Benchmarking pharmacogenomics genotyping tools: Performance analysis on short‐read sequencing samples and depth‐dependent evaluation

Author:

Halman Andreas123ORCID,Lunke Sebastian2ORCID,Sadedin Simon24ORCID,Moore Claire14ORCID,Conyers Rachel1456ORCID

Affiliation:

1. Cancer Therapies, Stem Cell Medicine Murdoch Children's Research Institute Parkville Victoria Australia

2. Victorian Clinical Genetics Services Murdoch Children's Research Institute Melbourne Victoria Australia

3. School of Population and Global Health The University of Melbourne Parkville Victoria Australia

4. Department of Paediatrics The University of Melbourne Parkville Victoria Australia

5. The Novo Nordisk Foundation Centre for Stem Cell Medicine, ReNEW, Melbourne Node Parkville Victoria Australia

6. Children's Cancer Centre, The Royal Children's Hospital Parkville Victoria Australia

Abstract

AbstractPharmacogenomics (PGx) investigates the influence of genetics on drug responses, enabling tailored treatments for personalized healthcare. This study assessed the accuracy of genotyping six genes using whole genome sequencing with four different computational tools and various sequencing depths. The effects of using different reference genomes (GRCh38 and GRCh37) and sequence aligners (BWA‐MEM and Bowtie2) were also explored. The results showed generally minor variations in tool performance across most genes; however, more notable discrepancies were observed in the analysis of the complex CYP2D6 gene. Cyrius, a CYP2D6‐specific tool, demonstrated the most robust performance, achieving the highest concordance rates for CYP2D6 in all instances, comparable to the consensus approach in most cases. There were rather small differences between the samples with 20× coverage depth and those with higher depth, but the decreased performance was more evident at lower depths, particularly at 5×. Additionally, variations in CYP2D6 results were observed when samples were aligned to different reference genomes using the same method, or to the same genome using different aligners, which led to reporting incorrect rare star alleles in several cases. These findings inform the selection of optimal PGx tools and methodologies as well as suggest that employing a consensus approach with two or more tools might be preferable for certain genes and tool combinations, especially at lower sequencing depths, to ensure accurate results. Additionally, we show how the upstream alignment can affect the performance of tools, an important factor to take into account.

Publisher

Wiley

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