Predicting response to short-acting bronchodilator medication using Bayesian networks.
OBJECTIVE
Bronchodilator response tests measure the effect of beta(2)-agonists, the most commonly used short-acting reliever drugs for asthma. We sought to relate candidate gene SNP data with bronchodilator response and measure the predictive accuracy of a model constructed with genetic variants.


METHODS
Bayesian networks, multivariate models that are able to account for simultaneous associations and interactions among variables, were used to create a predictive model of bronchodilator response using candidate gene SNP data from 308 Childhood Asthma Management Program Caucasian subjects.


RESULTS
The model found that 15 SNPs in 15 genes predict bronchodilator response with fair accuracy, as established by a fivefold cross-validation area under the receiver-operating characteristic curve of 0.75 (standard error: 0.03).


CONCLUSIONS
Bayesian networks are an attractive approach to analyze large-scale pharmacogenetic SNP data because of their ability to automatically learn complex models that can be used for the prediction and discovery of novel biological hypotheses.
Himes
Blanca E
BE
Harvard-MIT Division of Health Sciences and Technology, MA, USA. blanca_himes@hms.harvard.edu
Wu
Ann Chen
AC
Duan
Qing Ling
QL
Klanderman
Barbara
B
Litonjua
Augusto A
AA
Tantisira
Kelan
K
Ramoni
Marco F
MF
Weiss
Scott T
ST
D001249:Asthma / Q000188:drug therapy* / Q000235:genetics* / Q000503:physiopathology; D001499:Bayes Theorem; D001993:Bronchodilator Agents / Q000627:therapeutic use*; D002648:Child; D003627:Data Interpretation, Statistical; D005260:Female; D014644:Genetic Variation; D005838:Genotype; D006801:Humans; D016015:Logistic Models; D008297:Male; D016571:Neural Networks, Computer; D010597:Pharmacogenetics; D020641:Polymorphism, Single Nucleotide; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012129:Respiratory Function Tests
D001993:Bronchodilator Agents