Computational estimates of mitral annular diameter in systole and diastole cardiac cycle reveal novel genetic determinants of valve function and disease

Author:

Yu Mengyao,Tcheandjieu Catherine,Georges Adrien,Xiao Ke,Tejeda Helio,Dina Christian,Le Tourneau Thierry,Fiterau Ina,Judy Renae,Tsao Noah,Amgalan Dulguun,Munger Chad J,Engreitz Jesse M,Damrauer Scott,Bouatia-Naji Nabila,Priest James R.,

Abstract

ABSTRACTThe fibrous annulus of the mitral valve defines the functional orifice and anchors the anterior and posterior leaflets, playing an important role in normal cardiovascular physiology and valvular function. We derived automated estimates of mitral valve annular diameter in the 4-chamber view from 32,220 MRI images from the UK Biobank at ventricular systole and diastole as the basis for genome wide association studies. Mitral annular dimensions correspond to previously described anatomical norms and GWAS inclusive of four population strata identify ten loci, including novel loci (GOSR2, ERBB4, MCTP2, MCPH1) and genes related to cardiac contractility (BAG3, TTN, RBFOX1). ATAC-seq of primary mitral valve tissue localize multiple variants to regions of open chromatin in biologically relevant cell types and rs17608766 to an algorithmically predicted enhancer element in GOSR2. We observed strong genetic correlation with measures of contractility and mitral valve disease, and clinical correlations with heart failure, cerebrovascular disease, and ventricular arrythmias. A polygenic score of mitral valve annular diameter in systole was predictive of risk mitral valve prolapse across four cohorts (Odds ratio 1.19 per SD increase in polygenic score, 95% confidence interval 1.14 to 1.24, p=4.9E-11). In summary genetic and clinical studies of mitral valve annular diameter reveal new genetic determinants of mitral valve biology while highlighting known and previously unrecognized clinical associations. Polygenic determinants of mitral valve annular diameter may represent an independent risk-factor for mitral prolapse. Overall, computationally estimated phenotypes derived at scale from medical imaging represent an important substrate for genetic discovery and clinical risk prediction.

Publisher

Cold Spring Harbor Laboratory

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