Background
The podocyte is a fundamental unit of the kidney as it covers the glomerular basement membrane. The podocin gene encodes podocin, which has a hairpin-like structure and is an essential component of the slit diaphragm, playing an important role in glomerular filtration.(1) Podocin has also been shown to bind with cholesterol in lipid rafts where it may function as a scaffolding or signalling protein.(2)
Genome-wide association studies (GWAS) are an effective method of investigating the genetic background of complex diseases. Using GWAS, attempts are made to identify genetic variants that occur frequently and contribute to the genetic risk of specific diseases. A pooled GWAS study conducted in African Americans with the aim of validating the association of apolipoprotein L1 (APOL1) risk variants with non-diabetic nephropathy and identifying susceptibility loci that could interact with or act separately of APOL1 identified single-nucleotide polymorphism (SNP) rs16854341 in the podocin (NPHS2) gene as having significant interactions with APOL1.(3) To validate the results of this GWAS, a case–control study was conducted where rs16854341 was again found to significantly interact with APOL1 G1/G2 risk variants in African Americans with non-diabetic end-stage kidney disease (ESKD) compared to controls (interaction odds ratio (OR) = 0.6; p = 8.0 × 10−4).(4) Other significantly associated SNPs interacting with APOL1 G1/G2 included rs2802723 in the serologically defined colon cancer antigen 8 gene (SDCCAG8; interaction OR = 1.8; p = 5 × 10−4) and rs8014363 located near bone morphogenic protein 4 (BMP4; interaction OR = 1.6; p = 1 × 10−3). (4) However, before general acceptance of results from GWAS studies, replication and validation of the effect in the GWAS target population have to be done, after which sampling from other populations must occur to determine the effect of the SNP in different racial/ethnic groups.
The aim of the study was to determine the association of polymorphisms in the NPHS2, BMP4 and SDCCAG8 genes in patients with kidney disease and to test the interaction of these polymorphisms with APOL1 risk variants in black South Africans with hypertension-attributed chronic kidney disease (CKD).
Subjects and Methods
This case–control study was carried out at the Chris Hani Baragwanath Academic Hospital and Charlotte Maxeke Johannesburg Academic Hospital, Johannesburg, South Africa. Approval for the study was granted by the University of the Witwatersrand Human Research Ethics Committee. A total of 71 unrelated black South African patients ≥18 years of age and diagnosed clinically with hypertension-attributed CKD were recruited into the study. The diagnosis of hypertension-attributed CKD was made clinically by the treating physicians. For hypertension-attributed CKD, the diagnosis is made on clinical grounds, as a biopsy is not performed for most cases as it is not clinically mandated. A determination of hypertension-attributed CKD is made on the determination of elevated blood pressure, mild or no proteinuria (<2 g/day) and no evidence of other causes of kidney disease.(5) In order to make the diagnosis all other possible causes of renal disease need to be excluded. Thus the combination of these factors was taken into account for the diagnosis and meets the standard criteria for diagnosing hypertension-attributed CKD. In this study, not everyone was biopsied. Fifty-two first-degree relatives and 58 geographically and ethnically matched healthy controls with normal blood pressure and normal kidney function were also recruited. Ethnicity was recorded and based on self-reporting. More details on this case–control study have been published previously.(6,7) The following parameters were measured: weight, height, blood pressure, serum creatinine, total cholesterol, high- density lipoprotein (HDL) cholesterol with calculation of low-density lipoprotein (LDL) cholesterol, urine protein: creatinine ratio and urine albumin:creatinine ratio.
