Article Association of Systemic Lupus Erythematosus Susceptibility ...

Article

Association of Systemic Lupus Erythematosus Susceptibility Genes with IgA Nephropathy in a Chinese Cohort Xu-Jie Zhou, Fa-Juan Cheng, Li Zhu, Ji-Cheng Lv, Yuan-Yuan Qi, Ping Hou, and Hong Zhang

Abstract Background and objectives One hypothesis states that IgA nephropathy (IgAN) is a syndrome with an autoimmune component. Recent studies strongly support the notion of shared genetics between immune-related diseases. This study investigated single-nucleotide polymorphisms (SNPs) reported to be associated with systemic lupus erythematosus (SLE) in a Chinese cohort of patients with IgAN and in controls. Design, setting, participants, & measurements This study investigated whether SNP markers that had been reported to be associated with SLE were also associated with IgAN in a Chinese population. The study cohort consisted of 1194 patients with IgAN and 902 controls enrolled in Peking University First Hospital from 1997 to 2008. Results Ninety-six SNPs mapping to 60 SLE loci with reported P values ,131025 were investigated. CFH (P=8.4131026), HLA-DRA (P=4.9131026), HLA-DRB1 (P=9.4631029), PXK (P=3.6231024), BLK (P=9.3231023), and UBE2L3 (P=4.0731023) were identified as shared genes between IgAN and SLE. All associations reported herein were corroborated by associations at neighboring SNPs. Many of the alleles that are risk alleles for SLE are protective alleles for IgAN. By analyses of two open independent expression quantitative trait loci (eQTL) databases, correlations between genotypes and corresponding gene expression were observed (P,0.05 in multiple populations), suggesting a cis-eQTL effect. From gene-expression databases, differential expressions of these genes were observed in IgAN. Additive interactions between PXK rs6445961and HLA-DRA rs9501626 (P=1.5131022), as well as multiplicative interactions between CFH rs6677604 and HLA-DRB1 rs9271366 (P=1.7731022), and between HLA-DRA rs9501626 and HLA-DRB1 rs9271366 (P=3.2331022) were observed. Disease risk decreased with accumulation of protective alleles. Network analyses highlighted four pathways: MHC class II antigen presentation, complement regulation, signaling by the B-cell receptor, and ubiquitin/ proteasome-dependent degradation. Conclusion From this “systems genetics” perspective, these data provide important clues for future studies on pleiotropy in IgAN and lupus nephritis. Clin J Am Soc Nephrol 9: 788–797, 2014. doi: 10.2215/CJN.01860213

Introduction Over the past two decades, considerable progress has been made in unraveling the complex pathogenesis of IgA nephropathy (IgAN). However, the exact pathogenesis remains poorly determined. Current data suggest that genetic factors combined with environmental factors lead to increased synthesis of aberrantly galactosylated IgA1, formation of glycan-specific antibodies to IgG and IgA, and mesangio-podocytic-tubular cross-talk in the occurrence and development of the disease (1–6). Whether IgAN should be termed an “autoimmune disease” is controversial. However, recent genome-wide association studies (GWAS) strongly indicate that many of its associated loci also affect other autoimmune and infectious diseases (5,7–9), further supporting the notion of shared genetics of immunerelated diseases (10). Recent estimates suggest that the 788

Copyright © 2014 by the American Society of Nephrology

Renal Division, Peking University First Hospital; Peking University Institute of Nephrology; Key Laboratory of Renal Disease, Ministry of Health of China; and Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing, People’s Republic of China Correspondence: Dr. Hong Zhang, Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, No. 8 Xi Shi Ku Street, Xi Cheng District, Beijing 100034, People’s Republic of China. Email: hongzh@bjmu. edu.cn

identified loci collectively explain ,10% of the genetic risk for IgAN, highlighting the fact that much of the heritable basis for IgAN has yet to be identified. Previously, we reported that genetic factors have an appreciable influence on the production of undergalactosylated IgA1 and that GWAS data strongly implicate new clues as to the pathogenesis of IgAN (7,8,11–15). We also reported on the overlap between several autoimmune diseases: systemic lupus erythematosus (SLE), rheumatoid arthritis, ANCA-associated small vasculitis, and anti–glomerular basement membrane disease (16–25). Thus, we hypothesized that refinement of GWAS data or identification of IgAN susceptibility genes could be underpinned by investigation of the genetic variants reported to be associated with other immune-related diseases. Identification of novel IgAN genes and shared genetic www.cjasn.org Vol 9 April, 2014

