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. Author manuscript; available in PMC: 2021 Mar 1.
Published in final edited form as: Clin Transplant. 2020 Apr 25;34(6):e13827. doi: 10.1111/ctr.13827

Genome-wide association study for time to failure of kidney transplants from African American deceased donors

Jasmin Divers 1, Lijun Ma 2, William Mark Brown 3, Nicholette D Palmer 4, Young Choi 2, Ajay K Israni 5, Stephen O Pastan 6, Bruce A Julian 7, Robert S Gaston 7, Pamela J Hicks 4, Amber M Reeves-Daniel 2, Barry I Freedman 2
PMCID: PMC7694574  NIHMSID: NIHMS1609505  PMID: 32080893

Abstract

Two renal-risk variants in the apolipoprotein L1 gene (APOL1) in African American (AA) deceased donors (DD) are associated with shorter renal allograft survival after transplantation. To identify additional genes contributing to allograft survival, a genome-wide association study was performed in 532 AA DDs. Phenotypic data were obtained from the Scientific Registry of Transplant Recipients. Association and single-nucleotide polymorphism (SNP)-by-APOL1 interaction tests were conducted using death-censored renal allograft survival accounting for relevant covariates. Replication and inverse-variance-weighted meta-analysis were performed using data from 250 AA DD in the Genomics of Transplantation study. Accounting for APOL1, multiple SNPs near the Nudix Hydrolase 7 gene (NUDT7) showed strong independent effects (P = 1.6 × 10−8−2.2 × 10−8). Several SNPs in the Translocation protein SEC63 homolog (SEC63; P = 2 × 10−9-3.7 × 10−8) and plasmacytoma variant translocation 1 (PVT1) genes (P = 4.0 × 10−8-7 × 10−8) modified the effect of APOL1 on allograft survival. SEC63 is expressed in human renal tubule cells and glomeruli, and PVT1 is associated with diabetic kidney disease. Overall, associations were detected for 41 SNPs (P = 2 × 10−9-5 × 10−8) contributing independently or interacting with APOL1 to impact renal allograft survival after transplantation from AA DD. Given the small sample size of the discovery and replication sets, independent validations and functional genomic efforts are needed to validate these results.

Keywords: African Americans, APOL1, chronic kidney disease, graft failure, genome-wide association study, kidney transplantation

1 |. INTRODUCTION

Relative to European Americans (EAs), African Americans (AAs) dis-proportionately develop end-stage kidney disease (ESKD).1 Although ESKD can be treated with dialysis, kidney transplantation is preferable, leading to improved quality of life and longer patient survival at far lower cost.2 Approximately 92% of transplanted kidneys function beyond 1 year; however, long-term graft failure remains high despite recent medical advances.3

Thirteen percent of the AA population carries two copies of the apolipoprotein L1 gene (APOL1) renal-risk-variants (G1/G1, G1/G2, or G2/G2).4 These nephropathy-risk variants are observed through-out sub-Saharan African with various frequencies and in the New World among populations with recent African ancestry (eg, African Americans, Afro-Caribbeans, Afro-Latinos).5,6 Variations in the distribution of the APOL1 nephropathy-risk variants contribute to the racial and ethnic disparities observed in nephropathy and with poorer outcomes after kidney transplantation.7–9 They are virtually absent in individuals who lack recent African ancestry.10 We reported that variations in APOL1 dramatically impacted allograft survival for deceased-donor kidney transplants (DDKT) from donors with recent African ancestry; two APOL1-renal-risk variants conferred increased risk for early allograft failure, relative to kidneys from AAs with zero or one APOL1-renal-risk variant.11–13 Presence of two APOL1-renal-risk variants in recipients did not translate into heightened risk for earlier allograft failure after DDKT from an AA donor.14

In addition to APOL1, we and others have shown that single nucleotide polymorphisms (SNPs) in the caveolin-1 (CAV1) and ATP-binding cassette, subfamily B, member 1 (ABCB1) genes in EA, and Chinese and Japanese kidney donors impact renal allograft survival after transplantation.15–20 Little is known about the association between genetic factors and kidney allograft survival beyond these genes. The objective of this study was to identify genetic factors associated with renal allograft survival independent from APOL1, and those that interact with APOL1 to affect allograft survival among recipients of AA deceased-donor kidneys.

2 |. MATERIALS AND METHODS

2.1 |. Subjects

The discovery dataset encompassed 978 kidney transplantations from 532 AA deceased donors that were recovered and/or transplanted at the Wake Forest School of Medicine (WFSM), University of Alabama at Birmingham School of Medicine (UAB), and Emory University School of Medicine (Emory). Significant results were validated in an additional 465 kidney transplantations recovered from 250 AA deceased donors from the Deterioration of Kidney Allograft Function study (DeKAF Genomics). DeKAF Genomics received samples from the following Organ Procurement Organizations (OPOs): LifeSource (Minnesota), LifeQuest (Florida), New Jersey Organ & Tissue Sharing Network (New Jersey), Organ Donor Center of Hawaii (Hawaii), Southwest Transplant Alliance (Texas), One Legacy (California), New England Organ Bank (Massachusetts), LifeBanc (Ohio), and Louisiana Organ Procurement Agency (Louisiana). Samples were identified solely by United Network of Organ Sharing (UNOS) identification numbers. The phenotypic data used in these analyses were obtained from the Scientific Registry of Transplant Recipients (SRTR). WFSM received Institutional Review Board approval for genotyping DNA samples. Deceased individuals are not considered study participants. Therefore, informed consent was not required by the IRB. Outcomes of kidney transplantation were assessed among recipients using UNOS identification numbers in the SRTR. The SRTR data system includes data on all donor, wait-listed candidates, and transplant recipients in the United States, submitted by the members of the Organ Procurement and Transplantation Network (OPTN).21 The Health Resources and Services Administration in the United States Department of Health and Human Services provides oversight to the activities of the OPTN and SRTR contractors. The clinical and research activities reported are consistent with the Principles of the Declaration of Istanbul as outlined in the Declaration of Istanbul on Organ Trafficking and Transplant Tourism.

