Abstract

Recent reports have associated NCF2, encoding a core component of the multi-protein NADPH oxidase (NADPHO), with systemic lupus erythematosus (SLE) susceptibility in individuals of European ancestry. To identify ethnicity-specific and -robust variants within NCF2, we assessed 145 SNPs in and around the NCF2 gene in 5325 cases and 21 866 controls of European-American (EA), African-American (AA), Hispanic (HS) and Korean (KR) ancestry. Subsequent imputation, conditional, haplotype and bioinformatic analyses identified seven potentially functional SLE-predisposing variants. Association with non-synonymous rs17849502, previously reported in EA, was detected in EA, HS and AA (PEA = 1.01 × 10−54, PHS = 3.68 × 10−10, PAA = 0.03); synonymous rs17849501 was similarly significant. These SNPs were monomorphic in KR. Novel associations were detected with coding variants at rs35937854 in AA (PAA = 1.49 × 10−9), and rs13306575 in HS and KR (PHS = 7.04 × 10−7, PKR = 3.30 × 10−3). In KR, a 3-SNP haplotype was significantly associated (P = 4.20 × 10−7), implying that SLE predisposing variants were tagged. Significant SNP–SNP interaction (P = 0.02) was detected between rs13306575 and rs17849502 in HS, and a dramatically increased risk (OR = 6.55) with a risk allele at each locus. Molecular modeling predicts that these non-synonymous mutations could disrupt NADPHO complex assembly. The risk allele of rs17849501, located in a conserved transcriptional regulatory region, increased reporter gene activity, suggesting in vivo enhancer function. Our results not only establish allelic heterogeneity within NCF2 associated with SLE, but also emphasize the utility of multi-ethnic cohorts to identify predisposing variants explaining additional phenotypic variance (‘missing heritability’) of complex diseases like SLE.

INTRODUCTION

Systemic lupus erythematosus (SLE or lupus) is a clinically heterogeneous, systemic autoimmune disease affecting ∼2 million people in the USA, about 90% of whom are women (1). The profound morbidity and mortality of SLE stem from chronic inflammation and multiple organ damage. Because lupus patients have weakened immune systems from standard immunosuppressant treatment (2–7) and immune dysregulation, infection is a leading cause of morbidity and mortality, accounting for >25% of SLE-related deaths (2,3,8–9).

Both innate immunity and global regulation of the immune system depend on generation of reactive oxygen species (ROS) such as superoxide (O2•−) and hydrogen peroxide (H2O2). Pathogen clearance by phagocytosis depends on ROS production by phagocytes, i.e. neutrophils, monocytes and macrophages, and deficient ROS generation may give rise to infection susceptibility. Conversely, overproduction of ROS may lead to oxidative stress that stimulates autoimmune responses (10–12). Given the delicate ROS balance required for proper immune function, it is not surprising that mutations in ROS regulators may contribute to both immune dysfunction and susceptibility to chronic infections (13). At the core of the ROS production machinery is nicotinamide adenine dinucleotide phosphate oxidase (NADPHO), a multi-subunit membrane protein that catalyzes oxidation of NADPH. NADPHO consists of five core phagocytic oxidase (‘phox’) proteins, p22phox/cytochrome b-245 alpha (CYBA), gp91phox/Nox2/cytochrome b-245 beta (CYBB), p47phox (NCF1), p67phox (NCF2) and p40phox(NCF4). The NADPHO complex is activated by one or more soluble GTPases in each phagocytic class (Supplementary Material, Fig. S1). The dominant NADPHO-activating GTPase is Rac1 in monocytes and Rac2 in neutrophils. Both GTPases appear to play a role in macrophages (14). A specific guanine nucleotide exchange factor (GEF), such as Vav1, is required to activate the GTPase (15).

Recently NCF2, which encodes p67phox, was identified as an SLE susceptibility gene (16–18). An intronic SNP (rs10911363) was implicated in SLE pathogenicity in individuals with European ancestry (16,17). In Chinese individuals this SNP was not associated with SLE, but it was associated with arthritis and anti-nuclear antibody production (19). Fine-mapping by Jacob et al. (18) suggested that missense variant rs17849502 (H389Q) underlies NCF2-SLE association in European-derived populations. However, this could not be confirmed in other ethnic populations because of low minor allele frequency (MAF) and small sample sizes. Such disparate and inconclusive results are not uncommon for SLE, given the strong influence of ancestry on SLE predisposition and progression. SLE pathology is influenced by numerous molecular pathways and ethnicity-specific genetic associations (20,21), which suggests that distinct sets of genetic interactions modulate SLE risk in different populations. Also, prevalence and severity of SLE are markedly higher in individuals with African, Asian or Hispanic ancestries compared with those with European ancestry (22–24). Thus, analyzing multiple ethnic populations is crucial for understanding the disease mechanisms of SLE (25).

To thoroughly investigate SLE-NCF2 association in ethnically diverse populations, and to identify both ethnicity-specific and -robust causal variants, we analyzed a large multi-ethnic cohort. The objectives of this study were to (i) perform comprehensive analysis using dense fine-mapping, (ii) identify robust, independent SLE-predisposing variants and (iii) predict and validate the molecular effects of SLE risk alleles through bioinformatics, molecular modeling, and in vitro assays.

RESULTS

Study populations and genotypic data

Our large-scale association study included 12 256 participants from four ethnically diverse populations including European-American (EA), African-American (AA), Hispanic (HS) and Korean (KR) individuals (Supplementary Material, Table S1). All SLE patients satisfied American College of Rheumatology classification for SLE (26,27). Controls had no history of autoimmune disease and were considered healthy.

Our study design with workflow is shown in Figure 1. Individuals were genotyped on the Illumina ImmunoChip array (28) as a part of separate projects led by collaborators. Additional healthy controls were obtained from dbGaP (29). Subjects were excluded from analysis for low call rate (<98%), cryptic relatedness to another study participant (identity by descent >25%), or being outliers identified by principal component analysis (PCA) (Supplementary Material, Fig. S2). After quality control (QC) there were 4804 SLE cases and 7452 healthy controls: EA (1476 cases, 2693 controls), HS (829 cases, 1181 controls), AA (686 cases, 371 controls) and KR (1813 cases, 3207 controls) (Supplementary Material, Table S1).

Figure 1.

