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

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.

Automated summary

By utilizing genome-wide association study and family history data, prediction models for asthma, atopic dermatitis (AD), and asthma-AD comorbidity were constructed. The addition of family history risk scores significantly improved predictability of asthma and asthma-AD comorbidity, while prediction of AD was mainly attributed to GWAS SNPs.