2.2 |. Genotyping

DNA samples from deceased donors were provided by the HLA laboratories at Wake Forest, UAB and Emory. DNA in the Wake Forest discovery sample was extracted from peripheral blood using the PureGene system (Gentra Systems). Directly genotyped data were obtained using the Illumina 5M chip. Genotypic imputation was performed using the 1000 Genomes (phase 1 version 3, cosmopolitan panel).22 Genotyping for the replication analysis was performed using the Sequenom genotyping system (Sequenom Inc).

2.3 |. SNP quality control and imputation

Directly genotyped variants underwent stringent quality control (QC) checks which led to the exclusion of 14 individuals: 5 had call rates <90%, 2 had discordant self-reported and genetically determined sex, 5 had a heterozygosity score outside of the mean ±4 times the standard error interval, and 2 had the same sample identifiers. In addition, two AA deceased donors were excluded from this analysis because APOL1 genotyping failed. We used a classification scheme to rank SNPs and prioritize association results, based on the estimated minor allele frequency (MAF), P-value of the Hardy-Weinberg equilibrium (HWE) test, and call rate. Imputation was performed using IMPUTE2 with phased haplotypic data obtained from Shapeit2.23 Imputation was based on 3 436 913 autosomal SNPs in 534 AA deceased donors. All directly genotyped and imputed SNPs were considered in the analyses. However, results are reported for common variants with a HWE P-value ≤ 10−4, call rate ≥95%, confidence score ≥0.50, MAF ≥5%, and ≥30 allograft failures among recipients of kidneys procured from donors with two APOL1 renal-risk variants and at least 5 failures among the APOL1 2 renal-risk variants carriers for SNP-by-APOL1 interaction tests.

2.3.1 |. APOL1 genotyping

Two SNPs in the APOL1 G1 renal-risk allele (rs73885319; rs60910145) and an insertion/deletion for the G2 renal-risk allele (rs143830837) were genotyped using custom assays designed at WFSM on the Sequenom platform. APOl1 genotyping was performed in the discovery and replication samples using the same protocol. For each sample, genotype calls were visually inspected for quality control.13,24 Also, genotyping of 25 blind duplicates resulted in a concordance rate of 100% and the genotyping efficiency for the three SNPs was >99% in all AA deceased donors. Possession of two APOL1 renal-risk variants constituted the “renal-risk genotype” (G1/G1, G2/G2, or G1/G2).

2.4 |. Estimating admixture proportion

Admixture proportion estimations and principal component analyses were performed using a set of 3000 SNPs that have been linkage disequilibrium (LD)-pruned to reduce the correlation between markers.25,26

2.5 |. Statistical analysis

The distribution of demographic variables for recipients and deceased kidney donors, based on donor APOL1 renal-risk genotypes, was contrasted using Wilcoxon two-sample tests (continuous variables) and chi-square tests (binary variables). The main outcome was time to renal allograft failure, determined by the interval between the date of transplantation and the date of allograft loss. In those with a functioning allograft, the final observation date was censored for death with function or at last follow-up prior to June 30, 2016. Cox proportional hazard models were subsequently fitted, using the sandwich estimator to provide a robust estimation of the covariance matrix associated with the parameter estimates to account for the correlation between allograft failure rate and time to failure of kidneys donated by a single individual to two recipients.27–30 Samples with missing genotype, phenotype, and covariate data were excluded. The variables considered in this analysis have low counts of missing data (<5%), limiting the appeal for data imputation techniques. Deceased-donor age and recipient age were categorized using the outcome-oriented approach of Contal and O’Quigley, suggesting cut points for donor age at 20, 35, and 45 years, and recipient age at 30 and 45 years.31 Therefore, analyses treated donor-age groups 0–20, 20–35, 35–45, and 45+ years, and recipient-age groups 0–30, 30–45, and 45+ years as ordinal variables. Two models were fitted for all transplanted kidneys from AA deceased donors: (a) a minimally adjusted model that, in addition to the marker being tested, included the observed APOL1 genotypes and the individual admixture proportion; and (b) a fully adjusted model that also controls for the ages of the donor and the recipient, sex, recipient race, transplant center, HLA match, cold ischemia time (CIT), panel reactive antibodies (PRA), donor age, and donor type (standard donor vs. extended-criteria donor). Results from the fully adjusted model are presented in this manuscript. The centered cross product between each SNP and the number of APOL1 renal-risk variants was included in both models to test for interaction effects. The genotypic data used for each SNP were the observed genotypes for SNPs that were on the Illumina Chip; the expected number of minor alleles was used for imputed SNPs. Imputed SNPs that reach our preset significance levels were then directly genotyped to validate the observed results.

