Genetic architecture of cardiac dynamic flow volumes

Gomes, Bruna; Singh, Aditya; O’Sullivan, Jack W.; Schnurr, Theresia M.; Goddard, Pagé C.; Loong, Shaun; Amar, David; Hughes, J. Weston; Kostur, Mykhailo; Haddad, Francois; Salerno, Michael; Foo, Roger; Montgomery, Stephen B.; Parikh, Victoria N.; Meder, Benjamin; Ashley, Euan A.

doi:10.1038/s41588-023-01587-5

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2024

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Cited by 3 publications

References 86 publications

(69 reference statements)

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Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data

Lehmann,

Gomes,

Vetter

et al. 2024

The Lancet Digital Health

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Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data

Lehmann,

Gomes,

Vetter

et al. 2024

The Lancet Digital Health

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Genetic analysis of cardiac dynamic flow volumes identifies loci mapping aortic root size

Munroe,

Aung,

Ramírez

2024

Nat Genet

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Utilizing multimodal AI to improve genetic analyses of cardiovascular traits

Zhou,

Cosentino,

Yun

et al. 2024

Preprint

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Electronic health record (EHR) and biobank datasets contain multiple high-dimensional clinical data (HDCD) modalities (e.g., ECG, Photoplethysmography (PPG), and MRI) for each individual. Access to multimodal HDCD provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a novel multimodal deep learning method, M-REGLE, for discovering genetic associations from a joint representation of multiple complementary HDCD modalities. We showcase the effectiveness of this model by applying it to several cardiovascular modalities. M-REGLE jointly learns a lower-dimensional representation (i.e., latent factors) of multimodal HDCD using a convolutional variational autoencoder, performs genome-wide association studies (GWAS) on each latent factor, then combines the results to study the genetics of the underlying system. To validate the advantages of M-REGLE and multimodal learning, we apply it to common cardiovascular modalities (PPG and ECG), and compare its results to unimodal learning methods in which representations are learned from each data modality separately, but the downstream genetic analyses are performed on the combined unimodal representations. M-REGLE identifies 19.3% more loci on the 12-lead ECG dataset, 13.0% more loci on the ECG lead I + PPG dataset, and its genetic risk score significantly outperforms the unimodal risk score at predicting cardiac phenotypes, such as atrial fibrillation (Afib), in multiple biobanks.

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Genetic architecture of cardiac dynamic flow volumes

Abstract: doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Cited by 3 publications

References 86 publications

Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data

Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data

Genetic analysis of cardiac dynamic flow volumes identifies loci mapping aortic root size

Utilizing multimodal AI to improve genetic analyses of cardiovascular traits

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