Sixty participants had renal biopsy information available for review.

The genotyped population was 56% female, mean±SD (median) age at enrolment was 56.0±12.2 (56.0) years (range 19–99), age at diagnosis of ESRD was 51.5±13.5 (52.0) years (range 15–85) and mean BMI at enrolment was 29.3±7.4 (28.4) kg/m2 (range 15.2–64.8).

Linkage analysis results
Multipoint single-locus linkage analysis provided modest evidence of linkage to ESRD (Table 1; ASM analysis). The strongest evidence for linkage was to 13q33.3 near D13S796 [93.4 cM, log of the odds (LOD) = 1.72]. Three other chromosomal regions exhibited a LOD score >1: 9q34.3 near D9S1826 (159.5 cM, LOD = 1.22), 4p15.32 near D4S2639 (32.9 cM, LOD = 1.11) and 1q25.1 near D1S1589 (192.4 cM, LOD = 1.01).


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Table 1. Results of the genome-wide non-parametric linkage analyses of all-cause ESRD in African American families

 
As with all complex genetic traits, it is expected that multiple loci will probably contribute to susceptibility to ESRD. As such, analytical methods that account for genetic heterogeneity may improve the ability to detect linkage. The results of the multilocus NPL analysis provided evidence that ESRD is linked to three chromosomal regions (Table 1). Specifically considering these three loci in one model jointly and computing the corresponding 1 degree of freedom test, the evidence for linkage was greatest at 13q33.3 near D13S796 (LOD = 1.59) and 9q34.3 near D9S1826 (LOD = 1.24). Evidence for another locus at 4p15.32 near D4S2639 (LOD = 1.11) increased. After adjusting for the evidence for linkage at these three loci, no other region of the genome provided significant evidence for linkage. The evidence for linkage at any one of these loci (chromosomes 13, 9 and 4) did not appear to be influenced by the evidence for linkage at the remaining two loci, i.e. there was no evidence of an epistatic relationship among these loci.

NPL regression analysis: interaction with phenotypic traits
In addition to potential genetic heterogeneity, it is possible that other phenotypic characteristics of the individual influence the effect of a predisposing gene. To test whether age at onset of ESRD or BMI might influence the evidence for linkage, a series of locus x trait interaction analyses were computed using NPL regression analysis. The results of the NPL regression locus-specific interaction analyses are summarized in Table 2. Here the regions showing evidence of a statistical interaction with the specific trait are listed with the mean trait values for those families that link vs those families that do not link to the region. The Pearson's correlation coefficient summarizes the correlation between the evidence for linkage within a family and the mean trait value.


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Table 2. Results of NPL regression interaction analyses across the genome for age at ESRD diagnosis and BMI in African American ESRD families

 
Age at diagnosis of ESRD
The evidence for linkage tended to be split between three loci where linked pedigrees had an earlier mean age at ESRD onset (13q13.1, 14q21.1 and 1q41) and six loci where linked pedigrees had a later mean age at ESRD onset (2p24.2, 4q13.1, 6q16.1, 7p21.1, 12p11.23 and 19q13.43) (Table 2).

Body mass index
The evidence for linkage varied by BMI at 10 primary loci; three loci were linked in pedigrees with lower BMI and seven in pedigrees with higher BMI (Table 2).

Ordered subsets analysis with phenotypic traits
OSA is an analytical approach designed to test for linkage by attempting to identify the subset of families that maximize the evidence for linkage. In contrast to the NPL regression interaction analysis that tests for an interaction in the entire set of data, OSA tests for differences in the evidence for linkage based on the subset of pedigrees. Thus, the two approaches attempt to test the same hypothesis by using fundamentally different but complementary approaches (i.e. stratification for OSA and regression models for NPL regression). The OSA approach will be statistically more powerful if there are distinct subpopulations identified through the subsets of the covariate that are genetically more homogeneous, and the NPL regression approach should be more powerful if these ad hoc subsets are not genetically more homogeneous. Regions displaying a significant change in the chromosome-specific P-value for the change in the LOD score ({Delta}P<0.05) are shown in Table 3.