2.5.1 |. Significant effects, correction for multiple testing and replication in DeKAF Genomics

The analysis involved more than 13 million directly genotyped and imputed SNPs. A strict Bonferroni correction would place the significance threshold at 3.8 × 10−9 for a two-sided test, a highly conservative threshold. The sample sizes required to provide adequate for genetic association studies in AAs at this significance threshold are not feasible for a single study (or even two). These limitations are more pronounced when the focus is on a survival outcome where the number of events (allograft failures), instead of only the sample size, is the main driver of statistical power. We prioritized initial association results that reached an adjusted P-value ≤ 7.5 × 10−7, with a minimum of 30 failures among individuals with two APOL1-renal-risk variants.32,33 SNP-by-APOL1 association effects were followed up and ultimately reported if a minimum of 10 failures were observed among the minor allele homozygotes and at least 30 failures among AA deceased donors with two APOL1 renal-risk variants. The significance level of 7.5 × 10−7 strikes a balance between the stringency needed to reduce the number of false positives and the underrepresentation of AAs and individuals from other minority ethnic groups in large genomic studies and consortia. This underrepresentation is even more problematic in kidney disease and outcomes of kidney transplantation among AA recipients who are more likely to receive DDKTs from AA donors. With 212 failures, these analyses had 80% statistical power to detect genetic associations with kidney allograft survival with hazard ratios (HR) of 2.5 or higher and minor allele frequencies of at least 12.5% at the 7.5 × 10−7 significance threshold. Not all of the reported results meet the conservative GWAS significance level of 5 × 10−8. As described elsewhere, forcing that every GWAS analysis meets this stringent threshold, independently of the phenotype and population of interest, could lead to significant loss of statistical power.34,35 These agnostic results offer meaningful insight into various biologic processes, including regulation of metabolic activities in renal tubular cells and response to immunosuppression medication. Data on the contribution of donor-level, and particular of AA deceased-donor, genetic factors on the outcome of kidney transplantation are scant. Therefore, these results are meant to start the process of independent validation that has been successful in many other diseases.

2.5.2 |. Follow-up genotyping and functional validation

Primers were designed for direct genotyping of imputed SNPs that reached the statistical significance level if they were not in strong linkage disequilibrium with another SNP that was directly genotyped on the Illumina array. After validating the results, a set of 50 SNPs was selected for direct genotyping in the DeKAF Genomics samples for replication and inverse-variance-weighted meta-analysis. SNP selection for replication was based on a number factors, including statistical significance, number of associated SNPs in the region of interest, and biologic relevance.

Human kidney tissue was collected from patients undergoing radical or partial nephrectomy. Preoperative BUN, serum creatinine, eGFR, urinalysis, and urine protein-to-creatinine ratio were obtained, and patients with preoperative MDRD eGFR > 60 mL/min per 1.73 m2 (>1 mL/s/m2) were assessed. Age, sex, presence of hypertension, diabetes, cardiovascular disease, family history of kidney disease, and medications were recorded. The Institutional Review Board of Wake Forest School of Medicine approved the study, and all patients provided written informed consent. Human kidney cryosections were sliced at 6-μm intervals using a cryotome (Leica Biosystems). Immunofluorescence microscopy for APOL1 (Abcam, ab-108315), NUDT7 (Santa Cruz, sc-390911), ATAD3B (Santa Cruz, sc-514615), SEC63 (Santa Cruz, sc-517139), podocyte cell marker podocin (Santa Cruz, sc-22294), and endothelial cell marker CD31 (Abcam, ab-32457) was performed on human kidney cryosections using established protocols.36,37 Secondary antibodies (goat anti-rabbit Alexa Fluor 594 and goat anti-mouse Alexa Fluor 488, donkey anti-goat Alexa Fluor 594, and donkey anti-mouse Alexa Fluor 488) were used to display fluorescent signals (Jackson ImmunoResearch). Microscopy was performed using an Olympus IX71 fluorescence microscope.

3 |. RESULTS

Table 1 displays demographic characteristics of AA deceased donors and recipients from WFSM, UAB, and Emory, based on the donor’s number of APOL1 renal-risk variants. As expected, these characteristics did not vary with donor APOL1 genotype. AA deceased donors had a median (first quartile, third quartile) age of 35.0 (19.0, 49.0) years and were more likely to be male (60.0%). The median age of recipients of kidneys from AA deceased donors was 51.0 (39.0, 60.0), and they were also more likely to be male (59.6%). Two hundred ten allograft failures were observed. Consistent with previously reported results, the proportion of graft failures was higher among the 153 recipients of kidneys from AA donors who carried two APOL1-renal-risk variants (n = 45, 29.4%), relative to the 825 whose transplanted kidney was procured from 0/1 APOL1 renal-risk-variant donors (n = 165, 20.0%; P = .01). Immunosuppression varied between patients and centers across the United States, but typically included antibody induction, a calcineurin inhibitor, and an anti-proliferative agent, with or without corticosteroids. The follow-up duration after engraftment was 53.3 (27.9, 83.4) months for the 825 recipients of kidneys from donors with fewer than two APOL1 renal-risk variants and 50.4 (27.6, 84.0) months for the 153 recipients of kidneys from donors with two APOL1 renal-risk variants.

TABLE 1.