Genomic DNA extraction and genotyping
Genomic DNA was extracted from whole blood samples collected in an EDTA tube, using the salting out procedure (8) and the Maxwell® automated nucleic acid extraction platform from Promega® (Madison, WI, USA), according to the manufacturer's instruction. SNPs in podocin (NPSH2), the serologically defined colon cancer gene antigen 8 (SDCCGA8) and bone morphogenetic protein 4 genes (BMP4) (SNPs rs16854341, rs2802723 and rs8014363, respectively) were genotyped using TaqMan® SNP genotyping assay (Thermofisher Scientific, Waltham, MA, USA). The sequence of the primers for the genotyping assays were as follows: BMP4-TAAATAAAAGTGATTGTTGGCTTTG[C/T]TTGTTGTATTTAAATTAAATGTTAA; NPHS2-AGAGAGGAGGAGGAAGTGACAGATA[A/G]ATAGCTAGCATGAAGGCTGTTTGGG; SDCCAG8-TGTCGAATAGCTTGCCTCCCC TAGT[C/T]CAGTGAGTCCCTTGCA TGTAACATC. Genotyping of the APOL1 risk variants was carried out previously.(6)
Reactions were carried out according to the manufacturers’ protocol. Briefly, polymerase chain reaction (PCR) was performed in the presence of 2X TaqMan® Universal PCR Master Mix (Thermofisher Scientific, Waltham, MA, USA) and genomic DNA. A total of 13.75 µl of the master mix solution was combined together with 11.25 µl of sample DNA to a final reaction volume of 25 µl. Thermocycling conditions included an initial denaturation step for 10 min at 95°C, after which 40 cycles were run, each consisting of denaturation (95°C for 15 s) and annealing (60°C for 60 s). Real-time detection of fluorescence signal was performed using the Applied Biosystems 7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA). Genotypes were identified using the allelic discrimination plots generated by the Sequence Detection System software version 2.3 (Applied Biosystems, Foster City, CA, USA).
Statistical analysis
STATA v12.0 (Texas, USA) was used for data analysis. Normally distributed continuous data are presented as the mean ± standard deviation, and variables with non-Gaussian distribution as the median (interquartile range). Categorical data are presented as frequencies and percentages. Differences between the groups were assessed using Student's t-test for continuous variables with normal distribution. In the case of non-normal distribution, a Mann–Whitney or Wilcoxon test was used. For categorical variables, a chi-squared test or a Fisher exact test where necessary was performed. The chi-squared test was used to determine significant differences in allele/genotype frequencies between CKD cases and healthy controls and between first-degree relatives and healthy controls. Genotype distribution was analysed under dominant genetic models. All tests were two sided and p < 0.05 was considered statistically significant.
For NPHS2, A represents the ancestral allele, and G represents the minor allele.
For BMP4, T represents the ancestral allele, and C represents the minor allele.
For SDCCAG8, C allele represents the ancestral allele, and T represents the minor allele.
Results
A total of 181 participants were recruited (71 patients with hypertension-attributed CKD, herein referred to CKD cases, 52 first-degree relatives and 58 healthy controls. Due to inadequate samples and some amplification failures, we successfully genotyped the following participants for the different SNP assays:
For NPHS2 SNP rs16854341, we had a total of 171 (94%) participants [70 (98.6%) CKD cases, 47 (90.4%) first-degree relatives and 54 (93.1%) of controls].
For BMP4 SNP rs8014363, we had a total of 178 (98.3%) participants [69 (97.1%) CKD cases, 51 (98%) first-degree relatives and 58 (100%) of controls].
For SDCCAG8 SNP rs28027230, we had a total of 168 (92.8%) of participants [70 (98.6%) CKD cases, 42 (80.7%) first-degree relatives and 56 (96.6%) of controls].
Genotype and allele frequencies in CKD cases and controls
Genotypic and allelic frequencies in CKD cases and controls are shown in Table 1. There were no significant differences in the genotypic and allelic frequencies of rs8014363, rs16854341 and rs28027230 between CKD cases and controls (all p > 0.05).
Genotype and allelic frequencies in CKD cases and controls
| dbSNP ID | CKD cases | Control | p-Value* |
|---|---|---|---|
| Rs8014363 BMP4 | |||
| Genotype frequency | |||
| CC or CT | 38 (55%) | 38 (65%) | 0.232 |
| TT | 31 (45%) | 20 (34%) | |
| Allele frequency | |||
| C | 48 (35% | 51 (44%) | 0.135 |
| T | 90 (65%) | 65 (56%) | |
| Rs16864341 NPHS2 | |||
| Genotype frequency | |||
| AG or GG | 23 (33%) | 13 (24%) | 0.285 |
| AA | 47 (67%) | 41 (76%) | |
| Allele frequency | |||
| G | 25 (18%) | 15 (14%) | 0.400 |
| A | 115 (82%) | 93 (86%) | |
| Rs28027230 SDCCAG8 | |||
| Genotype frequency | |||
| TT or CT | 69 (99%) | 53 (95%) | 0.211 |
| CC | 1 (1%) | 3 (5%) | |
| Allele frequency | |||
| C | 21 (15%) | 16 (14%) | 0.874 |
| T | 119 (85%) | 96 (86%) | |
Data given as n (%).
*Pearson chi square test.
