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Genome-Wide Association of CKD Progression: The Chronic Renal Insufficiency Cohort Study
† ‡ | Afshin Parsa,* Peter A. Kanetsky, Rui Xiao,§ Jayanta Gupta, Nandita Mitra,§ †† ‡‡ Sophie Limou,¶ Dawei Xie,§ Huichun Xu,** Amanda Hyre Anderson, Akinlolu Ojo, || †††‡‡‡ John W. Kusek,§§ Claudia M. Lora, L. Lee Hamm,¶¶ Jiang He,¶¶ Niina Sandholm,*** ||| Janina Jeff,§§§ Dominic E. Raj, Carsten A. Böger,¶¶¶ Erwin Bottinger,§§§ †††† ‡‡‡‡ Shabnam Salimi,**** Rulan S. Parekh, Sharon G. Adler, Carl D. Langefeld,§§§§ |||| †††‡‡‡ Donald W. Bowden, the FIND Consortium, Per-Henrik Groop,*** †††‡‡‡ Carol Forsblom,*** Barry I. Freedman,¶¶¶¶ Michael Lipkowitz,***** ††††† †† Caroline S. Fox, Cheryl A. Winkler,§ and Harold I. Feldman, and the Chronic Renal Insufficiency Cohort (CRIC) Study Investigators
Due to the number of contributing authors, the affiliations are listed at the end of this article.
ABSTRACT The rate of decline of renal function varies significantly among individuals with CKD. To understand better the contribution of genetics to CKD progression, we performed a genome–wide association study among partic- ipants in the Chronic Renal Insufficiency Cohort Study. Our outcome of interest was CKD progression measured as change in eGFR over time among 1331 blacks and 1476 whites with CKD. We stratified all analyses by race and subsequently, diabetes status. Single-nucleotide polymorphisms (SNPs) that surpassed a significance threshold 2 of P,1310 6 for association with eGFR slope were selected as candidates for follow-up and secondarily tested for association with proteinuria and time to ESRD. We identified 12 such SNPs among black patients and six such SNPs among white patients. We were able to conduct follow-up analyses of three candidate SNPs in similar (replication) cohorts and eight candidate SNPs in phenotype-related (validation) cohorts. Among blacks without diabetes, rs653747 in LINC00923 replicated in the African American Study of Kidney Disease and Hypertension 2 2 cohort (discovery P=5.42310 7;replicationP=0.039; combined P=7.42310 9). This SNP also associated with 2 ESRD (hazard ratio, 2.0 (95% confidence interval, 1.5 to 2.7); P=4.90310 6). Similarly, rs931891 in LINC00923 2 associated with eGFR decline (P=1.44310 4) in white patients without diabetes. In summary, SNPs in LINC00923, an RNA gene expressed in the kidney, significantly associated with CKD progression in individuals with nondiabetic CKD. However, the lack of equivalent cohorts hampered replication for most discovery loci. Further replication of our findings in comparable study populations is warranted.
J Am Soc Nephrol 28: ccc–ccc, 2016. doi: 10.1681/ASN.2015101152
It is well established that progression of CKD varies Received October 20, 2015. Accepted August 25, 2016. substantially among individuals, despite similar disease A.P. and P.A.K. contributed equally to this work. R.X., J.G., and etiologies, BP,and/or glycemic control.1,2 However, our N.M. contributed equally to this work. fl understanding of factors in uencing rates of CKD pro- Published online ahead of print. Publication date available at gression is limited, with known clinical factors account- www.jasn.org. 2 ing for less than one half of the observed variability. Correspondence: Dr.AfshinParsa,UniversityofMaryland Various studies have implicated a common pathway School of Medicine, 685 West Baltimore Street, MSTF 357, Bal- for CKD progression, and results strongly suggest that timore, MD 21201. Email: [email protected] the genetic basis for progression of renal disease is, in Copyright © 2016 by the American Society of Nephrology
J Am Soc Nephrol 28: ccc–ccc, 2016 ISSN : 1046-6673/2803-ccc 1 CLINICAL EPIDEMIOLOGY www.jasn.org part, distinct from the underlying genetic determinants of CKD Graphic summaries of eGFR decline by race and diabetes status onset.3–5 Inherited variation in apolipoprotein L1 (APOL1)has are shown in Supplemental Figure 1. Mean follow-up for the been shown to explain a significant proportion of the observed various strata ranged between 4.0 and 4.2 years. racial differences in CKD progression among blacks and com- pared with whites, irrespective of initial CKD etiology6; however, Genotype Associations and Replication more comprehensive data on genetic associations across the ge- Among blacks, we report 12 single-nucleotide polymorphisms nome are still lacking. Several large population–based genome– (SNPs) with minor allele frequency (MAF) .0.03 in distinct 2 wide association studies (GWASs) have identified variants gene regions that were independently associated (P#1310 6) associated with measures of eGFR, leading to the recognition with eGFR slope (Table 2), of which four met the genome-wide 2 of novel biologic pathways related to level of renal function.7– threshold for significance of P#5310 8 (Supplemental Figure 2). 11 However, whether these gene regions are related to differ- Among whites, we discovered six SNPs in distinct gene 2 ential progression of CKD remains to be elucidated.12 regions that were associated (P#1310 6)witheGFRslope To better understand the contribution of inherited genes to (Table 3), which did not reach the threshold for genome- CKD progression, we performed a GWAS of eGFR change over wide significance (Supplemental Figure 3). time among participants with established CKD enrolled in a Of the 18 total candidate SNPs across both groups, seven were prospective, observational cohort study: the Chronic Renal not readily available from either observed or imputed genotypes Insufficiency Cohort (CRIC) Study. in the replication studies (Figure 1), and appropriate proxy SNPs in strong linkage disequilibrium (LD) with our discovery SNPs could not be identified. Reasons for unavailability of existing RESULTS genotyped SNPs in the replication studies were either that the discovery SNP was specific to our genotype platform (i.e.,not Study Participants available in the HapMap database) or unavailable imputed data Table 1 summarizes selected demographic and clinical informa- in some of the black replication cohorts, limiting the number of tion overall and in persons with and without diabetes for the 3074 available markers. We were able to attempt replication for three CRIC Study participants who were genotyped and included in our SNPs associated with progression in the CRIC Study black non- analyses; 49% were of African descent, and 46% had diabetes. As diabetic subcohort in the African American Study of Kidney expected, many of the baseline characteristics were different be- Disease and Hypertension (AASK), also consisting of nondia- tween black and white participants overall as well as within strata betic blacks with repeated eGFR measures over time, and vali- defined by diabetes status. Our eGFR slopes (Table 1) were on the dation of another eight SNPs in cohorts with related phenotypes basis of standardized annual measures, with a median number of and/or populations (Figure 1). six measures and interquartile ranges of five to eight and four to One of the three SNPs for which we had access to a similar seven measures in our white and black cohorts, respectively. population and outcome measure (i.e., the AASK), rs653747,
Table 1. Baseline demographics in whites and blacks combined and stratified by baseline diabetes status Reported Combined Diabetes Nondiabetes Characteristic Variable White, n=1581 Black, n=1493 White, n=632 Black, n=771 White, n=949 Black, n=722 Age, yr Mean (SD) 59.0 (11.0) 58.0 (10.7) 59.5 (10.0) 59.8 (9.4) 58.7 (11.6) 56.1 (11.6) Sex Men 945 (59.8) 726 (48.6) 416 (65.8) 359 (46.6) 529 (55.7) 367 (50.8) eGFR, ml/min per 1.73 m2 Mean (SD) 43.8 (12.8) 43.7 (14.0) 41.8 (12.2) 42.0 (13.3) 45.1 (13.1) 45.6 (14.5) eGFR slope Mean (SD) 20.8 (3.4) 22.2 (4.7) 21.3 (4.3) 22.9 (5.0) 20.5 (2.6) 21.4 (4.3) Follow-up period, yr Mean (SD) 4.1 (1.2) 4.1 (1.2) 4.0 (1.2) 4.0 (1.2) 4.1 (1.1) 4.2 (1.2) Current smoker Yes 148 (9.4) 285 (19.1) 55 (8.7) 128 (16.6) 93 (9.8) 157 (21.7) Hypertension Yes 1250 (79.1) 1387 (92.9) 556 (88.0) 733 (95.1) 694 (73.1) 654 (90.6) Systolic BP, mmHg Mean (SD) 121.8 (18.5) 132.9 (23.2) 125.5 (19.2) 136.2 (23.5) 119.4 (17.6) 129.3 (22.4) Diastolic BP, mmHg Mean (SD) 69.0 (11.4) 73.8 (13.9) 66.8 (11.3) 71.4 (13.5) 70.4 (11.2) 76.4 (14.0) ACE-I or ARB Yes 1053 (67.0) 1063 (71.8) 510 (81.2) 619 (80.8) 543 (57.6) 444 (62.1) BMI, kg/m2 Mean (SD) 31.2 (7.5) 33.5 (8.4) 33.8 (8.2) 35.2 (8.2) 29.4 (6.3) 31.7 (8.1) LDL, mg/dl Mean (SD) 99.2 (32.2) 106.2 (37.2) 89.1 (29.9) 102.4 (38.1) 106.0 (31.9) 110.2 (35.8) Triglycerides, mg/dl Mean (SD) 163.8 (119.0) 140.2 (111.0) 182.6 (135.9) 152.7 (133.1) 151.3 (104.5) 126.8 (78.7) Lipid-lowering drugs Yes 995 (63.3) 830 (56.0) 508 (80.9) 540 (70.5) 487 (51.6) 290 (40.6) Hemoglobin, g/dl Mean (SD) 13.2 (1.6) 12.2 (1.7) 12.6 (1.6) 11.8 (1.6) 13.5 (1.5) 12.6 (1.7) Hemoglobin A1C, % Mean (SD) 6.4 (1.4) 6.9 (1.7) 7.5 (1.5) 7.9 (1.8) 5.6 (0.5) 5.8 (0.6) Uric acid, mg/dl Mean (SD) 7.0 (1.9) 7.7 (1.9) 7.3 (1.9) 7.9 (1.9) 6.9 (1.8) 7.6 (1.8) 24-h Urine protein, g Mean (SD) 0.7 (1.9) 1.1 (2.3) 1.1 (2.6) 1.5 (2.8) 0.4 (1.1) 0.6 (1.3) Proteinuria is indicated by 24-hour urine collection. ACE-I, angiotensin–converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index.
