Spatially Distinct Genetic Determinants of Aortic Dimensions Influence Risks of Aneurysm and Stenosis

Abstract

Background: The left ventricular outflow tract (LVOT) and ascending aorta are spatially complex, with distinct pathologies and embryologic origins. Prior work examined the genetics of thoracic aortic diameter in a single plane.

Objectives: We sought to elucidate the genetic basis for the diameter of the LVOT, aortic root, and ascending aorta.

Methods: Using deep learning, we analyzed 2.3 million cardiac magnetic resonance images from 43,317 UK Biobank participants. We computed the diameters of the LVOT, the aortic root, and at 6 locations of ascending aorta. For each diameter, we conducted a genome-wide association study and generated a polygenic score. Finally, we investigated associations between these scores and disease incidence.

Results: A total of 79 loci were significantly associated with at least 1 diameter. Of these, 35 were novel, and most were associated with 1 or 2 diameters. A polygenic score of aortic diameter approximately 13 mm from the sinotubular junction most strongly predicted thoracic aortic aneurysm (n = 427,016; mean HR: 1.42 per SD; 95% CI: 1.34-1.50; P = 6.67 × 10^-21). A polygenic score predicting a smaller aortic root was predictive of aortic stenosis (n = 426,502; mean HR: 1.08 per SD; 95% CI: 1.03-1.12; P = 5 × 10^-6).

Conclusions: We detected distinct genetic loci underpinning the diameters of the LVOT, aortic root, and at several segments of ascending aorta. We spatially defined a region of aorta whose genetics may be most relevant to predicting thoracic aortic aneurysm. We further described a genetic signature that may predispose to aortic stenosis. Understanding genetic contributions to proximal aortic diameter may enable identification of individuals at risk for aortic disease and facilitate prioritization of therapeutic targets.

Keywords: aorta; cardiovascular disease; genetics; left ventricular outflow tract; machine learning.

Figure 1:

Genetic associations with LVOT, aortic root, and ascending aorta diameters Loci with P<5×10⁻⁸ are shown in blue (if also associated at genome-wide significance with two or more anatomically contiguous traits) or red (if associated at genome-wide significance with up to one anatomically contiguous trait. A selected subset of nearest genes of loci with P < 5×10⁻⁸ are overlaid.

Figure 2:

Shared genetic determinants across the LVOT and ascending aorta A: Rows denote associated GWAS as shown with arrows. Columns denote SNPs associated with two or more traits at p<5×10⁻⁸; labels display closest genes. Where multiple SNPs share a closest gene, a number displays the gene’s instance in the combined significant GWAS results. See Supplemental Table 8 for corresponding SNP names. Color indicates absolute effect size (|β|) per standard error of a significantly associated SNP. To illustrate anatomic relationship of significant SNPs, column position is hierarchically clustered to group associations of similar significance (p). For this illustrative purpose, SNPs not meeting genome-wide significance are assigned p=0. B: Venn diagram of genes nearest to loci found to be associated at p<5×10⁻⁸ with one or more traits. Traits are binned into groups based on proximity to the heart as labeled. Loci are defined using a 500 kilobase radius (see Supplemental Methods: Genotyping and genome-wide association studies).

Figure 3:

Disease prediction A: Top: Cox proportional hazards models predicting incidence of thoracic aortic aneurysm using polygenic scores of larger diameters and covariates. Vertical position denotes trait used to create score. Horizontal bars denote effect size (β) and significance. Middle: Overlay of model performance. Rectangle color represents relative effect size (β); location represents trait used to create polygenic score. Bottom: Kaplan-Meier plot: cumulative incidence of thoracic aortic aneurysm for strata of a polygenic score derived from Aorta 1. B: Top: Cox proportional hazards models predicting incidence of aortic stenosis using polygenic scores of smaller diameters and covariates. Vertical position denotes trait used to create score. Horizontal bars denote effect size (β) and significance. Middle: Overlay of model performance. Rectangle color represents relative effect size (β); location represents trait used to create polygenic score. Bottom: Kaplan-Meier plot: cumulative incidence of aortic stenosis for strata of a polygenic score derived from Aortic root.

Central Illustration:. Overview of study steps

Flow diagram of major study steps, visualized by representative examples. Left column, from top: CMR image; deep learning output; output for the diameter-extracting algorithm. Right column, from top: phenotype histograms; output of multiple GWAS; disease prediction models (overlaid: prediction strength comparison).