Predicting response to short-acting bronchodilator medication using Bayesian networks.

AIMS

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.

MATERIALS & 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).

CONCLUSION

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.

Investigators
Abbreviation
Pharmacogenomics
Publication Date
2009 - 01 - 01
Volume
10
Issue
9
Page Numbers
1393-412
Pubmed ID
19761364
Medium
Print
Full Title
Predicting response to short-acting bronchodilator medication using Bayesian networks.
Authors
Himes BE, Wu AC, Duan QL, Klanderman B, Litonjua AA, Tantisira K, Ramoni MF, Weiss ST