Year of Award
Master of Science (MS)
Department or School/College
Biomedical and Pharmaceutical Sciences
Erica L. Woodahl
Richard Bridges, Keith Parker, Nigel Priestley
Pharmacogenetics, pharmacogenomics, CYP2D6
University of Montana
Other Pharmacy and Pharmaceutical Sciences
BACKGROUND: CYP2D6 is difficult to accurately genotype due to a large number of single nucleotide variants (SNVs), indels, and structural variation such as deletions, duplications, and CYP2D6/CYP2D7 hybrid genes. CYP2D6 targeted genotyping panels are of limited utility; clinically relevant variants that are not genotyped will be missed. Sequencing solves this problem but requires additional tools to address structural variation. The goal of our study was to determine the predictive power of Stargazer, a novel allele-calling program, which combines SNV/indel calls with structural variation identification.
METHODS: In a panel of 309 human livers, CYP2D6 diplotypes and activity scores were initially assigned manually using PGRNSeq SNV/indel data and then reassigned after inclusion of Stargazer-derived structural variation data. We determined CYP2D6 activity in human liver microsomes with metoprolol and dextromethorphan as probe substrates. Then, we used linear regression to assess the relationship between activity and activity scores assigned using SNV/indel data alone versus SNV/indel + structural variation data.
RESULTS: Without incorporating structural variation data, diplotypes were incorrectly assigned for 67 samples (22%); activity scores were incorrect for 26 samples (8.4%). Structural variants included 23 deletions, 47 duplications, and 39 hybrids. When diplotypes were assigned based on SNV/indel data alone, activity score explained 31% of the variation in CYP2D6 activity with metoprolol (R2 = 0.31, p < 0.001) and 36% with dextromethorphan (R2 = 0.36, p < 0.001). When reassigned with SNV/indel plus structural variation data, this increased to 36% for metoprolol (R2 = 0.36, p < 0.001) and 41% for dextromethorphan (R2 = 0.41, p < 0.001).
CONCLUSION: The accuracy of CYP2D6 phenotype prediction can be improved by using a next-generation sequencing approach coupled with a tool such as Stargazer to detect common and rare SNVs and indels as well as structural variation in CYP2D6.
Dalton, Rachel, "A Multifactorial Cytochrome P450 2D6 Genotype-Phenotype Prediction Model to Improve Precision of Clinical Pharmacogenomic Tests" (2018). Graduate Student Theses, Dissertations, & Professional Papers. 11111.
© Copyright 2018 Rachel Dalton