Predicting allergic diseases in children using genome-wide association study (GWAS) data and family history

Abstract

The recent rise in the prevalence of chronic allergic diseases among children has increased disease burden and reduced quality of life, especially for children with comorbid allergic diseases. Predicting the occurrence of allergic diseases can help prevent its onset for those in high risk groups. Herein, we aimed to construct prediction models for asthma, atopic dermatitis (AD), and asthma-AD comorbidity (also known as atopic march) using a genome-wide association study (GWAS) and family history data from patients of Korean heritage. Among 973 patients and 481 healthy controls, we evaluated single nucleotide polymorphism (SNP) heritability for each disease using genome-based restricted maximum likelihood (GREML) analysis. We then compared the performance of prediction models constructed using Least Absolute Shrinkage and Selection Operator (LASSO) and penalized ridge regression methods. Our results indicate that the addition of family history risk scores to the prediction model greatly increase the predictability of asthma and asthma-AD comorbidity. However, prediction of AD was mostly attributable to GWAS SNPs.

Keywords: Asthma; Atopic dermatitis; Family history; Genome-wide association study; Prediction model.

Fig. 1

Vertical bar plots of AUCs of the (a) LASSO and (b) ridge regression prediction models with or without the addition of family history risk scores. The top 100, 500, 1,000, 5,000, and 10,000 SNPs selected with best linear unbiased prediction values (BLUPs) were separately used for prediction model building, and the best AUCs are shown. ∗ paired DeLong test p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. AD, Atopic Dermatits