Clin J Am Soc Nephrol 9: 788–797, April, 2014

pathways could improve understanding of common genetic mechanisms and eventually the development of improved methods of diagnosis, prognosis, and targeted therapies. SLE is an autoimmune disease. Lupus nephritis is characterized by multiple immune complexes depositing in the kidney, including IgA molecules. IgAN is an immune complex–mediated GN defined by the predominant IgA molecule that deposits in the kidney. A recent study showed the pathogenicity of anti-glycan antibodies in IgAN, which suggested that IgAN is a type of autoimmune disease (26). A new theory suggests that most types of GN are primarily autoimmune diseases. Certain pathogenic similarities between autoimmune diseases (e.g., greater prevalence among Asians than Europeans, chronic course, renal involvement, circulating immune complexes, complement activation, morphologic similarities, certain pathways being involved in ESRD) prompted us to investigate the overlap in genetic susceptibility between SLE and IgAN. Well established cooccurrences of SLE with IgAN suggest common etiologic factors (27–29). Little progress has been made regarding the identification of genetic factors specific to lupus nephritis, but a genetic cause in SLE has been substantiated. More than 40 genes have been robustly associated with SLE. We investigated whether single-nucleotide polymorphism (SNP) markers that had been reported to be associated with SLE were also associated with IgAN in a Chinese population.

Materials and Methods The protocol of this study complied with the Declaration of Helsinki. The protocol was approved by the Ethics Committee of Peking University First Hospital (Beijing, China). Written informed consent was obtained from each patient. Study Population The samples used in the present study have been described previously. Briefly, exclusion of duplicates and first-degree relatives yielded 1194 IgAN cases and 902 healthy controls recruited in the Renal Division of Peking University First Hospital from 1997 to 2008 (8). All the cases were confirmed by renal biopsy, and all the controls were healthy blood donors without indicators of renal disease. Quality control was undertaken as described (8). Unexpected relatedness was excluded with a PLINK pi-hat cutoff of 0.125. We included men and women of Northern Chinese Han ancestry. Selection and Genotyping of SNPs We systemically examined data from GWAS, as well as large-scale replications conducted in SLE genetics through December 1, 2012. The reported SNPs associated with SLE in the GWAS context with a P value ,1310 2 5 were selected for analysis (30–42). The reported risk variants for SLE using data from the Catalog of Published Genome-Wide Association Studies from the National Human Genome Research Institute (http://www.genome. gov/gwastudies) were also checked. Finally, a panel of 96 SNPs representative of 60 genes or loci was selected (Supplemental Table 1). Genotyping was undertaken using the Illumina Human 610-Quad BeadChip, which involved 498,322 SNPs with a mean call rate of 0.9992.

Shared Genetic Study in IgAN and SLE, Zhou et al.

789

Statistical Analyses Only SNPs meeting the quality-control criteria of ,1% overall missing data as well as consistency with Hardy– Weinberg equilibrium genotype frequency expectations (P,0.05) were included. As reported previously, after adjustment for population substructure, the inflation factor using all SNPs was l=1.02, indicating a minimal effect of residual population structure. Thus, no further genomic control corrections were applied. Genotype frequencies between IgAN cases and controls were compared using the chi-squared trend test implemented in PLINK software to determine whether individual SLE susceptibility loci were also associated with IgAN. Genetic models were defined relative to the minor allele. To reduce the risk of false-positive findings, all positive associations were checked further by associations at neighboring SNPs. To test for additive interactions, the methods were taken using a 232 factorial design to calculate the attributable proportion due to interaction, the relative excess risk due to interaction, and the synergy index (20,43). P values ,0.05 for attributable portion due to interaction were considered to be indicators of additive interactions. Ninetyfive percent confidence intervals (95% CIs) were calculated using the delta method (44). Multiplicative interaction was assessed by adding an interaction variable (SNP3SNP) to the regression models. P,0.05 was considered to be evidence for multiplicative interactions. Analyses of carriage of SLE alleles in patients with IgAN were carried out to determine whether there was an overall enrichment of SLE susceptibility variants in IgAN cases. Analyses were also undertaken to determine whether combining those risk alleles conferred a higher risk of disease. Analyses of Bioinformatics To explore whether the identified SNPs had expression quantitative trait loci (eQTLs) effects, Genevar software was used to determine associations between sequence variation and gene expression (http://www.sanger.ac. uk/resources/software/genevar). The sequence variation and gene-expression profiling data were from lymphoblastoid cell lines of 726 HapMap3 individuals. Another global map of the effects of polymorphism on gene expression in 400 children from families recruited through a proband with asthma was also investigated to associate gene expression on the basis of imputed genotypes (45). The differential expressions of suspected IgAN candidate genes were compared with those of healthy controls using publically available data from the ArrayExpress Archive database (http://www.ebi.ac.uk/arrayexpress/) using “IgA nephropathy” as the search term. Three experiments (EGEOD-37460, E-GEOD-35489, and E-GEOD-14795) involving comparatively large samples were included in the current analysis. The former two experiments took kidney biopsy samples and the latter experiment took whole-blood samples for gene-expression analyses. The normalized data available on the public databases were tested as reported previously. To integrate data in biologic networks, Cytoscape software (which allows visualization of data in the context of networks) was applied (46). Cytoscape is widely used open-source software for the analyses of bimolecular interaction networks. MiMI integrates data on 119,880 molecules, 330,153 interactions, and 579 complexes from