Workflow and study design of the present study.

Figure 1.

Workflow and study design of the present study.

A total of 145 SNPs in and around the NCF2 region (Build 37; Chr1: 183,523,470-183,573,858) were examined. SNPs were removed if they were out of Hardy–Weinberg equilibrium (PHWE < 0.001 in controls), had >10% missing genotype calls, clustered too poorly to separate genotype calls, or had MAF <0.5%. After QC, 118 SNPs were analyzed, including 74 SNPs in EA, 83 in HS, 94 in AA and 75 in KR. Case–control association studies were analyzed by χ2 test, and odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated for each population using PLINK (30).

Initial case–control association analysis

The initial analysis showed significant association (P < 0.05) with SLE for 26 SNPs in EA, 5 in HS, 6 in AA, and 41 in KR (Supplementary Material, Table S2). The strongest association was at rs17849501 (A202A) in EA and HS [PEA = 5.23 × 10−33, OR (95% CI) = 2.73 (2.31–3.24); PHS = 2.97 × 10−6, OR (95% CI) = 1.94 (1.46–2.56)]. Our results confirmed strong SLE association in EA and HS with the previously identified risk allele at rs17849502 (H389Q) (18) [PEA = 1.93 × 10−28, OR (95% CI) = 2.48 (2.10–2.92); PHS = 7.01 × 10−7, OR (95% CI) = 2.03 (1.53–2.69)]. Both of these SNPs were monomorphic in KR, and both failed to pass QC in AA due to low MAF (0.006 in controls). Novel association at non-synonymous SNP rs35937854 (V297A) was the strongest association in AA [PAA = 7.15 × 10−4, OR (95% CI) = 2.20 (1.38–3.51)], but was monomorphic in other populations. In KR, the strongest association was at intronic SNP rs10911357 [PKR = 7.75 × 10−4, OR (95% CI) = 1.16 (1.06–1.26)].

Imputation-based association analysis, adjusted for multiple testing and ancestry correction

To increase the statistical power of our analysis, we performed a comprehensive ethnicity-specific imputation-based analysis. We incorporated an additional 11 968 EA and 1603 AA healthy out-of-study controls from dbGaP, as well as 521 HS SLE cases and 843 healthy HS controls obtained through collaboration (Supplementary Material, Table S1; Fig. 1). We also increased fine-mapping density by imputing additional NCF2 SNPs from 1000 Genomes Project data using MACH-ADMIX (31). QC criteria described earlier were applied to these samples and SNPs. Additionally, SNPs had to pass imputation quality metric (Rsq > 0.7).

After imputation and QC, 250 SNPs were available for analysis (Supplementary Material, Table S3). We adjusted the significance threshold of α = 0.05 to account for multiple testing. Spectral decomposition of the pair-wise LD matrices (32,33) was used to account for inter-SNP correlation due to linkage disequilibrium (LD), allowing us to determine the effective number of independent SNPs separately for each population. Based on this calculation, we determined a population-specific adjusted experiment-wise significance threshold at the 5% level: EA (P ≤ 1.33 × 10−3, 152 total SNPs and 6 independent SNPs), HS (P ≤ 1.70 × 10−3, 134, 11), AA (P ≤ 4.95 × 10−4, 214, 5) and KR (P ≤ 2.00 × 10−3, 164, 5).

Imputation-based analysis supported our initial results. rs17849502 was the most strongly associated SNP in EA [PEA = 1.01 × 10−54, OR (95% CI) = 2.57 (2.28–2.91)] and was also highly significant in HS [PHS = 3.68 × 10−10, OR (95% CI) = 2.02 (1.62–2.53)]. In both populations, rs17849501 was similarly associated [PEA = 4.45 × 10−54, OR (95% CI) = 2.56 (2.26–2.90); PHS = 3.34 × 10−10, OR (95% CI) = 2.02 (1.61–2.52)], and was in strong LD with rs17849502 (Supplementary Material, Fig. S3). Since the MAF in AA was too low for reliable imputation (MAF rs17849502 = 0.0066, MAF rs17849501 = 0.0056 in controls), we genotyped rs17849502 and rs17849501 in a subset of 603 AA cases and 384 AA controls using a TaqMan assay. Despite the reduced sample size and low MAF, both SNPs were significantly associated with SLE [Prs17849502 = 0.02, OR (95% CI) = 2.76 (1.14–6.68); Prs17849501 = 0.007, OR (95% CI) = 3.88 (1.36–11.10)]. Consistent with our initial results, the strongest association in AA was at rs35937854 [V297A; PAA = 1.49 × 10−9, OR (95% CI) = 2.34 (1.76–3.11)]. In KR, the strongest SLE association was an imputed intronic SNP, rs10911359 [PKR = 6.82 × 10−4, OR (95% CI) = 1.16 (1.06–1.26)].

Robustness of our analyses was assessed using covariate adjusted logistic regression implemented in PLINK (see Methods and Materials). Using ADMIXTURE (34) software, we detected three ancestral components as optimal for describing AA, KR and EA, whereas four ancestral components best described HS. Individual ancestry proportions were used as covariates to correct for potential effects of population structure on association (Supplementary Material, Table S3). Results were confirmed by principal component analysis (PCA, implemented in GCTA) (35) using the first three principal components covariates (Supplementary Material, Table S3). For both covariate adjusted result sets the magnitude of significance was consistent with the unadjusted results, with the exception of rs17849502/rs17849501 in EA, and rs13306575 in HS, where ancestry might have a small impact on the strength of association (Supplementary Material, Table S3). Both associations remain significant following ancestry adjustment.

Identification of independent variants in four ethnicities

We performed conditional analysis to detect independently associated variants in each population (Supplementary Material, Table S4). Population-specific haplotypes were constructed using independent SNPs identified through conditional analysis. Interactions between independently associated SNPs in each population were also assessed using additional methods (described below).

SLE association of the NCF2 region in EA was explained by rs17849502 (H389Q) and rs17849501 (A202A). Conditioning on either SNP explained SLE significance in EA (using threshold P > 1.33 × 10−3) (Supplementary Material, Fig. S4). Because of the strong LD between these SNPs (D′ = 1.0, r2 = 0.99, 9743 bp apart), we could not dissect separate contributions of each SNP (Supplementary Material, Fig. S3) and considered these SNPs together.