Genotype and allele frequencies in first-degree relatives and controls
Genotype and allelic distributions in first-degree relatives and controls are shown in Table 2. There were no significant differences in the genotypic and allelic frequencies of rs16854341 and rs28027230 between first-degree relatives and controls (all p > 0.05). However, for rs8014363, the minor allele (C allele) frequency was significantly lower in first-degree relatives compared to controls (p = 0.015).
Genotype and allelic frequencies in first-degree relatives and controls
| dbSNP ID | First-degree relatives | Control | p-Value* |
|---|---|---|---|
| Rs8014363 BMP4 | |||
| Genotype frequency | |||
| CC or CT | 25 (50%) | 38 (66%) | 0.103 |
| TT | 25 (50%) | 20 (34%) | |
| Allele frequency | |||
| C | 28 (28%) | 51 (44%) | 0.015 |
| T | 72 (72%) | 65 (56%) | |
| Rs16864341 NPHS2 | |||
| Genotype frequency | |||
| AG or GG | 14 (30%) | 13 (24%) | 0.475 |
| AA | 32 (70%) | 41 (76%) | |
| Allele frequency | |||
| G | 14 (15%) | 15 (14%) | 0.790 |
| A | 78 (85%) | 93 (86%) | |
| Rs28027230 SDCCAG8 | |||
| Genotype frequency | |||
| TT or CT | 41 (100%) | 53 (95%) | 0.132 |
| CC | 0 (0%) | 3 (5%) | |
| Allele frequency | |||
| C | 10 (12%) | 16 (14%) | 0.673 |
| T | 72 (88%) | 96 (86%) | |
Data given as n (%).
*Pearson chi square test.
Relationship between genotype at rs16854341 in NPHS2 gene and clinical features in CKD cases
Clinical features of CKD cases with two genotypes at rs16854341 [AA vs non-AA (AG or GG)] are shown in Table 3. There were significantly more males with the non-AA genotype than females (p = 0.025). There was no difference in BMI, systolic BP, serum creatinine or proteinuria between cases with the AA genotype compared to those with the non-AA genotype (all p > 0.05). There was a trend for cases with the AG or GG genotype to have higher diastolic BPs than those with the AA genotype, though the difference was not statistically significant (p = 0.073).
Relationship between genotype at rs16854341 in the NPHS2 gene and clinical and demographic features in CKD cases
| Rs16854341 | AA | AG or GG | p-Value* |
|---|---|---|---|
| N (%) | 47/70 | 23/70 | |
| Gender (M/F) | 26/21 | 19/4 | 0.025# |
| Age, years | 48 (41–53) | 47 (42–54) | 0.740 |
| Age at diagnosis of CKD, years | 45 (39–52) | 44 (40–52) | 0.523 |
| BMI, kg/m2 | 28 (24–31) | 30 (22–29) | 0.520 |
| Systolic BP, mmHg | 141 (132–165) | 145 (131–180) | 0.260 |
| Diastolic BP, mmHg | 85 (78–94) | 90 (80–108) | 0.073 |
| Serum creatinine, µmol/L | 771 (505–1154) | 638 (472–1015) | 0.512 |
| CKD-EPI eGFR, ml/min per 1.73 m2 | 7 (4–11) | 9 (5–13) | 0.252 |
| Total cholesterol mmol/L | 4.2 (3.6–4.8) | 4.2 (3.8–4.9) | 0.600 |
| HDL, mmol/L | 1.0 (0.8–1.2) | 1.2 (0.9–1.4) | 0.185 |
| LDL, mmol/L | 2.3 (1.8–2.9) | 2.4 (2.0–2.8) | 0.741 |
| Urine PCR g/mmol | 0.1 (0.1–0.2) | 0.1 (0.1–0.1) | 0.948 |
Data given as median (interquartile range) unless specified.
p-value: *Wilcoxon test; #Pearson chi square test.
Abbreviations: CKD, chronic kidney disease; BP, blood pressure; BMI, body mass index; eGFR, estimated glomerular filtration rate; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; HDL, high density lipoprotein; LDL, low density lipoprotein; PCR, protein:creatinine ratio.
Relationship between Genotype at rs8013363 in BMP4 Gene and Clinical Features in CKD Cases
Clinical features of CKD cases with two genotypes at rs8013363 [TT vs non-TT (CC or CT)] are shown in Table 4. T here was no difference in gender distribution, BMI, mean arterial pressure, serum creatinine or proteinuria between cases with the AA genotype compared to those with the non-AA genotype (all p > 0.05). CKD cases with the TT genotype tended to be younger and were diagnosed with hypertension-attributed CKD at a younger age than those with the non-TT genotypes (p = 0.066 and p = 0.048, respectively).