Demographic data for the discovery sample (Wake Forest, UAB, and Emory) kidney transplant recipients, based on the number of APOL1 renal-risk variants of African American deceased donors

Donors All, N = 532 APOL1 = 0, N = 216 APOL1 = 1, N = 233 APOL1 = 2, N = 83 P-value
Age at death (y) 35.0 (19.5,49.0) 34.0 (19.0,49.0) 37.0 (20.0,49.0) 36.0 (20.0,47.0) .78
Terminal serum creatinine (μmol/L) 97.2 (73.8, 131.3) 97.2 (70.7, 124.6) 106.1 (79.6, 134.4) 97.2 (79.6, 122.9) .21
Sex (Male), N (%) 319 (60.0%) 135 (62.5%) 138 (59.2%) 46 (55.4%) .51
Non-heartbeating donor, N (%) 19 (3.6%) 9 (4.2%) 7 (3.0%) 3 (3.7%) .80
Standard criteria donor, N (%) 437 (82.4%) 178 (82.4%) 189 (81.1%) 70 (84.3%) .80
Recipients N=978 N=394 N=431 N=153 P-value
Age at transplant (y) 51.0 (39.0, 60.0) 51.0 (39.0, 61.0) 50.0 (39.0, 59.0) 51.0 (39.0, 60.0) .63
Panel reactive antibodies (%) 6.0 (0.0, 34.0) 6.0 (0.0, 33.5) 7.5 (0.0, 40.0) 5.0 (0.0, 32.8) .24
Body mass index (kg/m2) 26.8 (23.4, 30.9) 26.4 (23.1, 29.9) 27.5 (24.3, 30.9) 27.1 (23.5, 31.7) .01
Cold ischemia time (h) 19.5 (14.5, 26.0) 19.5 (14.4, 26.3) 20.6 (16.0, 26.9) 19.0 (14.0, 25.2) .38
HLA mismatches (N) 5.0 (4.0, 5.0) 5.0 (4.0, 5.0) 4.0 (3.8, 5.0) 5.0 (4.0, 5.2) .04
Allograft survival (mo) 56.4 (27.6, 82.8) 56.4 (27.6, 82.8) 48.0 (24.0, 84.0) 50.4 (27.6, 84) .59
Last serum creatinine (μmol/L) 127.7 (97.2, 173.7) 123.8 (96.4, 160.9) 132.6 (97.2, 180.3) 132.6 (88.0, 185.6) .5
Last follow-up GFR ( mL/s/m2) 0.9 (0.7, 1.2) 0.9 (0.7, 1.3) 0.9 (0.6, 1.2) 0.9 (0.6, 1.4) .39
Type 2 diabetes, N (%) 221(22.6%) 86 (21.8%) 103 (23.9%) 32 (20.9%) .67
Sex (male), N (%) 683 (59.6%) 232 (58.9%) 255 (59.2%) 196(62.8%) .69
African American, N (%) 579 (59.1%) 221 (56.1%) 251 (58.2%) 107 (69.9%) .01
Graft failure within 15 d, N (%) 21 (2.1%) 7 (1.8%) 7 (1.6%) 7 (4.6%) .08
Graft failure within 6 mo, N (%) 41 (4.2%) 13 (3.3%) 18 (4.2%) 10 (6.5%) .24
Graft failure within 1 y, N (%) 57 (5.8%) 18 (4.6%) 29 (6.7%) 10 (6.5%) .38
Graft failure, Total, N (%) 210 (21.4%) 68(17.3%) 97 (22.5%) 45 (29.4%) .006
Death, N (%) 244 (24.9%) 89 (22.6%) 115 (26.7%) 40 (26.1%) .37
Return to dialysis, N (%) 200 (20.4%) 63(16.0%) 95 (22.0%) 42 (27.5%) .006
Death with functioning allograft, N (%) 141 (14.4%) 56 (14.2%) 64 (14.9%) 21 (13.7%) .93
Acute rejection, N (%) 182 (18.6%) 67 (17.0%) 86 (20.0%) 29 (19.0%) .55
First-week dialysis, N (%) 218 (22.2%) 78 (19.8%) 103 (23.9%) 37 (24.2%) .31
Drug induction, N (%) 840 (85.7%) 346 (89.2%) 363 (85.4%) 131 (87.3%) .28
Primary diagnosis, N (%)
 Diabetes 221 (22.6%) 86 (21.8%) 103 (23.9%) 32 (20.9%) 0.11
 Hypertension 253 (25.8%) 110 (27.9%) 103 (23.9%) 40 (26.1%)
 GN 177 (18.1%) 67 (17.0%) 69 (16.0%) 41 (26.8%)
 Cystic 46 (4.7%) 18 (4.6%) 22 (5.1%) 6 (3.9%)
 Other 281 (28.7%) 113 (28.7%) 134 (31.1%) 34 (22.2%)
Diabetes, N (%)
 No 189 (19.3%) 72 (18.3%) 71 (16.5%) 46 (30.1%) .007
 Yes—Insulin-dependent 56 (5.7%) 24 (6.1%) 25 (5.8%) 7 (4.6%)
 Yes—Not insulin-dependent 35 (3.6%) 9 (2.3%) 19 (4.4%) 7 (4.6%)
 Missing 698 (71.2%) 289 (73.4%) 316 (73.3%) 93 (68.8%)
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Table 2 shows demographic characteristics of AA deceased donors and recipients from DeKAF Genomics, according to the number of donor APOL1-renal-risk variants. Similar to the discovery dataset, demographic characteristics of AA deceased donors and recipients did not vary according to donor APOL1-renal-risk genotypes. Kidney allograft failure rate was strongly associated with the APOL1 status of the deceased kidney donors; (n = 15, 26.3%) among the 57 recipients of an AA deceased-donor kidney with two APOL1-renal-risk-variants, vs. (n = 57, 13.9%; P = .02) among the 408 who received a kidney from donors with 0/1 APOL1-renal-risk variants. However, AA deceased donors in DeKAF Genomics were significantly older than AA donors from WFSM, UAB, and Emory (42.0 (27.0, 52.0) vs 35 (19.0, 49.0) years; P < .0001). They also had shorter follow-up (37.6 (23.8, 60.0) vs 56.4 (27.6, 82.8) months; P < .0001) and lower overall graft failure rates (15.5% vs 21.4%; P = .01).

