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

Area of Science:

• Genetics
• Pediatrics
• Immunology

Background:

• Rising prevalence of childhood allergic diseases increases health burden.
• Early prediction of allergic diseases aids in prevention for high-risk groups.
• Comorbid allergic diseases, like the atopic march, significantly reduce quality of life.

Purpose of the Study:

• To develop predictive models for asthma, atopic dermatitis (AD), and their comorbidity (atopic march).
• To utilize genome-wide association study (GWAS) data and family history in Korean populations.
• To compare the efficacy of LASSO and penalized ridge regression for prediction.

Main Methods:

• Genome-wide association study (GWAS) on 973 patients and 481 controls of Korean heritage.
• Evaluation of single nucleotide polymorphism (SNP) heritability using GREML analysis.
• Construction and comparison of prediction models using LASSO and penalized ridge regression.

Main Results:

• Family history risk scores substantially enhanced prediction accuracy for asthma and asthma-AD comorbidity.
• Genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) were the primary predictors for atopic dermatitis (AD).
• Prediction models incorporating family history showed improved performance for asthma and the atopic march.

Conclusions:

• Family history is a critical factor in predicting childhood asthma and the atopic march.
• Genome-wide association study (GWAS) data is essential for predicting atopic dermatitis (AD) independently.
• Integrated genetic and familial data can improve personalized risk assessment for allergic diseases.