The CYP2D6 VCF Translator

Qiao, Wanqiong; Wang, J; Pullman, Benjamin; Chen, R; Yao, Yang; Scott, Stuart A.

doi:10.1038/tpj.2016.14

Cited by 11 publications

(7 citation statements)

References 10 publications

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“…In addition, a cumbersome informatics issue is con verting identified CYP2D6 variants from the genome assembly used for sequencing (e.g., GRCh37/hg19) to the M33388.1 GenBank reference sequence used to define CYP2D6 star (*) alleles. This is typically accomplished by manual curation and/or with the CYP2D6 haplotype tables at PharmGKB [60], but recently has been automated by the freely available CYP2D6 VCF Translator [61]. Some of the currently available CYP2D6 phasing and diplo typing tools are explained in the following sections (Cypiripi, Constellation/Astrolabe, PharmCAT).…”

Section: Cyp2d6 Phasing and Star (*) Allele Haplotypingmentioning

confidence: 99%

Sequencing the CYP2D6 Gene: From Variant Allele Discovery to Clinical Pharmacogenetic Testing

et al. 2017

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CYP2D6 is one of the most studied enzymes in the field of pharmacogenetics. The CYP2D6 gene is highly polymorphic with over 100 catalogued star (*) alleles, and clinical CYP2D6 testing is increasingly accessible and supported by practice guidelines. However, the degree of variation at the CYP2D6 locus and homology with its pseudogenes make interrogating CYP2D6 by short-read sequencing challenging. Moreover, accurate prediction of CYP2D6 metabolizer status necessitates analysis of duplicated alleles when an increased copy number is detected. These challenges have recently been overcome by long-read CYP2D6 sequencing; however, such platforms are not widely available. This review highlights the genomic complexities of CYP2D6, current sequencing methods and the evolution of CYP2D6 from allele discovery to clinical pharmacogenetic testing.

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Section: Cyp2d6 Phasing and Star (*) Allele Haplotypingmentioning

confidence: 99%

Sequencing the CYP2D6 Gene: From Variant Allele Discovery to Clinical Pharmacogenetic Testing

et al. 2017

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“…To address these challenges, bioinformatics tools that have recently been developed for calling star alleles in CYP2D6 and other highly polymorphic pharmacogenes using genome sequencing data and/or targeted-capture panels such as PGRNseq, include Astrolabe (formerly Constellation) 15 , Aldy 16 , Stargazer 17 , VCF Annotator 18 , Cypiripi 19 , and PharmCAT 20 . These tools automate the detection of diplotype combinations based on PharmVar and the Pharmacogenomics Knowledgebase (PharmGKB) star allele catalogues thus facilitating clinical interpretation.…”

Section: Introductionmentioning

confidence: 99%

A systematic comparison of pharmacogene star allele calling bioinformatics algorithms: a focus on CYP2D6 genotyping

et al. 2020

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Genetic variation in genes encoding cytochrome P450 enzymes has important clinical implications for drug metabolism. Bioinformatics algorithms for genotyping these highly polymorphic genes using high-throughput sequence data and automating phenotype prediction have recently been developed. The CYP2D6 gene is often used as a model during the validation of these algorithms due to its clinical importance, high polymorphism, and structural variations. However, the validation process is often limited to common star alleles due to scarcity of reference datasets. In addition, there has been no comprehensive benchmark of these algorithms to date. We performed a systematic comparison of three star allele calling algorithms using 4618 simulations as well as 75 whole-genome sequence samples from the GeT-RM project. Overall, we found that Aldy and Astrolabe are better suited to call both common and rare diplotypes compared to Stargazer, which is affected by population structure. Aldy was the best performing algorithm in calling CYP2D6 structural variants followed by Stargazer, whereas Astrolabe had limitations especially in calling hybrid rearrangements. We found that ensemble genotyping, characterised by taking a consensus of genotypes called by all three algorithms, has higher haplotype concordance but it is prone to ambiguities whenever complete discrepancies between the tools arise. Further, we evaluated the effects of sequencing coverage and indel misalignment on genotyping accuracy. Our account of the strengths and limitations of these algorithms is extremely important to clinicians and researchers in the pharmacogenomics and precision medicine communities looking to haplotype CYP2D6 and other pharmacogenes using high-throughput sequencing data.

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“…Why not just sequence the entire gene, then? Next generation sequencing techniques, along with software tools for analyzing and calling the CYP2D6 genotype, are accordingly flooding the CYP2D6 variant detection field [14][15][16]. The main concerns with next generation sequencing methods are the misalignment of sequence reads to the highly homologous CYP2D7 and nondetection of the structural variants (hybrids and/or duplications and multiplications), although the technology is becoming increasingly refined to avoid these blunders.…”

mentioning

confidence: 99%