TABLE 2.

Demographic data for the replication sample (DeKAF Genomics) kidney transplant recipients, based on the number of APOL1 renal-risk variants of African American deceased donors

Donors All, N = 250 APOL1 = 0, N = 102 APOL1 = 1, N = 117 APOL1 = 2, N = 31 P-value
Age at death (y) 42.0 (27.0, 52.0) 42.0 (28.0, 51.0) 43.0 (25.0, 52.0) 41.0 (26.5, 51.0) 0.94
Terminal serum creatinine (μmol/L) 101.7 (79.6, 150.3) 101.7 (76.0, 141.4) 97.2 (79.6, 150.3) 132.6 (76.9, 203.3) 0.15
Donor sex (Male), N (%) 139 (55.6%) 51 (50%) 68 (58.1%) 20 (64.5%) 0.2
Non-heartbeating donor, N (%) 15 (6.0%) 8 (7.8%) 6 (5.1%) 1 (3.2%) 0.55
Standard criteria donor, N (%) 195 (78.0%) 80 (78.4%) 90 (76.9%) 25 (80.6%) 0.9
Recipients All, N = 465 APOL1 = 0, N = 185 APOL1 = 1, N = 223 APOL1 = 2, N = 57 P-value
Age at transplant (y) 52.0 (42.0, 61.0) 52 (40.0, 62.0) 53 (43.5, 61.0) 51 (44.0, 59.0) 0.83
Panel reactive antibodies (%) 7.0 (0.0, 55.5) 11.0 (0.0, 67.0) 11.0 (0.0, 67.0) 10.0 (0.0, 55.0) 0.52
Body mass index (kg/m2) 27.9 (24.1, 31.9) 28.4 (24.2, 32.4) 27.4 (24.0, 31.3) 28.1 (24.0, 31.8) 0.49
Cold ischemia time (h) 15.1 (11.0, 20.8) 15.3 (10.9, 20.7) 15.0 (11.0, 20.0) 15.6 (10.1, 23.6) 0.78
HLA mismatches (N) 5.0 (4.0, 5.0) 4.0 (4.0, 5.0) 5.0 (4.0, 5.0) 5.0 (4.0, 5.0) 0.13
Allograft survival (mo) 37.6 (23.8, 60.0) 36.0 (23.8, 64.7) 37.6 (24.0, 60.0) 37.6 (24.0, 60.0) 0.36
Last serum creatinine (μmol/L) 123.8 (92.4, 168.0) 118.9 (91.1, 175.0) 123.8 (93.7, 159.1) 123.7 (100.8, 185.6) 0.57
Last follow-up GFR ( mL/s/m2) 0.9 (0.6, 1.2) 0.9 (0.6, 1.3) 0.9 (0.7, 1.2) 0.9 (0.5, 1.0) 0.36
Type 2 diabetes, N (%) 91 (20.2%) 41 (22.2%) 45 (20.2%) 8 (14%) 0.41
Sex (male), N (%) 286 (61.5%) 112 (60.5%) 139 (62.3%) 35 (61.4%) 0.93
African American, N (%) 200 (43.0%) 74 (40%) 99 (44.4%) 27 (47.4%) 0.52
Graft failure within 15 d, N (%) 4 (0.9%) 2 (1.1%) 0 (0%) 2 (3.5%) 0.04
Graft failure within 6 mo, N (%) 10 (2.2%) 3 (1.6%) 1 (0.4%) 6 (10.5%) 0.0002
Graft failure within 1 y, N (%) 16 (3.4%) 4 (2.2%) 5 (2.2%) 7 (12.3%) 0.003
Graft failure, Total, N (%) 72 (15.5%) 23 (12.4%) 34 (15.2%) 15 (26.3%) 0.04
Death, N (%) 89 (19.1%) 31 (16.8%) 45 (20.2%) 13 (22.8%) 0.51
Return to dialysis, N (%) 69 (14.8%) 23 (12.4%) 32 (14.3%) 14 (24.6%) 0.08
Death with functioning allograft, N (%) 51 (11.0%) 20 (10.8%) 24 (10.8%) 7 (12.3%) 0.94
Acute rejection, N (%) 104 (22.4%) 40 (21.6%) 47 (21.1%) 17 (29.8%) 0.35
First-week dialysis, N (%) 131 (28.2%) 56 (30.3%) 57 (25.6%) 18 (31.6%) 0.48
Drug induction, N (%) 421 (91.7%) 176 (96.2%) 196 (89.1%) 49 (87.5%) 0.02
Primary diagnosis, N (%)
 Diabetes 94 (20.2%) 41 (22.2%) 45 (20.2%) 8 (14%) 0.84
 Hypertension 125 (26.9%) 54 (29.2%) 55 (24.7%) 16 (28.1%)
 GN 66 (14.2%) 22 (11.9%) 35 (15.7%) 9 (15.8%)
 Cystic 34 (7.3%) 14 (7.6%) 15 (6.7%) 5 (8.8%)
 Other 146 (31.4%) 54 (29.2%) 73 (32.7%) 19 (33.3%)
Diabetes, N (%)
 No 30 (6.5%) 14 (7.6%) 12 (5.4%) 4 (7.0%) 0.52
 Yes, Insulin-dependent 3 (0.6%) 1 (0.5%) 1 (0.4%) 1 (1.8%)
 Yes, Not insulin-dependent 2 (1.1%) 2 (1.1%) 0 (0%) 0 (0%)
 Missing 168 (90.8%) 168 (90.8%) 210 (94.2%) 52 (91.2%)
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3.1 |. SNP association with allograft survival

