Integration of Genomic Risk Scores to Improve the Prediction of Childhood Asthma Diagnosis

Abstract

Genome-wide and epigenome-wide association studies have identified genetic variants and differentially methylated nucleotides associated with childhood asthma. Incorporation of such genomic data may improve performance of childhood asthma prediction models which use phenotypic and environmental data. Using genome-wide genotype and methylation data at birth from the Isle of Wight Birth Cohort (n = 1456), a polygenic risk score (PRS), and newborn (nMRS) and childhood (cMRS) methylation risk scores, were developed to predict childhood asthma diagnosis. Each risk score was integrated with two previously published childhood asthma prediction models (CAPE and CAPP) and were validated in the Manchester Asthma and Allergy Study. Individually, the genomic risk scores demonstrated modest-to-moderate discriminative performance (area under the receiver operating characteristic curve, AUC: PRS = 0.64, nMRS = 0.55, cMRS = 0.54), and their integration only marginally improved the performance of the CAPE (AUC: 0.75 vs. 0.71) and CAPP models (AUC: 0.84 vs. 0.82). The limited predictive performance of each genomic risk score individually and their inability to substantially improve upon the performance of the CAPE and CAPP models suggests that genetic and epigenetic predictors of the broad phenotype of asthma are unlikely to have clinical utility. Hence, further studies predicting specific asthma endotypes are warranted.

Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity

“…We also demonstrated that the greater diversity of GBMI improved polygenic prediction in asthma, particularly for populations of non-European ancestry. Previous studies on asthma PRS in the literature have primarily focused on using PRS to predict asthma in pediatric cohorts, and overall found limited performance of PRS [28][29][30]85 . Most of these studies used the P+T approach, while a recently published paper, Namjou et al (2022) 32 , applied PRS-CS to the TAGC multi-ancestry GWAS and found improved discriminatory power of their PRS (receiver-operating characteristic area under the curve, or AUC, of 0.66-0.70 across two pediatric cohorts) compared to the prior studies that used P+T.…”

“…For asthma, PRS could ultimately play a role in predicting disease severity and development in the clinical setting and serve as a tool for investigating gene-environment interactions in the research setting. So far, some GWAS have been applied to developing PRS for asthma [28][29][30][31][32] , but these models have had limited predictive ability, likely due to the insufficient sample sizes and diversity of existing datasets of asthma. This underscores the genetic complexity of asthma and highlights the need for more large-scale, genomic studies of asthma.…”

External Validation of the Predicting Asthma Risk in Children (PARC) tool in a clinical cohort

“…These prediction tools vary in their target population, geography, predictors used and applicable settings. Most use validated questions on respiratory symptoms and some include invasively measured traits such as atopy, fractional exhaled nitric oxide (FeNO), lung function and genetic markers [7][8][9][10][11][12][13][14][15] .…”

External validation of the Predicting Asthma Risk in Children tool in a clinical cohort

“…These prediction tools vary in their target population, geography, predictors used, and applicable settings. Most use validated questions on respiratory symptoms and some include invasively measured traits such as atopy, fractional exhaled nitric oxide (FeNO), lung function, and genetic markers 7–15 …”

“…Most use validated questions on respiratory symptoms and some include invasively measured traits such as atopy, fractional exhaled nitric oxide (FeNO), lung function, and genetic markers. [7][8][9][10][11][12][13][14][15] The Predicting Asthma Risk in Children (PARC) tool has the advantage that it predicts asthma risk in preschool children by using questions asked in routine clinical care and avoids the use of invasive measures that may not be available in primary care or resource-poor settings. The tool was developed by Pescatore et al 16 using data of preschool children from the population-based Leicester Respiratory Cohort (LRC) who had seen their doctor at age 1-3 years with symptoms of cough or wheeze, and aimed to predict whether they would have asthma 5 years later (Supporting Information: Table S1).…”