2 Journal of the American Society of Nephrology J Am Soc Nephrol 28: ccc–ccc,2016 www.jasn.org CLINICAL EPIDEMIOLOGY
Table 2. Top candidate loci for eGFR decline and replication or validation results in blacks P Value Study SNP Chromosome Gene Area MAF Allelic b SEM b Original Meta Replication results 2 2 D-CRIC, non-DM rs653747 15 LINC00923 0.47 20.27 0.05 5.42310 7 7.2310 9 R-AASK rs653747 0.48 20.27 0.13 0.04 2 2 D-CRIC, non-DM rs12770303 10 MIR378C (TCERG1L) 0.11 20.52 0.09 5.85310 9 1.6310 8 R-AASK rs12770303 0.13 20.19 0.19 0.32 2 2 D-CRIC, non-DM rs1220081 9 LINC01241 0.36 0.31 0.58 8.7310 8 1.17310 6 R-AASK rs1220081 0.35 20.03 0.14 0.83 2 D-CRIC, non-DM rs4492355 8 ADCY8 0.05 20.78 0.15 1.25310 7 NA R-AASK NA NA NA NA NA Validation results 2 D-CRIC all rs73690944 7 BBS9 0.06 20.44 0.09 7.9310 7 NA V-AASK, non-DM No proxy NA NA NA NA 2 D-CRIC all rs9290337 3 EGFEM1P 0.26 1.06 0.21 5.36310 7 0.06 V-AASK, non-DM rs9290337 0.29 20.20 0.15 0.17 2 2 D-CRIC all rs12540238 7 SEMA3A 0.07 20.40 0.08 4.46310 7 1.1310 6 V-AASK, non-DM rs12540238 0.05 20.04 0.32 0.91 2 D-CRIC all rs62231385 20 LINC01441 0.09 20.37 0.07 1.49310 7 NA V-AASK, non-DM No proxy NA NA NA NA NA 2 D-CRIC, DM rs116356141 10 RPA2P2 0.06 20.58 0.11 2.42310 7 NAa V-FIND, DMb rs12573318 0.08 20.08c 0.19 0.67 V-WF, DMb rs12573318 0.09 20.19c 0.15 0.19 2 D-CRIC, DM rs198257 14 TMEM260 OTX2 0.31 0.31 0.06 7.45310 7 NAa V-FIND, DMb rs198257 0.34 20.04c 0.11 .703 V-WF, DMb rs4567630 0.32 20.03c 0.08 .766 2 D-CRIC, DM rs10927223 1 C1orf100 0.03 21.39 0.19 4.66310 13 NA V-FIND, DM No proxy NA NA NA NA 2 D-CRIC, DM rs79239111 5 ALDH7A1 0.06 20.86 0.16 2.6310 8 NA V-FIND, DM No proxy NA NA NA NA Results show our top eGFR decline–associated SNPs in blacks adjusted for age, sex, and population admixture along with replication/validation results. b,Allelic effect estimate for eGFR slope on the basis of additive model; Meta, combined P values of discovery and replication cohorts; D-CRIC, non-DM, discovery group CRIC Study participants without diabetes; R-AASK, replication in the AASK cohort; NA, results not available; D-CRIC all, all discovery group CRIC Study participants; V-AASK, non-DM, validation in the AASK cohort participants without diabetes; D-CRIC, DM, discovery group CRIC Study participants with diabetes; V-FIND, DM, validation in the FIND Study cohort patients with diabetes; V-WF, DM, validation in the Wake Forest Study cohort patients with diabetes. aMetaresults not calculated due to significant heterogeneity in cohort study designs. bCase-control study. cCase-control–based logistic model. replicated (P=0.04). The allele effect sizes in the CRIC Study and the correction for multiple testing (Table 4). Within our black sub- AASK were identical, with each allele being associated with a 0.27 group, after adjustment for multiple comparisons (P,0.0038), ml/min per 1.73 m2 faster annual decline in eGFR. When combin- we found four of our 12 eGFR slope candidate SNPs to be ing the results from the CRIC Study and the AASK, the discovery associated with ESRD (Table 4). Although eGFR decline and 2 P value of became more statistically significant from P=5.42310 7 time to ESRD are not independent markers of CKD progres- 2 to P=7.42310 9, thus exceeding the genome–wide significance sion, the association with incident ESRD provides an additional threshold. The minor allele rs653747-T has a frequency of 47% gauge of the potential clinical significance of these findings. and is in LINC00923, an RNA gene on chromosome 15q21. We were unable to validate any of the eight tested SNP markers in our Association with Proteinuria validation study populations. A list of the genes located in proximity Within our white subgroup, two of the six eGFR slope can- of our top SNPs is summarized in Supplemental Tables 1 and 2 didate SNPs were associated with 24-hour proteinuria but along with a brief summary of their known functions. were not robust to statistical correction for multiple testing (Table 4). Within our black subgroup, after adjustment for Association with Incident ESRD multiple comparisons (P,0.0038), we found five of our 12 We secondarily looked at the association between our eGFR slope candidate SNPs to be associated with proteinuria eGFR decline candidate SNPs and time to ESRD. Within (Table 4). We also noted physiologic direction–consistent our white subgroup, one of the six candidate SNPS was asso- association between our identified SNP alleles with eGFR ciated with incident ESRD, which was robust to statistical slope, proteinuria, and time to ESRD (e.g.,fastereGFR
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Table 3. Top candidate loci for eGFR decline and validation results in whites significant, consistent with a high level of Study SNP Chr Gene Region MAF Allelic b SEM b P Value collinearity between the two measures. 2 D-CRIC all rs1182475 20 PHACTR3 0.35 0.65 0.13 8.0310 7 V-CKDGen rs1182475 0.36 20.001 0.016 0.96 Pathway-Based Analysis V-FinnDiane rs1182475 0.31 0.07 0.15 0.65 We completed gene set analysis (GSA) path- 2 D-CRIC, DM rs710956 19 KDM4B 0.55 20.28 0.08 8.65310 7 way–based analysis separately within the V-FinnDiane rs710956 0.64 20.21 0.16 0.18 black and white subgroups as well as within 2 D-CRIC, non-DM rs10501939 11 CNTN5 0.08 20.43 0.06 8.46310 7 the four subgroups jointly defined by ances- V-CKDGen rs10501939 0.07 0.013 0.03 0.66 tral race and presence of diabetes. From 27 D-CRIC, non-DM rs17012778 2 LTBP1 0.11 20.38 0.08 4.03310 these, we identified 12 and 15 biologic path- V-CKDGen rs17012778 0.10 0.026 0.03 0.32 ways that were statistically significantly asso- 2 3 27 D-CRIC, non-DM rs73133086 12 BBS10 (OSBPL8) 0.08 0.47 0.09 1.36 10 ciated with eGFR change in six of six and five V-CKDGen No proxy NA NA NA NA 27 of six strata, respectively (Supplemental Ta- D-CRIC, non-DM rs10926598 1 EXO1 MAP1LC3C 0.06 20.59 0.11 1.62310 V-CKDGen No proxy NA NA NA NA ble 3). Interestingly, we noted associations of Results show our top eGFR decline associated SNPs in whites adjusted for age, sex and population eGFR decline with several cardiomyopathies admixture, along with replication/validation results. Meta-analysis results were not calculated in whites as well as ion channel–related pathways. due to heterogeneity in cohorts study design. Chr, chromosome; b, Allelic effect estimate on the basis Our molecular network analysis of our 18 of additive model; D-CRIC all, all discovery group CRIC Study participants; V-CKDGen, validation in the CKDGen cohort; V-FinnDiane, validation in the FinnDiane cohort; D-CRIC, DM, discovery group CRIC independent SNP markers was on the basis of Study participants with diabetes; D-CRIC, non-DM, discovery group CRIC Study participants without 16 a priori proximity–based assigned genes, diabetes; NA, not available. which revealed 11 of the potential 16 candidate genes to functionally interact with 268 mole- cules, of which 206 are known to be involved decline was associated with increased proteinuria and in- in renal, cardiovascular, or immunologic disease (Supplemental creased hazard ratio for ESRD). Figure 4, Supplemental Table 4). More specifically, we found eight We also noted, as expected, a significant correlation be- of our aprioriassigned gene products to be associated with renal tween eGFR decline and our 24-hour measures of proteinuria and/or urologic function, as noted in Supplemental Figure 5. This of 20.61 among blacks (P,0.001) and 20.42 among whites represented a statistically significant over–representation of estab- (P,0.001). After adjusting for proteinuria, the association lished renal and/or urologic disease–related molecules (P,0.0001) between our SNP markers and eGFR decline was no longer (additional details are in Supplemental Material).
Figure 1. Study participant selection for GWAS groups and matching validation or replication studies. Total of six GWAS groups (all, diabetes mellitus [DM], and non-DM groups in whites and blacks separately). Candidate SNP indicates the number of candidate SNPs identified. Tested SNP indicates the number of SNPs available for testing in the validation or replication study group. Replication indicates a confirmatory study that was similar to the CRIC Study. Validation indicates a confirmatory study that differed from the CRIC Study on the basis of either study population and/or outcome measure. AA, black; EA, white; QC, quality control.
4 Journal of the American Society of Nephrology J Am Soc Nephrol 28: ccc–ccc,2016 www.jasn.org CLINICAL EPIDEMIOLOGY
Table 4. Association between eGFR slope loci, proteinuria, and ESRD among whites and blacks Proteinuria ESRD Group SNP Gene b SEM b P Value HR 95% CI P Value Blacks All rs9290337 EGFEM1P 20.05 0.02 0.03 0.88 0.72 to 1.07 0.21 All rs73690944 BBS9 0.08 0.05 0.09 1.21 0.85 to 1.73 0.28 All rs12540238 SEMA3A 0.10 0.04 0.02 1.48 1.09 to 2.01 0.01 All rs62231385 Unknown 0.07 0.04 0.06 1.35 1.02 to 1.77 0.03 2 DM rs116356141 RPA2P2 0.26 0.07 3.90310 4a 1.77a 1.23 to 2.54 0.002a 2 2 DM rs10927223 C1orf100 0.32 0.09 8.03310 4a 2.01 1.19 to 3.42 9.6310 3 2 DM rs79239111 ALDH7A1 0.23 0.09 8.44310 3 1.83 1.13 to 2.97 0.01 2 DM rs198257 TMEM260-OTX2 20.11 0.04 0.003a 0.65a 0.51 to 0.82 4.23310 4a Non-DM rs4492355 ADCY8 0.11 0.05 0.04 1.98 1.14 to 3.45 0.02 2 Non-DM rs1220081 LINC01241 20.04 0.02 0.08 0.60a 0.45 to 0.81 9.93310 4a 2 2 Non-DM rs653747 LINC00923 0.08 0.02 3.90310 4a 2.01a 1.49 to 2.71 4.87310 6a 2 Non-DM rs12770303 TCERG1L-MIR378C 0.13 0.03 2.53310 4a 1.62 1.11 to 2.36 0.01 Whites All rs1182475 PHACTR3 0.02 0.02 0.28 0.99 0.76 to 1.29 0.95 DM rs710956 KDM4B 0.06 0.03 0.08 1.33 0.93 to 1.90 0.12 Non-DM rs10501939 CNTN5 0.07 0.03 0.03 0.73 0.35 to 1.51 0.40 Non-DM rs17012778 LTBP1 0.03 0.03 0.29 0.96 0.49 to 1.88 0.91 Non-DM rs73133086 BBS10 0.08 0.03 0.01 1.13 0.55 to 2.3 0.74 2 Non-DM rs10926598 EXO1 0.04 0.04 0.33 3.92a 2.00 to 7.68 6.83310 5a Results show our top eGFR decline–associated SNPs with proteinuria and time to ESRD in the same CKD group in which the top SNP association was identified. All analyses were adjusted for age, sex, eGFR, and population admixture. Variants positively and negatively associated with proteinuria are associatedwithincreased and decreased hazards for ESRD, respectively. b, Allelic effect estimate on the basis of additive model adjusted for age, sex, and baseline eGFR; HR, hazard ratio for incident ESRD adjusted for age, sex, and baseline eGFR; 95% CI, 95% confidence interval; All, all CRIC Study participants; DM, CRIC Study participants with di- abetes; Non-DM, CRIC Study participants without diabetes. aP value indicates statistical significance after Bonferroni adjustment for number of tested SNPs in each racial group.
