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

Jaehyun Park; Haerin Jang; Mina Kim; Jung Yeon Hong; Yoon Hee Kim; Myung Hyun Sohn; Sang-Cheol Park; Sungho Won; Kyung Won Kim

doi:10.1016/j.waojou.2021.100539

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

World Allergy Organ J. 2021 May 8;14(5):100539. doi: 10.1016/j.waojou.2021.100539. eCollection 2021 May.

Authors

Jaehyun Park¹, Haerin Jang^{2

3}, Mina Kim^{2

3}, Jung Yeon Hong^{2

3}, Yoon Hee Kim^{3

4}, Myung Hyun Sohn^{2

3}, Sang-Cheol Park⁵, Sungho Won^{1

5

6}, Kyung Won Kim^{2

3}

Affiliations

¹ Interdisciplinary Program of Bioinformatics, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea.
² Department of Pediatrics, Severance Hospital, Seoul, Republic of Korea.
³ Institute of Allergy, Institute for Immunology and Immunological Diseases, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
⁴ Department of Pediatrics, Gangnam Severance Hospital, Seoul, Republic of Korea.
⁵ Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea.
⁶ Department of Public Health Sciences, Seoul National University, Seoul, Republic of Korea.

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.