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Table 3. Ordered subsets analysis (OSA) across the genome for age at ESRD diagnosis and BMI in African American ESRD families

 
Age at diagnosis of ESRD
The OSA provided supporting evidence that the age at ESRD onset tended to influence the evidence for linkage (Table 3). Subsetting on the 13% of the pedigrees with the earliest mean age at ESRD significantly increased the evidence for linkage to 2q32.1 near D2S1391 (P-value for the change in the LOD score {Delta}P = 0.0168, LOD = 3.05). The mean age at ESRD in linked families was 30.4±4.3 vs 54.6±10.1 years in the remaining families. Subsetting on the 16% of families with the latest mean age at ESRD onset significantly increased the evidence for linkage to 10q26.3 near D10S1248 ({Delta}P = 0.0486, LOD = 2.47). Only the 10q locus exhibited modest evidence for linkage in the entire sample (Table 1). The mean age at ESRD in linked families was 70.0±4.9 vs 47.9±10.2 years in the remaining families.

Body mass index
The subset of pedigrees with the highest mean BMI tended to provide the greatest evidence for linkage. Subsetting for the pedigrees with the largest BMI increased the LOD score to 4.94 at 13p33.3 near D13S796 ({Delta}P = 0.0024) in 61% of the pedigrees and to 5.72 at 6q25.3 near D6S1035 ({Delta}P = 0.0036) in 15% of the pedigrees. It is noteworthy that both of these regions provided only modest evidence of linkage in the entire sample (Table 1). The mean BMI in families linked to the chromosome 13 locus was 32.6±4.6 vs 23.4±2.2 kg/m2 in the remaining families.



   Discussion
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 
This investigation represents the largest effort to date to map the chromosomal locations of genes specifically contributing to the common causes of ESRD in the African American population. It is likely that African Americans reported as having hypertensive, diabetic or chronic glomerular disease-associated ESRD comprised a heterogeneous grouping of patients. We postulated that susceptibility genes for the common, complex aetiologies of ESRD exist and that a family-based genome scan could identify homogeneous patient subsets with similar genetic susceptibility. Proceeding from the assumption that ESRD was genetically complex with genetic and environmental components, we incorporated relatively novel approaches for evaluating multigenic and phenotypic influences (i.e. NPL regression multilocus modelling and OSA).

Only limited evidence of linkage was evident at the first stage of analysis (Table 1) with four LOD scores ≥1.0. The highest LOD score was 1.72 on chromosome 13p33.3 at 93.4 cM. The support (LOD-1) region for this linkage peak at 78.9–107.9 cM contains 54 known and predicted genes (Build 34.3), with plausible candidates including the genes for ephrin-B2 (EFNB2), and collagens {alpha}1 (IV) (COL4A1) and {alpha}2 (IV) (COL4A2). Ephrin-B2 is involved in glomerular vasculogenesis and maintenance of glomerular structures [11]. Type IV collagen is a major component of glomerular basement membranes, and increased collagen synthesis is observed in diabetic renal microangiopathy [12]. The human homologues of the fawn hooded rat (FHR) Rf-1 and the Rf-4 nephropathy genes also reside within the chromosome 10q and 1q peaks, respectively, that we identified (Dr Howard Jacob, personal communication). The FHR is a rodent model of human hypertensive nephrosclerosis/focal segmental glomerulosclerosis.

In the multilocus analysis, incorporating an evaluation of heterogeneity, three chromosomal regions showed evidence of significant interaction in the multilocus models, with the strongest evidence (LOD = 1.59) on 13q33.3. When analytical approaches that incorporate phenotypic trait data were applied, as summarized in Tables 2 and 3, evidence for multiple chromosomal loci contributing to ESRD susceptibility was revealed. Using the OSA approach to subset families based on BMI and age at ESRD onset, we identified subsets which in some cases showed dramatic increases in LOD scores, compared with the entire family set. The chromosome 13 locus at 90.87 cm for high BMI was identified using two complementary analytical methods (NPL and OSA), providing strong evidence for linkage in 61% of all pedigrees. Subsetting based on BMI was performed because there is a stronger familial clustering of ESRD in individuals with high BMI [8] and the impact of birthweight on adult BMI, risk for diabetes and renal disease [13]. Subsetting based upon age at ESRD onset was performed since there is a stronger familial aggregation of early-onset ESRD [6]. Subsetting by age at onset in complex diseases such as ESRD implies that genes have an age-dependent penetrance function. Some of these genes may be ‘turned on’ earlier or others may have a later effect, especially if they interact with environmental factors.