Secondary Analyses ily identified from GWASs in population-based cohorts that Crossrace Comparison assessed cross-sectional and/or longitudinal measures of We also assessed potential consistency of genetic associations eGFR. Of the 39 candidate markers previously shown to be identified in the CRIC Study across racial groups. To account associated with cross–sectional eGFR measures and/or ESRD for differences in LD and haplotype blocks across race, we among whites,8–10,13–15 we found three (WDR37, SLC3A2, elected to look up associations of all SNP markers within a and DAB2) (Table 5) to be associated with eGFR decline in 100-kb flanking region of each discovery SNP. Of the 12 iden- the CRIC Study (Supplemental Table 7). Among blacks, none tified candidate gene regions among the CRIC Study black of the 12 GWAS–based previously identified candidate SNPs participants, five (BBS9, C1orf100, ALDH7A1, TUSC1,and were associated with eGFR decline11 (Supplemental Table 8). LINC00923) had SNPs that were associated with eGFR decline The positive association between APOL1 genotype and eGFR in the white cohort. Of the six in white candidate SNPs, four decline in the CRIC Study has been previously published.6 gene regions (PHACTR3, LTBP1, CNTN5,andBBS10)were associated with eGFR decline in the black cohort (Supplemen- tal Tables 5 and 6). Of the total nine crossrace–associated gene DISCUSSION regions, none of the lead and crossrace-identified SNPs were identical, although they were both located within the same Current evidence suggests that genetic variation may account gene for BBS9, LINC00923, CNTN5,andLTBP1. All associations for a meaningful portion of the observed variability in CKD were adjusted at the gene level for the number of independent decline. In a prospectiveCKD cohort, we performed a GWAS to SNPs across each specific gene region. These findings provide assess genetic factors associated with longitudinal decline suggestive evidence of crossrace validation of these genomic in renal function separately among blacks and whites and areas. stratified by diabetes status. We identified a total of four SNP markers in four distinct gene regions that were associated with the rate of renal function decline with genome-wide sig- Replication of Published Renal Function Gene Markers 2 We also sought to determine whether previously published nificance (P#5310 8) and noted an additional 14 distinct 2 genetic markers of renal function traits could be validated in gene regions that reached nominal significance (P,10 6). the CRIC Study. Specifically, we selected SNP markers primar- Although replication and validation efforts were limited by
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Table 5. Validated whites candidate SNP look up in the CRIC Study SNP Associated Gene Allelic b, All P Value, All b,DM P Value, DM Allelic b, Non-DM P Value, Non-DM rs10794720 WDR37 20.21 0.0037a 20.27 0.021a 20.16 0.085 rs489381 SLC3A2 20.11 0.048a 20.24 0.0064a 20.0002 0.99 rs11959928 DAB2 0.11 0.0045a 0.23 0.0001a 20.028 0.55 Results show the three of 39 literature–based renal–related candidate SNPs that were significantly associated with eGFR decline in our cohort. Supplemental Tables 7and8showcompletelistsofcandidategeneresults.b, Allelic effect estimate on the basis of additive model adjusted for age and sex; All, all white CRIC Study participants; DM, participants with diabetes; Non-DM, participants without diabetes. aSignificant P value. differences in study design, we were able to securely replicate upstream is arrestin domain containing 4 (ARRDC4), a protein one SNP, rs653747, located in RNA coding gene LINC00923 coding gene. ARRDC4 is expressed in renal tubules and glomer- among blacks without diabetes and another LINC00923 SNP, uli, although there is no apparent LD between our markers and rs931891, which was also strongly associated with eGFR de- ARRDC4 variants, making it less likely to be driving the noted cline among whites without diabetes. LINC00923 SNP associations (Supplemental Figure 6). The strength of association between our rs653747 A notable strength of the CRIC Study is in the enrollment of LINC00923 SNP in blacks, supported by the replication in individuals with established CKD along with standardized pro- the AASK, and validation of the rs931891 LINC00923 SNP spectively assessed measures of renal disease progression and in whites in addition to the secondary associations with pro- confounders. Capitalizing on this design, our outcome of in- teinuria and increased risk for incident ESRD provide evi- terest has been on the basis of the carefully collected repeated dence for the clinical relevance of this gene region to CKD longitudinal measures of kidney function (eGFR) in well char- outcomes. The two intragenic SNPs in blacks and whites are acterized patients. However, few existing CKD cohorts with not in LD, which is not surprising, because LD patterns across comparable longitudinally collected phenotypic measures are races are very varied. LINC00923 is a long intergenic nonpro- available. Thus, there are limited suitable studies to replicate tein coding RNA (lncRNA) spanning 302 kb and has eight our findings. We selected the CKDGen9 and the Finnish described splice variants (http://www.ensembl.org). Notably, Diabetic Nephropathy Study (FinnDiane)25,26 cohorts for val- LINC00923 RNA has been shown to be expressed in renal idation of our findings among whites. The CKDGen aggre- glomeruli and endothelial cells. Interestingly, in renal gates multiple population–based cohorts (non-CKD), and HEK293 cells, hypertension–related, calcium–regulated gene thus, as a whole, it represents populations and methodologies (HCaRG) –transfected cells showed an increase in LINC00923 that incorporate variable amounts of observation time and expression (http://www.ncbi.nlm.nih.gov/geo/tools/profile- often have limited measures of eGFR. The FinnDiane consists Graph.cgi?ID=GDS2426:234017_at).16 HCaRG has been of relatively younger participants with type 1 diabetes and shown to be involved in several major processes contributing varied kidney function with an overall normal mean eGFR. to kidney repair, control of cell proliferation, differentiation, This is in contrast to the CRIC Study, in which participants are and cell migration.16–18 These findings suggest that older, have primarily type 2 diabetes, and have reduced levels LINC00923 may be involved in HCaRG pathway–mediated of kidney function (CKD). kidney repair processes. However, the specific role and targets For the CRIC Study black population, we sought replication have yet to be elucidated. Interestingly, rs653747 is predicted in the AASK27 and validation in the Family Investigation of to alter the transcription factors binding domains in Pou2f2 Nephropathy and Diabetes (FIND) Study28 and the Wake For- and Pou6f1 (http://www.broadinstitute.org/mammals/haploreg/ est Study cohorts.29,30 The AASK study population is modest haploreg.php). Pou2f2 (also known as Oct2) is known to regulate in size, includes black with CKD who did not have diabetes at Ig gene expression dysregulation and has been associated with study entry, and is the only CKD replication cohort in which a Hodgkin lymphoma,19 but there is no biologic confirmation of standardized eGFR slope measure was available. The FIND these potential predicted effects to date. Study and the Wake Forest Study were ESRD case-controls lncRNA genes, similar to the nonprotein coding microRNAs studies with no longitudinal measures of CKD progression. genes, are increasingly the target of intense scrutiny, because they Additionally, our reliance on in silico–based replication efforts are emerging as a key class of gene expression regulators. Most precluded replication for seven of our SNPs due to lack notably, various lncRNAs have been shown to be associated with of coverage or appropriate SNP proxies on the available diabetes, vascular complications, AKI-related outcomes, diabetic replication genotyping platforms. It should be noted that nephropathy, blood vessel inflammation, kidney extracellular the previously reported association between APOL1 and matrix accumulation, inflammation, and fibrosis.20–24 Thus, a CKD progression is dependent on an APOL1 G1/G2 recessive potential direct role of LINC00923 and CKD progression is bi- haplotype analysis,6 whichwasnotfullygenotypedonour ologically plausible. Conceivably, our observed LINC00923 SNP GWAS platform and accordingly, was not evident in our associations may be driven by genetic variations in other genes GWAS analyses that use a standard single–marker analysis that might be in LD with these variants. For example, 300 kb within an additive model. Separate APOL1 G1/G2 direct
6 Journal of the American Society of Nephrology J Am Soc Nephrol 28: ccc–ccc,2016 www.jasn.org CLINICAL EPIDEMIOLOGY genotyping and haplotypes analyses for our CRIC Study par- cohorts. In addition, a new cohort of 1500 study participants is ticipants have previously shown a strong association with currently being recruited into the CRIC Study and may be a CKD progression.6 more suitable cohort for replication in the future. As such, Of interest, two of our top SNPs markers (rs73690944 and many of our current findings can be viewed within the context rs73133086) (Supplemental Figures 2J and 3D) fell within the of a discovery study, with full replication/validation pending. BBS9 and BBS10 gene regions known to cause renal malfor- Our study has several limitations. Foremost, despite iden- mations and renal failure (Bardett–Biedl syndrome 9 and 10, tifying numerous novel candidate loci and genes, we were only respectively).31 A recent curation of all known Mendelian able to attempt replication for a limited number of our iden- genes associated with renal phenotypes showed approximately tified SNPs due to lack of comparable cohorts and/or lack of 1% of the genome (278 of approximately 25,000 genes) to have existing coverage of some of our candidate SNPs in our rep- an established renal phenotype,32 which is in stark contrast to lication cohort datasets. In addition, we had limited statistical the 11% noted in our analysis and suggests that common var- power in our diabetes status–based stratified analyses. We also iants within Mendelian genes may contribute to general CKD recognize that the assignment of SNPs to genes by proximity, progression. However, because we are not able to confirm as often standard in genome–wide association–based pathway that these associated variants are causal for differential BBS9 analyses, can result in wrongful gene assignment. However, and BBS10 expression, these findings should be viewed as such nonsystemic errors generally result in biases toward the suggestive. null, potentially leading to false negative as opposed to false We also performed several predefined secondary analyses. positive findings. Lastly, the precise etiology of CKD in par- We found nearly one third of our eGFR decline gene markers to ticipants was not ascertained. Strengths of our study include have a significant association with proteinuria, suggesting a the relatively large well characterized CRIC Study with sub- potential shared genetic pathway between proteinuria and stantial representation of both blacks and whites from centers CKD progression. However, because renal decline and pro- across the United States, participants with diabetes, low rates teinuria were not independent of each other, we are unable of missing data, and measurement of rate of change of kidney to delineate if the joint associations between our eGFR decline function on the basis of standardized repeated measures. SNPs and proteinuria are due to shared genetic versus non- In summary, we have identified LINC00923 RNA gene var- genetic factors or a combination of both. iants, an RNA gene expressed in glomeruli, to be associated Several large population–based GWASs have successfully with CKD progression in individuals without diabetes as identified numerous gene loci that have been associated with noted by measures of eGFR decline, proteinuria, and incident cross-sectional measures of eGFR and albuminuria, of which a ESRD. We also found additional potentially promising novel few are associated with CKD.13,15,33 We tested to see if any candidate genes associated with renal function decline that of these candidate SNPs would be associated with rate of warrant further replication in comparable study populations eGFR decline in patients with established CKD and found with longitudinal measures of kidney function. three of them to be associated with CKD progression. These findings further highlight the clinical relevance of gene vari- ants identified in a nondiseased population to clinical disease CONCISE METHODS progression. Our pathway analysis revealed statistically significant en- Study Population richment of numerous canonical biologic pathways. Three of A total of 3939 men and women were enrolled in the CRIC Study our top 17 identified pathways were related to cardiovascular between June of 2003 and August of 2008. The study design details and complications: arrythmogenic, dilated, and hypertrophic participant characteristics at baseline have been previously pub- cardiomyopathy. These suggestive findings promote the lished.35,36 Briefly, participants were eligible for the study if they hypothesis that there may potentially be shared genetic path- were between 21 and 74 years of age with an eGFR between 20 and ophysiologic pathways between cardiomyopathy and CKD 70 ml/min per 1.73 m2. Exclusion criteria included GN requiring progression. immunosuppressive therapy, advanced heart failure, cirrhosis, and Lastly, our molecular networks analysis of our 18 identified polycystic kidney disease. Institutional review boards at participating gene loci showed a clear over-representation of renal disease– institutions approved the study protocol, and all study participants related pathways. This noted enrichment in renal-related provided written informed consent, including specific consent for pathways suggests the presence of additional true positive as- genetic investigations. sociations among our identified candidate SNPs. Indeed, our single replicated marker was within the only set of well Data Collection matched nondiabetic black individuals from the AASK co- Demographic characteristics, self–reported medical history, anthro- hort. Importantly, new large–scale cohorts of patients with pometric measures, and medication use were ascertained at baseline. CKD, such as the German Chronic Kidney Disease Study,34 Serum creatinine was measured annually using an enzymatic method that share common key study design elements with the CRIC on an Ortho Vitros 950 (Ortho Biotech Products, Bridgewater, NJ) Study have been initiated and can serve as future replication through October of 2008 and by the Jaffe method on a Beckman
