Loss of Cardioprotective Effects at the ADAMTS7 Locus as a Result of Gene-Smoking Interactions

BACKGROUND: Common diseases such as coronary heart disease (CHD) are complex in etiology. The interaction of genetic susceptibility with lifestyle factors may play a prominent role. However, gene-lifestyle interactions for CHD have been difficult to identify. Here, we investigate interaction of smoking behavior, a potent lifestyle factor, with genotypes that have been shown to associate with CHD risk. METHODS: We analyzed data on 60 919 CHD cases and 80 243 controls from 29 studies for gene-smoking interactions for genetic variants at 45 loci previously reported to be associated with CHD risk. We also studied 5 loci associated with smoking behavior. Study-specific gene-smoking interaction effects were calculated and pooled using fixed-effects meta-analyses. Interaction analyses were declared to be significant at a P value of <1.0x10(-3) (Bonferroni correction for 50 tests). RESULTS: We identified novel gene-smoking interaction for a variant upstream of the ADAMTS7 gene. Every T allele of rs7178051 was associated with lower CHD risk by 12% in never-smokers (P= 1.3x10(-16)) in comparison with 5% in ever-smokers (P= 2.5x10(-4)), translating to a 60% loss of CHD protection conferred by this allelic variation in people who smoked tobacco (interaction P value= 8.7x10(-5)). The protective T allele at rs7178051 was also associated with reduced ADAMTS7 expression in human aortic endothelial cells and lymphoblastoid cell lines. Exposure of human coronary artery smooth muscle cells to cigarette smoke extract led to induction of ADAMTS7. CONCLUSIONS: Allelic variation at rs7178051 that associates with reduced ADAMTS7 expression confers stronger CHD protection in never-smokers than in ever-smokers. Increased vascular ADAMTS7 expression may contribute to the loss of CHD protection in smokers.Peer reviewe

A comparative power analysis of <i>M</i> and Cochran’s Q to detect systematic heterogeneity.

<p>The nine panels show (from left to right) simulations for 10, 15 and 30 studies, examined at 50, 25 and l0 variants; Data points for the <i>M</i> statistic are represented by filled circles whilst those for Cochran’s Q are denoted by filled triangles. Each data point represents a meta-analysis scenario where effect sizes for the non-outlier studies were held constant (log_e(odds ratio) = 0.182 i.e. odds ratio = 1.2) to model homogeneous effects. The effect sizes of variants in the outlier study were the product of the non-outlier effect size (i.e. log_e(odds ratio) = 0.182) and a parameter (fold-change) to model a continuous series of systematic heterogeneity patterns. All studies were equally weighted (standard error of log_e(odds ratio) = 0.1).</p

Heterogeneity in the CARDIoGRAMplusC4D meta-analysis can be explained by differences in age of CAD onset, family history and ancestry.

<p><i>M</i> statistics for each study in the CARDIoGRAMplusC4D meta-analysis (Y- axis) are plotted against the average variant effect size (expressed as odds ratios) (X-axis) in each study. Panel A shows the ancestry of each study, panel B distinguishes early-onset from late-onset studies and panel C identifies studies ascertained with a positive family history of coronary artery disease. Panel D is a composite plot showing the degree of genetic enrichment among the studies in the meta-analysis, which ranged from non-enriched (late-onset studies without a positive family history of coronary artery disease) to doubly enriched (early-onset studies with a positive family history of coronary artery disease). The dashed lines indicate the Bonferroni corrected 5% significance threshold (<i>M</i> = ±0.483) to allow for multiple testing of 48 studies.</p

The power of the <i>M</i> statistic to detect systematic outlier studies.

<p>A power analysis of the <i>M</i> statistics for meta-analysis scenarios with varying numbers of studies and variants. The three panels show (from left to right) simulations for 10, 15 and 30 studies; 50, 25 and 10 variant simulations are shown by filled diamonds, filled circles, or open squares respectively. Each data point represents a meta-analysis simulation with 1,000 replicates, where an outlier study was assigned genetic effects that are x-fold stronger than the effects assigned to the remaining studies showing typical effects. Effect sizes for variants in the studies showing typical effects were allocated from an L—shaped distribution (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006755#pgen.1006755.s011" target="_blank">S2 Table</a>) whilst effect sizes for variants in the outlier study were calculated as a multiple of the typical effect size. For example, effect sizes for variants in an outlier study 2-fold-stronger than studies showing typical effects would be computed as (2 x ({0.04, 0.12, 0.2, 0.28, 0.4}, σ = 0.10).</p

Forest plot of <i>M</i> statistics summarizing systematic patterns of heterogeneity among studies in the CARDIOGRAMplusC4D GWAS meta-analysis.

