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PHM 838 - Pharmacogenomics*



Pharmacogenomics is one if the most rapidly
growing areas of pharmacology. The promise of
individualized (personalized) health care and
treatment strategies warrants understanding of
common and rare genetic mutations that impact disease risk and stratify treatment options.

In this course we will dissect the basics of
genomics and its interplay with traits, efficacy,
toxicity, kinetics and dosage involving drugs and drug pathways. The course will cover the
background of pharmacogenomics through
lectures and literature discussions, with hands-on learning through pharmacogenomic database searches and a related student project.


Concepts in Pharmacogenomics
2nd edition
Martin M Zdanowicz
American Society of Health-System
Pharmacists, 2017

Course Topics

  • Genomics basics: inheritance, linkage
    disequilibrium, genome browser extraction, gene annotation, gene regulation
  • expression Quantitative Trait Loci (eQTLs)
  • Genome-Wide Associate Studies (GWAS)
  • Population genomics
  • Pharmacogenomic variants in public
    databases (pharmGKB)
  • Pharmacology systems mapping and
    extraction of human variants

Course Information


  • Knowledge of general principles of pharmacology, physiology,
    and genetics

  • Course Number: PHM 980
  • Sections Available: 737 (online)
  • Semesters: Fall 2019
  • Credits: 2 credits

Successful completion of this course will count as 2 credits of science elective toward the MS degree. 

It may not substitute for either of the required 2 credits of PHM 980 section 799, Special Problems in Pharmacology and Toxicology (writing your final/capstone review paper).


Online Fall (offered on a limited basis)




  • Online Graduate / MS


Online Graduate / MS

PHM 838
(formerly 980/007)
Course Flyer (.pdf)

Jeremy Prokop, PhD
Received his PhD in Integrated Biosciences from
the U of Akron and was a Postdoctoral Fellow in
Molecular Genetics at the Medical College of
Wisconsin. His research focuses on the genetic
mechanisms of disease. The lab utilizes computational tools for examining the roles of genomic variants on protein function and
extends this research into in vitro models to gain insight to predict disease causation and treatment.