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Synchron System (Beckman Coulter, Inc., Brea CA) thereafter. All Three distinct sets of results were available for the white and black serum creatinine measures were standardized to isotope dilution subgroups corresponding to analysis of main effects as well as analyses mass spectrometry traceable values. Serum cystatin C was measured stratified by diabetes status for a total of six GWAS analyses (e.g., using a particle–enhanced nephelometric immunoassay on a Dade- among all blacks, among blacks with diabetes, and among blacks Behring BNII (Dade Behring, Inc., Newark, NJ). GFR was estimated without diabetes). from a previously developed and validated equation on the basis of measures from the CRIC Study participants.37 Diabetes was defined Replication and Validation Studies as a fasting glucose $126 mg/dl, a nonfasting glucose $200 mg/dl, or We used a two-tiered approach to determine those SNP markers to use of insulin or an oral hypoglycemic agent. bring forth into replication or validation. Our primary approach was focused on associations of eGFR slope and observed genotypes with an 26 Genotyping MAF$0.03 and a P value #1310 . We then augmented this analytic A total of 3635 CRIC Study participants were genotyped using the approach by assessing imputed genotypes with MAF$0.05. Our a Illumina HumanOmni 1-Quad Array Platform, and 3527 samples priori–set necessary conditions for claiming positive replication passed quality control metrics. Using principal components and for any given SNP were on the basis of meeting three criteria: (1) multidimensional scaling, we identified subgroups of 1581 and direction consistent effect association, (2)aP value ,0.05, and (3)a 1493 participants with white and black ancestry, respectively combined P value for our discovery and replication cohort that is (other ancestries were excluded) (Supplemental Material). We lower than the discovery P value. As suggested by Igl et al.,42 we used also completed SNP marker–level quality control in each subgroup. the term replication when the confirmatory study was similar to the Lastly, we completed genome-wide imputation on the basis of CRIC Study in terms of study population and phenotypic measures. the 1000 Genomes mixed race/ethnicity (ALL) genetic backbone We used the term validation when the confirmatory study differed in (NCBI build 37, release date March of 2012; n=1093). More some important aspect (for example, sample population or phenotype detailed information on genotyping methods is provided in measurement) from the CRIC Study. Hence, we sought to replicate or Supplemental Material. validate findings from our discovery phase in several existing studies that assessed eGFR change over time, and we also attempted to validate GWAS Outcomes and Primary Exposures selected discovery findings in ESRD case-control studies when no Our primary outcome was the rate of kidney function decline (eGFR available cohorts with eGFR change were available (Figure 1). For slope), which was calculated from a linear model of multiple eGFR discovery-phase findings among blacks, in silico and de novo replication values per person measured annually. To ensure the adequacy of or validation was provided by the AASK27 cohort with data on eGFR using a linear model of decline in eGFR in the CRIC Study population, slope in a CKD population and the Wake Forest Study29,30 and the the model performance of linear models and nonlinear models had FIND Study28 ESRD case-control studies. For discovery findings been previously compared and found to be equivalent. Accordingly, among whites, in silico replication was provided by the CKDGen con- the default modeling for eGFR decline in the CRIC Study–based sortium9,43 and the FinnDiane,25,26 both reporting eGFR slope but in analyses is on the basis of linear modeling. Our primary exposures populations with normal-range eGFR. These studies are described in were SNP-based genotypes. We used genetically derived ancestry to further detail in Supplemental Tables 9 and 10. determine race subgroups (white and black) for primary analyses Results for our CRIC Study discovery and the AASK replication (details in Supplemental Material) with secondary stratification by cohorts were meta-analyzed into a summary statistic. However, the presence or absence of diabetes. given significant heterogeneity in the CRIC Study versus our validation cohorts in terms of both population and outcome mea- Statistical Analyses sures, as noted above, those results were not combined into a single We used descriptive statistics to compare clinical characteristics across summary statistic. race and diabetic subgroups, including mean duration of follow-up and mean of eGFR slopes. Association with Proteinuria and Incident ESRD We used PLINK software to conduct sample– and SNP marker– Proteinuria is one of the strongest predictors of CKD.44,45 Thus, we level quality control assessments38 and Eigenstrat39 to estimate principal tested whether any of our 18 candidate discovery SNPs associated components for genetic ancestry analysis. To determine associations with eGFR decline in the CRIC Study were independently associated of genotype and eGFR outcome, we regressed eGFR slopes on genotypes with our 24 hour–based baseline urinary protein measures. using linear regression models implemented in PLINK assuming an Wealso secondarily looked atthe associationof our 18eGFR slope– additive genetic model and adjusting for age, sex, and population associated SNPs with time to ESRD using Cox proportional hazards stratification (top three and 10 principle components for analyses models to adjust for age, sex, ancestry, and baseline eGFR. among the white and black subgroups, respectively). Imputation was conducted using IMPUTE, version 2 software.40 To account for un- Pathway Analyses certainty involved in the imputation process, we analyzed associa- In contrast to single–SNP marker analyses, pathway-based ap- tions of imputed SNP markers with eGFR outcomes using SNPTEST proaches assess the combined effects of many loci within defined software41 and included age, sex, and population stratification as biologic pathways. We used the software tool GSA-SNP to conduct covariates. standardized pathway–based analysis of canonical pathways
8 Journal of the American Society of Nephrology J Am Soc Nephrol 28: ccc–ccc,2016 www.jasn.org CLINICAL EPIDEMIOLOGY
(Biocarta, KEGG, and Reactome) and gene ontology (GO) gene sets University, Cleveland, OH), and Raymond R. Townsend (University of (GO biologic processes, GO cellular components, and GO molecular Philadelphia, Philadelphia, PA). functions) obtained from the Molecular Signatures Database, version 4.46,47 For each pathway, GSA-SNP software provides a P value cor- rected for false discovery rate to account for multiple comparisons. DISCLOSURES We also used Ingenuity Systems (www.ingenuity.com) to explore None. the molecular network and interactions among our identified top candidate genes (details in Supplemental Material). REFERENCES Crossrace Comparison We also assessed for potential association of identified eGFR decline 1. Borch-Johnsen K, Nørgaard K, Hommel E, Mathiesen ER, Jensen JS, gene regions across racial groups. Toaccount for differences in LD and Deckert T, Parving HH: Is diabetic nephropathy an inherited complication? Kidney Int 41: 719–722, 1992 haplotype blocks across race, we elected to look up associations of all 2. Hunsicker LG, Adler S, Caggiula A, England BK, Greene T, Kusek JW, fl SNP markers within a 100-kb anking region of each discovery SNP. Rogers NL, Teschan PE: Predictors of the progression of renal disease in We then adjusted for gene–level multiple comparisons by accounting the Modification of Diet in Renal Disease Study. Kidney Int 51: 1908– for the total number of independent SNPs across each crossrace gene 1919, 1997 region look up using SNPSpD (http://genepi.qimr.edu.au/general/ 3. Satko SG, Freedman BI, Moossavi S: Genetic factors in end-stage renal disease. Kidney Int Suppl 94: S46–S49, 2005 daleN/SNPSpD/). The threshold for each crossrace gene look up 4. Spray BJ, Atassi NG, Tuttle AB, Freedman BI: Familial risk, age at onset, was derived by dividing P=0.05 by the derived number of indepen- and cause of end-stage renal disease in white Americans. JAmSoc dent SNPs within each specific gene region. Nephrol 5: 1806–1810, 1995 5. Schelling JR, Zarif L, Sehgal A, Iyengar S, Sedor JR: Genetic suscepti- bility to end-stage renal disease. Curr Opin Nephrol Hypertens 8: 465– 472, 1999 ACKNOWLEDGMENTS 6. Parsa A, Kao WH, Xie D, Astor BC, Li M, Hsu CY, Feldman HI, Parekh RS, Kusek JW, Greene TH, Fink JC, Anderson AH, Choi MJ, Wright JT Jr., Support for the Chronic Renal Insufficiency Cohort (CRIC) Study was Lash JP, Freedman BI, Ojo A, Winkler CA, Raj DS, Kopp JB, He J, obtained under a cooperative agreement from National Institute of Jensvold NG, Tao K, Lipkowitz MS, Appel LJ; AASK Study Investigators; Diabetes and Digestive and Kidney Diseases (grants U01DK060990, CRIC Study Investigators: APOL1 risk variants, race, and progression of chronic kidney disease. N Engl J Med 369: 2183–2196, 2013 U01DK060984, U01DK061022, U01DK061021, U01DK061028, 7. Thameem F, Igo RP Jr., Freedman BI, Langefeld C, Hanson RL, U01DK060980, U01DK060963, and U01DK060902). This project has Schelling JR, Elston RC, Duggirala R, Nicholas SB, Goddard KA, Divers been funded in whole or in part with federal funds from National J, Guo X, Ipp E, Kimmel PL, Meoni LA, Shah VO, Smith MW, Winkler CA, Cancer Institute, National Institutes of Health (NIH) contract Zager PG, Knowler WC, Nelson RG, Pahl MV, Parekh RS, Kao WH, HHSN26120080001E. This research was supported, in part, by the Rasooly RS, Adler SG, Abboud HE, Iyengar SK, Sedor JR; Family In- vestigation of Nephropathy and Diabetes Research Group: A genome- Intramural Research Program of the NIH, National Cancer Institute, wide search for linkage of estimated glomerular filtration rate (eGFR) in Center for Cancer Research. In addition, this work was supported, in the Family Investigation of Nephropathy and Diabetes (FIND). PLoS part, by Perelman School of Medicine at the University of Pennsylvania One 8: e81888, 2013 Clinical and Translational Science awards NIH/National Center for 8. Chasman DI, Fuchsberger C, Pattaro C, Teumer A, Böger CA, Endlich K, Advancing Translational Sciences (NCATS) UL1TR000003 and Olden M, Chen MH, Tin A, Taliun D, Li M, Gao X, Gorski M, Yang Q, Hundertmark C, Foster MC, O’Seaghdha CM, Glazer N, Isaacs A, Liu K01DK092353, Johns Hopkins University grant UL1 TR-000424, CT, Smith AV, O’Connell JR, Struchalin M, Tanaka T, Li G, Johnson AD, University of Maryland grant GCRC M01 RR-16500, the Clinical and Gierman HJ, Feitosa MF, Hwang SJ, Atkinson EJ, Lohman K, Cornelis Translational Science Collaborative of Cleveland, grant UL1TR000439 MC, Johansson A, Tönjes A, Dehghan A, Lambert JC, Holliday EG, from the NCATScomponent of the NIH and NIH Roadmap for Medical Sorice R, Kutalik Z, Lehtimäki T, Esko T, Deshmukh H, Ulivi S, Chu AY, Research, Michigan Institute for Clinical and Health Research grant Murgia F, Trompet S, Imboden M, Coassin S, Pistis G, Harris TB, Launer LJ, Aspelund T, Eiriksdottir G, Mitchell BD, Boerwinkle E, Schmidt H, UL1TR000433, University of Illinois at Chicago grant CTSA Cavalieri M, Rao M, Hu F, Demirkan A, Oostra BA, de Andrade M, UL1RR029879, NIH grant T32 AG00262, the Passano Foundation, Turner ST, Ding J, Andrews JS, Freedman BI, Giulianini F, Koenig W, Tulane University Translational Research in Hypertension and Renal Illig T, Meisinger C, Gieger C, Zgaga L, Zemunik T, Boban M, Minelli C, Biology grant P30GM103337, and Kaiser Permanente grant NIH/ Wheeler HE, Igl W, Zaboli G, Wild SH, Wright AF, Campbell H, NCRR UCSF-CTSI UL1 RR-024131. Ellinghaus D, Nöthlings U, Jacobs G, Biffar R, Ernst F, Homuth G, fl Kroemer HK, Nauck M, Stracke S, Völker U, Völzke H, Kovacs P, The content of this publication does not necessarily re ect the Stumvoll M, Mägi R, Hofman A, Uitterlinden AG, Rivadeneira F, views or policies of the Department of Health and Human Services, Aulchenko YS, Polasek O, Hastie N, Vitart V, Helmer C, Wang JJ, and mention of trade names, commercial products, or organizations Stengel B, Ruggiero D, Bergmann S, Kähönen M, Viikari J, Nikopensius does not imply endorsement by the US Government. T, Province M, Ketkar S, Colhoun H, Doney A, Robino A, Krämer BK, The CRIC Study investigators include Lawrence J. Appel (University Portas L, Ford I, Buckley BM, Adam M, Thun GA, Paulweber B, Haun M, Sala C, Mitchell P, Ciullo M, Kim SK, Vollenweider P, Raitakari O, of Illinois at Chicago, Chicago, IL), Alan S. Go (Kaiser Permanente of Metspalu A, Palmer C, Gasparini P, Pirastu M, Jukema JW, Probst- Northern California, Oakland, CA), James P.Lash (University of Illinois Hensch NM, Kronenberg F, Toniolo D, Gudnason V, Shuldiner AR, at Chicago, Chicago, IL), Mahboob Rahman (Case Western Reserve Coresh J, Schmidt R, Ferrucci L, Siscovick DS, van Duijn CM, Borecki IB,