In HS both rs17849502 and rs17849501 were also highly significant [PHS = 7.04 × 10−7, OR (95% CI) = 1.56 (1.31–1.86)] and in high LD (D′ = 1.0, r2 = 0.98). Additionally, non-synonymous variant rs13306575 (R395W) was independently associated with SLE (Table 1) [PHS = 7.04 × 10−7, OR (95% CI) = 1.56 (1.31–1.86)]. rs13306575 was also significant in KR [PKR = 3.30 × 10−3, OR (95% CI) = 1.43 (1.13–1.83)] in the same direction (Fisher's combined P-value for PHS&KR = 4.85 × 10−8). The omnibus haplotype association was highly significant (Phap = 4.75 × 10−14) (Supplementary Material, Table S5). Conditioning on haplotypes with a risk allele at only one SNP did not explain the omnibus haplotype significance (Pcond:GT = 2.33 × 10−9; Pcond:AG = 5.60 × 10−10), but conditioning on the protective haplotype ‘GG’ accounted for the entire omnibus haplotype association (Pcond:GG = 0.09) (Supplementary Material, Fig. S5). Haplotype analysis including rs17849501/rs13306575 produced similar results (data not shown). Thus in HS, SLE association is explained by rs13306575 and rs17849502/rs17849501, the latter acting as an indistinguishable effect.

Table 1.

Single SNP allelic association results for four populations. Seven SNPs were independent in at least one ethnicity (bold). Three SNPs caused a change of amino acid (non-synonomus), one was synonymous, and the remaining two were intronic. Minor allele frequencies for SLE cases (MAF-A) and healthy controls (MAF-U), along with P-values and odds ratios (OR) are presented.

SNP Position Allele European American
 
Hispanic
 
African American
 
Korean
 
(function) (feature) (1476/14,661)* (1350/2024)* (686/1974)* (1813/3207)* 
   MAF-A MAF-U P-value OR MAF-A MAF-U P-value OR MAF-A MAF-U P-value OR MAF-A MAF-U P-value OR 
rs13306575 183532437 A/G monomorphic 0.100 0.066 7.04x10-7 1.56 monomorphic 0.034 0.024 3.30x10-3 1.43 
(R395W) exon 14 
rs17849502 183532580 T/G 0.120 0.050 1.01x10-54 2.57 0.069 0.035 3.68x10-10 2.02 0.021 0.008 0.02+ 2.76 monomorphic 
(H389Q) exon 13 
rs35937854 183536089 G/A monomorphic monomorphic 0.066 0.029 1.49x10-9 2.34 monomorphic 
(A297V) exon 10 
rs34680162 183538434 C/A monomorphic monomorphic 0.045 0.025 2.33x10-4 1.83 monomorphic 
intron 8 
rs17849501 183542323 T/C 0.119 0.050 4.45x10-54 2.56 0.070 0.036 3.34x10−10 2.02 0.020 0.005 0.007+ 3.88 monomorphic 
(A202A) exon 6 
rs10911359 183543053 C/T 0.513 0.524 0.256 0.96 0.377 0.375 0.825 1.01 0.637 0.649 0.417 0.95 0.388 0.354 6.82x10-4 1.16 
intron 5 
rs34423782 183554714 C/T 0.936 0.929 0.185 1.11 Failed QC 0.838 0.824 0.246 1.10 0.986 0.978 1.73x10-3 1.67 
intron 4 
SNP Position Allele European American
 
Hispanic
 
African American
 
Korean
 
(function) (feature) (1476/14,661)* (1350/2024)* (686/1974)* (1813/3207)* 
   MAF-A MAF-U P-value OR MAF-A MAF-U P-value OR MAF-A MAF-U P-value OR MAF-A MAF-U P-value OR 
rs13306575 183532437 A/G monomorphic 0.100 0.066 7.04x10-7 1.56 monomorphic 0.034 0.024 3.30x10-3 1.43 
(R395W) exon 14 
rs17849502 183532580 T/G 0.120 0.050 1.01x10-54 2.57 0.069 0.035 3.68x10-10 2.02 0.021 0.008 0.02+ 2.76 monomorphic 
(H389Q) exon 13 
rs35937854 183536089 G/A monomorphic monomorphic 0.066 0.029 1.49x10-9 2.34 monomorphic 
(A297V) exon 10 
rs34680162 183538434 C/A monomorphic monomorphic 0.045 0.025 2.33x10-4 1.83 monomorphic 
intron 8 
rs17849501 183542323 T/C 0.119 0.050 4.45x10-54 2.56 0.070 0.036 3.34x10−10 2.02 0.020 0.005 0.007+ 3.88 monomorphic 
(A202A) exon 6 
rs10911359 183543053 C/T 0.513 0.524 0.256 0.96 0.377 0.375 0.825 1.01 0.637 0.649 0.417 0.95 0.388 0.354 6.82x10-4 1.16 
intron 5 
rs34423782 183554714 C/T 0.936 0.929 0.185 1.11 Failed QC 0.838 0.824 0.246 1.10 0.986 0.978 1.73x10-3 1.67 
intron 4 

*Sample sizes (cases/controls); + Due to low MAF, P-value was determined in a subset of N cases and N controls sequenced by Taqman, and P-value met the threshold for significance in this experiment; OR = odds ratio

We observed a significant SNP–SNP interaction between independent, missense SNPs rs17849502 and rs13306575 using a logistic regression model implemented in R (PC-corrected P = 0.024, ancestry-corrected P = 0.025). This result was confirmed by parametric FITF (36), epistasis test (P = 0.018), PLINK two-locus test (P = 0.02) and GAIA (P = 0.038). We confirmed this interaction (sign test P = 1 × 10−3) using non-parametric generalized multifactor-dimensionality reduction (GMDR). In GMDR, the best-fitted interaction model was between these two SNPs, with a cross-validation consistency of 9 out of 10, and a testing accuracy of 55%. We then compared cases and controls with no risk alleles to individuals with a risk allele at both SNPs and found a dramatic increase in SLE risk [OR (95% CI) = 6.55 (2.84–15.11)]. Possessing a risk allele at only one SNP showed a marginal increase in SLE risk [risk at rs13306575, OR = 1.52 (1.25–1.85); risk at rs17849502, OR = 1.78 (1.38–2.28)].

In AA, non-synonymous rs35937854 (V297A) and intronic rs34680162 were independently associated with SLE (Table 1). Conditional analysis on overall haplotype association (Phap = 2.93 × 10−11) confirmed that neither SNP fully explained association (Pcond: rs35937854 = 1.15 × 10−4; Pcond: rs34680162 = 2.20 × 10−9) (Supplementary Material, Fig. S6 and Table S5). No observed haplotypes contained risk alleles at both SNPs. Conditioning on haplotypes including risk alleles at only one SNP did not explain the omnibus haplotype significance (Pcond:AC = 2.20 × 10−9; Pcond:GA = 1.15 × 10−4), but conditioning on the protective haplotype ‘AA’ explained haplotypic association (P = 0.28). No SNP–SNP interactions were observed.

In KR, we identified three novel independently associated NCF2 SNPs: non-synonymous variant rs13306575, and intronic variants rs10911359 and rs34423782 (Table 1) in low LD with one another (Supplementary Material, Fig. S3). No SNP fully explained the overall omnibus haplotype association (Phap = 4.20 × 10−7; Pcond: rs13306575 = 9.50 × 10−6; Pcond: rs10911359 = 3.14 × 10−4; Pcond: rs34423782 = 2.72 × 10−5). Likewise, conditioning on single haplotypes did not explain overall haplotype significance (Pcond: GTT = 2.72 × 10−5; Pcond: GCC = 3.14 × 10−4; Pcond:ATC = 1.26 × 10−5; Pcond:GTC = 3.17 × 10−5) (Supplementary Material, Fig. S7). No haplotypes containing more than one risk allele were observed, and no SNP–SNP interactions were observed. Both rs10911359 and rs34423782 lie within active chromatin regions in GM12878 cells (a lymphoblastoid cell line); both risk alleles disrupt CpG dinucleotides within tracks of methylated sequence (ENCODE/MeDIP track).

We assessed the heterogeneity of OR across populations for independently associated SNPs that passed QC in at least two populations. Control allele frequencies were consistent with HapMap frequencies for each population. We found that for all but one SNP the ORs were homogeneous across populations. For rs10911359, which was only significant in Koreans, the ORs were heterogeneous (P = 0.0056).

In silico functional analysis

Together, the conditional analyses identified seven associated SNPs with six independent effects (counting rs17849501 and rs17849502 together) in the NCF2 region; three of these were non-synonymous variants. Because each non-synonymous mutation was monomorphic in at least one population, a single amino acid change (e.g. H389Q) (18) cannot explain the NCF2 association in all populations. To better understand how genetic variation in these populations influences SLE susceptibility, independently associated variants, coding and non-coding, were scanned in silico to assess their possible functional effects on NCF2 or directly adjacent genes.

We focused on probes targeting NCF2 and its adjacent genes, SMG7 and ARPC5, using genotype data from HapMap and the 1000 Genomes Project and expression data from GeneVar (37,38) for expression quantitative trait locus (eQTL) analysis. A linear model was fitted for population-specific eQTL expression data versus NCF2 SNP genotypes for 153 HapMap samples using PLINK. Five SNPs (rs13306575, rs17849502, rs34680162, rs17849501, rs10911359; Supplementary Material, Fig. S8, Table S6) were significantly associated with cis-eQTLs at the SMG7 probe (GI_42476073-A). These results were also assessed using data from the mRNA browser (39,40). Since none of these SNPs were available in the mRNA browser, we used tagged SNPs that were in LD (r2 or D′ ≥ 0.8) with our SNPs (Supplementary Material, Table S6) to assess associations with mRNA transcript abundance.

Four tagged SNPs, rs1044879 (D′ = 1, r2 = 1 with rs17849501 and rs17849502 in CEU, PmRNA = 1.4 × 10−17), rs796860 (D′ = 1, r2 = 0.05 with rs10911359 on YRI, PmRNA = 5.0 × 10−4), rs2296164 (D′ = 1, r2 = 1 with rs10911359 in CEU and CHB + JPT, PmRNA = 2.6 × 10−10) and rs2274064 (D′ = 1, r2 = 0.02 with rs10911359 and rs34423782 in CHB and JPT, PmRNA = 6.2 × 10−10) were significantly associated with transcript abundance of SMG7, but not NCF2 or ARPC5. Of note, although two tag-SNP pairs (rs796860- rs10911359 and rs2274064- rs34423782) had low correlation (r2), their minor alleles were on the same haplotype (D′ = 1). rs10911359 is in perfect correlation with rs2296164, leaving only rs34423782 imperfectly tagged. These significant associations between tagged SNPs in NCF2 and SMG7 transcript abundance demonstrate that genetic variants within NCF2 associated with SLE may have functional effects on expression of the adjacent gene SMG7. SMG7 encodes a component of the mRNA ‘decay body’, which mediates nonsense-mediated mRNA decay (NMRD) (41). Disruptions to NMRD have been implicated in auto-antibody development, in particular, to lupus auto-antigen La (42). Furthermore, SMG7 levels have been shown to correlate negatively with lupus anti-nuclear antibody titers (43), suggesting that SMG7 in particular and NMRD in general may play a protective role against auto-antigen generation and SLE progression.

Molecular modeling of the novel coding variants

We modeled protein structural effects of non-synonymous rs13306575 (R395W), which was independently associated with SLE in HS and KR. The risk allele at rs13306575 results in an R395W mutation in exon-14 of the p67phox protein encoded by NCF2. A previous crystal structure of the PB1 domain complex of p40phoxand p67phox (44) showed that R395 forms a salt bridge with the carbonyl oxygen of P339 in p40phox and R395W may disrupt this salt bridge (Fig. 2A). Additionally, the tryptophan side-chain introduces significant clashes within the PB1 domain, likely destabilizing the domain and its interactions.

Figure 2.

(A) Effect of rs13306575 (R395W) on the interactions between p67phox and p40phox. Structure (PBD# 1OEY) of p67phox-R395 (wild-type) (top) making an ion pair with the carbonyl oxygen of P339 in p40phox; lost in R395W (mutant); the tryptophan side also introduces clashes (bottom). (B) Effect of rs35937854 (V297A) on the conformation and stability of the first SH3 domain.

Figure 2.

