PAnno: A pharmacogenomics annotation tool for clinical genomic testing

Front Pharmacol. 2023 Jan 26:14:1008330. doi: 10.3389/fphar.2023.1008330. eCollection 2023.

Abstract

Introduction: Next-generation sequencing (NGS) technologies have been widely used in clinical genomic testing for drug response phenotypes. However, the inherent limitations of short reads make accurate inference of diplotypes still challenging, which may reduce the effectiveness of genotype-guided drug therapy. Methods: An automated Pharmacogenomics Annotation tool (PAnno) was implemented, which reports prescribing recommendations and phenotypes by parsing the germline variant call format (VCF) file from NGS and the population to which the individual belongs. Results: A ranking model dedicated to inferring diplotypes, developed based on the allele (haplotype) definition and population allele frequency, was introduced in PAnno. The predictive performance was validated in comparison with four similar tools using the consensus diplotype data of the Genetic Testing Reference Materials Coordination Program (GeT-RM) as ground truth. An annotation method was proposed to summarize prescribing recommendations and classify drugs into avoid use, use with caution, and routine use, following the recommendations of the Clinical Pharmacogenetics Implementation Consortium (CPIC), etc. It further predicts phenotypes of specific drugs in terms of toxicity, dosage, efficacy, and metabolism by integrating the high-confidence clinical annotations in the Pharmacogenomics Knowledgebase (PharmGKB). PAnno is available at https://github.com/PreMedKB/PAnno. Discussion: PAnno provides an end-to-end clinical pharmacogenomics decision support solution by resolving, annotating, and reporting germline variants.

Keywords: diplotype; drug responce; drug-genotype; genomics; haplotype; pharmacogenomics; precision medicine; star allele.

Grants and funding

This work was supported in part by the National Natural Science Foundation of China (31720103909 and 32170657), the National Key R&D Project of China (2018YFE0201603, and 2021YFF1201305), Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) and the 111 Project (B13016).