Browsing by Author "Cosentino, Justin"

Now showing 1 - 3 of 3
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    Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases
    (medRxiv, 2023-08-29) Yun, Taedong; Cosentino, Justin; Behsaz, Babak; McCaw, Zachary R.; Hill, Davin; Luben, Robert; Lai, Dongbing; Bates, John; Yang, Howard; Schwantes-An, Tae-Hwi; Zhou, Yuchen; Khawaja, Anthony P.; Carroll, Andrew; Hobbs, Brian D.; Cho, Michael H.; McLean, Cory Y.; Hormozdiari, Farhad; Medical and Molecular Genetics, School of Medicine
    High-dimensional clinical data are becoming more accessible in biobank-scale datasets. However, effectively utilizing high-dimensional clinical data for genetic discovery remains challenging. Here we introduce a general deep learning-based framework, REpresentation learning for Genetic discovery on Low-dimensional Embeddings (REGLE), for discovering associations between genetic variants and high-dimensional clinical data. REGLE uses convolutional variational autoencoders to compute a non-linear, low-dimensional, disentangled embedding of the data with highly heritable individual components. REGLE can incorporate expert-defined or clinical features and provides a framework to create accurate disease-specific polygenic risk scores (PRS) in datasets which have minimal expert phenotyping. We apply REGLE to both respiratory and circulatory systems: spirograms which measure lung function and photoplethysmograms (PPG) which measure blood volume changes. Genome-wide association studies on REGLE embeddings identify more genome-wide significant loci than existing methods and replicate known loci for both spirograms and PPG, demonstrating the generality of the framework. Furthermore, these embeddings are associated with overall survival. Finally, we construct a set of PRSs that improve predictive performance of asthma, chronic obstructive pulmonary disease, hypertension, and systolic blood pressure in multiple biobanks. Thus, REGLE embeddings can quantify clinically relevant features that are not currently captured in a standardized or automated way.
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    Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction
    (Springer Nature, 2024) Yun, Taedong; Cosentino, Justin; Behsaz, Babak; McCaw, Zachary R.; Hill, Davin; Luben, Robert; Lai, Dongbing; Bates, John; Yang, Howard; Schwantes-An, Tae-Hwi; Zhou, Yuchen; Khawaja, Anthony P.; Carroll, Andrew; Hobbs, Brian D.; Cho, Michael H.; McLean, Cory Y.; Hormozdiari, Farhad; Medical and Molecular Genetics, School of Medicine
    Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale datasets, their use for genetic discovery remains challenging. Here we introduce an unsupervised deep learning model, Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE), for discovering associations between genetic variants and HDCD. REGLE leverages variational autoencoders to compute nonlinear disentangled embeddings of HDCD, which become the inputs to genome-wide association studies (GWAS). REGLE can uncover features not captured by existing expert-defined features and enables the creation of accurate disease-specific polygenic risk scores (PRSs) in datasets with very few labeled data. We apply REGLE to perform GWAS on respiratory and circulatory HDCD-spirograms measuring lung function and photoplethysmograms measuring blood volume changes. REGLE replicates known loci while identifying others not previously detected. REGLE are predictive of overall survival, and PRSs constructed from REGLE loci improve disease prediction across multiple biobanks. Overall, REGLE contain clinically relevant information beyond that captured by existing expert-defined features, leading to improved genetic discovery and disease prediction.
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    Utilizing multimodal AI to improve genetic analyses of cardiovascular traits
    (medRxiv, 2024-03-20) Zhou, Yuchen; Cosentino, Justin; Yun, Taedong; Biradar, Mahantesh I.; Shreibati, Jacqueline; Lai, Dongbing; Schwantes-An, Tae-Hwi; Luben, Robert; McCaw, Zachary; Engmann, Jorgen; Providencia, Rui; Schmidt, Amand Floriaan; Munroe, Patricia; Yang, Howard; Carroll, Andrew; Khawaja, Anthony P.; McLean, Cory Y.; Behsaz, Babak; Hormozdiari, Farhad; Medical and Molecular Genetics, School of Medicine
    Electronic health records, biobanks, and wearable biosensors 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 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.