(A) Effect of rs13306575 (R395W) on the interactions between p67phox and p40phox. Structure (PBD# 1OEY) of p67phox-R395 (wild-type) (top) making an ion pair with the carbonyl oxygen of P339 in p40phox; lost in R395W (mutant); the tryptophan side also introduces clashes (bottom). (B) Effect of rs35937854 (V297A) on the conformation and stability of the first SH3 domain.

Similarly, the risk allele at rs35937854 produces a V297A substitution in exon-10 and is predicted to disrupt a highly conserved binding site (ENCODE/HMR track) for the transcriptional activator Myb (Myeloblastosis oncogene). Myb is critical for hematopoiesis, particularly monocyte–macrophage differentiation and proliferation (45). The V297A substitution in p67phox is located in the first SH3 domain, which interacts with gp91phox (46). This mutation is predicted to destabilize SH3 domain folding (Fig. 2B), which may in turn weaken the interaction with gp91phox. These observations suggest that rs13306575 and rs35937854 risk alleles may have both direct and indirect effects on protein function.

Luciferase reporter assay with the synonymous variant

Bioinformatic analysis demonstrated that the synonymous variant rs17849501 (A202A) may act as a cis-regulatory element to influence protein and mRNA expression. During protein translation, the wild-type codon (GCG) pairs with the least abundant alanine tRNA, whereas the complementary tRNA for the mutant codon (GCA) is abundant (www.kazusa.or.jp/codon), suggesting that the risk allele could increase translation efficiency. Moreover, rs17849501 is in a highly conserved region annotated as active chromatin in GM12878 cells (ENCODE/Broad Histone track). This region is notably enriched for trimethylated histone H3-Lys36 (H3K36Me3), a transcriptional elongation signal, and trimethylated histone H3-Lys27 (H3K27Me3), frequently associated with transcriptional silencing. Furthermore, the cytosine in the rs17849501 wild-type allele is in a methylated CpG motif (ENCODE/MeDIP-Seq track). Based on this evidence, we directly assessed transcriptional activity of the rs17849501 locus with a luciferase expression assay. The wild-type rs17849501 locus enhanced transcription in MonoMac-6 cells by ∼15-fold over background. The rs17849501 locus carrying the risk allele boosted expression by a further ∼40% (P = 9.7 × 10−8) (Fig. 3). Thus the rs17849501 risk allele may increase transcriptional enhancer activities by suppressing silencing function of the non-risk allele, perhaps through epigenetic interactions. In combination with potential increased translational efficacy, rs17849501 could dysregulate NADPHO even though the mutation does not change protein sequence.

Figure 3.

Enhancer activity of exonic DNA sequences carrying the rs17849501 wild-type (501G) or risk allele (501A). The 106-mer DNA sequences carrying rs17849501 non-risk (G) and risk (A) alleles were cloned into an enhancer/silencer assay vector (TKmin-MGL.1, Xactagen, USA) for luciferase activity carrying a minimal TKmin promoter. These clones were transiently transfected by electroporation to MonoMac-6 cells, and luciferase activity was measured at 6 h after transfection (Klan and Steinhilber, 2003). Transfection efficiency was more than >90% in all cases (measure of CMV promoter control, data not shown). TKmin = vector only, MCS = vector with multiple cloning sites, 501G = vector with wild-type allele, 501A = vector with risk allele. Bars represent the average promoter activity and standard deviation for nine replicates, expressed in relative luminescence units.

Figure 3.

Enhancer activity of exonic DNA sequences carrying the rs17849501 wild-type (501G) or risk allele (501A). The 106-mer DNA sequences carrying rs17849501 non-risk (G) and risk (A) alleles were cloned into an enhancer/silencer assay vector (TKmin-MGL.1, Xactagen, USA) for luciferase activity carrying a minimal TKmin promoter. These clones were transiently transfected by electroporation to MonoMac-6 cells, and luciferase activity was measured at 6 h after transfection (Klan and Steinhilber, 2003). Transfection efficiency was more than >90% in all cases (measure of CMV promoter control, data not shown). TKmin = vector only, MCS = vector with multiple cloning sites, 501G = vector with wild-type allele, 501A = vector with risk allele. Bars represent the average promoter activity and standard deviation for nine replicates, expressed in relative luminescence units.

DISCUSSION

In the present study, we demonstrated that multiple independent and robustly associated variants within the NCF2 gene, both coding and non-coding, are predicted to influence SLE susceptibility. Intriguingly, the set of SLE-associated SNPs was quite different in each population.

This work also offers insight into possible mechanisms by which SLE-associated variants in NCF2 may influence SLE risk. First, associated SNPs have the potential to influence epigenetic regulation of this locus. For example, the synonymous rs17849501 variant seems to disrupt an endogenous silencer module. This SNP is in strong LD with rs17849502 and may contribute to the latter's SLE association. Secondly, several of the SNPs identified here may control expression of the adjacent SMG7 gene, which has also been implicated in SLE risk. Thirdly, three of the independently associated variants are non-synonymous mutations with the potential to disrupt protein function. For example, the novel amino acid mutation caused by rs35937854 (V297A) likely destabilizes the first p67phox SH3 domain and presumably its binding interactions. Finally, in HS two of the non-synonymous SNPs were polymorphic. Since multiple interacting loci may contribute to SLE susceptibility, we assessed SNP–SNP interaction (epistasis) to better understand NCF2 association with SLE. The SNPs that showed the strongest interaction, rs17849502 and rs13306575, yield amino acid substitutions that are separated by just 6 positions in the PB1 domain of p67phox. The rs17849502 mutation has been shown to adversely affect binding between the p67phox-PB1 domain and Vav1 (47), and the rs13306575 mutation likely affects binding of the p67phox-PB1 domain to p40phox (44). Genetic interaction between these variants suggests that destabilizing both of these interactions may have a synergistic disruptive effect on the formation and overall activity of the NADPHO complex.

