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

Additional file 3: of DNA methylation patterns associated with oxidative stress in an ageing population

Supplemental Tables. Table S1. Methylation sites associated with TGSH (FDR <0.05). Table S2. Methylation sites associated with GSH (FDR <0.05. Table S3. Methylation sites associated with GSSG (FDR <0.05). Table S4. Methylation sites associated with ratio of GSSG-to-GSH (FDR <0.05). Table S5. Methylation sites associated with levels of HCY (FDR <0.05). Table S6. Methylation sites associated with levels of oxLDL (FDR <0.05). Table S7. Methylation sites associated with levels of CD (FDR <0.05). Table S8: Methylation sites associated with BCD-LDL (FDR <0.05). (XLS 185 kb

Additional file 1: of DNA methylation patterns associated with oxidative stress in an ageing population

Supplementary Data. Figure S1. Plot showing the first two PC components of the PIVUS genotype data with the 1000G multi population reference panel. Figure S3. Comparison of regression coefficients from the primary and secondary models (additionally adjusted for BMI) for oxidative marker BCD-LDL. Table S9. Enrichment in JASPAR transcription factor binding site motifs in genes annotated to oxidative stress associated CpGs (Bonferroni-adjusted p-value < 0.05). Table S10. Enriched biological process among genes annotated to oxidative marker associated CpGs (adjusted p-value < 0.05). Table S11. Enriched annotation clusters among genes annotated to oxidative marker CpGs (enrichment score > 1). Table S12. Significant lead cis-meQTL SNPs of oxidative marker CpGs (FDR <0.05). Table S13. Overlap across genotype-CpG (FDR <0.05), genotype-phenotype (p-value <0.001), and CpG-phenotype (FDR <0.05) results. (DOCX 135 kb

Additional file 5: Table S14. of DNA methylation patterns associated with oxidative stress in an ageing population

Nominal significant associations (p-value <0.05) for significant meQTL SNPs in GWAS data from the CARDIOGRAMplusC4D and DIAGRAM consortia. (XLSX 59 kb

Additional file 2: Figure S2. of DNA methylation patterns associated with oxidative stress in an ageing population

DNA methylation sites associated with oxidative markers at a Bonferroni-corrected alpha threshold 0.05 (p-value <1.1E-07). CpG sites are ordered by chromosomal position from bottom (chr. 1) to top (chr. 22). (EPS 14 kb

Incidence rates of ischemic stroke by socioeconomic position for Swedish men and women in three age groups.

<p>All models were adjusted for birth country and stratified by sex and attained age. Note 1 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105279#pone-0105279-g002" target="_blank">Figure 2:</a> The shadowed area indicates a time period for which results cannot be interpreted. Note 2 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105279#pone-0105279-g002" target="_blank">Figure 2:</a> The incidence rate of ischemic stroke is increasing until 1997 due to changing in ICD codes 9 and 10, the result until 1997 is uncertain.</p

Incidence rate ratios (IRR) with 95% confidence intervals (CI) of myocardial infarction and ischemic stroke by socioeconomic position and calendar year, and stratified by sex and attained age.

<p>Incidence rate ratios (IRR) with 95% confidence intervals (CI) of myocardial infarction and ischemic stroke by socioeconomic position and calendar year, and stratified by sex and attained age.</p

Frequencies and incidence rates of myocardial infarction and ischemic stroke by sex, attained age, birth country and socioeconomic position.

1<p>Unadjusted incidence rate per 100,000 person-years.</p>2<p>Age and sex standardized incidence rate per 100,000 person-years using the Swedish population in 2011 as standard population).</p><p>Frequencies and incidence rates of myocardial infarction and ischemic stroke by sex, attained age, birth country and socioeconomic position.</p

Incidence rates of myocardial infarction by socioeconomic position for Swedish men and women in three age groups.

<p>All models were adjusted for birth country and stratified by sex and attained age. Note 1 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105279#pone-0105279-g001" target="_blank">Figure 1:</a> The shadowed area indicates a time period for which results cannot be interpreted.</p

Enabling Efficient and Confident Annotation of LC−MS Metabolomics Data through MS1 Spectrum and Time Prediction

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC–ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, our analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation