Application of long-read sequencing to elucidate complex pharmacogenomic regions: a proof of principle

Maaike Van Der Lee,Roberta Menafra,Henk-Jan Guchelaar,William J Rowell,Jesse J Swen,Seyed Yahya Anvar

doi:10.1038/s41397-021-00259-z

Abstract

The use of pharmacogenomics in clinical practice is becoming standard of care. However, due to the complex genetic makeup of pharmacogenes, not all genetic variation is currently accounted for. Here, we show the utility of long-read sequencing to resolve complex pharmacogenes by analyzing a well-characterised sample. This data consists of long reads that were processed to resolve phased haploblocks. 73% of pharmacogenes were fully covered in one phased haploblock, including 9/15 genes that are 100% complex. Variant calling accuracy in the pharmacogenes was high, with 99.8% recall and 100% precision for SNVs and 98.7% precision and 98.0% recall for Indels. For the majority of gene-drug interactions in the DPWG and CPIC guidelines, the associated genes could be fully resolved (62% and 63% respectively). Together, these findings suggest that long-read sequencing data offers promising opportunities in elucidating complex pharmacogenes and haplotype phasing while maintaining accurate variant calling.

Highlights

Pharmacogenomics (PGx) is crucial for individualizing drug dosages and thereby improving drug therapy outcomes [1, 2]
Variants could still be accurately identified, allowing for haplotype assignments according to current clinical practice which uses non-phased genetic data. In this proof-of-concept study, we have shown that long-read sequencing yields high quality variant calls in all selected pharmacogenes
Compared to the genome-wide analysis [19], results for PGx genes are superior with regards to variant calling accuracy and resolution of larger phased haploblocks

Summary

Introduction

Pharmacogenomics (PGx) is crucial for individualizing drug dosages and thereby improving drug therapy outcomes [1, 2]. It is essential to be able to explain all genetic components driving variable drug response in order to assess what part of variability is genetic and what part can be explained by other factors This is, challenged as the majority of pharmacogenes are at least in part located in complex genomic regions or contain variants like tandem-repeats and pseudogene hybrid conformations [9]. PGx diplotypes are phased based on linkage disequilibrium While this results in accurate haplotypes on a population scale it does not always result in accurate assumptions on an individual level. The complex gene CYP2D6, is involved in the metabolism of 20–30% of commonly prescribed drugs [18] and cannot be fully characterized by short-read sequencing

Objectives

Methods

Findings

Conclusion