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5 Defining the effects of genetic variation using machine learning analysis of CMRS: a study in hypertrophic cardiomyopathy and in a healthy population
  1. Antonio de Marvao1,
  2. Carlo Biffi2,
  3. Roddy Walsh3,
  4. Georgia Doumou1,
  5. Timothy Dawes2,
  6. Wenzhe Shi2,
  7. Wenjia Bai2,
  8. Alaine Berry1,
  9. Rachel Buchan3,
  10. Iain Pierce3,
  11. Pawel Tokarczuk1,
  12. Ben Statton1,
  13. Catherine Francis2,
  14. Jinming Duan2,
  15. Marina Quinlan1,
  16. Leanne Felkin2,
  17. Thu-Thao Le4,
  18. Anish Bhuva5,
  19. Hak Chiaw Tang4,
  20. Paul Barton2,
  21. Calvin Woon-Loong Chin4,
  22. Daniel Rueckert2,
  23. James Ware1,2,
  24. Sanjay Prasad1,2,
  25. Declan P O’Regan1,
  26. Stuart A Cook1,3,4
  1. 1MRC London Institute of Medical Sciences, Imperial College London, London, UK
  2. 2Imperial College London, London, UK
  3. 3Royal Brompton Hospital, London, UK
  4. 4National Heart Centre, Singapore
  5. 5Barts Heart Centre and Institute of Cardiovascular Science, University College London, London, UK


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.

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