Genome-wide association meta-analysis of spontaneous coronary artery dissection identifies risk variants and genes related to artery integrity and tissue-mediated coagulation

Abstract

Spontaneous coronary artery dissection (SCAD) is an understudied cause of myocardial infarction primarily affecting women. It is not known to what extent SCAD is genetically distinct from other cardiovascular diseases, including atherosclerotic coronary artery disease (CAD). Here we present a genome-wide association meta-analysis (1,917 cases and 9,292 controls) identifying 16 risk loci for SCAD. Integrative functional annotations prioritized genes that are likely to be regulated in vascular smooth muscle cells and artery fibroblasts and implicated in extracellular matrix biology. One locus containing the tissue factor gene F3, which is involved in blood coagulation cascade initiation, appears to be specific for SCAD risk. Several associated variants have diametrically opposite associations with CAD, suggesting that shared biological processes contribute to both diseases, but through different mechanisms. We also infer a causal role for high blood pressure in SCAD. Our findings provide novel pathophysiological insights involving arterial integrity and tissue-mediated coagulation in SCAD and set the stage for future specific therapeutics and preventions.

Fig. 1. GWAS meta-analysis main association results and gene prioritization at-risk loci.

a, Manhattan plot representation of SNP-based association meta-analysis in SCAD. The x axis shows the genomic coordinates and the y axis shows the −log₁₀[P value] obtained by two-sided Wald test. SNPs located around genome-wide significant signals (±500 kb) are highlighted. The labels show the rsIDs for the lead SNPs, with newly identified loci in red and previously known loci in black. The dashed red line represents genome-wide significance (P = 5 × 10⁻⁸) and the gray line suggestive association (P = 10⁻⁵). b, Summary of the strategy for the annotation of gene prioritization. The dots indicate genes fulfilling one of the following eight criteria: (1) colocalization of SCAD association signal and eQTL association in the aorta, coronary artery, tibial artery, fibroblasts or whole blood samples (GTEx version 8 release); (2) a TWAS hit in any of the above-mentioned tissues; (3) a cardiovascular (CV) phenotype in the gene knockout mouse; (4) existing evidence of gene function in cardiovascular disease (CVD) pathophysiology in humans; (5) the gene is an eGene for a nearby lead SNP in the above-mentioned GTEx tissues; (6) Hi-C evidence for a promoter of the gene in a chromatin loop from human aorta tissue that includes variants from the credible set of causal variants; (7) the closest gene upstream or downstream from the lead SNP; or (8) variants in the credible set of causal variants map in the gene. Criteria 1 and 2 (blue dots) were given a tenfold weighted score over criteria 3–8. Genes with the most criteria were prioritized in each locus and are shown here.

Fig. 2. Enrichment of SCAD SNPs in open chromatin regions from arterial cells and genetically predicted expression changes of nearby genes.

a, Top, representation of the fold-enrichment of SCAD SNPs (top y axis) and enrichment P value (log scale; bottom y axis) among the open chromatin regions of seven single-cell subclusters contributing to >1% of cells in artery tissue. The SCAD 95% credible set of causal SNPs and their linkage disequilibrium proxies were matched to random pools of neighboring SNPs using the GREGOR package. Enrichment represents the ratio of the number of SCAD SNPs overlapping open chromatin regions over the average number of matched SNPs overlapping the same regions. P values were evaluated by binomial one-sided test, with greater enrichment as the alternative hypothesis. The bottom dashed line represents significance (P < 0.05) after adjustment for 105 subclusters. Higher opacity is used to identify significant associations (adjusted P < 0.05). Bottom, composition of artery tissues relative to 105 single-cell subclusters, as determined by snATAC-seq in 30 adult tissues. Only subclusters representing >1% of cells from either the aorta or tibial artery were represented. b, Representation of the SCAD TWAS z score for each prioritized gene in GWAS loci. The point shape indicates the tissue used in the TWAS association. The point color distinguishes genes located at different loci. The absence of a symbol indicates that the gene did not show significant heritability based on the eQTL data in the corresponding tissue. TWAS P values were calculated by two-tailed z test against a null distribution calculated by permutation for each gene or tissue. Higher opacity is used to identify significant associations (Bonferroni adjusted P < 0.05), corresponding to a z score of >4.8 or <−4.8 (dashed gray lines).

Fig. 3. Colocalization and genetic correlation of SCAD genetic association with cardiovascular diseases and traits.

a, Heatmap representing the colocalization of SCAD signals with GWAS analysis of the following cardiovascular diseases or traits: cervical artery dissection (CeAD), multifocal FMD, migraine, blood pressure (SBP and DBP), LDL cholesterol blood concentration, hemoglobin concentration (HGB), any stroke (AS), intracranial aneurysm (IA) and CAD. The tile color represents the H4 coefficient of approximate Bayes factor (ABF) colocalization (that is, the posterior probability of the two traits sharing one causal variant at the locus (PP.H4.ABF; 0–1)) multiplied by the sign of colocalization (+1 if both traits have the same risk or higher mean allele and −1 if opposite allele)). b, Forest plot representing genetic correlations with SCAD. The Rho coefficient of genetic correlation (r_g), obtained using linkage disequilibrium score regression, is represented on the x axis (center of the error bar). The range of each bar represents the 95% CI. Unadjusted P values obtained by two-sided Wald test for genetic correlations are indicated. Asterisks indicate significance after Bonferroni correction for testing 26 traits (P < 1.9 × 10⁻³) (Supplementary Table 10).

Fig. 4. Mendelian randomization associations between main cardiovascular risk factors and SCAD or CAD.

a,b, Forest plots representing Mendelian randomization associations between cardiovascular risk factors and SCAD (n_cases = 1,917; n_controls = 9,292) (a) or CAD (n_cases = 181,522; n_controls = 984,168) (b). Association estimates (β; center of the error bars) obtained from Mendelian randomization analyses using the IVW method are represented on the x axis. The range of each bar represents the 95% CI. Unadjusted P values from the associations obtained by two-sided Wald test are indicated. n = 340,159 (SBP), 340,162 (DBP), 359,983 (BMI), 315,133 (HDL), 343,621 (LDL), 343,992 (triglycerides (TG)), 164,638 cases and 195,068 controls (smoking (SMK)) and 74,124 cases and 824,006 controls (type 2 diabetes (T2D)). The asterisks indicate significance after Bonferroni correction for testing nine traits (P < 5.6 × 10⁻³) (Supplementary Table 13).