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

Co-Localization analysis page and example.

<p>(A) Analysis can be carried in two organisms (human and mouse) and three regulation levels (CpG hydroxy/methylation, histone modification and transcription factor binding) (B) Correlation matrix created by selecting interested modifiers/regulators (Pou5f1, Nr5a2, Sox2, Nanog, H3K4me3 and H3K27me3) in mouse. The color of red and shape close to slash indicate more positive correlation, while the color of blue and shape close to backslash indicate negative correlation, and the color of grey and shape like circle indicate no correlation. (C) Venn-diagram of Pou5f1 targeted genes and Nr5a2 targeted genes. Gene list in each part of the plot can be downloaded separately to run enrichment analysis in DAVID.</p

Queries retrieved from SyStemCell, using mouse gene “Pou5f1” (Oct4) as an example.

<p>(A) Multi-level summary page and external annotation (only partial displayed). (B) DNA CpG Methylation information. (C) Histone modification information (only partial displayed) and (D) microRNA regulation information.</p

Conserved co-regulatory network in both Homo sapiens and Mus musculus species.

<p>Each interconnected edge (representing a pair of modifier/regulator) must satisfy three criteria, i.e., existed in both human and mouse, the Bonforroni adjusted p<0.001 and the intersection genes of the pair was enriched at least 2-fold. The gene symbols are shown as in Mus musculus species. The node size is in proportion to its degree and color represents different types of modifier/regulator, red, DNA hydroxy/methylation; blue, hisotone modification and yellow, transcription factor.</p

Database content of SyStemCell.

<p>(A) Summary of original papers on seven levels of regulation, where transcription products possess the largest proportion of all recorded papers in SyStemCell. (B) Summary of Top 5 stem cell types from original papers, where the proportion of ESC (Embryonic Stem Cells) ranks the first. MSC, Mesenchymal Stem Cells; HSC/HPC, Hematopoietic Stem/Progenitor Cells; NSC, Neural Stem Cells and iPSC, induced Pluripotent Stem Cells. (C) Summary of entry across seven regulatory levels. The entry counts are log2 transformed for each level. (D) Pie plot of regulatory levels occupied by all 43,434 genes in SyStemCell.</p

Nodes with high coreness in combinatorial TF-miRNA network of mouse ESC.

*<p>ESC, Embryonic Stem Cells.</p>**<p>Only the nodes with coreness larger than 10 are displayed in the table.</p

Browse page and dynamic selecting box.

<p>(A) Browse page for seven levels of regulatory information in SyStemCell. (B) Dynamic selecting box (using histone modification H3K27me3 in mouse ES and fibroblasts cells as an example). “Child” boxes are only displayed when their “Parent” boxes are selected.</p

Pipeline of data collection, curation and recording in SyStemCell.

<p>Pipeline of data collection, curation and recording in SyStemCell.</p

Motif patterns in the mouse ESC combinatorial network.

<p>Green nodes represent TFs, and red nodes represent miRNAs. Nodes in rectangle shape are ESC core TFs according to literatures. All the edges are retrieved from SystemCell except those in purple, which are supplemented by predicted miRNA-target relationships.</p

Genetic and modifiable risk factors combine multiplicatively in common disease.

BackgroundThe joint contribution of genetic and environmental exposures to noncommunicable diseases is not well characterized.ObjectivesWe modeled the cumulative effects of common risk alleles and their prevalence variations with classical risk factors.MethodsWe analyzed mathematically and statistically numbers and effect sizes of established risk alleles for coronary artery disease (CAD) and other conditions.ResultsIn UK Biobank, risk alleles counts in the lowest (175.4) and highest decile (205.7) of the distribution differed by only 16.9%, which nevertheless increased CAD prevalence 3.4-fold (p  0.94). Classical risk factors shifted effect sizes to the steep upslope of the logarithmic function linking risk allele numbers with CAD prevalence. Similar phenomena were observed in the Estonian Biobank and for risk alleles affecting diabetes mellitus, breast and prostate cancer.ConclusionsAlleles predisposing to common diseases can be carried safely in large numbers, but few additional ones lead to sharp risk increments. Here, we describe exponential functions by which risk alleles combine interchangeably but multiplicatively with each other and with modifiable risk factors to affect prevalence. Our data suggest that the biological systems underlying these diseases are modulated by hundreds of genes but become only fragile when a narrow window of total risk, irrespective of its genetic or environmental origins, has been passed