ArticleThe World Allergy Organization journal2021
Predicting allergic diseases in children using genome-wide association study (GWAS) data and family history.
Article in The World Allergy Organization journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 12 citations in OpenAlex.
- Genome-Wide Risk Assessment and Clinical Prediction of Iodinated Contrast Media-Induced Anaphylaxis.Allergy, asthma & immunology research · 2026Article
- Article
- Asthma-Genomic Advances Toward Risk Prediction.Clinics in chest medicine · 2024Review
- Prenatal Factors in the Development of Allergic Diseases.International journal of molecular sciences · 2024Review
- Genetic and Immunological Pathogenesis of Atopic Dermatitis.The Journal of investigative dermatology · 2024Review
- Intrinsic Effects of Exposome in Atopic Dermatitis: Genomics, Epigenomics and Regulatory Layers.Journal of clinical medicine · 2023Review
- Recent progress in the genetic and epigenetic underpinnings of atopy.The Journal of allergy and clinical immunology · 2023Review
- Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity.Cell genomics · 2022Article
- An update on the prevalence, chronicity, and severity of atopic dermatitis and the associated epidemiological risk factors in the Singapore/Malaysia Chinese young adult population: A detailed description of the Singapore/Malaysia Cross-Sectional Genetics Epidemiology Study (SMCGES) cohort.The World Allergy Organization journal · 2022Article
- Cows' Milk Allergy-Associated Constipation: When to Look for It? A Narrative Review.Nutrients · 2022Review
- Integration of Genomic Risk Scores to Improve the Prediction of Childhood Asthma Diagnosis.Journal of personalized medicine · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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