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. Author manuscript; available in PMC: 2015 Jan 1.
Published in final edited form as: Cytokine. 2013 Oct 29;65(1):10–16. doi: 10.1016/j.cyto.2013.10.002

Novel Gene Variants Predict Serum Levels of the Cytokines IL-18 and IL-1ra in Older Adults

AM Matteini 1,*, J Li 2, EM Lange 2, T Tanaka 3, LA Lange 2, RP Tracy 4, Y Wang 2, ML Biggs 5, DE Arking 6, MD Fallin 7, A Chakravarti 6, BM Psaty 8,9, S Bandinelli 10, L Ferrucci 3, AP Reiner 11, JD Walston 1
PMCID: PMC4060632  NIHMSID: NIHMS537266  PMID: 24182552

Abstract

Activation of inflammatory pathways measured by serum inflammatory markers such as interleukin-18 (IL-18) and interleukin-1 receptor antagonist (IL-1ra) is strongly associated with the progression of chronic disease states in older adults. Given that these serum cytokine levels are in part a heritable trait, genetic variation may predict increased serum levels. Using the Cardiovascular Health Study and InCHIANTI cohorts, a genome-wide association study was performed to identify genetic variants that influence IL18 and IL-1ra serum levels among older adults. Multiple linear regression models characterized the association between each SNP and log-transformed cytokine values. Tests for multiple independent signals within statistically significant loci were performed using haplotype analysis and regression models conditional on lead SNP in each region. Multiple SNPs were associated with these cytokines with genome-wide significance, including SNPs in the IL18-BCO gene region of chromosome 2 for IL-18 (top SNP rs2250417, P = 1.9×10−32) and in the IL1 gene family region of chromosome 2 for IL-1ra (rs6743376, P = 2.3×10−26). Haplotype tests and conditional linear regression models showed evidence of multiple independent signals in these regions. Serum IL-18 levels were also associated with a region on chromosome 2 containing the NLRC4 gene (rs12989936, P = 2.7×10−19). These data characterize multiple robust genetic signals that influence IL-18 and IL-1ra cytokine production. In particular, the signal for serum IL-18 located on chromosome two is novel and potentially important in inflammasome triggered chronic activation of inflammation in older adults. Replication in independent cohorts is an important next step, as well as molecular studies to better understand the role of NLRC4.

Keywords: chronic inflammation, genome-wide association studies, older adults

1. Introduction

Chronic activation of inflammatory pathways as measured by serum inflammatory markers is strongly associated with the progression of chronic disease states, sarcopenia, frailty, and mortality in older adults (1). Increased fat tissue, senescent cells, chronic disease states, damaged mitochondria, and genetic variation all likely play a role in triggering chronic activation in inflammatory pathways (1). Nuclear factor κB (NF-κB) is a nuclear receptor that functions as a gateway molecule for inflammatory pathway signaling, and most of the serum measures of inflammation previously utilized in studies of human subjects are influenced by its activity (1). Of the commonly measured serum markers of inflammation, interleukin 18 (IL-18), and interleukin-1 receptor antagonist (IL-1ra) are among the 5 best predictors of 10 year mortality in a longitudinal cohort study of community dwelling older adults (2). IL-1ra and IL-18 are part of the IL-1 family of cytokines that are generated very early in the inflammatory signaling process, and are thought to be important mediators of inflammation and the aging process(3).

Beyond their association with accelerated mortality in older adults, epidemiological studies show that increased IL-18 levels are strongly associated with insulin-resistance in diabetic and non-diabetic individuals, as well as type-II diabetes and a number of other metabolic risk factors (4,5). A recent meta-analysis of prospective data concluded that serum IL-18 is a strong predictor for cardiovascular disease, independent of the effects of IL-6 and CRP (6). Similarly, IL-1ra concentrations have been independently associated with metabolic syndrome (7). Mice deficient in IL-1ra develop a chronic inflammatory state leading to autoimmune arthritis (8). Both IL-18 and IL-1ra levels are associated with uric acid levels and are thought to be important in adipose tissue-driven inflammatory signaling (9,10).