<p>Sorted <i>M</i> statistics are presented for individual studies represented by filled squares with their 95% confidence intervals shown by horizontal lines; the sizes of the squares are proportional to each studies’ inverse-variance weighting. Studies showing weaker (<i>M</i> < 0) than average genetic effects can be distinguished from those showing stronger (<i>M</i> > 0) than average effects.</p

Empirical type- 1 error rates and power to detect an outlier study for <i>M</i> at threshold α = 0.05.

<p>Empirical type- 1 error rates and power to detect an outlier study for <i>M</i> at threshold α = 0.05.</p

Analysis of the Role of Interleukin 6 Receptor Haplotypes in the Regulation of Circulating Levels of Inflammatory Biomarkers and Risk of Coronary Heart Disease

<div><p>Variants at the interleukin 6 receptor (<i>IL6R</i>) gene regulate inflammation and are associated with risk of coronary heart disease (CHD). The aim of the present study was to investigate the effects of <i>IL6R</i> haplotypes on circulating levels of inflammatory biomarkers and risk of CHD. We performed a discovery analysis in SHEEP, a myocardial infarction (MI) case control study (n = 2,774) and replicated our results in two large, independent European populations, PROCARDIS, a CHD case control study (n = 7,998), and IMPROVE (n = 3,711) a prospective cardiovascular cohort study. Two major haplotype blocks (rs12083537A/G and rs4075015A/T—block 1; and rs8192282G/A, rs4553185T/C, rs8192284A/C, rs4240872T/C and rs7514452T/C—block 2) were identified in the <i>IL6R</i> gene. <i>IL6R</i> haplotype associations with C-reactive protein (CRP), fibrinogen, IL6, soluble IL6R (sIL6R), IL6, IL8 and TNF-α in SHEEP, CRP and fibrinogen in PROCARDIS and CRP in IMPROVE as well as association with risk of MI and CHD, were analyzed by THESIAS. Haplotypes in block 1 were associated neither with circulating inflammatory biomarkers nor with the MI/CHD risk. Haplotypes in block 2 were associated with circulating levels of CRP, in all three study populations, with fibrinogen in SHEEP and PROCARDIS, with IL8 and sIL6Rin SHEEP and with a modest, non significant, increase (7%) in MI/CHD risk in the three populations studied. Our results indicate that <i>IL6R</i> haplotypes regulate the circulating levels of inflammatory biomarkers. Lack of association with the risk of CHD may be explained by the combined effect of SNPs with opposite effect on the CHD risk, the sample size as well as by structural changes affecting sIL6R stability in the circulation.</p></div

Association of <i>IL6R</i> haplotypes in blocks 1 and 2 with difference in serum sIL6R levels in controls from the SHEEP compared to the reference haplotype where mean sIL6R (95%CI) are shown.

<p>Data represent the mean and relative 95%CI of the difference in serum sIL6R levels (ng/ml) observed in the presence of one copy of each haplotype configurations as compared to the reference haplotype.</p><p>Association of <i>IL6R</i> haplotypes in blocks 1 and 2 with difference in serum sIL6R levels in controls from the SHEEP compared to the reference haplotype where mean sIL6R (95%CI) are shown.</p

<i>IL6R</i> haplotype frequencies in cases and controls and risk of MI in SHEEP and of CHD in the PROCARDIS and IMPROVE studies associated with <i>IL6R</i> haplotypes.

<p>Only diplotypes and haplotypes with a frequency>1% are reported in the table. The most common diplotype in block 1 and the most common haplotype in block 2 are taken as reference categories.</p><p>Data from only three tag SNPs were available in the IMPROVE study (rs7553796 C/A (pairwise LD with rs4553185T/C, r2 = 0.96) rs8192284A/C and rs4072391T/C (pairwise LD with rs7514452T/C r2 = 0.98). Haplotype-21–1 represents both 12111 and 12121.</p><p><i>IL6R</i> haplotype frequencies in cases and controls and risk of MI in SHEEP and of CHD in the PROCARDIS and IMPROVE studies associated with <i>IL6R</i> haplotypes.</p

IL6R haplotypes selectively regulate inflammatory biomarkers.

<p><b>In particular, changes in CRP and fibrinogen are mirrored by inverse changes in sIL6R serum levels.</b><i>Mutations around the shedding site may affect the shedding/alternative splicing and therefore the relative amount of sIL6R in the circulation</i>. <i>The sIL6R</i>, <i>once released participates in the IL6 transignalling in cells not expressing IL6R (i.e</i>. <i>endothelial and smooth muscle cells) and the complex IL6/sIL6R is buffered in the circulation by its natural antagonist</i>, <i>sgp130</i>. <i>It is possible that the final effect of IL6R haplotypes on the CHD risk depends on the average effect of the association of different SNPs with the CHD risk present in the same haplotype and/or it may reflect changes in the secondary and tertiary structure of the IL6R and sIL6R that may affect the IL6/sIL6R interaction with its downstream mediators</i>.</p