J Am Soc Nephrol 28: ccc–ccc, 2016 Genetics of Differential CKD Progression 9 CLINICAL EPIDEMIOLOGY www.jasn.org
Kardia SL, Liu Y, Curhan GC, Rudan I, Gyllensten U, Wilson JF, Franke A, Mychaleckjy J, Li M, Reilly MP, Townsend RR, Adeyemo A, Zonderman Pramstaller PP, Rettig R, Prokopenko I, Witteman J, Hayward C, AB, de Andrade M, Turner ST, Mosley TH, Harris TB, Rotimi CN, Liu Y, Ridker PM, Parsa A, Bochud M, HeidIM,KaoWH,FoxCS,KöttgenA; Kardia SL, Evans MK, Shlipak MG, Kramer H, Flessner MF, Dreisbach CARDIoGRAM Consortium; ICBP Consortium; CARe Consortium; AW, Goessling W, Cupples LA, Kao WL, Fox CS; CKDGen Consortium: WTCCC2: Integration of genome-wide association studies with biological Genetic association for renal traits among participants of African an- knowledge identifies six novel genes related to kidney function. Hum Mol cestry reveals new loci for renal function. PLoS Genet 7: e1002264, Genet 21: 5329–5343, 2012 2011 9. Köttgen A, Pattaro C, Böger CA, Fuchsberger C, Olden M, Glazer NL, 12. 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AFFILIATIONS
*Division of Nephrology and Departments of **Medicine and ****Epidemiology and Public Health, University of Maryland School of Medicine, † ‡ Baltimore, Maryland; Department of Medicine, Baltimore Veterans Affairs Medical Center, Baltimore, Maryland; Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; §Department of Biostatistics and Epidemiology and †† | Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Health Sciences, College of Health Professions and Social Work, Florida Gulf Coast University, Fort Myers, FL; ¶Molecular Genetic Epidemiology Section, Basic Research Laboratory, Center for Cancer Research, National Cancer Institute and Basic Science Program, Leidos Biomedical Research, Inc., ‡‡ Frederick National Laboratory, Frederick, Maryland; Division of Nephrology, University of Michigan, Ann Arbor, Michigan; §§Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, || Bethesda, Maryland; Department of Medicine, Division of Nephrology, University of Illinois at Chicago, Chicago, Illinois; ¶¶Department of Medicine, Section of Nephrology, Tulane University, New Orleans, Louisiana; ***Folkhälsan Institute of Genetics, Folkhälsan Research Center, ††† ‡‡‡ Helsinki, Finland; Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland; §§§Department of Medicine, The Charles Bronfman Institute for ||| Personalized Medicine, Icahn School of Medicine Mount Sinai, New York, New York; Department of Medicine, The George Washington University School of Medicine, Washington, DC; ¶¶¶Department of Nephrology, University Hospital Regensburg, Regensburg, Germany; †††† Division of Nephrology, Department of Pediatrics and Medicine, Hospital for Sick Children, University Health Network and the University ‡‡‡‡ of Toronto, Toronto, Ontario, Canada; Department of Medicine, Division of Nephrology and Hypertension, Harbor-University of |||| California, Los Angeles Medical Center, Los Angeles, California; §§§§Departments of Biostatistical Sciences and Biochemistry and ¶¶¶¶Department of Internal Medicine, Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, North Carolina; ††††† *****Department of Medicine, Georgetown University Medical Center, Washington, DC; and Division of Intramural Research, National Heart, Lung and Blood Institute’s Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, Massachusetts
12 Journal of the American Society of Nephrology J Am Soc Nephrol 28: ccc–ccc,2016 SUPPLEMENTARY MATERIALS
Genome Wide Association of Chronic Kidney Disease Progression: The CRIC Study (Author list and affiliations listed at end of document)
Genotyping information page 2 Molecular pathway analysis information page 3 Replication cohort acknowledgments page 4 Supplementary Table 1. AA top hit region gene function page 5-6 Supplementary Table 2. EA top hit region gene function page 7 Supplementary Table 3. GSA pathway results page 8 Supplementary Table 4. Number of molecular interaction based on top candidate gene molecular networks page 9
Supplementary Table 5. Results of top gene marker association in AA, based on EA derived candidate gene regions page 10
Supplementary Table 6. Results of top gene marker association in EA, based on AA derived candidate gene regions page 11 Supplementary Table 7. EA Candidate SNP look up page 12 Supplementary Table 8. AA Candidate SNP look up page 13 Supplementary Table 9. Replication cohorts page 14 Supplementary Table 10. Replication cohort study characteristics page 15 Supplementary Figure 1a-b. Boxplot of eGFR decline in AA and EA page 16 Supplementary Figure 2a-l. Regional association plot of candidate SNPs identified in AA groups pages 17-22
Supplementary Figure 3a-f. Regional association plot of candidate SNPs identified in EA groups pages 23-25
Supplementary Figure 4. Molecular Interaction network of candidate genes for renal, cardiovascular and immunological diseases pages 26-27
Supplementary Figure 5. Molecular Interaction network of candidate genes for renal diseases pages 28-29
Supplementary Figure 6. ARRDC4 LD map page 30 Author list and affiliations page 31
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Supplemental Materials
Genotyping Genotyping was performed on a total of 3,635 CRIC participants who provided specific consent for investigations of inherited genetics (of a total of 3,939 CRIC participants). Genotyping was conducted at the Genetic Analysis Platform, Broad Institute of MIT and Harvard, using the Illumina HumanOmni1- Quad v1.0 microarray, which comprised of 1,140,419 SNPs. SNP genotypes were called using Illumina’s BeadStudio Genotyping Module (Illumina Inc, San Diego, CA, USA). Among the 3,635 participant samples, 81 were excluded based on quality control metrics including sex discordance (n=7), reduced or excess genotypic heterozygosity (heterozygosity rate ± 3 standard deviations from the mean; n=25), cryptic relatedness (π >0.185; n=64) or sample ID mismatches (n=28); 16 individuals were excluded because of more than one of these four metrics. All samples had a genotype call rate of at least 97%. This resulted in 3,527 samples that passed our quality control metrics. We then undertook principle component (PC) analyses to infer genetic ancestry on these remaining 3,527 samples. Using the cut points of PC 1 ≥ 0.0298 and PC 2 ≥ 0.0651, we identified a cluster of 1,581 participants of European ancestry (whites) and a cluster of 1,493 participants of African ancestry (blacks). Of the potential 1,140,419 markers on the genotype array, 184,789 were removed based on poor clustering or replication failures (i.e. assay performance). Then separately within the white and black subgroup populations, we completed SNP marker-level quality control on the remaining 955,630 SNP makers. In whites, we excluded a total of 214,437 markers (22.4%) because of minor allele frequency (MAF) < 0.03, 531 (0.06 %) because of deviation from Hardy-Weinberg equilibrium (P < 1 × 10-7), and 1,053 (0.11 %) because of genotype call rate < 0.95; 739,978 SNP markers remained in the discovery phase for white. Among blacks, we excluded a total of 111,478 markers (12.0%) because of MAF< 0.03, 1,047 (0.11 %) because of deviation from Hardy-Weinberg equilibrium (P < 1 × 10-7), and 1,109 (0.12 %) because of genotype call rate < 0.95; 839,205 SNP markers remained in the discovery phase for blacks. We completed genome-wide imputation based on the 1000 Genomes mixed race/ethnicity (ALL) genetic backbone (NCBI build 37, release date March 2012; n=1093) using IMPUTE2. Only those imputed SNP markers passing imputation quality control (info [a measure of r2] ≥ 0.80) were retained for analysis, resulting in a total of 8,191,067 and 11,726,769 variants with MAF >0.03 in our EA and AA cohorts, respectively.
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Molecular pathway analysis information Of our 18 identified candidate SNPs, based on physical proximity, we were able to assign 16 SNP to specific genes. Interaction networks centered around the molecule products of these 16 genes were constructed through the use of Integrative pathway analysis (Ingenuity® Systems, www.ingenuity.com). An interaction network is a graphical representation of the molecular relationships between genes and gene products. Gene products are represented as nodes, and the biological relationship between two nodes is represented as a line that is supported by at least one reference from the literature. In the graphic network, type categories of the molecules are represented by various shapes, and the labels along with each line indicate varieties of interactions between the connected molecules. Supplementary Figures 4and 5 list the legends for the type categories of the molecules, and the nature of the interactions between molecules. Only experimentally verified interaction relationships, either direct physical contact relationship or indirect regulation relationship through intermediate molecules, were considered. Exogenous chemicals were excluded from the interaction network analysis.
We found eight of our a-priori assigned gene products to be associated with renal or urological function (Supplementary Figure 5). Five of the SNPs were located within and three of the others SNPs or SNPs in LD with them were in close proximity (within 30kb) to the assigned genes (Supplementary Figures2- c,g,j,k and 3-b,c,d,e). In order to test for the statistical significance of our identified renal related molecular networks, the p-value was calculated based on Fisher's Exact Test to assess the degree of over- represented pathway compare to as expected by chance by accounting for: 1) the number of Functions/Pathways/Lists Eligible molecules that participate in that annotation as defined by the molecules in the selected Reference set; 2) the total number of molecules in the selected Reference set known to be associated with that function; 3) the total number of Functions/Pathways/Lists Eligible molecules in the selected Reference set; and 4) the total number of molecules in the Reference Set.
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Replication Cohorts acknowledgments
This study was supported by grants U01DK57292, U01DK57329, U01DK057300, U01DK057298, U01DK057249, U01DK57295, U01DK070657, U01DK057303, U01DK070657, U01DK57304 and DK57292-05 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and, in part, by the Intramural Research Program of the NIDDK. This project has been funded in whole or in part with federal funds from the National FIND study (AA) Cancer Institute, National Institutes of Health (NIH), under contract N01-CO-12400 and the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research. This work was also supported by the National Center for Research Resources for the General Clinical Research Center grants: Case Western Reserve University, M01-RR-000080; Wake Forest University, M01-RR-07122; Harbor-University of California, Los Angeles Medical Center, M01-RR-00425; College of Medicine, University of California, Irvine, M01-RR-00827– 29; University of New Mexico, HSC M01-RR-00997; and Frederic C. Bartter, M01-RR-01346. Genotyping was performed by the Center for Inherited Disease Research, which is fully funded through a federal contract from the NIH to Johns Hopkins University (N01-HG-65403). The results of this analysis were obtained using the S.A.G.E. package of genetic epidemiology software, which is supported by a U.S. Public Health Service Resource Grant (RR03655) from the National Center for Research Resources.