In addition to these associations between serum levels of IL-1ra and IL-18 and adverse health outcomes, there are several reported associations between polymorphisms within the genes encoding IL-18 and IL-1ra and aging-related conditions. The IL1RN gene encoding IL-1ra is located within a ~430 kb IL-1-related gene cluster on chromosome 2q13-21 that also contains the genes for IL-1B and IL-1A (11). Variations in this IL-1 gene cluster have been linked to a number of inflammatory conditions including lupus, vasculitis, renal failure, cognitive decline, and cardiovascular disease (12,13). IL-1ra expression is driven by AP-1, which is stimulated by the early inflammatory mediator IL-1B (14,15). IL-18 is encoded by a 19.5 kb gene (IL18) located on chromosome 11q22-11q23 (16). Variation in IL18 has been shown to correlate with adverse cardiac and other health outcomes (17). Using subjects from the InCHIANTI cohort, Melzer et al identified polymorphisms in IL1RN and IL18 that influence serum levels of the respective gene protein products (18). Additional GWA studies of variation in interleukin serum levels in small cohorts have yielded similar results (19,20)(21), but were most likely underpowered to pinpoint multiple independent signals within the regions or to characterize weaker signals in other parts of the genome that influence these important inflammatory serum levels in older adults. Given the importance of these proteins in inflammatory signaling, and prior knowledge that serum cytokine levels are in part a heritable trait, we hypothesized that genetic variation predicts increased serum levels of IL-18 and IL-1ra in older adults, and sought to identify gene variants that influence serum levels of these important inflammatory mediators in a large population of older adults (22).

2. Subjects and Methods

2.1 Subjects

The Cardiovascular Health Study (CHS) is a prospective observational cohort study designed to characterize risk factors of cardiovascular disease in older adults. The study recruited 5,888 men and women over the age of 65 at baseline based on age- and gender-stratified samples from Medicare eligibility lists from four field centers across the United States between 1989 and 1990. Details of sampling and exclusion criteria have been published elsewhere (23). For the purpose of this study, only samples from Caucasian participants were used. African Americans were excluded from analysis. The participants for this study included 3233 European Americans from the CHS cohort with biomarker phenotypes and GWAS data and who provided informed consent for genetic analyses at the time of blood draw. InCHIANTI is a prospective population-based cohort study of the factors that contribute to mobility decline in older Italian adults. The study sample (1,210 Caucasian participants aged 65–102 years) was randomly selected using a multistage stratified sampling method from two towns in the Chianti geographic area of Italy (Greve in Chianti and Bagno a Ripoli, Tuscany, Italy). The details of the data collection and sampling procedures have been described elsewhere (24). All participants gave written consent for study participation at the time of blood draw and the Italian National Research Council of Aging Ethical Committee approved the study.

2.2 Cytokines

Cytokines were measured from stored serum drawn at the baseline visit in both cohorts. For CHS participants, IL-1ra and IL-18 were measured by the Mesoscale chemiluminiscent multiplex system. The human 2-plex kits were purchased from MSD (Mesoscale Discovery Technology (MSD), Gaithersburg, MD USA). SULFO-TAG™ Human IL-18 Detection Antibody and Biotinylated Human IL-1ra Detection Antibody were used for the assay. MSD plates were measured on the MSD Sector Imager 2400 plate reader. The raw data was measured as electrochemiluminescence signal (light) detected by photo detectors and analyzed using the Discovery Workbench 3.0 software (MSD). A 4-parameter logistic fit curve was generated for each analyte using the standards and the concentration of each sample calculated. The kit’s sensitivity is 0.6pg/ml for IL-18 and 1.2 pg/ml for IL-1ra. In the InCHIANTI population, IL-1 RA was measured by ELISA using a commercial kit (BIOSOURCE international, Camarillo, CA). Serum IL-18 was measured using highly sensitive quantitative sandwich assays (Quantikine HS, R& D systems, Minneapolis, MN). Assay precisions and detectable limits were reported previously (25–27).

2.3 Genotyping

Genotyping in the CHS population was carried out using the Illumina 370CNV chip and genotype calls were done using Bead-Studio software, resulting in 307,655 genotyped SNPs. SNPs were retained for analysis using a call rate greater than 0.95 and Hardy-Weinberg equilibrium p-value ≥ 1E-5. Imputation to HapMap Phase II and III (CEU reference samples) was performed using MaCH 2.0V software, resulting in 2,543,887 and 1,387,466 SNPs respectively. In InCHIANTI, genotyping was performed with the Illumina Infinium HumanHap550 genotyping chip (chip versions 1 and 3) as previously described (19).

