Hundreds of cardiac MRI traits derived using 3D diffusion autoencoders share a common genetic architecture

Biobank-scale imaging provides an unprecedented opportunity to characterise how thousands of organ phenotypes vary in populations. However, deriving specific phenotypes from imaging data requires time-consuming expert annotation, limiting scalability. In this study, we develop a 3D diffusion autoencoder to derive latent phenotypes from temporally resolved cardiac MRI data of 71,017 UK Biobank participants. These phenotypes are reproducible, heritable (h2 = [4—18%]), and significantly associated with cardiometabolic traits. To establish the genetic basis of such traits, we perform a genome-wide association study, identifying 89 significant common variants (P < 2.3 × 10−9) across 42 loci, including seven novel loci. Extensive multi-trait colocalisation analyses (PP.H4 > 0.8) link variants across phenotypic scales, from intermediate cardiac traits to cardiac disease endpoints. In conclusion, this study showcases the use of diffusion autoencoding methods as powerful tools for unsupervised phenotyping, genetic discovery and disease risk prediction using cardiac MRI data. In this work, authors used unsupervised 3D diffusion autoencoders on 71k cardiac MRIs to learn latent heart representations predictive of cardiometabolic disease, identify 89 GWAS variants across 42 loci, and derive polygenic risk scores that stratify cardiovascular risk.