rs6677604 rs9782955 rs6445975 rs9501626 rs9271366 rs7812879 rs2254546 rs2736340 rs131654 rs5754217 CFH LYST PXK HLA-DRA HLA-DRB1 BLK BLK BLK UBE2L3 UBE2L3 194953541 234106500 58345217 32508322 32694832 11377591 11381089 11381382 20247190 20269675 1 1 3 6 6 8 8 8 22 22

The reported SLE risk alleles are set in boldface. Chr, chromosome; SNP, single-nucleotide polymorphism; MAF, minor allele frequency; OR, odds ratio; 95% CI, 95% confidence interval; SLE, systemic lupus erythematosus. a ORs were calculated on the basis of SLE risk alleles for comparison. Reported ORs were derived from the references listed. b Only SNPs in CFH and HLA regions could retain statistical significance after multiple correction.

5.4731025 3.1531023 9.0031023 2.6031025 3.40310210 0.04 2.9631022 0.08 0.06 0.06 1.8031022 0.18 0.07 0.12 0.81 0.05 4.8831022 0.07 2.3831022 0.15 0.55 (0.42 to 0.72) 0.81 (0.67 to 0.98) 0.79 (0.68 to 0.92) 0.66 (0.55 to 0.79) 0.63 (0.53 to 0.75) 0.83 (0.72 to 0.96) 0.83 (0.72 to 0.95) 0.86 (0.75 to 0.99) 1.15 (1.02 to 1.30) 1.16 (1.02 to 1.31) 4.10/7.26 12.87/10.71 23.79/19.79 11.39/16.26 12.60/18.65 26.59/23.23 26.63/23.12 29.94/26.94 46.48/49.94 47.32/43.74

8.4131026 3.3131022 2.0131023 4.9131026 6.9631028 1.2331022 9.3231023 3.3331022 2.6331022 2.1131022

Dominant P Values Allele OR (95% CI) by SLE Risk Allelea Trend Test P Values MAF Case/ Control (%) Major/ Minor Allele SNP Locus Base Pair

Table 1. Association results for systemic lupus erythematosus risk variants in IgA nephropathy

Analyses of Neighboring SNPs Support Disease Effects To reduce the chance of false-positive findings using a single marker by chance, all the positive associations were checked further by analyzing the neighboring SNPs. Multiple significant association signals were observed (Figure 1). In CFH, HLA-DRA, and BLK, the top signals were from the SNPs selected. In HLA-DRA, rs2027856 (protective allele T; P=4.9131026; OR, 0.66; 95% CI, 0.55 to 0.79) also showed the same significance compared with rs9501626 in association with IgAN (r2 value between rs2027856 and rs9501626 is 1.00). In HLA-DRB1, rs9270984 (protective allele G; OR, 0.63; 95% CI, 0.53 to 0.74) and rs9271055 (protective allele T; OR, 0.63; 95% CI, 0.53 to 0.74) showed the same most significance with P=9.4631029 (r2=1.00 between rs9270984 and rs9271055; r2=0.84 between rs9270984 and rs9271366). HLADRB1 rs9270984 and rs9271055 showed no better fit (P=3.131029 for both) in association at the genotype level than that of rs9271366 (P=3.40310210) as well as their incomplete information in eQTL analyses; rs9271366 was still selected as tag SNPs in further analyses. In PXK, rs6445961 (r2=0.92 between rs6445961 and rs6445975) showed the most significant association, with P=3.6231024 (protective allele A; OR, 0.77; 95% CI, 0.66 to 0.89). In UBE2L3, rs2298428 (r2=0.95 between rs2298428 and rs5754217) showed the most significant association, with P=4.0731023 (risk allele T; OR, 1.21; 95% CI, 1.06 to 1.37). These findings suggested that associations with PXK, BLK, and UBE2L3 may be true associations because all the associations reported herein were corroborated by associations at neighboring SNPs. Nevertheless, the significances were too weak to meet the threshold in multiple testing. However, the associations between SNPs within LYST and IgAN were not convincing.