Using GWAPower software, power calculations were performed to assess the difference in power of the combined InCHIANTI-CHS meta-analysis compared to single study analyses, assuming linkage disequilibrium (r2) of 0.5 between a SNP and putative causal variant and 10% variance explained by the twelve covariates (Feng S, 2011 BMC Genetics). The power analysis demonstrated the InCHIANTI cohort (n=1,200) to have adequate power (0.85) to detect a true association with serum interleukin levels at h2 = 0.03. The CHS population (n= 3,200) demonstrated sufficient power (0.99) at h2 = 0.02. At the lowest effect size (h2 = 0.01) neither InCHIANTI nor CHS alone have adequate power to detect a true association with serum interleukin levels (0.05 and 0.74, respectively). The combined InCHIANTI-CHS meta-analysis achieved >95% power across all effect sizes (h2 ≥ 0.01). Although these estimates indicate that either the InCHIANTI and CHS cohort has sufficient power to independently detect a true association at small effect sizes (h2 = 0.03), the smallest effect sizes may only be uncovered with larger sample sizes (h2 ≤ 0.01). As demonstrated in Table 1, sample sizes similar to those in previously published GWA studies of IL-18, (i.e., n = 700) may have been underpowered to detect smaller effect SNP associations.

Table 1.

Statistical Power Estimates for InCHIANTI, CHS, and the meta-analyzed CHS-InCHIANTI results

h2 700 Subjects 1200
Subjects
(InCHIANTI)		 3200
Subjects
(CHS)		 4500 Subjects
(Meta-analyzed
sample)
1% 0.01 0.05 0.74 0.96
2% 0.10 0.45 0.999 1
3% 0.36 0.86 1 1
4% 0.66 0.98 1 1
5% 0.87 0.99 1 1
6% 0.96 0.999 1 1
7% 0.99 1 1 1
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2.4 Statistical analysis

Serum IL-1ra and IL18 levels were log-transformed for approximate normalization. Multiple linear regression models were built for each serum marker separately and analyzed using PLINK (http://pngu.mgh.harvard.edu/purcell/plink/) (28–30), with covariate adjustment for age, gender and the first 10 principal components reflecting background ancestry. SNPs were modeled assuming additive effects. GWAS analyses were performed in the CHS and InCHIANTI cohorts separately. Then, inverse variance weighted meta-analysis was performed in METAL (31) software using a fixed-effects model of beta coefficients and standard errors from CHS and InCHIANTI. Genome-wide significance was defined as a p-value less than 5×10−8.

Regions of interest from the GWAS results were further examined with haplotype and conditional analyses using the CHS population only. Multivariable regression models were built that included the most strongly associated SNP as a covariate to determine whether there were multiple independent association signals in a particular region. Region-specific plots were made to show the magnitude of association between all SNPs as well as the linkage disequilibrium (LD) between each SNP in the region and the most strongly associated SNP. Haplotype analyses were conducted using Haplo.stat (32), to examine specific combinations of allelic variants and whether the observed association signal is likely attributable to a less common unmeasured genetic variant (9). The ‘haplo.glm’ function implemented in the ‘haplo.stats’ R package was used to calculate beta coefficients (β), standard errors (SE) and P-values for each haplotype relative to the most common reference haplotype. The ‘haplo.score’ function implemented in the ‘haplo.stats’ R package was used to calculate the global score statistics to test the overall association between haplotypes and biomarker levels. Finally, to help interpret the relationship between the haplotype results and the unconditional and conditional association results from non-genotyped imputed SNPs, we downloaded genotype data from CEU HapMap samples and used the ‘haplo.em’ function to construct complete haplotypes in the regions of interest. Specifically, we used these data to identify which haplotypes in our haplotype analyses were expected to carry the minor allele for the top imputed SNPs that were identified in our individual SNP analyses.

3. Results

Descriptive statistics for the CHS and InCHIANTI samples are shown in Table 2. Q-Q plots for genome-wide SNP associations revealed no substantial evidence for inflated results due to population stratification, residual relatedness among subjects or experimental effects (not shown). Manhattan plots from the CHS analysis demonstrate 2 regions of genome-wide significance for plasma IL18 and one region for IL-1ra (Figures 1A,1B). Overall, there were 209 genome-wide significant SNPs for the IL-18 outcome (175 SNPs on chromosome 2 and 34 SNPs on chromosome 11) and 116 hits for IL-1ra (all SNPs on chromosome 2). The top SNPs for each outcome are listed in Tables 2 (IL-18) and 3 (IL1-RA). Manhattan plots for the InCHIANTI analysis were similar (not shown).