Complete results of the single SNP association with time to kidney allograft failure accounting for AA deceased-donor APOL1 status in the discovery dataset are shown in Figure 1. Table 3 presents a summary of variants that were significantly associated with time to allograft failure among kidney recipients in the inverse-variance meta-analysis that combined the results observed in the discovery and replication datasets. The meta-analysis was performed using parameter estimates obtained with the fully adjusted model. Significant associations were observed in the PIPSL gene (strongest effect with rs12552330) located in the 9p21.3 region, a region that has been linked with cardiovascular disease and cardiovascular outcomes in several populations. There is also some evidence that variants in this region could modify these risks among kidney recipients.38

FIGURE 1.

FIGURE 1

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SNP association with renal allograft survival after adjustment for the effect of APOL1, donor African ancestry proportion, sex, and age, and recipient sex, age, HLA match, cold ischemia time, and peak panel reactive antibodies among recipients of African American deceased-donor kidneys. Note: The significance line was placed at 6.125 (ie, log10(7.5 × 10−7). SNPs with P-values < 0.01 were not included in the plot to reduce the size of the file. Results are for the discovery dataset only. Full GWAS data were not available in DeKAF Genomics for these analyses

TABLE 3.

Summary of SNPs with full model meta-analysis P-values ≤ 7.5 × 10−7 after accounting for the effect of APOL1

Discovery (Wake Forest, UAB, Emory) Validation (DeKAF Genomics) Meta-Analysis
SNP Chromosome Position Nearest Gene Alleles MAF Estimate SE P-value Estimate SE P-value Estimate P-value DE I2
rsl923418 6 89 267 044 RNGTT C/A 0.197 0.54 0.11 6.5 × 10−7 0.27 0.23 0.23 0.49 5.5 × 10−7 + + 0.08
rsl2552330 9 21380 237 PIPSL A/C 0.078 0.70 0.13 2.2 × 10−7 0.15 0.31 0.63 0.61 7.5 × 10−7 + + 0.62
rs2113955 10 95 669 893 SLC35G1 G/C 0.492 −0.52 0.10 1.9 × 10−7 0.16 0.21 0.43 −0.39 1.5 × 10−5 − + 0.89
rsll3665227 11 106 277 946 G/A 0.086 0.68 0.14 6.4 × 10−7 0.74 0.47 0.11 0.68 1.8 × 10−7 + + 0.00
rs9319516 16 77 782 258 NUDT7 G/A 0.139 0.65 0.12 5.7 × 10−8 0.14 0.28 0.63 0.57 2.2 × 10−7 + + 0.65
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Note: Estimate, Coefficient estimate associated with the SNP in the model; SE, standard error; DE, direction of effect; I2, A measure of heterogeneity between studies; Adjustment, APOL1 genotype and the individual admixture proportion.

10q23.33 region near the SLC35G1 gene, a region that also contains PLCE1, a gene in which several mutations affect glomerulo-genesis and response to cyclosporine therapy.39,40 Several variants in the NUDT7 gene were among the top SNPs that showed independent effects after accounting for APOL1 (P-value = 1.57 × 10−8-3.78 × 10−8) in the discovery sample (Figure 2).

FIGURE 2.

FIGURE 2

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SNP-by-APOL1 interaction effect on renal allograft survival among recipients of African American deceased-donor kidneys. Note: The significance line was placed at 6.125 (ie, log10(7.5 × 10−7). SNPs with P-values < 0.01 were not included in the plot to reduce the size of the file. Results are for the discovery dataset only. Full GWAS data were not available in DeKAF Genomics for these analyses

Table 4 shows the top hits from the GWAS for SNP-by-APOL1 interaction. This analysis identified several variants that modify the association between APOL1 and allograft failure. For example, several common SNPs within the ATAD3B gene on 1p36.33 showed evidence of an interaction effect with APOL1 to affect AA deceased-donor kidney allograft survival with P-values between 7.5 × 10−7 and 8.0 × 10−7. Interaction with APOL1 was also observed with the SEC63 gene, modifying the association between APOL1 and time to kidney allograft failure, with interaction P-values ranging from 2 × 10−9 to 3.7 × 10−8. The joint effect of each SNP and APOL1 on allograft survival is shown in Figure S4. SEC63 encodes a protein translocation regulator, which appears at the endoplasmic reticulum (ER) and ER-mitochondria contact sites. SEC63 is presented in human renal tubules and glomeruli (Human Protein Atlas). The interaction between APOL1 and SEC63 is likely both physical and functional.

