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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
An integrative method for scoring candidate genes from association studies: application to warfarin dosing
Nicholas P Tatonetti1, Joel T Dudley, Hersh Sagreiya
1Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA, USA. nick.tatonetti@stanford.edu
BMC Bioinformatics
|November 4, 2010
Summary
This study introduces a novel method combining knowledge integration and SNP aggregation to enhance pharmacogenomics Genome-Wide Association Studies (GWAS). The approach improves the identification of drug-response genes and predicts patient responses to medications like warfarin.
Area of Science:
- Pharmacogenomics
- Genetics
- Computational Biology
Background:
- Identifying genes influencing drug response is crucial for personalized medicine.
- Pharmacogenomic Genome-Wide Association Studies (GWAS) are powerful but limited by study size.
- Severe adverse drug effects necessitate better methods for identifying predictive genetic markers.
Purpose of the Study:
- To develop a novel knowledge integration and SNP aggregation approach for identifying pharmacogenes.
- To enhance the power of GWAS in discovering genes associated with drug response phenotypes.
- To create a method for predicting drug response based on genetic variations.
Main Methods:
- Integrated pre-existing knowledge sources to prioritize candidate pharmacogenes.
- Developed a SNP aggregation method to assess the association of uncommon alleles with drug response.
- Defined a summary score per gene using allele frequencies.
- Trained linear and logistic regression models to predict drug response phenotypes.
Main Results:
- Applied the method to a warfarin GWAS dataset (181 individuals).
- Successfully identified both VKORC1 and CYP2C9 as warfarin pharmacogenes, improving upon original GWAS findings.
- Demonstrated the ability to discriminate between low-dose (AUROC=0.886) and high-dose (AUROC=0.764) warfarin responders.
Conclusions:
- The proposed method offers a new strategy for discovering candidate pharmacogenes from GWAS data.
- This approach enhances the power and utility of pharmacogenomic studies.
- It provides a foundation for developing predictive models in pharmacogenomics.
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