Table 2.

Descriptive Statistics for CHS and InCHIANTI

CHS (n=4,925) InCHIANTI (n=1,210)
IL-1ra (pg/mL), mean(std) 159.4 (36.4) 158.5 (127.8)
IL-18 (pg/mL), mean(std) 152.4 (68.8) 397.0 (156.3)
IL-6 (pg/mL), mean(std) 4.6 (5.1) 2.1 (4.0)
CRP (mg/L), mean(std) 3.1 (2.2) 5.1 (9.3)
Age (years), mean(std) 71.5 (6.4) 68.3 (15.5)
Male, n(%) 2135 (43.3) 540 (44.6)
Frailty
Robust, n (%) 2214 (49.4) 555 (48.1)
Prefrail, n (%) 2009 (44.8) 486 (42.1)
Frail, n (%) 262 (5.8) 114 (9.9)
Diabetes n (%) 715 (14.6) 125 (10.8)
CVD n (%) 1238 (25.1) 125 (10.8)
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Figure 1.

Figure 1

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Manhattan plots from CHS a) IL-18 and b) IL-1ra

Table 3.

Top significant SNP hits for plasma IL-18 levels for CHS, InCHIANTI and the meta-analyzed CHS-InCHIANTI results

CHS InChianti Meta-Analyzed
Chr SNP Position Gene Function A1,A2 MAF β SE P-VAL β SE P-VAL β SE P-VAL
11 rs5744256 111528058 IL18 intronic C/T 0.25 0.11 0.01 3.9e-18 0.12 0.02 2.0e-11 0.12 0.01 5.4e-28
11 rs1834481 111529037 IL18 intronic C,G 0.25 0.11 0.01 3.4e-18 0.12 0.02 2.0e-11 0.12 0.01 5.3e-28
11 rs5744222 111542224 - T,G 0.25 0.11 0.01 6.4e-18 0.12 0.02 7.9e-12 −0.11 0.01 8.6e-28
11 rs7131094 111550127 - T,C 0.24 0.11 0.01 6.6e-18 0.12 0.02 3.7e-11 −0.12 0.01 1.0e-27
11 rs10891343 111585594 BCO2 intronic T,C 0.48 0.10 0.01 7.3e-20 0.10 0.01 7.9e-13 0.10 0.01 4.8e-32
11 rs2250417 111590526 BCO2 intronic T,C 0.48 0.10 0.01 9.6e-20 0.10 0.01 1.3e-13 0.10 0.01 1.9e-32
2 rs2300702 316411522 SRD5A2 intronic G/C 0.42 0.09 0.01 4.2e-13 0.06 0.01 2.8e-5 0.07 0.01 1.6e-17
2 rs2268797 31637256 SRD5A2 intronic T/C 0.42 0.09 0.01 5.7e-13 0.06 0.01 2.3e-5 0.07 0.01 2.8e-17
2 rs6748621 32115705 DPY30 intronic T/C 0.40 −0.09 0.01 8.9e-15 0.05 0.01 6.3e-5 0.08 0.01 1.1e-16
2 rs6737500 32116591 DPY30 intronic T/C 0.41 0.09 0.01 9.5e-15 0.06 0.01 6.1e-5 0.08 0.01 1.1e-16
2 rs12989936 32122090 - C/T 0.41 0.09 0.01 2.5e-15 0.06 0.01 4.5e-5 0.08 0.01 2.3e-17
2 rs7577696 32132286 - G/A 0.41 0.09 0.01 7.4e-15 0.06 0.01 2.1e-5 0.08 0.01 2.7e-19
2 rs2280967 32143250 SPAST intronic T/C 0.40 0.09 0.01 6.5e-14 0.06 0.01 2.6e-5 0.07 0.01 3.2e-16
2 rs6760105 32160890 SPAST intronic G/A 0.40 0.09 0.01 8.1e-14 0.06 0.01 3.0e-5 0.06 0.01 3.6e-16
2 rs212745 32266336 SLC30A6 intronic C/G 0.40 0.08 0.01 1.4e-13 0.05 0.01 8.0e-5 0.07 0.01 2.1e-15
2 rs212679 32284611 SLC30A6 intronic C/A 0.40 0.09 0.01 4.3e-13 0.06 0.01 8.4e-5 0.07 0.01 1.9e-15
2 rs212713 32311041 NLRC4 intronic C/T 0.50 0.07 0.01 2.6e-9 0.04 0.01 9.6e-3 0.06 0.01 1.5e-10
2 rs479333 32342662 NLRC4 intronic C/G 0.38 0.09 0.01 3.3e-13 0.05 0.01 6.1e-4 0.07 0.01 1.3e-15
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174 SNPs were statisticall significant within the chromosome 2 region, with an average pairwise r2 = 0.75 with rs12989936.

