Genetic and Clinical Predictors for Asthma Risk Assessment among Children in Taiwan
Date Issued
2015
Date
2015
Author(s)
Tseng, Jui-Ju
Abstract
Background Asthma is the single most common chronic childhood disease affected by multiple genetic and environmental factors. Although many genetic variants have been noted to be associated with childhood asthma, up to the present, no risk assessment models that incorporate genetic and clinical predictors for asthma occurrence are currently available. Methods We analyzed 10 single nucleotide polymorphisms (SNPs) identified from 8 asthma-associated genes among subjects who participated in three children’s cohort studies in Taiwan (TCHS, GBCA and TCCAS). The parents of all participants were interviewed regarding the information of asthma risk factors. Modified Asthma Predictive Index (mAPI) and genetic risk scores (GRSs) of 10 SNPs were used in prediction model assessment. The performance of prediction ability was assessed by discrimination and reclassification statistics, and calibration. Cross validation with leave-one-out cross validation method was also conducted. Results In total, 640 asthma cases and 1921 control subjects were included in current study. After controlling for age, sex and BMI, two environmental factors, moist and water damage of wall, were significantly associated with childhood asthma and all these covariates were incorporated into baseline model. The prediction ability of childhood asthma improved greatly in combined model composed of GRSs and mAPI compared with clinical model, demonstrating an increased AUC from 0.748 to 0.780 (p<0.0001). Besides, the improvement in discrimination (IDI: 0.022, p<0.0001) and reclassification (continuous NRI: 0.344, p<0.0001) also showed while comparing clinical model and combined model. Considering the 18% general risk of asthma in children, the combined model concluded sensitivity, specificity, positive predictive value, and negative predictive value as 78.8%, 59.4%, 39.5%, and 89.3%, respectively. Conclusion We have successfully constructed a prediction model composed of genetic and clinical factors in asthma risk assessment. Adding GRS into clinical factor-based prediction model would increase the prediction ability substantially in predicting the occurrence of childhood asthma.
Subjects
childhood asthma
prediction model
genetic risk score
Type
thesis
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