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Updated: Jan 3, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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A comparison of genetic sampling methodologies for candidate-gene analyses.

Jessie Sun1, Steven R Tung2, Danxin Wang3

  • 1Department of Biological Chemistry, The Ohio State University, Columbus, Ohio, USA.

Journal of Translational Science
|November 22, 2019
PubMed
Summary

Candidate-gene analysis offers a reliable method for understanding how patient genetics impact drug response. This approach is valuable for advancing precision medicine, even with small patient cohorts.

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Area of Science:

  • Pharmacogenomics and Precision Medicine
  • Biomedical Informatics
  • Genetics

Background:

  • Advances in genetic testing and large datasets have driven progress in pharmacogenomics.
  • Genome Wide Association Studies (GWAS) and Phenome Wide Association Studies (PWAS) have significantly contributed to precision medicine.

Purpose of the Study:

  • To highlight the importance of candidate-gene analysis in pharmacogenomic research.
  • To demonstrate the utility of candidate-gene analysis for advancing precision medicine.
  • To compare methodologies for efficient candidate-gene study implementation.

Main Methods:

  • Focus on candidate-gene analysis, leveraging biological plausibility.
  • Utilizes smaller patient cohorts, not requiring large datasets.
  • Compares contemporary methodologies for sample collection, processing, and genotype testing.

Main Results:

  • Candidate-gene analysis is a reliable, albeit not cutting-edge, method.
  • This technique is crucial for translating pharmacogenomic research.
  • Small cohort studies (under 100 patients) can yield valuable insights.

Conclusions:

  • Candidate-gene analysis is an ideal starting point for clinicians to explore genetic influences on drug treatment.
  • Physician scientists can effectively implement candidate-gene studies in specialized patient populations.
  • Methodological comparisons aid in the efficient application of candidate-gene studies for personalized medicine.