rs11938093 - BTC

Magnitude 2.2 · 2 studies on file

Reported associations

• 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

• A systematic analysis of protein-altering exonic variants in chronic obstructive pulmonary disease. - American journal of physiology. Lung cellular and molecular physiology (2021) · Moll M, Jackson VE, Yu B, Grove ML, London SJ, Gharib SA, Bartz TM, Sitlani CM, Dupuis J, O'Connor GT, Xu H, Cassano PA, Patchen BK, Kim WJ, Park J, Kim KH, Han B, Barr RG, Manichaikul A, Nguyen JN, Rich SS, Lahousse L, Terzikhan N, Brusselle G, Sakornsakolpat P, Liu J, Benway CJ, Hall IP, Tobin MD, Wain LV, Silverman EK, Cho MH, Hobbs BD · PubMed 33909500

  Genome-wide association studies (GWASs) have identified regions associated with chronic obstructive pulmonary disease (COPD). GWASs of other diseases have shown an approximately 10-fold overrepresentation of nonsynonymous variants, despite limited exonic coverage on genotyping arrays. We hypothesized that a large-scale analysis of coding variants could discover novel genetic associations with COPD, including rare variants with large effect sizes. We performed a meta-analysis of exome arrays from 218,399 controls and 33,851 moderate-to-severe COPD cases. All exome-wide significant associations were present in regions previously identified by GWAS. We did not identify any novel rare coding variants with large effect sizes. Within GWAS regions on chromosomes 5q, 6p, and 15q, four coding varia

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