Genome Sequencing Enhances Precision and Clinical Utility of Pharmacogenetic Data Compared to Arrays
Year: 2026
Type: article
Abstract: While pharmacogenetic testing has traditionally relied on array-based genotyping platforms, these methods are limited by incomplete variant coverage and inability to detect novel alleles. We hypothesize that the performance of genome sequencing (GS) is superior to that of an array-based genotyping method for gene-drug pairs that are clinically implemented at the NIH Clinical Center. DNA was collected from a cohort of 293 patients from a single-center prospective cohort study at the National Institutes of Health, Bethesda, MD. Post hoc analysis was conducted in probands with genetically inferred ancestry: European, African, African American, and Asian. The primary endpoint was to determine concordance between GS, exome sequencing (ES), and a commercial genotyping array while assessing accuracy of non-concordant alleles using orthogonal sequencing. In a cohort of 293 individuals genotyped with a commonly used array, GS (n = 120) and/or ES (n = 185) was conducted. GS demonstrated superior accuracy, resolving unknown or ambiguous array-based allele calls in 5% of cases, ultimately achieving 99% baseline accuracy. Moreover, we confirmed discordances between array-based calls and GS in favor of GS through validation by orthogonal sequencing methods such as long-read sequencing. We highlight clinically significant inaccuracies that include critical variants missed by arrays and ES. From a cost perspective, GS proved comparable or superior to array-based methods, with significantly greater clinical applicability and flexibility. Our findings underscore the feasibility and superiority of integrating GS-based pharmacogenetics testing into clinical settings, demonstrating robust analytical performance, improved patient management potential, and a pathway toward broader, lifetime utility of genomic data.
DOI: 10.1002/jcph.70235
Authors: Ibrahim Numanagić, Morgan Similuk, Tristan M. Sissung, Ananth Hari, Michael K. B. Ford, Magdalena A. Walkiewicz, Jia Yan, Rajarshi Ghosh, Justin Lack, Wenjia Cao, Ryan N. Baugher, Todd B. Young, Teri Manolio, Leslie G. Biesecker, Stephen M. Hewitt, Suleyman Cenk Sahinalp, Steven M. Holland, William D. Figg
Language: English
Subfield: Pharmacology
Domain: Life Sciences
Open Access status: green
Funders: U.S. Department of Health and Human Services, Canada Foundation for Innovation, National Institutes of Health, Natural Sciences and Engineering Research Council of Canada, British Columbia Knowledge Development Fund, National Cancer Institute, National Institute of Allergy and Infectious Diseases
OpenAlex ID: W7167480667
