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Pharmacogenomics. 2009 Sep;10(9):1393-412. doi: 10.2217/pgs.09.93.

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

Author information

1
Harvard-MIT Division of Health Sciences and Technology, MA, USA. blanca_himes@hms.harvard.edu

Abstract

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.

PMID:
19761364
PMCID:
PMC2804237
DOI:
10.2217/pgs.09.93
[Indexed for MEDLINE]
Free PMC Article
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