3.1 Interleukin-18 GWAS Results- Chromosome 11

All top SNPs on chromosome 11 associated with IL-18 clustered within a region spanning less than 70 kb containing 3 genes: IL18, TEX12, and BCO2. The strongest signal (rs10891343 [imputed], PCHS = 7.3×10−20, PInCH = 7.9×10−13 and rs2250417 [genotyped], PCHS = 9.6×10−20, PInCH = 1.3×10−13 mapped to the BCO2 gene, which encodes beta-carotene oxygenase 2. SNPs within the IL18 gene were also highly significant (Table 3). In CHS, after conditioning on rs10891343, the remaining significant SNPs were no longer genome-wide statistically significant (PCHS<5×10−8); however, several SNPs in the intronic region of IL18 had nominally significant evidence that was not observed in the marginal single SNP tests, particularly SNP rs795467 (PCHS = 0.17 in the marginal test and PCHS = 1.1×10−4 in the conditional test). This observation suggests multiple associated loci or an underlying haplotype effect.

Haplotype analyses in CHS using genotyped SNPs further supported the single SNP and conditional SNP analyses. SNP rs2250417 was used to represent the index SNP and any other SNPs in LD with that SNP from the Illumina 370CNV array located within 500 Mb were then considered. Evaluation of HapMap CEU haplotype data confirmed the combination of rs1946518, rs5744222 and rs2250417 tagged separate common haplotypes in the region. SNPs rs5744222 and rs2250417 flank index SNP rs10891343 and places the minor allele of rs10891343 onto two separate haplotypes; rs1946518 splits the most common haplotype, which harbors the major allele for rs10891343. Frequency and effect size estimates for these haplotypes on log-transformed IL-18 serum levels are shown in Table 4. There was significant evidence for an overall association between haplotypes and IL-18 levels (PCHS < 0.0001). Both H2 (T-G-C, freq=0.26) and H3 (G-T-C, freq=0.25) haplotypes were significantly associated with lower IL-18 levels compared with the most common haplotype (G-G-T), with PCHS values of 8.4×10−7 and 1.0×10−16, respectively (Table 4). A post hoc comparison of these two haplotypes showed that their haplotypic effects were significantly different (PCHS = 3.8×10−6). Based on HapMap CEU data, the minor allele for our top imputed SNP rs10891343 rests on both H2 and H3. Interestingly, SNP rs795467, which became nominally significant in the conditional SNP analysis after conditioning on rs10891343, sits entirely on the H2 haplotype.

Table 4.

Association of three-SNP haplotypes in chromosome 11 with plasma IL-18 level in CHS European Americans

rs1946518 rs5744222 rs2250417 Freq β SE P
H1 T G T 0.15 −0.02 0.02 0.28
H2 T G C 0.26 −0.07 0.01 8.4e-07*
H3 G T C 0.25 −0.14 0.01 1.0e-16*
H4 (baseline) G G T 0.33
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A post hoc comparison of H2 and H3 showed that their strengths of association were significantly different (P=3.78e-06)

3.2 Interleukin-18 GWAS Results: Chromosome 2

The remaining 175 top IL18 SNPs clustered within an 8.8Mb region of chromosome 2 (Table 3), containing six genes: SRD5A2, MEMO1, DPY30, SPAST, SLC30A6 and NLRC4, with the strongest evidence of association located between DPY30 and SPAST genes (rs12989936 [imputed], PCHS =2.5×10−15, PInCH = 4.5×10−5. Linkage disequilibrium in the region is very high (Supplementary Figure 1) and in CHS, after conditioning on rs12989936, the remaining 174 SNPs in the region no longer showed genome-wide statistical significance (PCHS<5×10−8), though rs6543646 (PCHS = 2.6×10−5), a rare imputed variant (minor allele frequency [MAF] = 0.009 in HapMap CEU) which is located in the intron region between DPY30 and SPAST, demonstrated nominal evidence for association. The association effect estimate for rs12989936 was consistent with additive effects.