Recessive P Values

Analyses of SLE Risk Alleles in IgAN Show Suggestive IgAN Protective Alleles Among the selected 96 SNPs, 10 SNPs of the lupus risk alleles in the region of HLA, CFH (suggested to be a tagging SNP for CFHR1, 3D), PXK, BLK, UBE2L3, and LYST showed evidence for association at an allele-type level (P,0.05) (Table 1). Of note, the associations between alleles in the HLA region, CFH, and IgAN were the top signals in our previous reports on GWAS. Interestingly, in comparing odds ratio (OR) values for these alleles in SLE and IgAN, all the directions of association were opposite those observed in the SLE studies, except for UBE2L3. Control allele frequencies were similar to those reported in SLE GWAS data. Thus, these findings suggested that the SLE risk alleles may be protective for susceptibility to IgAN. However, only SNPs in CFH and HLA regions could retain statistically significant evidence for association (Table 1). Although nonsignificant after applying a Bonferroni correction, PXK, BLK, UBE2L3, and LYST remained interesting candidates for further investigation.

G/A C/T T/G C/A A/G C/T G/A T/C T/G G/T

Results

3.3731025b 6.3131023 3.7831023 4.6831026b 4.37310210b 3.2831022 2.3531022 7.7831022 0.17 2.3631022

Genotype P Values

SLE Risk Allele OR (Reference)

multiple, well known protein-interaction databases. An MiMI plugin, version 3.1.1, installed within Cytoscape 2.8.3, was used to determine the genetic interactions in positional/functional networks. Direct query of genes and their nearest neighbors from all data resources was done, and no further modifications were made.

1.19 (41) 1.18 (35) 1.20 (31) 1.86 (32) 1.26 (36) 1.45 (34) 1.42 (32) 1.35 (36) 1.28 (34) 1.20 (36)

Clinical Journal of the American Society of Nephrology

Chr

790



791

Figure 1. | Regional plots of identified loci in Chinese patients with IgA nephropathy. Genotyped SNPs are plotted with their P values (as– log10[P values]) as a function of genomic position (Human Genome Build 18) within a region surrounding the reported systemic lupus erythematosus risk alleles. SNP, single-nucleotide polymorphism.

eQTL Analyses Provides Functional Clues We investigated whether the most associated SNPs were expression SNPs because they affected the abundance of a protein or gene product by altering transcription. In lymphocyte cell lines from HapMap individuals, rs2298428 and rs9271366 were correlated consistently with UBE2L3 expression (P=0.01–5.03 10 2 5 ) and HLA-DRB1 expression (P=4.7310213–3.9310219), respectively, without population restrictions (Table 2). This conclusion was confirmed by data from lymphoblastoid cell lines from 405 siblings in the United Kingdom (45). These findings suggested that the associations between rs6677604 and CFH, rs2254546, and BLK were more pronounced in Asian populations (although similar trends between genotypes and gene expressions could be observed among different populations). For rs6445961 and PXK, the correlation was marginally significant in white patients living in Utah and Han Chinese from Beijing, China. However, different association patterns appeared to exist between white and Chinese individuals. Differential Gene-Expression Analyses Suggest Gene Involvement in IgAN We ascertained whether the associated genes described above were expressed differently in patients with IgAN and healthy controls. Except for PXK (for which data were

not available), all of the genes were differentially expressed from IgAN than those of controls, with elevated expressions of CFH, HLA-DRA, and HLA-DRB1 in renal biopsy specimens as well as BLK and UBE2L3 in wholeblood samples (Table 3). Only HLA genes were significantly differentially expressed when subjected to multiple testing, but only in renal biopsy specimens rather than in blood samples. Additive and Multiplicative Interaction Analyses Suggest Gene–Gene Interactions Fifteen tests involving different combinations of six of the most significantly associated SNPs (n3[n21])/2) within their respective loci (PXK rs6445961, UBE2L3 rs2298428, CFH rs6677604, HLA-DRB1 rs9271366, HLA-DRA rs9271366, and BLK rs2254546) were conducted in the Chinese population. Supplemental Table 2 shows the results of analyses for additive and multiplicative interactions between identified SNPs categorized by whether they had or did not have protective alleles. There was a modest additive (but not multiplicative) gene–gene interaction between PXK rs6445961 and HLA-DRA rs9501626, with the proportion of risk due to an additive interaction of 2.86 (0.55–5.16), interaction P=1.5131022 for IgAN. Significant multiplicative interactions were observed between FH rs6677604 and