Relationship between genotype at rs8013363 in the BMP4 gene and clinical and demographic features in CKD cases
| Rs8014363 | TT | CC or CT | p-Value* |
|---|---|---|---|
| N (%) | 31/69 | 38/69 | |
| Gender (M/F) | 18/13 | 27/11 | 0.260# |
| Age, years | 50 (41–57) | 46 (41–51) | 0.066 |
| BMI, kg/m2 | 28 (26–31) | 28 (24–29) | 0.420 |
| Age at diagnosis of CKD, years | 48 (40–54) | 44 (39–49) | 0.048 |
| Systolic BP, mmHg | 145 (132–169) | 141 (131–167) | 0.890 |
| Diastolic BP, mmHg | 86 (78–101) | 86 (79–97) | 0.861 |
| Mean arterial pressure, mmHg | 106 (96–124) | 105 (96–119) | 0.800 |
| Serum creatinine, µmol/L | 638 (457–1054) | 774 (517–1154) | 0.300 |
| CKD-EPI eGFR, ml/min per 1.73 m2 | 9 (4–13) | 7 (4–11) | 0.577 |
| Total cholesterol mmol/L | 4.2 (3.8–4.8) | 4.2 (3.6–4.9) | 0.830 |
| HDL, mmol/L | 1.0 (0.8–1.2) | 1.1 (0.9–1.4) | 0.153 |
| LDL, mmol/L | 2.5 (2.1–2.9) | 2.3 (1.8–2.9) | 0.491 |
| Urine PCR g/mmol | 0.1 (0.04–0.2) | 0.1 (0.1–0.1) | 0.983 |
Data given as median (interquartile range) unless specified.
p-value: *Wilcoxon test; #Pearson chi square test.
Abbreviations: CKD, chronic kidney disease; BP, blood pressure; BMI, body mass index; eGFR, estimated glomerular filtration rate; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; HDL, high density lipoprotein; LDL, low density lipoprotein; PCR, protein:creatinine ratio.
Relationship between genotype at rs2802723 in SDCCAG8 gene and clinical features in CKD cases
As only one CKD case and three controls had the CC genotype, with the rest having the non-CC genotype (TT or CT), no further analyses were performed.
Relationship between genotype at rs16854341 in NPHS2 and rs8013363 in BMP4 genes and clinical features in first-degree relatives
Clinical features of first-degree relatives with different genotype combinations at rs16854341 and rs8013363 are shown in Tables 5 and 6, respectively. For all the SNPs tested, there were no differences observed in BMI, mean arterial pressure, albuminuria or serum creatinine across any of the genotypes (all p > 0.05).
Relationship between rs16854341 polymorphisms in the NPHS2 gene and clinical and demographic features in first-degree relatives
| Rs16854341 | AA | AG or GG | p-Value* |
|---|---|---|---|
| N (%) | 32/46 | 14/47 | |
| Gender (M/F) | 10/22 | 6/8 | 0.447# |
| Age, years | 27 (22–45) | 46 (21–61) | 0.193 |
| BMI, kg/m2 | 27 (25 –33) | 27 (25–31) | 0.886 |
| Systolic BP, mmHg | 128 (116–149) | 131 (123–149) | 0.316 |
| Diastolic BP, mmHg | 75 (70–89) | 76 (74–85) | 0.616 |
| Mean arterial pressure, mmHg | 92 (85–109) | 95 (89–108) | 0.294 |
| Serum creatinine, µmol/L | 77 (65–84) | 75 (63–86) | 0.802 |
| CKD-EPI eGFR, ml/min per 1.73 m2 | 121 (92–138) | 101 (87–124) | 0.424 |
| Uric acid mmol/L | 0.3 (0.3–0.3) | 0.3 (0.2–0.4) | 0.870 |
| Total cholesterol mmol/L | 3.9 (3.6–4.9) | 4.5 (3.8–5.5) | 0.240 |
| HDL, mmol/L | 1.3 (1.2–1.4) | 1.3 (1.0–1.5) | 0.800 |
| LDL, mmol/L | 2.2 (1.7–3.0) | 2.9 (1.9–3.2) | 0.302 |
| Urine ACR mg/mmol | 1.4 (0.0–4.2) | 0.8 (0.0–1.9) | 0.526 |
Data given as median (interquartile range) unless specified.
p-value: *Wilcoxon test; #Pearson chi square test.
