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Updated: Jul 7, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Selecting predictive markers for pharmacogenetic traits: tagging vs. data-mining approaches.

Audrey Sabbagh¹, Emmanuelle Génin, Pierre Darlu

¹INSERM, UMR-S0535, Villejuif, France. sabbagh@vjf.inserm.fr

|January 29, 2008

Summary

The tagging approach effectively identifies genetic markers for predicting drug response. This method accurately classifies individual drug metabolizer status, proving valuable for pharmacogenomic studies.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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Last Updated: Jul 7, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Pharmacogenomics
Genetic association studies
Complex trait analysis

Background:

Tagging strategies are used to associate genetic variants with complex traits like disease susceptibility and drug response.
The utility of tag markers for predicting variable drug responses in clinical settings remains under investigation.
This study explores the effectiveness of tagging for selecting phenotype-associated markers relevant to drug response.

Purpose of the Study:

To evaluate the usefulness of the tagging approach in selecting phenotype-associated markers for drug response prediction.
To assess the accuracy of tagging markers in predicting individual drug metabolizer status.

Main Methods:

Applied various tagging methods to genotyping data for NAT2 and CYP2D6 drug-metabolizing enzymes.
Empirically evaluated the ability of selected tagging markers to predict individual metabolizer status.
Assessed the influence of linkage disequilibrium (LD) levels, tagging thresholds, and allele frequencies on tagging efficiency.

Main Results:

Selected tagging markers adequately represented functional genetic variation.
Achieved high classification accuracy for individual metabolizer status, approaching the maximum observed with the full polymorphism set.
Demonstrated the predictive power of tagging markers for drug response.

Conclusions:

The tagging approach is a valuable strategy for selecting candidate gene markers in pharmacogenomic research.
Tagging markers can effectively predict drug response, aiding in personalized medicine applications.