UBE2L3 CFH HLA-DRA HLA-DRB1 HLA-DRB1 BLK

rs2298428-C rs6677604-A rs9501626-A rs9270984-G rs9271366-G rs2254546-G

20.20 0.07 20.28 (0.01)a 0.02 (0.84) – 0.72 (1.30310213)a 0.74 (3.80310215)a 20.43 (8.2031025)a

0.27 (4.1031023)a

20.28 (3.3031023)a 0.12 (0.22) – 0.59 (1.00310211)a 0.63 (4.70310213)a 0.02 (0.82)

CHB (n=137)

CEU (n=165) 20.18 0.10 20.43 (5.0031025)a 0.26 (0.03)a – 0.68 (1.40310212)a 0.75 (3.10310216)a 20.51 (1.1031026)a

JPT (n=113)

HapMap 3 Unrelated Individuals (P Value)

0.02 0.83 – 0.11 (0.26) – 0.68 (4.90310216)a 0.73 (3.90310219)a 20.06 (0.57)

YRI (n=203)

20.390 (8.5031025)a – – – 0.878 (4.00310217)a ND

ND

Children Siblings of British Descent (n=405)

9.4160.94 11.5960.33 13.1060.26 – 4.9160.25 9.5860.18

IgAN (n=27)

8.9560.64 10.8960.54 12.5260.51 – 4.8260.17 9.6660.29

Controls (n=27)

IgAN (n=25) 5.7260.32 9.4260.76 11.3160.65 – 4.4860.13 7.9460.13

4.0931022a 6.5631027a,b 4.2231026a,b – 0.14 0.21

5.5160.14 8.6260.27 10.4360.28 – 4.4460.13 7.7560.16

Controls (n=6)

0.14 2.5631024a,b 5.5831025a,b – 0.53 3.2431023a

P Value

Experiment E-GEOD-35489

P Value

Experiment E-GEOD-37460

Renal Biopsies

Data are the means6SD. IgAN, IgA nephropathy. a P,0.05. b P values remained significant after multiple correction using Benjamini and Hochberg false-discovery rate methods.

CFH HLA-DRA HLA-DRB1 PXK BLK UBE2L3

Candidate Gene

Samples

96.90656.10 8576.4362251.01 16661.5865086.23 – 372.316148.09 492.78694.12

IgAN (n=12)

88.11661.04 8638.2462355.87 15779.1063730.21 – 245.606104.07 362.576132.65

Controls (n=8)

0.74 0.95 0.68 – 3.7531022a 1.9031022a

P Value

Whole Blood: Experiment E-GEOD-14795

Table 3. Differential candidate gene expressions in patients with IgA nephropathy compared with healthy controls from an open database

With the function of each increase of the risk allele, the table depicts the correlation between genotypes and gene expressions. Protective alleles were regarded as reference alleles in the correlation. Pearson correlation coefficients are presented with P values in brackets. CEU, Caucasians living in Utah who were of northern and western European ancestries; CHB, Han Chinese from Beijing, China; JPT, Japanese in Tokyo, Japan; YRI, Yoruba in Ibadan, Nigeria; ND, no data could be derived from the database. a P,0.05.

PXK

Gene

rs6445961-A

SNP

Table 2. Correlation between genotypes of identified IgA nephropathy–associated single-nucleotide polymorphisms with gene expression in Epstein–Barr virus–transformed lymphoblastoid cell lines from an open database

792 Clinical Journal of the American Society of Nephrology



HLA-DRB1 rs9271366 (P=1.7731022), as well as for HLADRA rs9501626 and HLA-DRB1 rs9271366 (P=3.2331022). Analyses of Joint Effects Suggest Cumulative Effects on the Risk of Disease To determine the cumulative effect of six SNPs, disease risk was assessed according to the number of protective alleles they had. Individuals with more protective alleles seemed to be less prone to IgAN (whole model P=5.96310213) (Table 4). With each increase in the number of protective alleles, the disease risk decreased by approximately 7% (r2=–0.97; P=1.3831023). The disease risk decreased up to seven-fold in individuals with eight or more protective alleles compared with those with fewer than 2. Integrating Identifies Molecules in Cytoscape-Supported Network Involvement The six identified molecules showed physical interactions between genes or through their products/neighbors (Figure 2). The network was divided mainly into four modules, representative of pathways: MHC class II antigen presentation, complement regulation, signaling by the B-cell receptor (BCR), and ubiquitin/proteasome-dependent degradation. Several cellular interrelated genes have been suggested to participate in the pathogenesis of IgAN as well as SLE: HLA, ITGAM, C3, CFI, FCGR, and PTEN (26,47–50). We also checked the differential expression of those interrelated genes: great enrichment of differences in gene expression between IgAN and healthy controls was observed (Supplemental Table 3). Whole genome-wide expression data were just from tens of samples, but C3 (it was linked with CFH), ITGAM (CFH), CD74 (HLA-DRB1), HLA-DMA (HLA-DRB1), HLA-DMB (HLA-DRA), EGFR (BLK), SMAD7 (UBE2L3), and PTEN (UBE2L3) still produced significant associations in the context of multiple testing.