TABLE 4.

Summary of SNPs-by-APOL1 interactions with full model meta-analysis P-values ≤ 7.5 × 10−7

Discovery (Wake Forest, UAB, Emory) Validation (DeKAF Genomics) Meta-Analysis
SNP Chromosome Position Nearest Gene Alleles MAF Estimate SE P-value Estimate SE P-value Estimate P-value Sign I2
rsl695847 1 1431806 ATAD3B A/C 0.09 −3.99 0.80 6.4 × 10−7 −1.34 3.90 0.73 −3.88 7.2 × 10−7 — 0.00
rs76455983 2 53 105 036 G/T 0.09 1.44 0.28 2.8 × 10−7 2.21 0.96 0.02 1.50 2.5 × 10−8 ++ 0.00
rs7582966 2 104 018 063 C/T 0.49 −1.27 0.24 1.6 × 10−7 0.13 0.88 0.89 −1.17 5.4 × 10−7 −+ 0.57
rs6906957 6 108 206 385 SEC63 A/G 0.23 1.58 0.26 2.0 × 10−9 0.87 0.64 0.18 1.48 1.3 × 10−9 ++ 0.03
rs73710129 8 129 073 950 PVT1 T/C 0.12 1.46 0.27 4.1 × 10−8 −0.15 0.64 0.82 1.22 6.5 × 10−7 +− 0.81
rs4889062 16 80 018 950 A/G 0.29 1.41 0.27 1.3 × 10−7 0.43 0.86 0.62 1.32 2.1 × 10−7 ++ 0.15
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Note: Estimate, Coefficient estimate associated with the SNP-by-APOLl interaction term in the model; SE, standard error; DE, direction of effect; I2, A measure of heterogeneity between studies; Adjustment, individual admixture proportion, ages of the donor and the recipient, sex, recipient race, transplant center, HLA match, cold ischemia time, panel reactive antibodies, donor age, and donor type (standard donor vs extended-criteria donor).

APOL1 co-immunoprecipitation and subsequent protein location analyses confirmed the physical relationship between SEC63 and the APOL1 protein complex in the ER and ER-mitochondria (See Figure S3). Additional co-immunoprecipitation may help determine whether protein complexes are formed between APOL1 and SEC63 or ATAD3B. Co-localization by immunofluorescence on kidney sections also provided evidence that the plasmacytoma variant translocation 1 (PVT1) may act on the same pathway with APOL1. PVT1 is associated with diabetic kidney disease, likely through mediation of the mechanisms involving extracellular matrix accumulation in the glomeruli.41,42 SNP-by-APOL1 interaction effects were also identified in several intergenic regions with multiple SNPs reaching the pre-specified significance thresholds on 1q41, 2p16.2, 2q12.1, 13q31.2, and 16q23.2.

Statistical power for determining an association with a survival outcome depends on the number of allograft failures. The combined endpoint of allograft failure and doubling of serum creatinine concentration after hospital discharge in the combined dataset added 93 failures. Thus, the GWAS was repeated using this combined endpoint. Results (not shown) were largely similar with a smaller standard error in some cases, resulting in lower P-values, or with more variants in the same region reaching significance.

4 |. DISCUSSION

Kidney transplantation remains the optimal and most cost-effective treatment for ESKD, leading to significant improvement in patient quality of life and better survival outcomes relative to dialysis.2 Carrying two copies of APOL1 renal-risk variants associates with CKD, ESKD, lupus ESKD, and shorter kidney allograft survival.8,11–13,24,43,44 In addition, APOL1 contributes to the observed ethnic disparities in kidney disease and poorer kidney transplant outcomes. This effect is due to HLA matching and other SES factors that render AA kidney transplant recipients more likely to receive allografts from AA deceased donors, relative to other racial/ethnic groups.7,45–49 To our knowledge, the present report is the first to present genome-wide association study data for renal allograft survival among recipients of kidneys from AA deceased donors. These analyses focused primarily on identifying genetic variants that were associated with kidney allograft survival after accounting for the effects of APOL1. We and others reported strong association between APOL1, progression of chronic kidney disease, end-stage kidney disease, and kidney allograft survival among recipients of AA DDKTs.50–52

Several SNPs in the APOL1-adjusted main effect and in the SNP-by-APOL1 interaction analyses were statistically significant at the strict Bonferroni-corrected threshold. However, these SNPs were not significantly associated with kidney allograft survival outcome in DeKAF Genomics and did not remain significant in the meta-analysis after strict adjustment for multiple testing. In fact, the unique nature of this study can be seen as one of its main limitations; there are few similar studies focused on kidney allograft survival among recipients of AA deceased-donor kidneys. DeKAF Genomics, with the stated limitation of older donors and recipients, had a lower proportion of APOL1 2 renal-risk variants carriers, significantly lower failures and shorter follow-up durations. However, DeKAF Genomics was the only other study in which attempts at independent validation were feasible. We note that APOL1 genotypes of deceased kidney donor were also associated with shorter renal allograft survival in the DeKAF Genomics portion of our cohort.

