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.

中文翻译：

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利用多模式人工智能改进心血管特征的遗传分析

电子健康记录 (EHR) 和生物样本库数据集包含每个人的多种高维临床数据 (HDCD) 模式（例如心电图、光电体积描记法 (PPG) 和 MRI）。获得多模式 HDCD 为复杂性状的遗传研究提供了独特的机会，因为与单个生理系统（例如循环系统）相关的不同模式编码互补和重叠的信息。我们提出了一种新颖的多模态深度学习方法 M-REGLE，用于从多种互补 HDCD 模态的联合表示中发现遗传关联。我们通过将其应用于多种心血管模式来展示该模型的有效性。 M-REGLE 使用卷积变分自动编码器联合学习多模态 HDCD 的低维表示（即潜在因子），对每个潜在因子进行全基因组关联研究 (GWAS)，然后结合结果来研究潜在因子的遗传学系统。为了验证 M-REGLE 和多模态学习的优势，我们将其应用于常见的心血管模态（PPG 和 ECG），并将其结果与单模态学习方法进行比较，在单模态学习方法中分别从每种数据模态中学习表示，但下游遗传分析是对组合的单峰表示进行。 M-REGLE 在 12 导联 ECG 数据集上识别出的位点增加了 19.3%，在 ECG 导联 I + PPG 数据集上识别出的位点增加了 13.0%，并且其遗传风险评分在预测心房颤动等心脏表型方面显着优于单峰风险评分（ Afib），在多个生物库中。