Frontiers in Pharmacology (Mar 2024)

Development and validation of a pharmacogenomics reporting workflow based on the illumina global screening array chip

  • Pamela Gan,
  • Muhammad Irfan Bin Hajis,
  • Mazaya Yumna,
  • Jessline Haruman,
  • Husnul Khotimah Matoha,
  • Dian Tri Wahyudi,
  • Santha Silalahi,
  • Dwi Rizky Oktariani,
  • Fitria Dela,
  • Tazkia Annisa,
  • Tessalonika Damaris Ayu Pitaloka,
  • Priscilla Klaresza Adhiwijaya,
  • Rizqi Yanuar Pauzi,
  • Robby Hertanto,
  • Meutia Ayuputeri Kumaheri,
  • Levana Sani,
  • Astrid Irwanto,
  • Ariel Pradipta,
  • Ariel Pradipta,
  • Kamonlawan Chomchopbun,
  • Mar Gonzalez-Porta

DOI
https://doi.org/10.3389/fphar.2024.1349203
Journal volume & issue
Vol. 15

Abstract

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Background: Microarrays are a well-established and widely adopted technology capable of interrogating hundreds of thousands of loci across the human genome. Combined with imputation to cover common variants not included in the chip design, they offer a cost-effective solution for large-scale genetic studies. Beyond research applications, this technology can be applied for testing pharmacogenomics, nutrigenetics, and complex disease risk prediction. However, establishing clinical reporting workflows requires a thorough evaluation of the assay’s performance, which is achieved through validation studies. In this study, we performed pre-clinical validation of a genetic testing workflow based on the Illumina Global Screening Array for 25 pharmacogenomic-related genes.Methods: To evaluate the accuracy of our workflow, we conducted multiple pre-clinical validation studies. Here, we present the results of accuracy and precision assessments, involving a total of 73 cell lines. These assessments encompass reference materials from the Genome-In-A-Bottle (GIAB), the Genetic Testing Reference Material Coordination Program (GeT-RM) projects, as well as additional samples from the 1000 Genomes project (1KGP). We conducted an accuracy assessment of genotype calls for target loci in each indication against established truth sets.Results: In our per-sample analysis, we observed a mean analytical sensitivity of 99.39% and specificity 99.98%. We further assessed the accuracy of star-allele calls by relying on established diplotypes in the GeT-RM catalogue or calls made based on 1KGP genotyping. On average, we detected a diplotype concordance rate of 96.47% across 14 pharmacogenomic-related genes with star allele-calls. Lastly, we evaluated the reproducibility of our findings across replicates and observed 99.48% diplotype and 100% phenotype inter-run concordance.Conclusion: Our comprehensive validation study demonstrates the robustness and reliability of the developed workflow, supporting its readiness for further development for applied testing.

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