Functional mutations in NADPHO genes that adversely affect complex assembly have been shown to disrupt ROS production and predispose patients to primary immunodeficiency, such as chronic granulomatous disease (CGD) (48). Assembly of the NADPHO complex requires precise coordination of several protein subunits (49–51), and multiple mutations affecting the expression and/or function of the various components of NADPHO (CYBA, CYBB, NCF1, NCF2, NCF4) (52–62) are strongly predisposing for CGD. In addition, rs13306575 (R395W), an SLE-associated SNP identified here, has been previously associated with inflammatory CGD (63,64), although the rs13306575 mutation was paired with an in-frame deletion of three amino acids in the N-terminal tetratricopeptide repeat domain, which binds Rac-GTP (65). One GCD patient with the R395W mutation had no detectable p67phox in her neutrophils based on western blot (64). Several other inflammatory diseases have also been associated with NADPHO genes, including inflammatory bowel disease with NCF2 (66), Crohn's disease with NCF4 (67,68) and rheumatoid arthritis with NCF2, NCF4 and RAC2 (69). A recent large-scale resequencing study demonstrated that rs17849502/rs17849501 is also associated with celiac disease (70). This indicates that NCF2 may be a common autoimmunity gene, increasing susceptibility for multiple autoimmune/inflammatory phenotypes (70). Because NADPHO is a central regulator of ROS levels, which are critical for regulation of inflammation, adaptive immunity, intracellular signaling, chemoattraction and autophagy (71–77), it is not surprising that mutations affecting this complex are implicated in inflammatory diseases.

Interestingly, recent reports demonstrated that physiologically appropriate NADPHO function is crucial for the formation of neutrophil extracellular traps (NETs) (78–80) and have linked improper clearance of NET debris with auto-antigen production in SLE (80–82). NETs are extracellular structures composed of chromatin and granular proteins that are secreted by neutrophils to entrap and kill microorganisms (83,84). Monocytes from SLE patients have a reduced ability to degrade in vitro generated NETs, and the abundance of NETs in SLE patients is correlated with serum levels of immunogenic complexes (82). Furthermore, patients that failed to degrade NETs had more active disease (85). Indeed, Campbell et al. (80) found that Nox2-deficient (and thus NADPHO-deficient) mice had severe lupus, including increased renal disease and elevated autoantibody profiles. These results suggest that failure to undergo normal NADPHO-dependent cell death may increase immunogenic cellular debris, breaking self-tolerance. The NCF2 variants discovered here could act through a similar mechanism, by disrupting ROS production, leading to accumulated NET debris and auto-antigenicity.

Our results suggest that NCF2 influences SLE risk through a complex balance between many underlying predisposing variants, some of which perturb expression and some of which affect the structure and function of the translated protein. Interactions with other genes would be expected to alter this balance, causing SNPs to have discordant effects on SLE risk in different populations when their intergenic interactions varied between populations. The potentially functional SNPs identified here have dramatically different effects on SLE risk in different populations, underscoring the importance of using large, multi-ethnic studies not only in elucidating the underlying mechanisms of SLE, but also in identifying predisposing variants explaining additional phenotypic variance (i.e. missing heritability) of complex diseases such as SLE. Our bioinformatics and functional work strongly suggests that these SNPs contribute directly to disruption of NADPHO. However, it is possible that the observed allelic heterogeneity results, at least in part, from unidentified causal SNPs tagged by the SNPs associated in this study. Further experiments may clarify this issue.

In summary, our strategy using a large, multi-ethnic case–control SLE cohort for dense ImmunoChip-based fine-mapping with imputation-based association analysis, followed by conditional analysis, bioinformatics analysis and molecular modeling, identified 6–7 (two behave dependently) potentially functional variants in NCF2. For many of the variants, we have implicated mechanisms by which they may impact NADPHO function. However, no variant was associated in all four populations, and some variants showed significant associations only within a single population. This high degree of allelic heterogeneity in NCF2 association with SLE suggests that several risk variants may be undiscovered. In particular, given that rare variants could not be successfully imputed, this study provides both the groundwork and the impetus for full gene sequencing in multiple ethnically diverse populations in a follow-up case–control study. Thus, understanding how these SNPs contribute to SLE can focus future functional experiments vital to uncovering how genetic variability predisposes for disease pathogenesis.

MATERIALS AND METHODS

Study samples, genotyping and quality control

The study samples were recruited through the Lupus Family Registry and Repository (86) at the Oklahoma Medical Research Foundation (OMRF), the Cincinnati Children's Hospital Medical Center (CCHMC), the University of Texas SouthWestern (UTSW), the Center for Genomics and Ontological Research, Spain (GENYO) and the Hanyang University, South Korea (HU). Protocols for this study were approved by the Institutional Review Boards (IRBs) where participants were recruited (OMRF, UTSW, CCHMC, GENYO and HU). Every individual, including SLE cases and healthy controls, provided written consent at enrollment. All SLE patients met a minimum of 4 out of 11 1997 American College of Rheumatology revised classification criteria for SLE (27,87). Controls were healthy individuals, who did not have lupus or any other autoimmune disease. Most study controls were recruited through the Lupus Family Registry and Repository (LFRR) (86). All controls were recruited from the same countries and areas where patients were recruited. Whenever possible, they were selected to have the same self-reported ethnicity as the patient population. Controls were recruited with IRB approval by participating physicians, or were recruited from health fairs, community health clinics, or area schools. Out of study GWAS controls were obtained with permission through dbGaP.

SLE cases and healthy controls were genotyped on the Illumina ImmunoChip array (28) (Supplementary Material, Table S1). For quality control (QC), subjects were excluded from analysis if they had >10% missing genotyping, were related to any other study participant (>25% identical by descent), or were population stratification outliers based on PCA. SNPs were removed for >10% missing genotyping, being out of Hardy–Weinberg equilibrium (PHWE < 0.001 in controls), or for poor clustering.

We increased statistical power to detect lupus-associated variants by incorporating out-of-study healthy controls obtained through dbGaP (Supplementary Material, Table S1). For AA, we added controls from the Health and Retirement Study (HRS) genotyped on the Illumina OMNI 2.5 platform. For EA, we incorporated control genotypes from the GENIE UK-ROI Diabetic Nephropathy GWAS (GoKIND), Genes and Blood Clotting Study (GABC) and NGRC Parkinson's Disease Study (CIDR) genotyped on the Illumina Omni1-Quad platform, and HRS control samples genotyped on the Illumina OMNI 2.5 platform. We also added data from HS SLE cases and controls genotyped on the Illumina HumanOmni 1 platform for a separate GWAS study (unpublished data). Genotypes were merged with our data for further QC (described subsequently) to insure high-quality imputation.

