Computational pharmacogenotype extraction from clinical next-generation sequencing

dc.contributor.authorShugg, Tyler
dc.contributor.authorLy, Reynold C.
dc.contributor.authorOsei, Wilberforce
dc.contributor.authorRowe, Elizabeth J.
dc.contributor.authorGranfield, Caitlin A.
dc.contributor.authorLynnes, Ty C.
dc.contributor.authorMedeiros, Elizabeth B.
dc.contributor.authorHodge, Jennelle C.
dc.contributor.authorBreman, Amy M.
dc.contributor.authorSchneider, Bryan P.
dc.contributor.authorSahinalp, S. Cenk
dc.contributor.authorNumanagić, Ibrahim
dc.contributor.authorSalisbury, Benjamin A.
dc.contributor.authorBray, Steven M.
dc.contributor.authorRatcliff, Ryan
dc.contributor.authorSkaar, Todd C.
dc.contributor.departmentMedicine, School of Medicine
dc.description.abstractBackground: Next-generation sequencing (NGS), including whole genome sequencing (WGS) and whole exome sequencing (WES), is increasingly being used for clinic care. While NGS data have the potential to be repurposed to support clinical pharmacogenomics (PGx), current computational approaches have not been widely validated using clinical data. In this study, we assessed the accuracy of the Aldy computational method to extract PGx genotypes from WGS and WES data for 14 and 13 major pharmacogenes, respectively. Methods: Germline DNA was isolated from whole blood samples collected for 264 patients seen at our institutional molecular solid tumor board. DNA was used for panel-based genotyping within our institutional Clinical Laboratory Improvement Amendments- (CLIA-) certified PGx laboratory. DNA was also sent to other CLIA-certified commercial laboratories for clinical WGS or WES. Aldy v3.3 and v4.4 were used to extract PGx genotypes from these NGS data, and results were compared to the panel-based genotyping reference standard that contained 45 star allele-defining variants within CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DPYD, G6PD, NUDT15, SLCO1B1, TPMT, and VKORC1. Results: Mean WGS read depth was >30x for all variant regions except for G6PD (average read depth was 29 reads), and mean WES read depth was >30x for all variant regions. For 94 patients with WGS, Aldy v3.3 diplotype calls were concordant with those from the genotyping reference standard in 99.5% of cases when excluding diplotypes with additional major star alleles not tested by targeted genotyping, ambiguous phasing, and CYP2D6 hybrid alleles. Aldy v3.3 identified 15 additional clinically actionable star alleles not covered by genotyping within CYP2B6, CYP2C19, DPYD, SLCO1B1, and NUDT15. Within the WGS cohort, Aldy v4.4 diplotype calls were concordant with those from genotyping in 99.7% of cases. When excluding patients with CYP2D6 copy number variation, all Aldy v4.4 diplotype calls except for one CYP3A4 diplotype call were concordant with genotyping for 161 patients in the WES cohort. Conclusion: Aldy v3.3 and v4.4 called diplotypes for major pharmacogenes from clinical WES and WGS data with >99% accuracy. These findings support the use of Aldy to repurpose clinical NGS data to inform clinical PGx.
dc.eprint.versionFinal published version
dc.identifier.citationShugg T, Ly RC, Osei W, et al. Computational pharmacogenotype extraction from clinical next-generation sequencing. Front Oncol. 2023;13:1199741. Published 2023 Jul 4. doi:10.3389/fonc.2023.1199741
dc.publisherFrontiers Media
dc.relation.journalFrontiers in Oncology
dc.rightsAttribution 4.0 Internationalen
dc.subjectNext-generating sequencing
dc.subjectPharmacogenetic algorithm
dc.subjectPharmacogenetics (PGx)
dc.subjectPharmacogenomics (PGx)
dc.subjectWhole exome sequencing (WES)
dc.subjectWhole genome sequencing (WGS)
dc.titleComputational pharmacogenotype extraction from clinical next-generation sequencing
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
679.33 KB
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
1.99 KB
Item-specific license agreed upon to submission