Genome-wide association studies (GWAS) have identified genetic variants robustly associated with asthma. A potential near-term clinical application is to calculate polygenic risk score (PRS) to improve disease risk prediction. The value of PRS, as part of numerous multi-source variables used to define asthma, remains unclear. This study aims to evaluate PRS and define most informative thresholds in relation to conventional clinical and physiological criteria of asthma using a multivariate statistical method. Clinical and genome-wide genotyping data were obtained from the Quebec City Case-Control Asthma Cohort (QCCCAC), which is an independent cohort from previous GWAS. PRS was derived using LDpred2 and integrated with other asthma phenotypes by means of Principal Component Analysis with Optimal Scaling (PCAOS). PRS was considered using 'ordinal level of scaling' to account for non-linear information. In two dimensional PCAOS space, the first component delineated individuals with and without asthma, whereas the severity of asthma was discerned on the second component. The positioning of high vs. low PRS in this space matched the presence and absence of airway hyperresponsiveness, showing that PRS delineated cases and controls at the same extent as a positive bronchial challenge test. The top 10% and the bottom 5% of the PRS were the most informative thresholds to define individuals at high and low genetic risk of asthma in this cohort. PRS used in a multivariate method offers a decision-making space similar to hyperresponsiveness in this cohort and highlights the most informative and asymmetrical thresholds to define high and low genetic risk of asthma.
Keywords: Asthma; Cohort study; Multivariate method; Optimal scaling; Polygenic risk score; Threshold.
© 2025. The Author(s).