Haplotype analyses were performed in an attempt to further characterize the association signal in the region. Since the best SNP signal (rs12989936) was imputed, directly genotyped rs212713 was used as a proxy in haplotype analyses (r2=0.66, D’=0.95 in HapMap CEU samples). Genotyped SNPs rs7559329 and rs10084280 were used for haplotype construction together with rs212713 based on evaluation of HapMap haplotype data (ordered: rs7559329, rs10084280 and rs212713). There was significant evidence for an overall association between chromosome 2 haplotypes and IL-18 levels (Table 5) (PCHS < 0.00001). Haplotypes H1, H2 and H4 were all significantly associated with lower IL-18 levels compared to the most common haplotype H5 (PCHS of 1.8×10−7, 0.0090 and 7.5×10−9, respectively). The effect for haplotype H3 was not significantly different than baseline haplotype H5 (PCHS = 0.13) and no significant difference were observed between the contrasts for the effect estimates for H1, H2 and H4 (data not shown). Based on HapMap CEU data, the minor allele for the top SNP, rs12989936, rests primarily on both H1 and H4, while the minor allele for the rare imputed variant, rs6543646, rests on both H2 and baseline haplotype H5.

Table 5.

Association of three-SNP haplotypes in chromosome 2 with plasma IL-18 level in CHS European Americans

rs7559329 rs10084280 rs212713 Freq β SE P
H1 C G C 0.12 −0.10 0.02 1.82e-07
H2 C G T 0.03 −0.10 0.04 0.0090
H3 T A C 0.10 −0.03 0.02 0.13
H4 T G C 0.27 −0.08 0.01 7.5e-09
H5 (baseline) T G T 0.47 -
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3.3 Interleukin 1-receptor antagonist GWAS Results: Chromosome 2

There were 116 SNPs significantly associated with IL-1ra serum levels all of which were located in a 58 kb region of chromosome 2 of the IL1 gene family. Top hits were found for SNP rs6743376 (PCHS = 2.0×10−21, PInCH = 2.3×10−5, and rs6761276 (PCHS = 4.2×10−21, PInCH = 4.5×10−6, both imputed missense mutations in the IL1F10 gene, coding Ala51Asp and Ile44Thr protein changes, respectively (Table 6). These loci are highly correlated (r2=0.74, D’=1.0, HapMap CEU samples). In the CHS cohort after conditioning on the rs6743376 signal, 55 SNPs were significantly associated with IL-1ra, particularly in the 47 kb region between the IL1F10 and IL1RN genes (Best SNP: rs11687782 [imputed], PCHS = 1.2×10−12) and the IL1RN gene itself (Best SNP: rs431726 [imputed], PCHS = 3.57×10−9). All associated SNPs in the IL1RN gene were intronic, with the exception of rs419598 which is a synonymous mutation. This evidence suggests that multiple loci from the IL1F10 and IL1RN genes as well as the surrounding region are associated with differences in IL-1ra plasma levels.

Table 6.

Top significant SNP hits for plasma IL-1ra levels for CHS, InCHIANTI and the meta-analyzed CHS-InCHIANTI results

CHS InChianti Meta-analysis
Chr SNP Position Gene Function A1,A2 MAF β SE P-VAL β SE P-VAL β SE P-VAL
2 Rs6743376 * 113548804 IL1F10 Ala51Asp A,C 0.43 0.14 0.01 2.0E-21 0.11 0.03 2.3E-5 0.13 0.01 2.27E-26
2 Rs6761276 * 113548783 IL1F10 Ile44Thr C,T 0.48 0.13 0.01 4.16E-21 0.11 0.03 4.48E-6 0.12 0.01 4.56E-25
2 Rs13386602 113551291 - A,C 0.44 0.13 0.01 7.12E-21 0.12 0.02 3.24E-6 0.13 0.01 6.12E-25
2 Rs6759676 113552819 - T,C 0.44 0.13 0.01 6.88E-21 0.11 0.02 3.3E-6 0.13 0.01 6.15E-25
2 Rs11678375 113552162 - T,C 0.44 0.13 0.01 7.1E-21 0.12 0.02 3.2E-6 0.13 0.01 6.19E-25
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*

SNPs rs6743376 and rs6761276 are highly correlated (r2=0.74, D’=1.0, HapMap CEU samples)