The NPL regression interaction analysis is based on the entire collection of pedigrees, while the OSA attempts to find the subset of pedigrees that maximize the evidence for linkage. Thus, if the evidence for linkage is in a relatively small proportion of the pedigrees (e.g. <15%) and correlates with a phenotype such as age at ESRD onset, OSA probably will have more statistical power. However, if the evidence for linkage is not restricted to a small proportion of pedigrees, the NPL regression interaction analyses will probably have more power. The fact that the NPL regression analyses identify more loci is likely to be due to the increased statistical power when using the entire sample and a locus-specific P-value; OSA reports a chromosome-wide P-value.

We previously have performed linkage analyses in subsets of these African American families containing multiple siblings concordant for type 2 diabetes mellitus-associated ESRD [9] and those having an index case with non-diabetic ESRD (typically hypertensive or chronic glomerular disease-associated ESRD) [10]. Analyses in the diabetes-concordant sib pairs with ESRD demonstrated suggestive evidence for linkage between markers on chromosomes 3q, 7p, 10q and 18q that is broadly consistent with several prior reports of linkage between diabetic nephropathy and these regions [14–17]. Analyses in the non-diabetic ESRD sib pairs demonstrated the strongest evidence for linkage between markers on chromosomes 9q, 1q and 13q. As was the case in the current analyses, the evidence for linkage in African American families with non-diabetic ESRD index cases on 13q was improved in the subset of families with higher BMI. Despite the increased power afforded by the larger number of families in the current report, the LOD scores identified using the NPL regression analysis and OSA were of similar magnitude to those observed in the smaller genome scans. The identification of the 1q and 13q peaks in the ‘non-diabetic ESRD’ genome scan [10], and in the current ‘all-cause ESRD’ genome scan, support the existence of true ESRD susceptibility genes in these regions. As expected in complex traits, there would be some concordant loci and some discordant loci between genome scans. This pattern of partial concordance in the same sample can be explained by both genetic heterogeneity and type I error.

Herein, we report the results of linkage analyses in the largest number of African American families with multiple ESRD members examined to date. Modest evidence for linkage was detected on chromosomes 13q33.3, 9q34.3, 4p15.32 and 1q25.2 in the overall family set. The locus on chromosome 13q33.3 demonstrated consistent and robust evidence for linkage in families with high BMI (using OSA and NPL regression interaction analyses). These regions will receive priority in our search for the genes that predispose to ESRD in African Americans. As with any linkage analysis of this type, the results will require replication in independent populations. The FIND is recruiting large numbers of families with diabetic nephropathy members [18]. The FIND also recruited unrelated African Americans with diabetic and non-diabetic forms of ESRD for mapping by admixture linkage disequilibrium (MALD) analyses. These MALD analyses contain cases with aetiologies of non-diabetic ESRD similar to those in our genome scan and will provide the opportunity to replicate our results using a complementary analytical technique.

The ultimate proof that an ESRD gene lies in the regions that we detected will require actual gene identification. We have been successful in identifying a gene associated with type 2 diabetes using genome scan techniques as applied in this report. After an initial linkage analysis [19], intensive single nucleotide polymorphism (SNP) association analyses were performed in plausible candidate genes residing in the LOD-1 interval of the major linkage peak. This detected strong association between type 2 diabetes mellitus and the protein tyrosine phosphatase 1B (PTP1B) gene in European Americans [20]. High density SNP arrays and fine mapping of the loci linked with all-cause ESRD will now be performed to refine these peaks and identify potentially causative genes. Our ultimate goal is to replicate the successful approach of SNP haplotype mapping of candidate genes, along with determination of population attributable risk, as was done with the PTP1B gene in type 2 diabetes mellitus [20]. Identification of the genes that produce ESRD susceptibility may help to explain the excessive incidence rates of kidney disease identified in the African American population and lead to novel therapies for the prevention of progressive renal failure.



   Acknowledgments
 
We wish to thank the patients, their relatives, the physicians and the staff of the Southeastern Kidney Council, Inc./ESRD Network 6 for their participation, and Drs Nancy Cox and Robert Elston for their support. This work was supported by grants R01 HL56266 (B.I.F.) and R01 DK53591 (D.W.B.). Genotyping services were provided by the Center for Inherited Disease Research (CIDR) through the International Type 2 Diabetes Linkage Analysis and the Family Investigation in Nephropathy and Diabetes (FIND) Consortia. CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University; contract number N01-HG-65403.

Conflict of interest statement. None declared.



   References
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 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 

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Received for publication: 6.10.04
Accepted in revised form: 5. 1.05