Extended Data Fig. 3: GWS loci replication pipeline. | Nature Genetics

Extended Data Fig. 3: GWS loci replication pipeline.

From: Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models

Extended Data Fig. 3: GWS loci replication pipeline.

A GWAS on the ML-based liability score identifies 265 novel COPD risk loci in addition to 91 previously known COPD loci with respect to8 and GWAS catalog entries (as of 2022-07-09) for COPD, emphysema, chronic bronchitis. Out of 265 additional COPD loci, 221 of which independently replicate as associated with COPD or COPD-related lung function as follows. We observed that 101 out of 265 was detected in a previous COPD GWAS8 after Bonferroni correction. Also, 198 out of 265 are previously known FEV1 or FEV1/FVC loci with respect to10 and GWAS catalog entries. The three datasets are GBMI (Global Biobank Meta-analysis Initiative)28, SpiroMeta27, and ICGC (International COPD Genetics Consortium)7 which all three exclude samples from UK Biobank. We defined two replication strategies: First, we defined supportive replication as consistent effect size direction across all studies with our ML-based COPD. The ICGC and GBMI GWAS are based on a COPD phenotype; thus, we expect their effect size signs to match our ML-based COPD. SpiroMeta phenotypes, on the other hand, capture lung function, so we expect their effect size signs to be the opposite of our ML-based COPD signs. Second, we defined strict replication as consistent effect size direction in any study with Bonferroni correction of P < 0.1 (one-sided) for that study.

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