5 Defining the effects of genetic variation using machine learning analysis of CMRS: a study in hypertrophic cardiomyopathy and in a healthy population

Introduction Hypertrophic cardiomyopathy (HCM) is characterised by great phenotypic diversity and broad spectrum of clinical courses. The genetic, environmental and phenotypic determinants of outcome remain poorly understood. We integrated machine-learning analysis of cardiovascular magnetic resonance (CMR) with computational modelling to define the effects of genetic variation on the heart in both HCM patients and heathy volunteers. Methods Healthy volunteers were recruited at Imperial College London (n=1367) and National Health Centre Singapore (n=754). Patients with HCM were enrolled at the Royal Brompton Hospital (n=622) and National Heart Centre Singapore (n=211). Participants underwent conventional CMR at 1.5 T. Using cardiac atlas and machine learning techniques, CMRs were segmented and co-registered providing statistical models of phenotypic variation. Subjects were sequenced with comprehensive gene panels and using stringent criteria identified as genotype positive (G+), negative (G-) or as carriers of variants of unknown significance (VUS). Results In healthy volunteers, sarcomeric G+variants were associated with increased septal and apical LV wall thickness. In HCM, sarcomeric thin filament G+displayed the mildest global hypertrophy. Sarcomeric thick filament G+variants were associated with asymmetric septal hypertrophy, when compared to G-, VUS and other G+. Conclusion We show that in a healthy population, rare variants in sarcomeric genes are penetrant and associated with increased wall thickness. This has potential clinical implications to the ~0.5% of the population that are carriers. In HCM, distinct patterns of hypertrophy were associated with specific genotypes. We demonstrate that machine-learning analysis of CMRs offers unparalleled insights into the earliest manifestations of cardiomyopathy and mutation-specific pathophysiology.