IntroductionClinical guidelines may reduce statistical power in epidemiological studies by discarding informative measures. Epidemiological studies of lung function may discard one-third to one-half of participants due to spirometry measures deemed "low quality" using criteria adapted from clinical practice. ObjectivesTo optimise the signal-to-noise ratio in epidemiological studies of lung function, we aimed to develop a data-driven method to refine spirometry quality control (QC) criteria. MethodsWe proposed a genetic risk score (GRS) informed strategy to categorise spirometer blows by quality criteria. GRS was built using SNPs associated with lung function traits in non-UK Biobank cohorts. In the UK Biobank, we applied a step-wise testing of the GRS association across groups of spirometry blows stratified by acceptability flags to rank the blow quality. We reassessed QC criteria by comparing the genetic associations under different acceptability flags and repeatability thresholds to determine the trade-off between sample size and measurement error. ResultsWe found that including blows previously excluded by strict QC criteria would maximise the statistical power for genome-wide association study and retain acceptable precision in the UK Biobank. This approach allowed the inclusion of 29% more participants compared to the strictest clinical guidelines and demonstrated genetic signals could be identified earlier. ConclusionsOur GRS-based method offers an important framework to challenge prevailing practices that exclude informative measures and limit power in epidemiological studies.
Chen et al. (Mon,) studied this question.