Population structure and sample quality control

In order to obtain a homogeneous sample, for EA we removed individuals with <90% European ancestry. For AA, we removed individuals with <25% African ancestry. All participants in this study were filtered by PCA to identify population outliers as previously reported (88). Samples exceeding three standard deviations along any statistically significant principal component were defined as outliers and removed from the study. For each population, we merged genotype data with unrelated HapMap reference population data. We selected LD pruned variants (r2 < 0.2) that had minor allele frequency (MAF) >1% and that were separated by at least 500 kilobases (kb). For PCA, we included 7170 SNPs for EA, 3501 SNPs for AA, 3646 SNPs for HS and 6928 SNPs for KR (Supplementary Material, Fig. S2). The number of SNPs chosen was a compromise between the size of the common data set and the number of independent SNPs. Although the number of SNPs to estimate population structure and ancestry proportions seems small, it is comparable to the sets used in other studies (89–90).

We used GCTA software (35) to estimate the first ten principal components for each ethnic population. All samples were plotted by their estimated ancestry proportion relative to the three continental reference populations (Supplementary Material, Fig. S3). We estimated the mean and standard deviation (SD) for each ethnic population for the first three principal components and removed samples that were outside three SD from the mean at each tail of the distribution.

To test for differences across data sets and potential population structure within each ethnic group, we selected variants from the combined data set common between unrelated HapMap samples (CEU, CHB + JPT and YRI) genotyped on the Illumina HumanOmni 2.5 platform. We selected LD pruned variants that had an MAF >1% and that were separated by at least 500 kb for estimation of admixture proportions. Ancestry proportion was estimated for all samples using ADMIXTURE (91) with 1–9 ancestry components. The optimum number of ancestry components was determined using the Bayesian Information Criterion (BIC) and the cross-validation error rate.

Imputation

Imputation is a statistical method used to probabilistically determine missing or untyped genotypes using a densely mapped reference panel. We performed imputation-based analysis in our ethnic-specific case-control samples. Genotypes included 145 SNPs from the ImmunoChip, 55 SNPs from EA and AA GWAS controls (34 overlapped between the platforms), and 19 SNPs from HS GWAS controls (all of which overlapped with ImmunoChip SNPs). We used publicly available 1000 Genomes Project data (2010-11-23 1000G Phase I) as reference panels for imputation, including 246 for AA (YRI + LWK + ASW), 381 for EA (GBR + FIN + IBS + CEU + TSI), 181 for HS (PUR + CLM + MXL) and 286 for Asians (AS; JPT + CHB + CHS). After QC 250 SNPs from the NCF2 region (Chr1: 183,523,470-183,573,858 bp) were available for analysis. Imputation was performed using MACH-ADMIX (31), which provided an imputation quality metric (Rsq). To insure high-quality imputation, only SNPs that passed post-imputation QC (Rsq > 0.7, PHWE > 0.001 in controls, MAF > 0.5%) were used for analysis.

Threshold for population-specific gene-wide significance adjusting for multiple testing and LD

Since we used a dense set of SNPs within NCF2 to correct for multiple testing in each population, we used the spectral decomposition method implemented in SNPSpD (32,33). For each population, the experiment-wide significance level (α = 0.05) was adjusted for multiple testing by estimating the effective number of independent SNPs by spectral decomposition of the pair-wise LD matrices adjusted with a Bonferroni correction. Since LD structures were different between the four populations, the effective numbers of independent SNPs (capturing most of the information across the entire NCF2 gene) were also different across populations.

Association analysis, population stratification adjustment and conditional analysis

We performed single SNP association analysis in each population to compare allele frequencies between cases and controls using the χ2 test (one degree of freedom), and calculated odds ratios (OR) and 95% confidence intervals (95% CI) using the PLINK (30) software package. In order to account for population structure, we used the first three principal components identified through PCA (92) as covariates in a logistic regression model. We also verified our results separately by adjusting three of the four individual ancestry proportions estimated by ADMIXTURE (93) as covariates. By using both types of stratification correction alternatively, we ensured that we accounted for stratification.

We used several methods to identify SNPs with independent effects in each ethnic population. First, we performed pair-wise conditional analysis in a step-wise manner using WHAP (94). Initially, we conditioned on the most significant SNP, then on the next most significant SNP, and so on, for each SNP that had passed the population-specific significance threshold in the imputation-based analysis. At each step, SNPs that lost significance after conditioning on the lead SNP were excluded from subsequent analyses until all residual association within the gene was accounted for. Secondly, we confirmed independent association using haplotype analysis implemented in PLINK (Supplementary Material, Table S5). Both omnibus and haplotype-specific association tests were used to evaluate these independent SNPs. An omnibus test was used to assess all haplotypes combined for SLE-association. Once association was observed with the omnibus test, a SNP-specific test as well as haplotype-specific test were used to identify whether any specific SNP or haplotype containing risk allele(s) explaining the omnibus association. Thirdly, we also verified independence between SNPs by their LD (r2).

SNP–SNP interaction

For genetically complex diseases such as SLE, multiple interacting loci may contribute to disease susceptibility. Therefore, we assessed SNP–SNP interaction (epistasis) to better understand NCF2 association with SLE. First, we examined SNP–SNP interactions for independently associated SNPs within each population using a logistic regression model implemented in R. To account for population structure, we also adjusted for either the first three principal components estimated by GCTA or by major individual ancestry populations estimated by ADMIXTURE, described earlier. Significance of the relevant model terms were evaluated by comparing the log-likelihood difference between the null model (only independent effects) and the full model (with SNP–SNP interaction) with a χ2 distribution with degrees of freedom (df) equal to the difference in the numbers of unconstrained parameters between models. We confirmed our results using the logistic regression model implemented in FITF (36), a PLINK (30) two-locus test and with GAIA (95).

Secondly, we verified our earlier results of potential SNP–SNP interaction using non-parametric generalized multifactor dimensionality reduction (GMDR) technique (96,97). A 10-fold cross-validation that randomly divides each data set into 10 randomly selected independent data sets (nine training sets and one testing set). This approach is the gold-standard in data mining and minimizes the bias introduced (due to random variation in the split or overestimation of the results) by averaging results across many divisions of the data (Dr Jason H. Moore, personal communication). The epistasis model (and its prediction error) was estimated for each training set and was then validated (98). The greatest cross-validation consistency (CVC) and the highest prediction accuracy were selected as the best predictor of this disease outcome. P-values were determined by the sign test, a robust non-parametric test implemented in the software.

