ClinPharmSeq: A targeted sequencing panel for clinical pharmacogenetics implementation

PLoS One. 2022 Jul 28;17(7):e0272129. doi: 10.1371/journal.pone.0272129. eCollection 2022.

Abstract

The accurate identification of genetic variants contributing to therapeutic drug response or adverse effects is the first step in implementation of precision drug therapy. Targeted sequencing has recently become a common methodology for large-scale studies of genetic variation thanks to its favorable balance between low cost, high throughput, and deep coverage. Here, we present ClinPharmSeq, a targeted sequencing panel of 59 genes with associations to pharmacogenetic (PGx) phenotypes, as a platform to explore the relationship between drug response and genetic variation, both common and rare. For validation, we sequenced DNA from 64 ethnically diverse Coriell samples with ClinPharmSeq to call star alleles (haplotype patterns) in 27 genes using the bioinformatics tool PyPGx. These reference samples were extensively characterized by multiple laboratories using PGx testing assays and, more recently, whole genome sequencing. We found that ClinPharmSeq can consistently generate deep-coverage data (mean = 274x) with high uniformity (30x or above = 94.8%). Our genotype analysis identified a total of 185 unique star alleles from sequencing data, and showed that diplotype calls from ClinPharmSeq are highly concordant with that from previous publications (97.6%) and whole genome sequencing (97.9%). Notably, all 19 star alleles with complex structural variation including gene deletions, duplications, and hybrids were recalled with 100% accuracy. Altogether, these results demonstrate that the ClinPharmSeq platform offers a feasible path for broad implementation of PGx testing and optimization of individual drug treatments.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alleles
  • Genotype
  • Haplotypes
  • High-Throughput Nucleotide Sequencing* / methods
  • Pharmacogenetics* / methods
  • Whole Genome Sequencing / methods

Grants and funding

This work was supported by the World Class 300 Project (R&D) (S2638360) of the Ministry of Trade, Industry and Energy (MOTIE; https://english.motie.go.kr/www/main.do) and Ministry of SMEs and Startups (MSS; https://www.mss.go.kr/site/eng/main.do) of Republic of Korea. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.