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- PMC3434304
Pharmacogenomics J. Author manuscript; available in PMC 2014 February 1.
Published in final edited form as:
Published online 2012 May 29. doi: 10.1038/tpj.2012.15
PMCID: PMC3434304
NIHMSID: NIHMS370595
Predicting Inhaled Corticosteroid Response in Asthma with Two Associated SNPs
Michael J. McGeachie, PhD,1,2,3 Ann C. Wu, MD,1,2,3 Hsun-Hsien Chang, PhD,1,3,4 John J. Lima, PharmD,5 Stephen P. Peters, MD, PhD,6 and Kelan G. Tantisira, MD1,2,3
The publisher's final edited version of this article is available at Pharmacogenomics J
Abstract
Inhaled corticosteroids are the most commonly used controller medications prescribed for asthma. Two single-nucleotide polymorphisms (SNPs), rs1876828 in CRHR1 and rs37973 in GLCCI1, have previously been associated with corticosteroid efficacy. We studied data from four existing clinical trials of asthmatics who received inhaled corticosteroids and had lung function measured by forced expiratory volume in one second (FEV1) before and after the period of such treatment. We combined the two SNPs rs37973 and rs1876828 into a predictive test of FEV1 change using a Bayesian model, which identified patients with good or poor steroid response (highest or lowest quartile, respectively) with predictive performance of 65.7% (p = 0.039 vs. random) area under the receiver-operator characteristic curve in the training population and 65.9% (p = 0.025 vs. random) in the test population. These findings show that two genetic variants can be combined into a predictive test that achieves similar accuracy and superior replicability compared with single SNP predictors.
Keywords: Pharmacogenetics, Asthma, Glucocorticoids, Predictive Modeling
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Abstract
Inhaled corticosteroids are the most commonly used controller medications prescribed for asthma. Two single-nucleotide polymorphisms (SNPs), rs1876828 in CRHR1 and rs37973 in GLCCI1, have previously been associated with corticosteroid efficacy. We studied data from four existing clinical trials of asthmatics who received inhaled corticosteroids and had lung function measured by forced expiratory volume in one second (FEV1) before and after the period of such treatment. We combined the two SNPs rs37973 and rs1876828 into a predictive test of FEV1 change using a Bayesian model, which identified patients with good or poor steroid response (highest or lowest quartile, respectively) with predictive performance of 65.7% (p = 0.039 vs. random) area under the receiver-operator characteristic curve in the training population and 65.9% (p = 0.025 vs. random) in the test population. These findings show that two genetic variants can be combined into a predictive test that achieves similar accuracy and superior replicability compared with single SNP predictors.
Keywords:
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