Once we detected significant interaction between a pair of SNPs, we then used χ2 test to assess the magnitude of association between individuals with at least one risk allele at either independently associated SNP, or at both independently associated SNPs, compared with those with no risk alleles as a baseline. ORs and P-values were used to assess the magnitude of the effect size.

In silico expression analysis

We obtained data from GeneVar (37,38) (Array Express Accession E-MTAB-264, protocol A-MEXP-930) for EBV-transformed lymphoblastoid cells from four HapMap populations (CEU = 35, CHB + JPT = 80, YRI = 38). Expression quantitative trait locus (eQTL; Illumina Human WG-6) data were merged with the corresponding genotypes at the independently associated variants identified in this study. Association analysis for cis-eQTLs in NCF2 (GI_4557786-S) and neighboring genes SMG7 (GI_42476073-A and GI_42475557-I) and ARPC5 (GI_13569955-S and GI_23238212-S) were performed in PLINK. To confirm these results, we used data from the mRNA by SNP browser (39,40) to investigate whether any of these SNPs were associated with changes in transcript abundance. The mRNA by SNP browser data were obtained from lymphoblastoid cells of 206 families of British descent (297 sibling pairs and 11 half-sibling pairs) using the Affymetrix HG-U133 Plus 2.0 Chip (39,40). Association between SNPs and expression probes in the mRNA browser was determined by regression analysis as described in Dixon et al. (40). We identified tagSNPs in linkage disequilibrium (r2 ≥ 0.2) with independently associated SNPs in CEU, YRI and CHB + JPT populations as a proxy for EA, AA and KR, respectively.

Luciferase reporter assay

We cloned exonic 106-mer sequence (bases 183,542,269-183,542,375) containing risk or non-risk alleles of rs17849501 into a luciferase assay vector, under a minimal thymidylate kinase promoter (TKmin, Xactagen Inc., USA). MonoMac-6 cells were transiently transfected in one of four groups: vector only, vector carrying multiple cloning sites (MCS), risk allele carrying DNA or non-risk allele carrying DNA sequences. Luciferase expression was measured at 6 h post-transfection as suggested (99), using the Gaussia Luciferase Assay (GLOW) with a Gaussia luciferase assay kit (Xactagen, LLC). Media was aspirated from transfected cells, which were plated and washed once. One hundred microliters of GLum.1 assay buffer (plus coelenterazine substrate) and 50 µl of GLum.1 assay reagent were added to each well and dark incubated for 5 min prior to reading luminescence (SpectraMax L, Molecular Devices: 475 nm). Measurements (relative luminescence units) were normalized to untransfected cells as controls. After normalization, statistical significance was calculated using t-tests assuming unequal variance.

Bioinformatics

All chromatin properties were examined using the UCSC Genome Browser (http://genome.ucsc.edu), based on the ENCODE annotations and other data contained in all ‘Regulation’ tracks.

Protein modeling

Structure figures were generated using PyMOL (http://www.pymol.org). The structure of the first SH3 domain (rs35937854/V297A) was taken from PDB file 2DMO. The structure of the PB1 domain (rs13306575/R395W and rs17849502/H389Q) was taken from PDB file 1OEY (44).

SUPPLEMENTARY MATERIAL

Supplementary Material is available at HMG online.

FUNDING

This work was supported by grants from the US National Institutes of Health (AI103399, AR060366, AI094377, AI083194, CA141700, AR058621, AI101934, AI082714, AR053483, GM103510, GM103456) and A121983, the Korea Healthcare Technology R&D Project, Ministry for Health & Welfare, Republic of Korea.

ACKNOWLEDGEMENTS

We are grateful to the affected and unaffected individuals who participated in this study. We thank the research assistants, coordinators, and physicians who helped in the recruitment of subjects, including the individuals in the coordinating projects. We are indebted to J. Donald Capra, M.D for critical reading and helpful comments on this manuscript.

Conflict of Interest statement. None declared.

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APPENDIX

We are also grateful for the assistance provided by the GENLES Network group: Eduardo Acevedo, MD (Hospital Nacional Guillermo Almenara Irigoyen, Lima, Perú), Ignacio García-De La Torre, MD (Hospital General de Occidente, Secretaría de Salud, Zapopan, Jalisco, México), Marco A. Maradiaga-Ceceña, MD (Hospital General de Culiacán, Culiacán, Mexico), Mario H. Cardiel, MD, MSc (Hospital General Dr Miguel Silva, Morelia, Mexico), Jorge A. Esquivel-Valerio, MD (Servicio de Reumatología. Hospital Universitario Dr José Eleuterio González de la Universidad Autonoma de Nuevo Leon, Monterrey, Nuevo León, Mexico), Jacqueline Rodriguez-Amado, MD (Servicio de Reumatología. Hospital Universitario Dr José Eleuterio González de la Universidad Autonoma de Nuevo Leon, Monterrey, Nuevo León, Mexico), José Francisco Moctezuma, MD (Hospital General de México, Mexico City, Mexico), Pedro Miranda, MD (Servicio de Reumatología, Hospital San Juan de Dios, Santiago, Chile), Carlos Perandones, MD (Centro de Educación Médica e Investigaciones Clínicas (CEMIC), Buenos Aires, Argentina), Cecilia Castel, MD (Servicio de Inmunología, Hospital Central de Mendoza, Mendoza, Argentina), Hugo A. Laborde, MD (Servicio de Reumatología, Hospital de Clinicas ‘José de San Martin’, Buenos Aires, Argentina), Paula Alba, MD (Laboratorio de Inmunología, Hospital Córdoba, Córdoba, Argentina), Jorge Musuruana, MD (Servicio de Reumatología, Hospital José Bernardo Iturraspe, Santa Fé, Argentina), Annelise Goecke, MD (Hospital Clínico, Universidad de Chile, Santiago de Chile, Chile), Carola Foster, MD (Laboratorio de Reumatología, Hospital Del Salvador, Santiago de Chile, Chile), Lorena Orozco (Instituto Nacional de Medicina Genómica, Mexico City, Mexico), Vicente Baca (Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico).

Author notes

These authors contributed equally to this work.
Members of GENLES are listed in the APPENDIX.

Supplementary data