Discussion In recent years, three GWASs in IgAN have been conducted. They uncovered several susceptibility loci and greatly broadened our understanding of the genetic architecture of the susceptibility to IgAN (5,7–9). Among these three GWAS, we took part in two of them (7,8). As reported, all the identified associations within the regions of MHC, 1q32, 8p23, 17p13, and 22q12 could be confirmed in our cohort (7,8), which proved to be the cornerstone of credibility of the present study. The findings of the present study added to the loci showing associations with IgAN, as well as overlap between IgAN and SLE: CFH, HLA-DRA, HLA-DRB1,

793

PXK, BLK and UBE2L3. Although some of the associations did not remain significant after the Bonferroni correction was applied, all the associations reported herein were corroborated by associations at neighboring SNPs, suggesting that they are true associations. It is widely accepted that initial GWAS can detect just the greatest effects rather than all the susceptibility variants. The ORs of all the novel variants were much weaker (0.8 or 1.2) than the ORs from variants within CFH and HLA (0.6), both of which were previously identified signals in GWAS. The observation that the associated allele was the reverse of that reported previously for SLE was in accordance with a report stating that the protective alleles within MHC, 1q32 and 22q12 regions for IgAN had been implicated as risk factors for other autoimmune disorders (8). Most of these associated loci showed the same tendency for disease susceptibility, so the result is not likely to be a coincidence. The different association directions of the same alleles nevertheless supported the notion of pleiotropy (effect of a single gene on multiple phenotypes), quantitative genetics (combination of the influences of multiple genes together with environmental variation resulting in continuous distributions of phenotypes), and the human “diseasome” (the synthesis of all human genetic disorders [“disease phenome”]) and all human disease genes [“disease genome”]). Ideally, GWAS testing for identifying the common or shared genetic influences on SLE and IgAN in the same population should be carried out and is underway. GWAS have been used to identify multiple SNPs associated with disease risk, and attention has turned to explaining the underlying molecular mechanisms of action (5). One hypothesis is that a proportion of the causal variants tagged by these disease-associated markers may affect the abundance of a protein (or the relative abundance of its different isoforms) by altering transcription. Efficient identification of additional susceptibility loci with more modest effects might benefit from the integration of statistical evidence with some assessment of functional candidacy. Here, we investigated the positive correlations between identified SNPs and their corresponding gene expression, especially for HLA-DRB1, UBE2L3, and BLK. The data further supported the candidacy of those genes as causal factors in IgAN. Data from Epstein– Barr virus B cell–transformed lymphoblastoid cell lines should be more illustrative than data based on other cell lines in IgAN, because gene expression and eQTLs can be tissue-specific and because IgAN is a disease characterized by production of the nephritogesnic IgA1 molecule from B cells. Confirmation from a different gene-expression database strongly supported the probability of reliability (45). In addition, immortalized lymphoblasts that were clonal

Table 4. Joint effects of newly identified loci stratified by the number of protective alleles

Protective Alleles (n)

Frequency in Cases/Controls (%/%)

#2 3 4 5 6 7 $8

5.4/1.9 13.5/10.3 25.7/19.5 26.3/25.4 19.0/21.4 6.4/13.3 3.7/8.0

Odds Ratio (95% CI) 1.00 (Reference) 0.46 (0.25 to 0.83) 0.46 (0.26 to 0.82) 0.36 (0.21 to 0.64) 0.31 (0.18 to 0.55) 0.17 (0.09 to 0.31) 0.16 (0.08 to 0.31)

P Value 9.1131023 6.6831023 2.7331024 3.0631025 1.4431029 8.7731029

794


Figure 2. | A network plot of connections between identified loci in Chinese patients with IgA nephropathy.