Wake Forest T2D- NIH R01 DK066358 (DWB), R01 DK053591 (DWB), R01 HL56266 (BIF), R01 DK070941 ESKD Study (BIF), General Clinical Research Center of Wake Forest University School of Medicine M01 RR07122. NIH R01 DK 070941 (BIF) and R01 DK53591 (DWB), and by the NIDDK Wake Forest non- and NCI Intramural Research Programs. MAB was supported by F32 diabetic ESKD Study DK080617 from the NIDDK. See funding sources of the CKDGen Cohorts in the manuscript with PMID 25493955. CKDGen CAB's work was supported by the Else Kröner-Fresenius-Stiftung. We thank all the FinnDiane researchers, as well as the physicians and nurses at each center participating in the recruitment of the patients and in the collection of samples and data (Sandholm et al. PloS Gen2012; Thorn LM et al., Diabetes Care 2005). The FinnDiane study was supported by grants from the Folkhälsan Research Foundation, Liv och Hälsa Foundation, the FinnDiane Willhelm and Else Stockmann Foundation, Helsinki University Central Hospital Research Funds (EVO), the Finnish Cultural Foundation, the Signe and Ane Gyllenberg Foundation, Finnish Medical Society (Finska läkaresällskapet), Academy of Finland, Novo Nordisk Foundation and Tekes.
AASK U01 DK048689, M01 RR-00071
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Supplementary Table 1. African American top hit region candidate gene function SNP to Gene Assignment (location or Gene name function and associations distance to gene) rs12057968 Chromosome 1 Open Reading Frame 100: Protein coding with unknown function. C1orf100 Expressed in kidney.
Intronic Adenylosuccinate synthetase: nearby gene with overlapping SNPs in LD with C1orf100. ADSS catalyzes the first committed step in the conversion of inosine monophosphate to (ADSS-23kb) adenosine monophosphate. May affect energy metabolism. Associated with schizophrenia. Expressed in kidney. Bardet-Biedl Syndrome 9: protein coding gene associated with Bardet-Biedl syndrome, rs73690944 which includes renal malformations and CKD. The exact function of this gene has not yet BBS9 been determined. Thought to function as a coat complex required for sorting of specific Intronic membrane proteins to the primary cilia. Also involved in parathyroid hormone action in
bones. Expressed in kidney. Transmembrane Protein 260: unclear function, potential association with b cell lymphoma. Expressed in kidney. rs198257 TMEM260 (58kb) Orthodenticle Homeobox 2: encodes a member of the bicoid subfamily of homeodomain- OTX2 (93kb) containing transcription factors. The encoded protein acts as a transcription factor with role in brain, and sensory organ development. Mutations in this gene cause syndromic microphthalmia 5 (MCOPS5) and combined pituitary hormone deficiency 6 (CPHD6). Expressed in kidney. Aldehyde dehydrogenase 7 family member: A1gene family enzymes. Play a major role in the detoxification of aldehydes generated by alcohol metabolism and protects cells from rs79239111 oxidative stress by metabolizing a number of lipid peroxidation-derived aldehydes. ALDH7A1(8kb) Expressed in kidney. GRAMD3(40kb) GRAM Domain Containing 3: contains a conserved region called the GRAM domain that is found in a variety of proteins associated with membrane-coupled processes and signal transduction. Precise function not elucidated. Expressed in kidney Semaphorin III: This gene is a member of the semaphorin family and encodes a protein rs12540238 with an Ig-like C2-type (immunoglobulin-like) domain, a PSI domain and a Sema SEMA3A domain. This secreted protein can function as either a chemorepulsive agent, inhibiting Intronic axonal outgrowth, or as a chemoattractive agent, stimulating the growth of apical dendrites. Can have inhibitory role in tumor growth and angiogenesis. Interacts with VEGF. Expressed in kidney. rs9290337 EGF like and EMI domain containing 1: Pseudogene, unclear function. Found, in GWAS, EGFEM1P to be associated with sporadic brain arteriovenous malformations. Intronic Expressed in kidney. Adenylate cyclase 8: membrane bound enzyme that catalyses the formation of cyclic rs4492355 AMP from ATP. The enzymatic activity is under the control of hormones, and different ADCY8(116kb) polypeptides participate in the transduction of the signal from the receptor to the catalytic moiety. Protects cardiomyocytes from Ca2+ overload. Expressed in kidney. MicroRNA 378c: miRNAs are short non-coding RNAs that are involved in post- rs12770303 transcriptional regulation of gene expression in multicellular organisms by affecting both MIR378C(34kb) the stability and translation of mRNAs. Specific function of MIR378C is unknown. Minimal to no renal expression.
(TCERG1L-163kb) Transcription Elongation Regulator 1-Like: Associated with insulin level and adiponectin microRNA site, post-transcriptional regulators. Expressed in renal tubules and glomeruli. Also strongly associated with insulin resistance and weakly with risk for DM-2 and IBD. Expressed in kidney. 5
SNP to Gene Assignment (location or Gene name function and associations distance to gene) Long Intergenic Non-Protein Coding RNA 923: non-protein coding RNA. Gene rs653747 regulatory class. May be involved in HCaRG pathway mediated kidney repair processes. LINC00923 rs653747 is predicted to alter the transcription factors binding domains in Pou2f2 and Intronic Pou6f1. Expressed in renal glomeruli and endothelial cells. rs116356141 Replication protein A2 pseudogene 2: Unknown function. Minimal to no renal expression RPA2P2(23kb)
rs62231385 Long Intergenic Non-Protein Coding RNA 1441: non-protein coding RNA gene of LINC01441(63kb) regulatory class. Potential gene regulation. Unknown function. Minimal to no renal expression.
rs1220081 Long intergenic non-protein coding RNA 1241: non-protein coding RNA. Gene LINC01241 regulatory class. Potential gene regulation. Intronic Unknown function. Low level renal expression
Tumor suppressor candidate 1: downregulated in non-small-cell lung cancer and small- (TUSC1-114kb) cell lung cancer cell lines. Expressed in kidney
Kb=kilobyte distance of the SNP to the assigned gene. Secondary genes with weaker potential association to the SNP are in parentheses. Gene function and renal expression data primary reference sources were: http://www.genecards.org; http://www.ncbi.nlm.nih.gov/gene/; and Online Mendelian Inheritance in Man (OMIM).
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Supplementary Table 2. European American top hit region candidate gene function
SNP to Gene Assignment (location or Gene name, function and associations distance to gene) rs1182475 Phosphatase And Actin Regulator 3: Encoded protein scapinin is associated with the PHACTR3 nuclear scaffold in proliferating cells, binds to actin and the catalytic subunit of protein Intronic phosphatase-1, suggesting function as a regulatory subunit of protein phosphatase-1. Regulatory role in gene expression and expressed in embryonic kidney cells. Contactin 5: member of the immunoglobulin superfamily, and contactin family, which rs10501939 mediate cell surface interactions during nervous system development. This protein is a CNTN5 glycosylphosphatidylinositol (GPI)-anchored neuronal membrane protein that functions Intronic as a cell adhesion molecule. Associated with atrial fibrillation in GWAS. Expressed in
kidney. rs710956 Lysine (K)-Specific Demethylase 4B: protein Coding gene, epigenetically regulates KDM4B gene expression by demethylating histone-3 tri- and dimethylated lys9, relaxing Intronic chromatin structure and permitting access by transcription factors. Associated with DNA repair and oxidoreductase activity. Expressed in kidney.
rs73133086 Bardet-Biedl Syndrome 10: Autosomal recessive disorder characterized by progressive BBS10 (45kb) retinal degeneration, obesity, polydactyly, mental retardation and renal malformation with CKD. Expressed in kidney.
(OSBPL8-53kb) OSBPL8 encodes a member of the oxysterol-binding protein (OSBP) family, a group of intracellular lipid receptors. Expressed in kidney. Latent Transforming Growth Factor Beta Binding Protein 1: belongs to the family of latent TGF-beta binding proteins. The secretion and activation of TGF-betas is regulated by their association with latency-associated proteins(LTBP1) and with latent rs17012778 TGF-beta binding proteins. May bind and regulate TGF-B1 and have a structural role LTBP1 in the extra cellular matrix. Intronic In renal transplantation; shown to be associated with increased expression of TGF-beta s and LTBP1, and the largest expression increase in the allografts occurred in the interstitium, followed by the glomeruli and blood vessels. Exonuclease 1Microtubule-Associated Protein 1 Light Chain 3 Gamma: Part of the rs10926598 RAD2 nuclease family and functions in DNA replication, repair, and recombination. EXO1(23kb) Also associated with chilblain lupus and xeroderma pigmentosum, group g. Noted to have increased expression of Exo-1 in cyst-lining epithelial cells of autosomal (MAP1LC3C-64kb) dominant polycystic kidney disease.
MAP1LC3C: Protein Coding gene with related pathways to cellular senescence and autophagy. Function not fully elucidated. Expressed in embryonic kidney cells.
Kb=kilobyte distance of the SNP to the assigned gene. Secondary genes with weaker potential association to the SNP are in parentheses. Gene function and renal expression data primary reference sources were: http://www.genecards.org; http://www.ncbi.nlm.nih.gov/gene/; and Online Mendelian Inheritance in Man (OMIM).
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Supplementary Table 3. GSA pathway results (corrected p values).