Haplotype analysis in CHS provided additional insight into the underlying association signals in the IL1RN gene region. Six haplotypes with frequencies greater than 0.02 were observed based on three-SNP haplotypes containing genotyped SNPs: rs2515402, rs6759676 and rs1542176. Four haplotypes (H1, H2, H4 and H5) were significantly associated with higher levels of log-transformed IL-1ra levels compared to baseline haplotype H6 (Table 7). Haplotype H2 had significantly stronger effects (PCHS < 0.05 for all contrasts) than the other positively associated haplotypes. The minor alleles for rs6743376 and rs6761276 were both predicted to be on haplotypes H1, H2, H3, and H4. The T allele for rs11687782, the top significantly associated SNP after adjusting for rs6743376, resides primarily on haplotypes H2, H4 and H5 according to HapMap CEU data, while the A allele rests on H1, H3 and H6. Thus, rs11687782 effectively separates H5 from H6 and captures the unexplained (with respect to rs6743376 and rs6761276) association for H5. The genotypes for the other imputed SNP with evidence in the conditional analyses, rs431726, also split H5 and H6.

Table 7.

Association of three-SNP haplotypes on chromosome 2 with plasma IL-1ra in CHS European Americans.

rs2515402 rs6759676 rs1542176 Freq β SE P
H1 C C T 0.16 0.17 0.03 1.8e-10
H2 C C C 0.16 0.26 0.02 7.1e-31
H3 C T T 0.05 0.05 0.04 0.17
H4 A C C 0.10 0.11 0.03 4.2e-05
H5 A T C 0.23 0.12 0.02 1.5e-07
H6(Baseline) A T T 0.27
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4. Discussion

In a GWAS study of older adult Caucasians, we have identified multiple SNPs associated with serum inflammatory mediators IL-18 and IL-1ra, including a novel region on chromosome 2 associated with IL-18 serum levels. In addition, this study confirms and extends previously identified genetic signals related to IL-18 and IL-1ra serum, including fine-mapping and identification of multiple SNPs within the IL1 gene cluster that are independently associated with IL-1ra levels. The newly identified IL-18 association signal spans 8.8Mb and 5 genes including SRD5A2, DPY30, SPAST, SLC30A6, and NLRC4. Of these five genes, NLRC4 is the most likely to be related to inflammation given it codes for a platform protein that acts to assemble the inflammasome, a cluster of proteins reflecting very early responders to inflammatory stimuli (33). NLRC4 is initially responsive to gut pathogens and to flagellin (33). It then activates IL-18 and IL-1B through caspase-1 dependent mechanisms and likely also triggers apoptosis in the absence of caspase-1 auto-processing (33,34). SNPs rs479333 and rs212713 are located within the intron of the NLRC4 gene and are significantly associated with serum levels of IL-18 (Table 2). However, other SNPs within that region are also highly associated with IL-18 and are in high linkage disequilibrium, making it difficult to pinpoint the locus that is most influencing the serum levels of IL-18. Given that the other genes in this region are not related to inflammatory pathway activation or inflammatory gene regulation, the NLRC4 gene variants seem the most likely to influence IL-18 serum levels through variation in inflammasome activity and hence cleavage and activation of IL-18. Future work of the NLRC4 region, including fine mapping and sequence analysis, should be done to better understand which SNPs are associated with serum IL-18 at this locus.

Previous GWAS and candidate gene studies have identified a significant association between serum IL-18 levels and SNPs within the IL18-BCO gene region (20,35–37). Top SNPs in these studies were consistent with top SNPs in our study, although it should be noted that two of the studies were previously performed in the InCHIANTI population, which was also included in this analysis. Ultimately, haplotype analyses with denser SNP panels, direct sequencing and functional analyses will be required to localize the causal variants in this region (38).

Two independent loci associated with IL-1ra serum levels were localized to chromosome 2 in the IL1 gene cluster. First, two missense variants (Ala51Asp and Ile44Thr) in the IL1F10 gene were significantly associated with increased serum IL-1ra in both the CHS and InCHIANTI populations. These polymorphisms are common and in high LD with each other. The association of IL-1ra serum levels with IL1F10 SNPs rs6761276 and rs6734238 was suggestive, but did not reach genome-wide significance, in a prior GWAS in 1,200 whites from InCHIANTI (18) or 700 African Americans (26). Second, when these IL1F10 polymorphisms were controlled for, an independent significant association was observed between intronic SNPs in the IL1RN gene and increased serum IL-1ra. This signal was also observed in the haplotype analysis, which showed a distinct haplotype not containing the risk alleles for either rs6761276 or rs6734238 that was associated with increased IL-1ra levels. In prior candidate gene analyses of IL1RN from InChianti (39) and a meta-analysis of 3 European studies (40), another common variant of IL1RN, tagged by the minor C allele of rs4251961 was associated with lower IL-1ra levels. In additional analyses that included both CHS and InChianti, the IL-1ra-lowering rs4251961 C allele was associated with higher plasma levels of several inflammation-sensitive biomarkers, including fibrinogen and C-reactive protein (39,41–43).