Abbreviations: CKD, chronic kidney disease; BP, blood pressure; BMI, body mass index; eGFR, estimated glomerular filtration rate; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; HDL, high density lipoprotein; LDL, low density lipoprotein; ACR, albumin:creatinine ratio.
Relationship between rs8014363 polymorphisms in the BMP4 gene and clinical and demographic features in first-degree relatives
| Rs8014363 | CC or CT | TT | p-Value* |
|---|---|---|---|
| N (%) | 25/50 | 25/50 | |
| Gender (M/F) | 9/16 | 7/18 | 0.544# |
| Age, years | 26 (22–46) | 34 (20–51) | 0.460 |
| BMI, kg/m2 | 27 (24–40) | 27 (24.6–35) | 1.000 |
| Systolic BP, mmHg | 127 (118–139) | 130 (120–149) | 0.190 |
| Diastolic BP, mmHg | 73 (70–86) | 74 (76–89) | 0.162 |
| Mean arterial pressure, mmHg | 91 (84–107) | 95 (90–110) | 0.105 |
| Serum creatinine, µmol/L | 76 (64–87) | 70 (64–82) | 0.607 |
| CKD-EPI eGFR, ml/min per 1.73 m2 | 125 (87–142) | 110 (92–137) | 0.839 |
| Uric acid mmol/L | 0.3 (0.2–0.3) | 0.3 (0.3–0.3) | 0.306 |
| Total cholesterol mmol/L | 4.0 (3.6–4.7) | 4.1 (3.7–5.4) | 0.532 |
| HDL, mmol/L | 1.3 (0.9–1.5) | 1.3 (1.2–1.5) | 0.224 |
| LDL, mmol/L | 2.3 (1.8–3.1) | 2.7 (1.9–3.2) | 0.496 |
| Urine ACR mg/mmol | 2.5 (0.4–4.2) | 0.9 (0.0–1.8) | 0.101 |
Data given as median (interquartile range) unless specified.
p-value: *Wilcoxon test; #Pearson chi square test.
Abbreviations: CKD, chronic kidney disease; BP, blood pressure; BMI, body mass index; eGFR, estimated glomerular filtration rate; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; HDL, high density lipoprotein; LDL, low density lipoprotein; ACR, albumin:creatinine ratio.
Association analyses
We found no association between the risk of kidney disease and rs16854341 (1.25 (95% CI [0.59–2.68]); p = 0.558, adjusted) and rs8013363 (0.96 (95% CI [0.48–1.92]); p = 0.901, adjusted), Table 7. The interactions of the SNP rs16854341 and rs8013363 with APOL1 are shown in Table 7. There was a trend for an increased risk of kidney disease in those who had two APOL1 risk variants and were major allele homozygotes at rs16854341 (4.78 (95% CI [0.87–26.31]); p = 0.072, adjusted) and at rs8014363 (5.16 (95% CI [0.92–29.87]); p = 0.062, adjusted).
Risk of developing chronic kidney disease
| Characteristic | Unadjusted | Adjusted* | ||
|---|---|---|---|---|
| Odds ratio (95% CI) | p-value | Odds ratio (95% CI) | p-Value | |
| Rs16854341 | ||||
| AG or GG genotype | 1 (Ref) | 1 (Ref) | ||
| AA genotype | 0.76 (0.39–1.47) | 0.410 | 1.25 (0.59–2.68) | 0.558 |
| AA genotype and 2 APOL1 risk alleles | 1.49 (0.35–6.26) | 0.435 | 4.78 (0.87–26.31) | 0.072 |
| Rs8014363 | ||||
| CC or CT genotype | 1 (Ref) | 1 (Ref) | ||
| TT genotype | 1.14 (0.62–2.10) | 0.669 | 0.96 (0.48–1.92) | 0.901 |
| TT genotype and 2 APOL1 risk alleles | 2.63 (0.60–11.71) | 0.202 | 5.16 (0.92–29.87) | 0.062 |
*Adjusted for age and sex.
Discussion
Our study represents the first cohort of black South African patients with hypertension-attributed CKD and their first-degree relatives to be evaluated for polymorphisms in the NPSH2, BMP4 and SDCCAG8 genes. Our study shows that these variants are indeed common polymorphisms and are present in the patients with hypertension-attributed CKD, their first-degree relatives and controls to a similar degree.
