A New Cloud-Native Tool for Pharmacogenetic Analysis

Author:

Yuan David Yu1ORCID,Park Jun Hyuk2,Li Zhenyu3,Thomas Rohan3ORCID,Hwang David M.345,Fu Lei34

Affiliation:

1. European Nucleotide Archive, European Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton, Cambridge CB10 1SD, UK

2. Department of Bioinformatics and Computational Biology, Faculty of Arts and Science, University of Toronto, Toronto, ON M5S 3G3, Canada

3. Department of Laboratory Medicine & Pathobiology, Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada

4. Precision Diagnostics and Therapeutics Program, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada

5. Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada

Abstract

Background: The advancement of next-generation sequencing (NGS) technologies provides opportunities for large-scale Pharmacogenetic (PGx) studies and pre-emptive PGx testing to cover a wide range of genotypes present in diverse populations. However, NGS-based PGx testing is limited by the lack of comprehensive computational tools to support genetic data analysis and clinical decisions. Methods: Bioinformatics utilities specialized for human genomics and the latest cloud-based technologies were used to develop a bioinformatics pipeline for analyzing the genomic sequence data and reporting PGx genotypes. A database was created and integrated in the pipeline for filtering the actionable PGx variants and clinical interpretations. Strict quality verification procedures were conducted on variant calls with the whole genome sequencing (WGS) dataset of the 1000 Genomes Project (G1K). The accuracy of PGx allele identification was validated using the WGS dataset of the Pharmacogenetics Reference Materials from the Centers for Disease Control and Prevention (CDC). Results: The newly created bioinformatics pipeline, Pgxtools, can analyze genomic sequence data, identify actionable variants in 13 PGx relevant genes, and generate reports annotated with specific interpretations and recommendations based on clinical practice guidelines. Verified with two independent methods, we have found that Pgxtools consistently identifies variants more accurately than the results in the G1K dataset on GRCh37 and GRCh38. Conclusions: Pgxtools provides an integrated workflow for large-scale genomic data analysis and PGx clinical decision support. Implemented with cloud-native technologies, it is highly portable in a wide variety of environments from a single laptop to High-Performance Computing (HPC) clusters and cloud platforms for different production scales and requirements.

Funder

Sunnybrook LMMD Strategic Innovation Fund

Publisher

MDPI AG

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