Revealing polygenic pleiotropy using genetic risk scores for asthma

The clinical value and most informative threshold of polygenic risk score in the Quebec City Case-Control Asthma Cohort

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.

Genetics of chronic respiratory disease

Chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD), asthma and interstitial lung diseases are frequently occurring disorders with a polygenic basis that account for a large global burden of morbidity and mortality. Recent large-scale genetic epidemiology studies have identified associations between genetic variation and individual respiratory diseases and linked specific genetic variants to quantitative traits related to lung function. These associations have improved our understanding of the genetic basis and mechanisms underlying common lung diseases. Moreover, examining the overlap between genetic associations of different respiratory conditions, along with evidence for gene-environment interactions, has yielded additional biological insights into affected molecular pathways. This genetic information could inform the assessment of respiratory disease risk and contribute to stratified treatment approaches.

EndoPRS: Incorporating Endophenotype Information to Improve Polygenic Risk Scores for Clinical Endpoints

Polygenic risk score (PRS) prediction of complex diseases can be improved by leveraging related phenotypes. This has motivated the development of several multi-trait PRS methods that jointly model information from genetically correlated traits. However, these methods do not account for vertical pleiotropy between traits, in which one trait acts as a mediator for another. Here, we introduce endoPRS, a weighted lasso model that incorporates information from relevant endophenotypes to improve disease risk prediction without making assumptions about the genetic architecture underlying the endophenotype-disease relationship. Through extensive simulation analysis, we demonstrate the robustness of endoPRS in a variety of complex genetic frameworks. We also apply endoPRS to predict the risk of childhood onset asthma in UK Biobank by leveraging a paired GWAS of eosinophil count, a relevant endophenotype. We find that endoPRS significantly improves prediction compared to many existing PRS methods, including multi-trait PRS methods, MTAG and wMT-BLUP, which suggests advantages of endoPRS in real-life clinical settings.