5 Defining the effects of genetic variation using machine learning analysis of CMRS: a study in hypertrophic cardiomyopathy and in a healthy population. (May 2018) - Marvao, Antonio de; Biffi, Carlo; Walsh, Roddy; Doumou, Georgia; Dawes, Timothy; Shi, Wenzhe; Bai, Wenjia; Berry, Alaine; Buchan, Rachel; Pierce, Iain; Tokarczuk, Pawel; Statton, Ben; et al.
Abstract : 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.