Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is highly heritable. While COPD is clinically defined by applying thresholds to summary measures of lung function, a quantitative liability score has more power to identify genetic signals. Here we train a deep convolutional neural network on noisy self-reported and International Classification of Diseases labels to predict COPD case–control status from high-dimensional raw spirograms and use the model’s predictions as a liability score. The machine-learning-based (ML-based) liability score accurately discriminates COPD cases and controls, and predicts COPD-related hospitalization without any domain-specific knowledge. Moreover, the ML-based liability score is associated with overall survival and exacerbation events. A genome-wide association study on the ML-based liability score replicates existing COPD and lung function loci and also identifies 67 new loci. Lastly, our method provides a general framework to use ML methods and medical-record-based labels that does not require domain knowledge or expert curation to improve disease prediction and genomic discovery for drug design.

慢性阻塞性肺病 (COPD) 是全球第三大死因，具有高度遗传性。虽然 COPD 在临床上是通过将阈值应用于肺功能的汇总测量来定义的，但定量责任评分更有能力识别遗传信号。在这里，我们在嘈杂的自我报告和国际疾病分类标签上训练深度卷积神经网络，以从高维原始呼吸图预测 COPD 病例控制状态，并将模型的预测用作责任评分。基于机器学习（基于 ML）的责任评分准确区分 COPD 病例和对照，并在没有任何特定领域知识的情况下预测 COPD 相关住院。此外，基于 ML 的责任评分与总体生存和恶化事件相关。基于 ML 的责任评分的全基因组关联研究复制了现有的 COPD 和肺功能位点，并确定了 67 个新位点。最后，我们的方法提供了一个通用框架来使用 ML 方法和基于医疗记录的标签，不需要领域知识或专家管理来改进药物设计的疾病预测和基因组发现。