Nonetheless, genome-wide significant associations observed in the discovery sample support strong biologic relevance. The strongest effect in the meta-analysis was observed with rs9319516. NUDT7 encodes Nudix Hydrolase 7, a coenzyme A diphosphatase. The presence of NUDT7 in human kidney, especially tubule cells, is evident (Human protein Atlas). NUDT7 is primarily expressed in peroxisomes, playing central roles in regulating metabolic activities in mammalian cells and autophagy.53 Subsequent pathway analysis may help identify interaction network linked to cellular dysfunction.

The role of APOL1 in autophagy is also recognized.54,55 Dual staining of NUDT7 with APOL1 on human kidney cryosections indicated that NUDT7 did not co-localize with APOL1. NUDT7 was more likely enriched in glomerular endothelial cells while APOL1 is enriched in podocytes (Figure S1).15 This finding supports the results of the current study: multiple NUDT7 variants were associated with transplant outcomes independent of APOL1 renal-risk variants. The present analyses also revealed an interaction effect between multiple SNPs in ATAD3B and APOL1, including rs1695847 which is also an eQTL of ATAD3B ATAD3B is a mitochondrial membrane protein characterized by reduced mitochondrial metabolism and low mtDNA copies that are typical for stem cells.56 The minor allele (“C”) of rs1695847 represents lower ATADB3 expression and appears to counterbalance the negative outcome of effects of the APOL1 renal-risk genotypes on kidney allograft survival. The co-localization of ATAD3B and APOL1 in human glomerular podocytes reinforced the hypothesis that APOL1 and ATAD3B could interact, and both are mitochondrial proteins in podocytes. Multiple SNPs in SEC63 interact with APOL1 to affect allograft survival. As an example, rs6935080, a SNP in high LD with rs6906957 (D′ = 0.95), is an eQTL, and the minor allele (“T”) is associated with higher SEC63 expression (P = 3.9 × 10−5) and amplifies the negative outcome of allograft survival induced by APOL1 risk genotypes. SEC63 mediates transport of certain precursor polypeptides across the endoplasmic reticulum (ER).57,58 APOL1 is also presented in ER59; therefore, co-localization of SEC63 and APOL1 in human podocytes suggests that APOL1 and SEC63 may play a synergistic role. SEC63 is enriched in the glomeruli, the glomerular endothelial cells in addition to the podocytes.

As in native-kidney disease, nephropathy susceptibility in persons with two APOL1-renal-risk variants likely results from additional modulating factors.60,61 Previous analyses revealed that recipient race/ethnicity, longer cold ischemia time, younger recipient age at transplantation, and induction immunosuppression also associate with allograft survival. These associations could modify the SNP and/or the SNP-by-APOL1 interaction effects identified in this report. However, these gene-environment interactions on allograft survival were beyond the scope of this manuscript.

In addition to the lack of a validation sample with comparable follow-up duration, limitations in this report include the retrospective study design with outcomes being captured using the SRTR. This registry is a collection of large databases that may not have been designed with scientific research as a primary objective. However, SRTR captures allograft loss well, because it links to a variety of sources such as dialysis claims data and includes initiation of renal replacement therapy even when this information is not provided by transplant centers, along with date of re-transplantation and date of death. Ultimately, survival analyses are powered by the number of events (allograft failures). Results presented in this report had to meet the following requirements: a minimum of 10 failures among the minor allele homozygotes and at least 30 failures among AA deceased donors with two APOL1 renal-risk variants. Given that fewer failures (72 in DeKAF Genomics, compared to 210 in the discovery dataset [Emory, UAB and Wake]) were observed in the validation dataset, the effect of these restrictions on false negatives and false positives is difficult to judge.

The present study revealed that variants NUDT7 (independent effect) and variants in ATAD3B, SEC63, and PVT1 appear to interact with APOL1 to influence allograft survival among recipients of AA DDKTs. Independent replication efforts and more extensive functional studies of these genetic variants in human kidney tissues/cells and transgenic animals will contribute to our understanding of the mechanisms that are associated with renal allograft survival.

Supplementary Material

Figures 1-3
Figure 4
Figure 5

ACKNOWLEDGEMENTS

This work was supported, in part, by NIH R01 DK070941 (BIF), NIH R01 DK084149 (BIF), NIH R01 MD009055 (JD, BIF), NIH/NIAD Genomics of Transplantation 5U19-AI070119 (AKI), and NIH R01 AI140303 (AKI). The data reported here have been supplied by the Hennepin Healthcare Research Institute (HHRI) as the contractor for the Scientific Registry of Transplant Recipients (SRTR). The interpretation and reporting of these data are the responsibilities of the authors and in no way should be considered as an official policy of or interpretation by the SRTR or the United States Government.

Funding information

National Institute on Aging, Grant/Award Number: AI070119 and AI140303; National Institute of Diabetes and Digestive and Kidney Diseases, Grant/Award Number: DK070941; National Institute on Minority Health and Health Disparities, Grant/Award Number: MD009055

Footnotes

SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section.

CONFLICT OF INTEREST

Wake Forest University Health Sciences and Barry Freedman have rights to an issued US patent related to APOL1 genetic testing (www.apol1genetest.com). Dr Freedman is a consultant for AstraZeneca Pharmaceuticals and Renalytix AI.

DATA AVAILABILITY STATEMENT

The data that used in these analyses will be deposited in dbGaP following an embargo from the date of publication to allow for commercialization of research findings.

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Figure 4
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