The function of the IL1F10 gene, recently renamed IL-38, is still largely unknown. However, IL1F10 shares about 40% homology with the IL1RN gene and is thought to play a role in inflammatory processes (44). Ala51Asp and Ile44Thr are located in the third exon of IL1F10. Using the prediction algorithm PolyPhen2 (45) neither amino acid substitution is predicted to alter protein structure/function. Two alternatively spliced transcript variants encoding IL1F10 protein have been reported, which differ in amino acid sequence between residues 9 and 39 due to an alternative splicing event that involves exon 3. Therefore, it is possible that one or both of these exonic variants influence the relative amounts of alternative isoforms. Polymorphisms in both the IL1RN and IL1F10 genes have also been implicated in variation in serum CRP levels (46,47). These signals are most likely altering the expression of the IL-1ra protein, although further research is needed to understand the exact mechanism.

Our results are therefore consistent with the existence of allelic heterogeneity within the IL1 family gene cluster and genetic complexity underlying the regulation of IL-1ra and production of other acute phase proteins. Several in vitro studies have confirmed an effect of the rs4251961 variant on IL1RN gene transcription or IL-1ra production in immune-stimulated cells (41). The rs4251961 variant is adjacent to a predicted GATA-1 transcription factor binding site within the IL1RN promoter. Co-transfection experiments suggested that GATA-1 increases transcription from the IL1RN promoter, and may act differentially depending upon rs4251961 genotype (48).

In summary, we report detailed genetic association findings generated from two large prospective cohorts of older adults regarding genetic influences on proinflammatory cytokine production. The difference between laboratory cytokine assays in CHS and InCHIANTI subjects may have decreased the ability to detect a true association and meta-analyzed results should be interpreted with great caution. However, effect sizes were consistent in independent study results, as well as the meta-analysis, leading us to believe that this bias did not affect the findings of this study. Although the combined sample size is relatively small for GWAS, the large effect sizes observed suggest that the genetic signals are robust. While some of our findings are consistent with previously reported genetic associations, the signal for serum levels of IL-18 located on chromosome 2 is novel and potentially important in inflammasome-triggered chronic activation of inflammation in older adults. Replication in additional independent cohorts is an important next step,particularly for the replication of the association between chromosome 2 region and IL-18 serum concentrations. Particularly, it would be interesting to test these associations in retrospective cohorts of older adults with known metabolic or cardiovascular diseases characterized by elevated serum IL-1RA and IL-18 levels. Ultimately, molecular studies will encourage a better understanding of the role of NLRC4 in the activation of chronic inflammation in older adults.

Supplementary Material

01

  • We proposed that gene variation predicts changes in IL-18 and IL-1ra serum levels.

  • We conducted a genome-wide association scan in prospective cohorts of older adults.

  • We characterized robust genetic signals that influence IL-18 and IL1-ra levels.

  • We identified SNPs near the NLRC4 gene associated with serum IL-18.

Acknowledgments

This study was supported by grants R01 AG027236 (Dr. Walston), R01 HL071862 (Dr. Reiner) and by the Johns Hopkins Older Americans Independence Center, P30 AG021334. The Cardiovascular Health Study was supported by contracts HHSN268201200036C, N01-HC-85239, N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133, and grant HL080295 from the National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through AG-023629, AG-15928, AG-20098, and AG-027058 from the National Institute on Aging (NIA). CHS genotyping was supported by HL087652. A full list of participating CHS investigators and institutions can be found at http://www.chs-nhlbi.org/pi.htm. The InCHIANTI study baseline (1998–2000) was supported as a "targeted project" (ICS110.1/RF97.71) by the Italian Ministry of Health and in part by the U.S. National Institute on Aging (Contracts: 263 MD 9164 and 263 MD 821336).

Footnotes

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