In the current study, we did not observe any differences in genotype or allele frequencies for any of the genes studied, between CKD cases and controls. There were no differences in proteinuria, serum creatinine or BMI based on the genotypes. However, patients who were major allele homozygotes at BMP4 gene were diagnosed with kidney disease at a younger age than those with other genotypes that are heterozygous or minor allele homozygous.
We found no association with kidney disease for SNPs in the NPSH2 and BMP4 genes. Podocin mutations have previously been implicated in kidney disease, particularly, in childhood steroid resistant nephrotic syndrome (9,10) and were first described by Boute et al.(11) There are approximately 50 NPHS2 gene mutations and variants and/or non-silent polymorphisms that have been reported and recognized as possibly having a role in proteinuria.(12) Similar to our study, after sequencing of the podocin gene in African Americans with non-diabetic ESKD, the majority of variants studied showed no evidence of association with ESKD.(13) However, significant associations of a rare variant, IVS3 + 9A of the NPHS2 gene with non-diabetic ESKD, were identified.(13) There was concern that this could have been a false-positive result as this is a very rare variant.(13) This is important because GWAS studies are prone to ‘false positive’ results due to the high number of statistical tests performed.(14)
There was a trend towards association with kidney disease in those who were major allele homozygotes at rs16854341 (NPSH2) and rs8014363 (BMP4) in the presence of two APOL1 risk alleles, though these associations did not reach statistical significance. In a study by Divers et al., the odds for ESKD were highest in those who had two APOL1 risk alleles and were homozygotes for the major allele at rs16854341, whereby each copy of the minor allele reduced the odds ratio of ESKD by 50%; the odds ratio of ESKD in individuals who were homozygous at the major allele was 7.03, compared to 3.52 for heterozygous individuals and 1.76 for those who were homozygous for the minor allele.(15) The presence of two APOL1 risk variants with zero, one and two rs8014363 minor alleles resulted in increased odds of kidney disease, with the odds ratio changing from 4.8 to 6.8 to 9.6, respectively.(4)
Similar to our study, McKnight et al. did not detect any significant associations between BMP2, BMP4 and BMP7, although this study was conducted in white patients with diabetic nephropathy.(16) Bone morphogenic proteins are members of the transforming growth factor β superfamily and function in embryonic development and cellular maintenance (17) and are important for kidney development.(18) They were first identified as factors that induce bone formation and cartilage development, but now they are known to be involved in other developmental processes. Bone morphogenic proteins 4 and 7 are expressed in the developing glomerulus and are important for normal glomerular development. Bone morphogenic protein 7 has been shown to reduce podocyte injury in hyperglycemia in vivo and has been shown to be a potentially therapeutic option for diabetic nephropathy.(19) Dysregulation of the BMP4 gene in the developing podocytes of transgenic mice results in abnormal glomerular capillary tuft formation.(20) Bone morphogenic protein 4 has been implicated in several aspects of embryonic development especially in those organs in which epithelial–mesenchymal interactions are essential for development.(21,22)
In a meta-analysis of two GWAS of more than 2000 individuals (extremely obese children and adolescents), a new locus for obesity in the SDCCAG8 gene was identified, with genetic variants in this gene being highly associated with early onset obesity.(23) SDCCAG8 is ubiquitously expressed, with most expression present in the hypothalamus and pituitary glands, suggesting that it plays a role in weight regulation.(23) Nephronophthisis is a recessive cystic kidney disease which is the most frequent genetic cause of ESKD in the first three decades of life.(24) Nephronophthisis-related ciliopathies are recessive disorders that feature dysplasia or degeneration occurring preferentially in the kidney, retina and cerebellum. Mutations of SDCCAG8 were identified as the cause of retinal–renal ciliopathies.(24)
The results of the current study are important because the frequency of the polymorphisms in the NPSH2 and BMP4 and SDCCAG8 genes among black South Africans was previously unknown. This study used rigorous phenotypic criteria for the inclusion of cases and controls. Subjects were recruited in a consecutive manner to avoid selection bias. A larger sample size could possibly provide increased power to detect potential differences in allele frequency that may not have been evident in the sample of individuals studied.
Conclusion
We found no association of the polymorphisms in the podocin and BMP4 gene with markers of kidney disease in black South African patients with hypertension-attributed CKD. However, in the presence of two APOL1 risk variants, there was a trend towards an increased risk of kidney disease. Future studies with larger samples are required to further characterize the interactions of these genes with APOL1.