Physiologic pathway AA_all AA_DM_ AA_non- EA_all_ EA_DM EA_non- p_value p_value DM p-value p_value p_value DM p_value KEGG_ARRHYTHMOGENIC_RIGHT_VENTRICULA R_CARDIOMYOPATHY_ARVC 2.75E-05 6.77E-04 3.95E-03 4.96E-06 4.02E-04 6.39E-05 CALCIUM_CHANNEL_ACTIVITY 1.83E-05 3.08E-03 2.05E-02 1.32E-02 3.82E-04 1.60E-03
KEGG_CALCIUM_SIGNALING_PATHWAY 4.74E-05 2.79E-05 1.75E-03 1.79E-02 3.82E-04 6.39E-05 TRANSMEMBRANE_RECEPTOR_PROTEIN_PHOSPH ATASE_ACTIVITY 5.31E-05 3.44E-04 1.19E-04 1.29E-02 1.76E-05 1.40E-03 REACTOME_CELL_CELL_COMMUNICATION 4.08E-03 7.16E-03 5.03E-03 4.91E-05 3.82E-04 3.68E-03
PID_RAC1_REG_PATHWAY 4.53E-03 3.61E-03 1.60E-02 8.29E-03 5.00E-03 3.74E-02
KEGG_AXON_GUIDANCE 5.03E-03 1.83E-03 1.75E-03 2.56E-02 9.07E-03 1.60E-03
KEGG_ECM_RECEPTOR_INTERACTION 6.57E-03 1.71E-03 1.94E-02 9.92E-03 3.82E-04 3.68E-03 TRANSMEMBRANE_RECEPTOR_PROTEIN_TYROSI NE_KINASE_ACTIVITY 1.48E-02 2.57E-02 1.75E-03 8.03E-04 1.56E-02 1.60E-03 REACTOME_NETRIN1_SIGNALING 1.95E-02 7.49E-03 3.95E-03 2.04E-02 4.93E-03 1.60E-03 TRANSMEMBRANE_RECEPTOR_PROTEIN_KINASE _ACTIVITY 2.36E-02 2.78E-03 1.75E-03 2.75E-04 2.10E-03 3.68E-03 REACTOME_COLLAGEN_FORMATION 2.51E-02 8.29E-03 3.17E-03 7.73E-04 3.82E-04 1.60E-03 METAL_ION_TRANSMEMBRANE_TRANSPORTER_ ACTIVITY 4.98E-06 1.56E-05 4.23E-03 NS 2.67E-02 1.60E-03 CATION_CHANNEL_ACTIVITY 5.96E-06 7.21E-04 1.34E-02 NS 8.30E-03 1.76E-03
GATED_CHANNEL_ACTIVITY 1.83E-05 7.21E-04 3.29E-02 NS 2.11E-02 1.30E-02 REACTOME_TRANSMISSION_ACROSS_CHEMICAL _SYNAPSES 1.83E-05 5.54E-05 1.75E-03 NS 4.97E-03 1.60E-03 ION_CHANNEL_ACTIVITY 2.75E-05 3.71E-03 2.95E-02 NS 1.63E-02 1.60E-03
SUBSTRATE_SPECIFIC_CHANNEL_ACTIVITY 2.75E-05 4.66E-03 3.04E-02 NS 1.77E-02 1.60E-03 REACTOME_NEUROTRANSMITTER_RECEPTOR_BI NDING_AND_DOWNSTREAM_TRANSMISSION_IN_ 1.84E-04 4.66E-03 3.51E-03 NS 2.93E-03 6.73E-03 THE_POSTSYNAPTIC_CELL KEGG_DILATED_CARDIOMYOPATHY 2.26E-04 8.49E-03 NS 8.86E-03 9.07E-03 2.16E-03
KEGG_FOCAL_ADHESION 6.47E-04 2.79E-05 6.87E-03 NS 1.05E-03 1.34E-02
KEGG_HYPERTROPHIC_CARDIOMYOPATHY_HCM 1.64E-03 1.14E-02 NS 1.28E-02 2.31E-02 1.04E-02
KEGG_LONG_TERM_POTENTIATION 2.03E-03 1.83E-03 1.21E-02 NS 2.90E-02 8.81E-03
KEGG_GNRH_SIGNALING_PATHWAY 6.80E-03 9.08E-03 NS 4.14E-02 2.91E-02 2.66E-02
REACTOME_ION_CHANNEL_TRANSPORT 1.93E-02 3.98E-02 1.98E-03 NS 2.01E-02 1.34E-02 REACTOME_RAS_ACTIVATION_UOPN_CA2_INFU X_THROUGH_NMDA_RECEPTOR 4.66E-02 1.13E-02 2.64E-02 NS 3.27E-02 3.39E-02 PROTEIN_TYROSINE_KINASE_ACTIVITY NS 2.07E-03 1.58E-02 9.92E-03 4.93E-02 1.60E-03 Genome wide analyses based pathway enrichment results that are consistently significant across at least five of our 6 GWAS group are summarized. P-value corrected for false discovery rate (FDR) to account for multiple comparisons. EA= European American, AA = African American, All = all CRIC participants, DM = participants with diabetes, non-DM = participants without diabetes
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Supplementary Table 4. Summary of number of molecular interaction based on 17 candidate genes
Direct or indirect Direct interaction interaction only All diseases 268 154 Renal/Urological Diseases, CVD, Immune-inflammatory diseases, 220 115 Nutritional, Metabolic, Neurological diseases Renal/Urological Disease, CVD, Immune-inflammatory diseases, 206 103 Nutritional Diseases, Metabolic diseases Renal/Urological Disease, CVD, Immune-inflammatory diseases 200 98 Renal/Urological diseases only 90 37
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Supplementary Table 5. Results of top gene marker association in African Americans, based on European American derived candidate gene regions
Effective # of independent SNPs Lead SNP Gene Top SNP in in a 100 kb in EA Region AA Chr Position Beta S.E. P-value flanking region rs1182475 PHACTR3 rs368697 20 58089238 0.15 0.05 9.66E-04 31.9 (P<0.0016) rs7267619 20 58097983 0.15 0.05 2.57E-03
rs710956 KDM4B rs1809980 19 4856287 -0.16 0.06 8.18E-03 17.0 (P<0.0029) rs2656950 19 4856694 -0.16 0.06 1.05E-02 rs10501939 CNTN5 rs73562437 11 100054028 -0.37 0.11 4.73E-04 25.2 (P<0.002) rs61315699 11 100054375 -0.30 0.09 7.62E-04 rs17012778 LTBP1 rs59136259 2 33596669 0.20 0.06 6.04E-04 20.5 (P<0.0024) rs11899010 2 33596090 0.19 0.06 7.76E-04 rs73133086 BBS10 rs11116625 12 76605001 -0.39 0.12 1.28E-03 18.2 (P<0.0027) rs11116643 12 76605407 -0.38 0.12 1.67E-03 rs10926598 EXO1 rs6698254 1 242002481 0.19 0.07 5.82E-03 28.2 (P<0.0018) rs9428894 1 241969926 -0.39 0.14 6.58E-03
Results of top gene marker association in African Americans, based on European American derived candidate gene regions are shown. The across-race gene region consists of a 100kb flanking region around the lead EA SNP. Results shown are for the top two AA SNPs within the gene region. Statistical significance is based on Bonferroni correction for number of independent SNPs across each gene region (i.e., 0.05/number of effective independent SNPs in region, r2 <0.3). Statistical significant findings are noted in bold. Beta = allelic effect estimate based on additive model. S.E. = standard error. Chr= chromosome, AA = African American, EA = European American
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Supplementary Table 6. Results of top gene marker association in European Americans, based on African American derived candidate gene regions
Effective # of Lead SNP Gene SNPs in a 100 kb in AA Region Top rs_id in EA Chr Position Beta S.E. P-value flanking region rs9290337 EGFEM1P rs329072 3 168183506 -0.07 0.04 7.05E-02 3.8 (P<0.13) rs228745 3 168187667 -0.07 0.04 8.68E-02
rs73690944 BBS9 rs7805496 7 33566689 0.15 0.05 1.52E-03 21.3 (P<0.0023) rs10235388 7 33515400 -0.13 0.04 1.57E-03
rs12540238 SEMA3A rs76656442 7 83932729 0.23 0.09 6.81E-03 18.0 (P<0.0028) rs41487549 7 83838877 0.22 0.08 7.43E-03
rs62231385 unknown rs2208080 20 54151344 0.11 0.05 1.76E-02 23.6 (P<0.0022) rs6069408 20 54355399 0.13 0.05 1.87E-02
rs116356141 RPA2P2 rs3913914 10 83045104 -0.15 0.07 3.66E-02 10.0 (P<0.005) rs12355292 10 82972870 -0.31 0.15 3.96E-02
rs10927223 C1orf100 rs186848326 1 244459274 -0.49 0.16 1.91E-03 24.2 (P<0.0021) rs76346105 1 244455502 -0.47 0.16 2.60E-03
rs79239111 ALDH7A1 chr5:125981804:D 5 125981804 -0.21 0.06 7.38E-04 6.0 (P<0.0083) rs1060856 5 125880109 -0.27 0.08 1.29E-03 rs4836280 5 125981300 -0.18 0.06 1.77E-03
rs198257 TMEM260 rs2345152 14 57077724 0.18 0.08 3.05E-02 23.1 (P<0.0022) rs4898915 14 57156142 0.13 0.06 3.06E-02
rs4492355 ADCY8 rs12546270 8 132216569 0.12 0.05 1.45E-02 7.0 (P<0.007) rs12334868 8 131954652 0.14 0.06 1.66E-02
rs1220081 TUSC1 rs79837678 9 25564715 -0.35 0.12 3.32E-03 15.2 (P<0.42) rs12238103 9 25566567 -0.30 0.11 9.27E-03
rs653747 LINC00923 rs931891 15 98293207 -0.49 0.13 1.44E-04 26.7 (P<0.0019) rs35503124 15 98293268 -0.49 0.13 1.45E-04
rs12770303 MIR378C rs4750850 10 132902960 -0.14 0.06 1.47E-02 31.3 (P<0.0016) rs61862923 10 132650146 0.16 0.07 1.70E-02
Results of top gene marker association in European American, based on African Americans derived candidate gene regions are shown. The across-race gene region consists of a 100kb flanking region around the lead AA SNP. Results shown are for the top EA loci SNPs within the gene region. Statistical significance is based on Bonferroni correction for number of independent SNPs across each gene region (i.e., 0.05/number of effective independent SNPs in region, r2 <0.3). Statistical significant findings are noted in bold. Beta = allelic effect estimate based on additive model. S.E. = standard error. D=deletion and I =insertion. Results based on I/D are not fully considered given that study is SNP based, but are still shown for general interest.
