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Pharmacogenet Genomics. 2018 Nov;28(11):256-259. doi: 10.1097/FPC.0000000000000355.

Leveraging electronic health records to assess the role of ADRB2 single nucleotide polymorphisms in predicting exacerbation frequency in asthma patients.

Author information

1
Center for Applied Genomics, Children's Hospital of Philadelphia.
2
The University of Pennsylvania.
3
The Perelman School of Medicine, University of Pennsylvania.
4
Divisions of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.

Abstract

Asthma is the leading chronic disease in children. Several studies have identified genetic biomarkers associated with susceptibility and severity in both adult and pediatric cases. In this study, we evaluated outcomes in 400 African American and European American pediatric cases all of whom were regular users of inhaled corticosteroids. Patients were stratified by genotype using two single nucleotide polymorphisms in the β-2-adrenergic receptor (ADRB2) gene - rs1042713 and rs1042714, previously associated with asthma outcome. These correspond to nonsynonymous single nucleotide polymorphisms at positions 16 [arginine to glycine (Arg16Gly); rs1042713] and 27 [glutamic acid to glutamine (Glu27Gln); rs1042714], which are relatively common (minor allele frequencies ∼40-50%), and have been well characterized in asthma pharmacogenetics. We controlled for adherence to the National Heart, Lung and Blood Institute guidelines using deep mining of electronic health record data to determine treatment course. We found no significant effect for rs1042713 (Arg16Gly) but did identify an effect for rs1042714, where participants homozygous for Gln27 had increased exacerbations while taking inhaled corticosteroids in comparison with those who were either heterozygous or homozygous for Glu27. This is consistent with previous studies and demonstrates for the first time that the Glu27 variant in the ADRB2 gene is associated with increased frequencies of asthma exacerbations. Moreover, this study also lends an important proof-of-principle on how electronic health records linked to genotype can be efficiently and systematically mined to delineate health outcomes.

PMID:
30334910
PMCID:
PMC6417505
[Available on 2019-11-01]
DOI:
10.1097/FPC.0000000000000355
[Indexed for MEDLINE]

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