Left Ventricular Wall
Catalog entries using this tag (links open the entry card on its page):
Entries
Ning C (DL LVRWT GWAS)
PUBMED_LINK
FULL NAME
Genome-Wide Association Analysis of Left Ventricular Imaging-Derived Phenotypes Identifies 72 Risk Loci
DESCRIPTION
Built a CNN-based deep learning algorithm for automated segmentation of left ventricular myocardium from cardiac MRI, enabling precise calculation of 12 regional wall thickness (LVRWT) measurements in 42,194 UK Biobank participants. GWAS of these 12 CNN-derived LVRWT traits identified 72 significant genetic loci involved in heart development and contraction pathways. Mendelian randomization confirmed causal relationships with hypertrophic cardiomyopathy. The PRS of inferoseptal LVRWT enabled identification of high-risk individuals.
KEYWORDS
deep learning, cardiac MRI, CNN, left ventricular wall thickness segmentation, hypertrophic cardiomyopathy, UK Biobank
TITLE
Genome-wide association analysis of left ventricular imaging-derived phenotypes identifies 72 risk loci and yields genetic insights into hypertrophic cardiomyopathy.
Main citation
Ning C, Fan L, Jin M, ...&, Miao X. (2023) Genome-wide association analysis of left ventricular imaging-derived phenotypes identifies 72 risk loci and yields genetic insights into hypertrophic cardiomyopathy. Nat Commun, 14 (1) 7900. doi:10.1038/s41467-023-43771-5. PMID 38036550
ABSTRACT
Left ventricular regional wall thickness (LVRWT) is an independent predictor of morbidity and mortality in cardiovascular diseases (CVDs). To identify specific genetic influences on individual LVRWT, we established a novel deep learning algorithm to calculate 12 LVRWTs accurately in 42,194 individuals from the UK Biobank with cardiac magnetic resonance (CMR) imaging. Genome-wide association studies of CMR-derived 12 LVRWTs identified 72 significant genetic loci associated with at least one LVRWT phenotype.
DOI
10.1038/s41467-023-43771-5