could more readily be studied without the environmental influences or transcriptome diversity found in mixed lymphocyte populations in vivo (51). Also, when differential gene expressions in IgAN patients were checked, the expression of all of those genes was upregulated in IgAN patients. However, the associations seemed to have tissue specific-characteristics because elevated expressions of CFH, HLA-DRA and HLA-DRB1 seemed to be restricted to renal biopsies and BLK and UBE2L3 to whole-blood samples. More widespread gene-expression analyses will be warranted, especially in specific cell clones. It seemed that HLADRA and HLA-DRB1 protective alleles corresponded to lower gene expressions, whereas PXK, BLK, and UBE2L3 protective alleles corresponded to higher gene expressions, which may indicate an abnormal balance between antigen presentation and lymphocyte signaling. Nevertheless, future studies linking alleles and differential gene expressions in specific tissues will be needed. In addition, rare variants, which may have a greater effect in conferring disease risk and may contribute to a substantial fraction of heritability, will need further evaluations in future genetic studies in IgAN. Furthermore, to determine whether the identified genes cause effects in a joint manner or epistatic fashion, we conducted gene–gene interaction analyses as well as cumulative gene effect analysis. Investigating genetic interactions

has proved difficult, and an optimal statistical approach is not available, so combining several analytical methods may be best for detecting epistatic interactions. Gene–gene interactions can be assessed with additive or multiplicative mathematical models. We demonstrated significant additive and multiplicative interactions among the identified SNPs: that is, additive interactions between PXK rs6445961and HLADRA rs9501626, as well as multiplicative interactions between CFH rs6677604 and HLA-DRB1 rs9271366, and between HLA-DRA rs9501626 and HLA-DRB1 rs9271366. However, because of the moderate effects of these alleles and a low incidence of IgAN (estimated incidence in the general population, 25–50 cases per 100,000 individuals), our study remained underpowered to detect epitasis with our sample size (calculated power for epistasis was approximately 0.1–0.2). In joint analyses, we observed that the disease risk decreased by about 7% with each increase in the alleles, and it decreased up to 7-fold in individuals with eight or more protective alleles compared with those who have fewer than two. These results repeatedly supported the notion that the identified genes were the susceptibility genes for IgAN. One of the most compelling reasons for identifying the genetic underpinnings of common diseases is to generate new hypotheses about the mechanisms and pathogenesis of disease (5). Hence, we checked further the newly


identified genes in a pathway-based manner. A molecular network using a correlation structure was produced in which all the identified genes were connected to each other by intermediary genes, and four modules were highlighted. Great enrichment of differences in gene expression between IgAN and healthy controls was observed even though the whole genome-wide expression data were just from tens of samples. The pathways were MHC class II antigen presentation, complement regulation, signaling by the BCR, and ubiquitin/proteasome-dependent degradation. The role of MHC and complement in IgAN has been supported strongly by several observational studies. BLK encodes a tyrosine kinase that is involved in the regulation of B-cell activation. B-cell signaling may have a key role in the pathogenesis of IgAN through elevation of IgA levels in serum, production of autoantibodies, antigen presentation to T cells, and cytokine production (52). Also, B-cell depletion has proved successful in the treatment of GN. UBE2L3 encodes a ubiquitin-conjugating enzyme involved in ubiquitin/proteasome-dependent degradation, which is important in the cell cycle, cell differentiation, apoptosis, sodium-channel function, and modulation of inflammatory responses. The ubiquitin/proteasome pathway has been suggested to be implicated in the development of multiple kidney diseases (53), and proteasome inhibitors have been efficacious in some forms of renal disorders, such as lupus nephritis (54), renal ischemia-reperfusion injury (55), and ANCA-induced GN (56). PXK encodes a multimodular protein composed of a phox homology domain, a protein kinase–like domain, and a WiskottAldrich syndrome protein homology 2 domain. The gene product of PXK regulates the activity of Na-ATPase and K-ATPase ion transport, and is expressed in the kidney (57,58). Recent data suggest that PXK has a critical role in trafficking of the EGF receptor through modulation of ligandinduced ubiquitination of the receptor. Thus, the present study provided important clues for better elucidation of IgAN pathophysiology in the future and possible therapy optimization. Nevertheless, one must be cautious because most of the findings from the initial GWA studies were association signals rather than direct information about susceptibility genes. The degree of shared genes between IgAN and SLE is substantial, but is likely to still be an underestimate. First, we analyzed only associated loci at the P,131025 level, and the SNPs or genes meeting this criterion are increasing with enrollment of larger sample sizes. Second, because of different linkage disequilibrium between variants in cases and controls, a different variant in the same locus may be responsible for disease risk in a second phenotype. Third, GWASs directly conducted in patients with lupus nephritis are still underway. For these reasons, the gene overlap between the two diseases may be higher than that identified in the present study. In conclusion, we identified CFH, HLA-DRA, HLA-DRB1, PXK, BLK, and UBE2L3 as shared loci between IgAN and SLE. Many of the alleles that are risk alleles for SLE are protective alleles for IgAN. Genotypes were correlated with the corresponding gene expression, suggesting a cis-eQTL effect. Positive gene–gene interactions were observed, and disease risk decreased with accumulation of protective alleles. Four pathways (MHC class II antigen presentation, complement regulation, signaling by the BCR,


