rs10930578 - MAP3K20
Magnitude 2.2 · 1 study on file
Reported associations
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Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models. - Nature genetics (2023) · Cosentino J, Behsaz B, Alipanahi B, McCaw ZR, Hill D, Schwantes-An TH, Lai D, Carroll A, Hobbs BD, Cho MH, McLean CY, Hormozdiari F · PubMed 37069358
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 exac
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Lifestyle context
Concrete actions anchored to the cited research. We do not prescribe, we describe.
Discuss with your doctor
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COPD genetic risk and baseline screening strategy Moderate
rs10930578 G-allele is associated with increased chronic obstructive pulmonary disease liability in large GWAS study
Screening
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respiratory function and pulmonary symptoms Moderate
Genetic predisposition to COPD warrants baseline assessment and periodic monitoring for early disease detection
baseline spirometry if not recently performed; annual assessment per physician recommendation