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. 2025 Mar 31;16(1):2266.
doi: 10.1038/s41467-025-57457-7.

A genome-wide association study of imaging-defined atherosclerosis

Anders Gummesson  1   2 , Per Lundmark  3 , Qiao Sen Chen  4 , Elias Björnson  5 , Koen F Dekkers  3 , Ulf Hammar  3 , Martin Adiels  6 , Yunzhang Wang  7 , Therese Andersson  8 , Göran Bergström  5   9 , Carl-Johan Carlhäll  10   11   12 , David Erlinge  13 , Tomas Jernberg  7 , Fredrik Landfors  8 , Lars Lind  14 , Maria Mannila  15 , Olle Melander  16   17 , Carlo Pirazzi  18 , Johan Sundström  19   20 , Carl Johan Östgren  10   12 , Cecilia Gunnarsson  21 , Marju Orho-Melander  16 , Stefan Söderberg  8 , Tove Fall  3 , Bruna Gigante  4   15
Affiliations

A genome-wide association study of imaging-defined atherosclerosis

Anders Gummesson et al. Nat Commun. .

Abstract

Imaging-defined atherosclerosis represents an intermediate phenotype of atherosclerotic cardiovascular disease (ASCVD). Genome-wide association studies (GWAS) on directly measured coronary plaques using coronary computed tomography angiography (CCTA) are scarce. In the so far largest population-based cohort with CCTA data, we performed a GWAS on coronary plaque burden as determined by the segment involvement score (SIS) in 24,811 European individuals. We identified 20 significant independent genetic markers for SIS, three of which were found in loci not implicated in ASCVD before. Further GWAS on coronary artery calcification showed similar results to that of SIS, whereas a GWAS on ultrasound-assessed carotid plaques identified both shared and non-shared loci with SIS. In two-sample Mendelian randomization studies using SIS-associated markers in UK Biobank and CARDIoGRAMplusC4D, one extra coronary segment with atherosclerosis corresponded to 1.8-fold increased odds of myocardial infarction. This GWAS data can aid future studies of causal pathways in ASCVD.

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Conflict of interest statement

Competing interests: The authors declare the following competing interests: S.S. reports speakers’ honoraria and participation in the scientific board for the Exposure study (Janssen). The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1. GWAS results for atherosclerosis outcome variables.
A–C Manhattan plots of 2-sided unadjusted p-values for GWAS results for segment involvement score (SIS; blue, n = 24,811 participants), coronary artery calcium score (CACS; yellow, n = 26,000 participants), and carotid plaque (CarPlaq; turquoise, n = 26,807 participants), respectively. Plots depict unadjusted p-values below 0.001 from the GRAB/POLMM package for ordinal outcomes (SIS, CarPlaq) or Regenie software for continuous outcomes (CACS). Red dashed line depicts the genome-wide significant threshold of p = 5 × 10−8. D Quantile - quantile plot of observed vs. expected p-values, plotted against each other to highlight potential p-value inflation. Plotted are the 10,000 most extreme observed p-values for each outcome and a sample of 300,000 values per outcome. E Plots comparing beta coefficients between SIS and CACS for SNPs associated with at least one of the outcome variables. F Plots comparing beta coefficients between SIS and CarPlaq for SNPs associated with at least one of the outcome variables. Source data are provided as a Source Data file.
Fig. 2
Fig. 2. Colocalization analyses.
A Single variant colocalization for significant independent SNPs from segment involvement score (SIS) and carotid plaque (CarPlaq) GWAS. This analysis is based on the log approximate Bayes factor, calculating five posterior probabilities for five hypotheses: H0 (blue): none of the variants are associated with any of the traits; H1 (orange) and H2 (turquoise): one variant associates with one or the other trait; H3 (red): two different variants associate with each of the two traits; H4 (green): one variant associates with both traits. B Representative scatter plot of the multivariant colocalization between SIS and CarPlaq using the Sum of Single Effects (SuSiE). Here, genetic variants within a window of 250 kb in the locus near rs186696265 (PLG locus) were fine-mapped for SIS and CarPlaq. In the scatter plot, one set of genetically colocalized SNPs between CarPlaq (blue) and SIS (red) are identified as triangles marked by black circles. Enlarged points represent fine-mapped SNPs for each trait. Below the scatter plot, the middle panel displays a line plot showing the genes and region associated with each SNP. At the bottom, the triangular heatmap shows the linkage disequilibrium (LD) structure for the region surrounding rs186696265. Source data are provided as a Source Data file.
Fig. 3
Fig. 3. Analysis of potential effect mediators for each of the 20 SNPs associated with segment involvement score (SIS).
Associations (beta-coefficients between SNPs and SIS, derived from n = 24,811 participants) were adjusted for the potential mediators lipoprotein(a) [Lp(a)], low density lipoprotein cholesterol (LDL-C), triglyceride (TG), haemoglobin A1c (HbA1c), body mass index (BMI), and systolic blood pressure (SBP). Dots represent beta-coefficients and error bars represent 95% confidence intervals. Source data are provided as a Source Data file.
Fig. 4
Fig. 4. Genome-wide genetic correlation (rg) between atherosclerosis variables and other atherosclerosis-related phenotypes.
A Genetic correlations with phenotypes within SCAPIS. B Genetic correlations using GWAS data from external consortia. The rg value was estimated by linkage disequilibrium score regression. Numbers in bold refer to genetic correlations with two-sided P-value < 0.05, without adjustments for multiple comparisons. Source data are provided as a Source Data file. SIS segment involvement score, CACS coronary artery calcium score, CarPlaq carotid plaque, MI myocardial infarction, BMI body mass index, SBP systolic blood pressure, HbA1c haemoglobin A1c, LDL-C low density lipoprotein cholesterol, TG triglyceride, HDL-C high density lipoprotein cholesterol, Lp(a) lipoprotein(a).
Fig. 5
Fig. 5. Relationship between coronary plaque burden and myocardial infarction (MI).
A, B shows the relationship between genetically induced change in segment involvement score (SIS; quantified as a continuous variable in n = 24,811 SCAPIS participants) and MI evaluated in UK Biobank (n = 487,202) and CARDIoGRAMplusC4D (n = 60,811 cases and 123,504 controls), respectively. Dots represent beta-coefficients (beta-SIS on x-axis and log odds ratio for MI on y-axis), and error bars represent standard errors. Source data are provided as a Source Data file.
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