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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
Published on: November 3, 2010
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Evidence for extensive pleiotropy among pharmacogenes
Matthew T Oetjens1, William S Bush2, Joshua C Denny3
1Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA.
Pharmacogenomics
|June 2, 2016
Summary
This study identified novel genotype-phenotype links by examining pharmacogene variants in electronic health records. Findings reveal potential pleiotropy, impacting drug response and disease risk.
Area of Science:
- Genetics
- Pharmacogenomics
- Clinical Informatics
Background:
- Pharmacogenes play a crucial role in drug metabolism and response.
- Understanding pleiotropy, where one gene influences multiple traits, is essential for personalized medicine.
- Electronic health records (EHRs) offer a rich source for studying genotype-phenotype associations.
Purpose of the Study:
- To systematically screen for potential pleiotropy involving pharmacogenes.
- To identify novel genotype-phenotype associations using EHR data.
- To investigate the link between functional pharmacogene variants and various clinical phenotypes.
Main Methods:
- Utilized a custom grouping of International Classification of Diseases, Ninth Revision (ICD-9) billing codes from deidentified EHRs.
- Tested 184 functional variants across 34 pharmacogenes for associations in 6892 patients.
- Employed statistical analyses to determine phenome-wide significance of identified genotype-phenotype combinations.
Main Results:
- Replicated known associations, including ABCG2 (rs2231142) with gout and SLCO1B1 (rs4149056) with jaundice.
- Identified novel phenome-wide significant associations, such as SLC15A2 rs1143672 with renal osteodystrophy.
- Demonstrated the utility of EHR data in uncovering pharmacogene-phenotype relationships.
Conclusions:
- This systematic screen successfully identified novel genotype-phenotype combinations with clinical relevance.
- The findings highlight the potential for pleiotropy in pharmacogenes, influencing diverse disease outcomes.
- Leveraging EHR data provides a powerful approach for pharmacogenomic discovery and clinical application.
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