Unsupervised representation learning improves genomic discovery for lung function and respiratory disease prediction

BackgroundHigh-dimensional clinical data are becoming more accessible in biobank-scale datasets. However, accurately phenotyping high-dimensional clinical data remains a major impediment to genetic discovery.MethodsWe introduce a general deep learning framework, RE presentation 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 anon-linear, low-dimensional, disentangled embeddingof the data and can also incorporate expert clinical metrics. We demonstrate the utility of REGLE by application to spirograms, which measure lung function. We generate two types of synthetic representations of pulmonary functions we call spirogram encodings (SPINCs) and residual spirogram encodings (RSPINCs).FindingsGenome-wide association studies on (R)SPINCs identify more genome-wide significant loci than existing methods while replicating most known lung function loci. Furthermore, (R)SPINCs are associated with overall survival and, under the latent causal variable model, they exhibit significantly high genetic causality proportion with asthma, chronic obstructive pulmonary disease (COPD), and inflammatory diseases. Finally, we construct a set of polygenic risk scores (PRS) that are generally predictive of pulmonary traits and diseases. We demonstrate superior performance predicting asthma and COPD, in multiple ancestries and across four biobanks, compared to PRSs constructed using expert-defined pulmonary function measurements.InterpretationREGLE is a method for generating low-dimensional, disentangled representations of high-dimensional clinical data that does not require labels, and improves upon expert-defined phenotypes for genetic discovery and disease prediction. It can flexibly incorporate expert-defined or clinical features and provides a framework to create accurate disease-specific PRS in datasets which have minimal expert phenotyping. (R)SPINCs are quantifying clinically relevant features that are not currently captured in a standardized or automated way.FundingGoogle LLC.