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Supplementary Table 7. European American Candidate SNPs looked up in CRIC
Gene p-value p-value Beta p-value SNP Region ALL beta All beta_DM DM non-DM non-DM rs10794720 WDR37 -0.2127 0.0037 -0.2648 0.0214 -0.1620 0.0849
SLC3A2 rs489381 -0.1130 0.0482 -0.2352 0.0064 -0.0002 0.9980 DAB2 rs11959928 0.1055 0.0045 0.2286 0.0001 -0.0282 0.5534 rs1394125 UBE2Q2 0.0573 0.1247 0.0345 0.5511 0.1086 0.0246 rs7588550 ERBB4 -0.1730 0.0539 -0.2423 0.1030 -0.1272 0.2542 rs10109414 STC1 0.0206 0.5822 0.0204 0.7294 0.0181 0.7049 rs10206899 NAT8 -0.0273 0.5467 0.0060 0.9313 -0.0389 0.5113 rs10490130 LRP2 -0.0553 0.3968 -0.0178 0.8739 -0.0923 0.2470 rs10774021 SLC6A13 0.0504 0.1846 0.0264 0.6591 0.0834 0.0869 rs10868025 FRMD3 0.0014 0.9709 0.0363 0.5449 -0.0485 0.3071 rs11078903 CDK12 -0.0025 0.9520 -0.0810 0.2094 0.0738 0.1716 DNAJC16; rs12124078 CASP9 -0.0249 0.5286 -0.0330 0.5929 -0.0081 0.8729 RGMA; rs12437854 MCPT2 0.0163 0.8264 -0.0997 0.4289 0.0899 0.3246 rs12460876 SLC7A9 -0.0198 0.5959 0.0303 0.6084 -0.0776 0.1043 rs1260326 GCKR -0.0183 0.6199 -0.0081 0.8896 -0.0122 0.7971 rs12917707 UMOD -0.0289 0.5882 -0.1071 0.1932 0.0713 0.3029 rs13538 ALMS1 -0.0252 0.5746 0.0066 0.9243 -0.0372 0.5249 rs17319721 SHROOM3 -0.0457 0.2122 -0.0135 0.8146 -0.0678 0.1491 rs2279463 SLC22A2 -0.0087 0.8747 0.0486 0.5707 -0.0813 0.2526 SPATA5L1; rs2453533 GATM 0.0373 0.3086 0.0647 0.2538 0.0191 0.6876 rs2453589 SLC47A1 -0.0522 0.1694 -0.0861 0.1539 -0.0236 0.6251 rs267734 ANXA9 -0.0855 0.0709 -0.0900 0.2188 -0.0855 0.1651 rs298148 INO80 -0.0392 0.3969 -0.0353 0.6172 -0.0485 0.4232 rs347685 TFDP2 0.0024 0.9536 0.0492 0.4499 -0.0574 0.2932 rs3741414 INHBC -0.0101 0.8217 -0.0522 0.4653 -0.0094 0.8677 rs3925584 MPPED2 0.0129 0.7232 0.0236 0.6781 0.0103 0.8278 rs4744712 PIP5K1B 0.0140 0.7085 0.0204 0.7411 0.0074 0.8738 rs4751890 PLEKHA1 -0.0145 0.6973 0.0487 0.4013 -0.0806 0.0938 rs491567 WDR72 -0.0466 0.2892 -0.1677 0.0180 0.0749 0.1781
DACH1 rs626277 -0.0253 0.5019 0.0404 0.5042 -0.0691 0.1479 SLC34A1 rs6420094 0.0551 0.1635 0.0650 0.2907 0.0745 0.1450 rs6465825 TMEM60 0.0172 0.6434 0.0325 0.5805 -0.0251 0.5958 rs6499166 SLC7A6 -0.0249 0.5281 -0.0668 0.2673 0.0054 0.9168 rs653178 ATXN2 0.0629 0.0904 0.0553 0.3591 0.0526 0.2589 rs7208487 FBXL20 0.0153 0.7518 0.0582 0.4355 -0.0386 0.5387 rs78055747 PRKAG2 0.0374 0.3449 0.0191 0.7650 0.0577 0.2477 rs7918972 CUBN -0.0695 0.2174 -0.0201 0.8214 -0.1098 0.1281 rs881858 VEGFA 0.0358 0.3639 0.0177 0.7717 0.0455 0.3736 rs9895661 BCAS3 -0.0223 0.6344 0.0653 0.3958 -0.0982 0.0897
Results are based on selection of 39 published renal candidate genes and association with eGFR decline in our cohorts. All = all participants, DM = participants with diabetes, non-DM = participants without diabetes Beta = allelic effect estimate based on additive model
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Supplementary Table 8. African American Candidate SNPs looked up in CRIC
SNP Gene p-value-all Beta total p-value DM beta_DM p-value_ beta non-DM non-DM rs1153859 GATM 0.250 -0.043 0.198 -0.066 0.614 -0.027 rs11650989 BCAS3 0.579 -0.026 0.345 0.064 0.034 -0.137 rs12302645 ATXN2 0.990 -0.001 0.764 0.031 0.665 -0.050 rs13022873 GCKR 0.751 -0.016 0.464 0.048 0.215 -0.088 rs1750571 VEGFA 0.065 -0.135 0.401 -0.078 0.185 -0.153 rs3738479 ANXA9 0.278 -0.043 0.483 -0.037 0.611 -0.029 rs3798156 SLC22A2 0.423 0.049 0.677 0.035 0.317 0.089 rs3822460 DAB2 0.307 0.050 0.236 0.078 0.887 0.010 rs4293393 UMOD 0.833 0.010 0.935 0.005 0.415 0.054 rs485514 SLC6A13 0.272 0.072 0.628 -0.044 0.073 0.165 rs6781340 TFPD2 0.429 0.031 0.385 -0.047 0.047 0.111 rs6973213 TMEM60 0.952 -0.003 0.323 -0.074 0.586 0.040
Results are based on selection of 12 published renal candidate genes and association with eGFR decline in our cohorts. All = all participants, DM = participants with diabetes, non-DM = participants without diabetes Beta = allelic effect estimate based on additive model
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Supplementary Table 9. Replication cohorts
Study name Study design Genotyped Study inclusion, exclusions Analysis Study References (article, PMID) sample size FIND study Case control: 1,293 Inclusion: African American Adjusted for age, Igo RP et al., AM J Nephrol 2011 (AA) Diabetic nephropathy participants, all had DM duration >5 gender, center, and (PMID: 21454968) vs diabetic control years and/or DR, with UACR >1 g/g ancestry. or ESKD. Unrelated controls had DM duration >9 years, UACR <30 mg/g, and serum creatinine <1.6 mg/dl (men) or <1.4 mg/dl (women). Wake Forest Case control: T2D 1,741 African American ESKD cases with Adjusted for age, McDonough CW et al., Kidney Int. 2011 T2D-ESKD nephropathy vs non- T2D of >5 yr duration pre-ESKD gender, and ancestry. (PMID: 21150874) Study diabetic controls and/or DR vs non-diabetic non-neph controls. Wake Forest Pooled case-control 1,000 African American ESKD cases with Pooled GWAS Bostrom MA et al., Hum Genet. 2010 non-diabetic analysis: non- FSGS, HIVAN, non-specific CGN, (PMID:20532800) ESKD Study diabetic ESKD versus hypertension-attributed or unknown non-diabetic non- cause vs. non-nephropathy controls neph controls. CKDGen Population based 45,530 European,population cohort with at Adjusted for age and Gorski M et al., Kidney Int. 2014 cohort: meta-GWAS least 2 eGFR estimates over 2 years gender. (PMID:25493955) of change in eGFR apart over time FinnDiane Nationwide, 881 Inclusion: Type 1 diabetes (age at Adjusted for age, Sandholm et al. PloS Gen2012: prospective onset less than or equal to 40 years), gender and T1D (PMID:23028342) multicenter study. follow up at least 3 years, baseline duration at baseline Thorn LM et al., Diabetes Care 2005, Longitudinal, eGFR eGFR <= 120 ml/min, GWAS data (PMID:16043748) slope. Type 1 available. Exclusion: ESRD (=dialysis diabetic cohort. or transplant) at baseline. AASK Longitudinal, eGFR 696 African American, hypertension Adjusted for age, Appel LJ et al., JASN 2003 slope in CKD attributed. Exclusion: diabetes, other gender and ancestry (PMID:12819323) cause for CKD, eGFR >20 and < 65 ml/min, urine protein/creatinine ratio >2.5g/g
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Supplementary Table 10. Replication study sample characteristics
eGFRcrea, Imputation Sex, ml/min/1.73 yes/no_(platfor Study Sample size Age, years % women m2 Genotyping Platform m) 979 severe DKD FIND study case 58.5 (11.3) 76.4% NA Affymetrix 6.0 Yes (Hapmap 3) (AA) 314 DM non-neph control 60.1 (11.1) 58.3% NA Wake Forest 920 T2D-ESKD 61.6 (10.5) 61.0% NA T2D-ESKD Affymetrix 6.0 Yes (Hapmap 3) 821 T2D non-neph Study control 49.0 (11.9) 55.9% NA Pooled GWAS in 500 non-DM ESKD Wake Forest cases and 500 non- non-diabetic DM non-CKD Illumina HumanHap550 ESKD Study controls NA - Duo BeadChip No Range 43.3 Range 2.1 Range 81.2 CKDgen Mixed platforms Meta-GWAS 45,530 to 73 to 61.3 to107.4 ( PMID: 25493955) Yes Illumina Human Yes (HapMapII FinnDiane 881 40.8 (11.2) 55.4% 85.2 (21.9) 610Quad CEU/ MACH) Taqman for the top hit SNPs replication (MALD data for AASK 696 54.1 (10.6) 40.17% 47.3 (13.6) population stratification) No NA = not available
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Supplementary Figure 1. Boxplot of eGFR decline in AA and EA
Figure 1a. Distribution of eGFR slope in AA by diabetes status 20 ) ² m 3 7 .
1 0 r a e y / n i m / l m (
e p o l s
R -20 F G e
-40
non-Diabetes Diabetes
Figure 1b. Distribution of eGFR slope in EA by diabetes status 30
) 20 ² m 3 7 . 1 r
a 10 e y / n i m / l
m 0 (
e p o l s
R -10 F G e
-20
-30
non-Diabetes Diabetes
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Supplementary Figures 2 a-l. Regional association plots for all African ancestry gene variants with renal function decline
2a
2b
17
2c
2d
(Reference: ensemble; genome assembly: GRCh38.p5) Position on chr 15 (Mb) 18
2e
2f
19
2g
2h
20
2i
2j
21
2k
2l
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Supplementary Figures 3a-f. Regional association plots for all European ancestry gene variants with renal function decline 3a
3b
23
3c
3d
24
3e
3f
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Supplementary Figures 4: 11 out of 16 identifiable gene products associated with our discovered 18 independent SNP markers interact with 268 molecules of which 200 molecules are known to have roles in the pathological mechanism of renal, cardiovascular or immunological diseases. Type categories of the molecules were represented by various shapes. The labels along with each line indicate varieties of interactions between the connected molecules, such as A (Activation), E (Expression), PD (Protein-DNA binding), PP (protein-protein binding), P Phosphorylation/Dephosphorylation).
26
Figure 4 (continuation)
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Supplementary Figure 5: Among the 268 interacting molecules, 90 of them have experimentally verified roles in renal/urological functions or diseases according to literature. Type categories of the molecules were represented by various shapes; The labels along with each line indicate varieties of interactions between the connected molecules, such as A (Activation), E Expression, PD (Protein-DNA binding), PP (protein-protein binding), P (Phosphorylation/Dephosphorylation).
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Legend for interaction networks, Figures 4a, 4b and 5
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Supplementary Figure 6: Extended linkage disequilibrium pattern for rs653747 in AA and rs931891 in EA, including ARRDC4 gene region.
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Author list and affiliations: Afshin Parsa1,2*, Peter A. Kanetsky3*, Rui Xiao4**, Jayanta Gupta5**, Nandita Mitra4**, Sophie Limou6, Dawei Xie4, Huichun Xu7, Amanda H. Anderson8, Akinlolu Ojo9, John W. Kusek10, Claudia M. Lora11, Lee L. Hamm12, Jiang He12, , Niina Sandholm13-15, Janina Jeff16, Dominic E. Raj17, Carsten A. Böger18, Erwin Bottinger16, Shabnam Salimi19, Rulan S. Parekh20, Sharon G. Adler21, Carl D. Langefeld22, Donald W. Bowden23, FIND Consortium, Per-Henrik Groop13-15, Carol Forsblom13-15, Barry I. Freedman24, Michael Lipkowitz25, Caroline S. Fox26, Cheryl A. Winkler6, Harold I. Feldman8; and the Chronic Renal Insufficiency Cohort (CRIC) Study Investigators
*these authors contributed equally **these authors contributed equally
1-Division of Nephrology, University of Maryland School of Medicine, Baltimore, MD, USA 2-Department of Medicine, Baltimore VA Medical Center, Baltimore, MD, USA 3-Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida, USA 4-Department of Biostatics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA 5- Department of Health Sciences College of Health Professions and Social Work, Florida Gulf Coast University, Fort Myers, FL, USA 6-Molecular Genetic Epidemiology Section, Basic Research Laboratory, Basic Science Program, NCI, Leidos Biomedical Research, Inc., Frederick National Laboratory, Frederick, MD, USA 7-Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA 8-Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA 9-Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA 10-National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA 11-Department of Medicine, Division of Nephrology, University of Illinois at Chicago, USA 12-Department of Epidemiology, Tulane University, New Orleans, Louisiana, USA 13- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland 14- Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland 15-Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland 16-The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine Mount Sinai, N.Y., USA 17-The George Washington University School of Medicine, Washington, DC, USA 18-Department of Nephrology, University Hospital Regensburg, Regensburg, Germany 19-Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA 20-Nephrology, Hospital for Sick Children, University Health Network and the University of Toronto, Canada 21-Department of Medicine, Division of Nephrology and Hypertension, Harbor-UCLA Medical Center, CA, USA 22-Department of Biostatistical Sciences; Wake Forest School of Medicine, Winston-Salem, NC, USA 23-Department of Biochemistry; Wake Forest School of Medicine, Winston-Salem, NC, USA 24-Department of Internal Medicine, Section on Nephrology; Wake Forest School of Medicine, Winston- Salem, NC, USA 25-Department of Medicine, Georgetown University Medical Center, Washington DC, USA 26-NHLBI's Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, MA, USA 31