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and ubiquitin/proteasome-dependent degradation) were highlighted. From the “systems genetics” perspective, our data represent important clues for future studies on pleiotropy in IgA nephropathy and lupus nephritis. Acknowledgments We thank our collaborators, Ali G. Gharavi and Krzyszt of Kiryluk (Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY), for kindly providing GWAS data and giving advice for the manuscript. We thank Sai-Nan Zhu (Department of Biostatistics, Peking University First Hospital) for assistance with statistical analysis. We thank ELIXIGEN for their editing assistance. We are grateful to the patients and their families for their participation in this study. This work was supported by grants from the Major State Basic Research Development Program of China (973 program, No. 2012CB517700), the National Natural Science Foundation of China (No. 81200524), the Research Fund of Beijing Municipal Science and Technology for the Outstanding Program (20121000110), the Foundation of Ministry of Education of China (20120001120008), and the Natural Science Fund of China to the Innovation Research Group (81021004). Disclosures None. References 1. Floege J: The pathogenesis of IgA nephropathy: What is new and how does it change therapeutic approaches? Am J Kidney Dis 58: 992–1004, 2011 2. Lai KN: Pathogenesis of IgA nephropathy. Nat Rev Nephrol 85: 275–283, 2012 3. Boyd JK, Cheung CK, Molyneux K, Feehally J, Barratt J: An update on the pathogenesis and treatment of IgA nephropathy. Kidney Int 81: 833–843, 2012 4. Suzuki H, Kiryluk K, Novak J, Moldoveanu Z, Herr AB, Renfrow MB, Wyatt RJ, Scolari F, Mestecky J, Gharavi AG, Julian BA: The pathophysiology of IgA nephropathy. J Am Soc Nephrol 22: 1795–1803, 2011 5. Kiryluk K, Novak J, Gharavi AG: Pathogenesis of immunoglobulin A nephropathy: Recent insight from genetic studies. Annu Rev Med 64: 339–356, 2013 6. Mestecky J, Raska M, Julian BA, Gharavi AG, Renfrow MB, Moldoveanu Z, Novak L, Matousovic K, Novak J: IgA nephropathy: Molecular mechanisms of the disease. Annu Rev Pathol 8: 217–240, 2013 7. Yu XQ, Li M, Zhang H, Low HQ, Wei X, Wang JQ, Sun LD, Sim KS, Li Y, Foo JN, Wang W, Li ZJ, Yin XY, Tang XQ, Fan L, Chen J, Li RS, Wan JX, Liu ZS, Lou TQ, Zhu L, Huang XJ, Zhang XJ, Liu ZH, Liu JJ: A genome-wide association study in Han Chinese identifies multiple susceptibility loci for IgA nephropathy. Nat Genet 44: 178–182, 2012 8. Gharavi AG, Kiryluk K, Choi M, Li Y, Hou P, Xie J, Sanna-Cherchi S, Men CJ, Julian BA, Wyatt RJ, Novak J, He JC, Wang H, Lv J, Zhu L, Wang W, Wang Z, Yasuno K, Gunel M, Mane S, Umlauf S, Tikhonova I, Beerman I, Savoldi S, Magistroni R, Ghiggeri GM, Bodria M, Lugani F, Ravani P, Ponticelli C, Allegri L, Boscutti G, Frasca G, Amore A, Peruzzi L, Coppo R, Izzi C, Viola BF, Prati E, Salvadori M, Mignani R, Gesualdo L, Bertinetto F, Mesiano P, Amoroso A, Scolari F, Chen N, Zhang H, Lifton RP: Genomewide association study identifies susceptibility loci for IgA nephropathy. Nat Genet 43: 321–327, 2011 9. Feehally J, Farrall M, Boland A, Gale DP, Gut I, Heath S, Kumar A, Peden JF, Maxwell PH, Morris DL, Padmanabhan S, Vyse TJ, Zawadzka A, Rees AJ, Lathrop M, Ratcliffe PJ: HLA has strongest association with IgA nephropathy in genome-wide analysis. J Am Soc Nephrol 21: 1791–1797, 2010 10. Zhernakova A, van Diemen CC, Wijmenga C: Detecting shared pathogenesis from the shared genetics of immune-related diseases. Nat Rev Genet 10: 43–55, 2009

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Received: February 13, 2013 Accepted: October 30, 2013 Published online ahead of print. Publication date available at www. cjasn.org. This article contains supplemental material online at http://cjasn. asnjournals.org/lookup/suppl/doi:10.2215/CJN.01860213/-/ DCSupplemental.