Liability scores for chronic obstructive pulmonary disease obtained from our deep learning model improve genetic association discovery and risk prediction. We trained our model using full spirograms and noisy medical record labels obtained from self-reporting and hospital diagnostic codes, and demonstrated that the machine-learning-based phenotyping approach can be generalized to diseases that lack expert-defined annotations.
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References
Alipanahi, B. et al. Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology. Am. J. Hum. Genet. 108, 1217–1230 (2021). This paper reports that ML-based phenotyping improves the discovery of glaucoma risk loci in a UK Biobank GWAS.
Han, X. et al. Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA. Am. J. Hum. Genet. 108, 1204–1216 (2021). This paper reports ML-based disease phenotyping of two large independent glaucoma datasets.
Silverman, E. K. Genetics of COPD. Annu. Rev. Physiol. 82, 413–431 (2020). This review article presents the genetic components of COPD and resultant challenges.
Hobbs, B. D. et al. Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis. Nat. Genet. 49l, 426–432 (2017). This is the largest meta-analysis and GWAS of spirometry-based COPD that excludes UK Biobank participants.
Sakornsakolpat, P. et al. Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations. Nat. Genet. 51, 494–505 (2019). This is the largest meta-analysis and GWAS of spirometry-based COPD that includes UK Biobank participants.
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This is a summary of: Cosentino, J. et al. Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models. Nat. Genet. https://doi.org/10.1038/s41588-023-01372-4 (2023).
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Deep learning model improves COPD risk prediction and gene discovery. Nat Genet 55, 738–739 (2023). https://doi.org/10.1038/s41588-023-01388-w
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DOI: https://doi.org/10.1038/s41588-023-01388-w
