Characterization of Reference Materials for Genetic Testing of CYP2D6 Alleles

Gaedigk, Andrea; Turner, Amy; Everts, Robin E.; Scott, Stuart A.; Aggarwal, Praful; Broeckel, Ulrich; McMillin, Gwendolyn A.; Melis, Roberta; Boone, Erin C.; Pratt, Victoria M.; Kalman, Lisa V.

doi:10.1016/j.jmoldx.2019.06.007

Cited by 55 publications

(96 citation statements)

References 31 publications

(36 reference statements)

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“…Three command-line-based bioinformatic tools, Astrolabe (previously Constellation) [15], Aldy [16], and Stargazer [17] were used to call CYP2D6 variants, including SVs. Using downloaded genetic reference data, we compared the CYP2D6 variant calls of these three tools to the GeT-RM 2019 consensus genotypes [21]. As shown in Table 2, of 21 samples Stargazer called 11, Astrolabe 12, and Aldy 19 correctly (note that for NA18524 Aldy detected all existing star allels but not in the right diplotype).…”

Section: Comparison Of Cyp2d6 Calling Tools From Wgs Datamentioning

confidence: 99%

“…All three SV detection tools were applied using default settings (Supplementary Figure S4). For each reference sample, the results of SV calling were compared to the consensus CYP2D6 genotype obtained from the GeT-RM projects 2019 [21]. For resolving ambiguous calls, BAM files were manually evaluated using the Integrative Genomics Viewer v2.4.19 [22].…”

Section: Evaluation Of Cyp2d6 Variant Callersmentioning

confidence: 99%

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Added Value of Clinical Sequencing: WGS-Based Profiling of Pharmacogenes

Caspar

Schneider

Meienberg

et al. 2020

IJMS

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Although several pharmacogenetic (PGx) predispositions affecting drug efficacy and safety are well established, drug selection and dosing as well as clinical trials are often performed in a non-pharmacogenetically-stratified manner, ultimately burdening healthcare systems. Pre-emptive PGx testing offers a solution which is often performed using microarrays or targeted gene panels, testing for common/known PGx variants. However, as an added value, whole-genome sequencing (WGS) could detect not only disease-causing but also pharmacogenetically-relevant variants in a single assay. Here, we present our WGS-based pipeline that extends the genetic testing of Mendelian diseases with PGx profiling, enabling the detection of rare/novel PGx variants as well. From our in-house WGS (PCR-free 60× PE150) data of 547 individuals we extracted PGx variants with drug-dosing recommendations of the Dutch Pharmacogenetics Working Group (DPWG). Furthermore, we explored the landscape of DPWG pharmacogenes in gnomAD and our in-house cohort as well as compared bioinformatic tools for WGS-based structural variant detection in CYP2D6. We show that although common/known PGx variants comprise the vast majority of detected DPWG pharmacogene alleles, for better precision medicine, PGx testing should move towards WGS-based approaches. Indeed, WGS-based PGx profiling is not only feasible and future-oriented but also the most comprehensive all-in-one approach without generating significant additional costs.

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Section: Comparison Of Cyp2d6 Calling Tools From Wgs Datamentioning

confidence: 99%

Section: Evaluation Of Cyp2d6 Variant Callersmentioning

confidence: 99%

Added Value of Clinical Sequencing: WGS-Based Profiling of Pharmacogenes

Caspar

Schneider

Meienberg

et al. 2020

IJMS

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“…The research participant-derived data used in the study are part of the Polaris pharmacogenomics cohort ( https://github.com/Illumina/Polaris/wiki/HiSeqX-PGx-Cohort#Pratt2016 ) whose samples were originally collected as part of the HapMap and 1000 genomes projects 22 . The Centers for Disease Control and Prevention (CDC)-based Genetic Testing Reference Material Coordination Program (GeT-RM) has characterised these samples through extensive orthogonal testing by various laboratories 23 , 24 . In doing so, GeT-RM has contributed invaluable reference materials for benchmarking CYP2D6 genotyping approaches.…”

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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“…Sample and genotype data were compiled from previous studies (Abduljalil et al, 2010;Gaedigk et al, 2012;Montane Jaime et al, 2013;Tay-Sontheimer et al, 2014;Brown et al, 2016;Bérard et al, 2017;Gaedigk et al, 2019b) as well as ongoing studies (Genomicand Ontogeny-Linked Dose Individualization and cLinical Optimization for Kids (GOLDILOKs), CYP2D6 genotype and neurocognitive dysfunction in methamphetamine users with and without HIV and medication use in pregnant women). Studies into which participants were initially enrolled in were approved by the institutions at which the studies were performed.…”

Section: Study Samplesmentioning

confidence: 99%

“…Genomic DNA was prepared from whole blood or liver tissue and genotyped for a minimum of four SNPs (rs5758550 or rs133333, rs16947, rs3892097, and rs1065852) as previously described (Gaedigk et al, 1999;Gaedigk et al, 2006;Gaedigk et al, 2007;Gaedigk et al, 2008;Gaedigk et al, 2010a;Gaedigk et al, 2010b;Gaedigk et al, 2012;Gaedigk et al, 2015;Gaedigk et al, 2019b) and detailed in Supplemental Table 1. This format for methods reporting has been recommended by PharmVar and PharmGKB to facilitate standardized genotype method reporting (Nofziger et al, 2020).…”

Section: Genotypingmentioning

confidence: 99%

Long-Distance Phasing of a Tentative “Enhancer” Single-Nucleotide Polymorphism With CYP2D6 Star Allele Definitions

et al. 2020

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Background: The CYP2D6 gene locus has been extensively studied over decades, yet a portion of variability in CYP2D6 activity cannot be explained by known sequence variations within the gene, copy number variation, or structural rearrangements. It was proposed that rs5758550, located 116 kb downstream of the CYP2D6 gene locus, increases gene expression and thus contributes to variability in CYP2D6 activity. This finding has, however, not been validated. The purpose of the study was to address a major technological barrier, i.e., experimentally linking rs5758550, also referred to as the "enhancer" single-nucleotide polymorphism (SNP), to CYP2D6 haplotypes >100 kb away. To overcome this challenge is essential to ultimately determine the contribution of the "enhancer" SNP to interindividual variability in CYP2D6 activity. Methods: A large ethnically mixed population sample (n=3,162) was computationally phased to determine linkage between the "enhancer" SNP and CYP2D6 haplotypes (or star alleles). To experimentally validate predicted linkages, DropPhase2D6, a digital droplet PCR (ddPCR)-based method was developed. 10X Genomics Linked-Reads were utilized as a proof of concept. Results: Phasing predicted that the "enhancer" SNP can occur on numerous CYP2D6 haplotypes including CYP2D6*1, *2, *5, and *41 and suggested that linkage is incomplete, i.e., a portion of these alleles do not have the "enhancer" SNP. Phasing also revealed differences among the European and African ancestry data sets regarding the proportion of alleles with and without the "enhancer" SNP. DropPhase2D6 was utilized to confirm or refute the predicted "enhancer" SNP location for individual samples, e.g., of n=3 samples genotyped as *1/*41, rs5758550 was on the *41 allele of two samples and on the *1 allele of one sample. Our findings highlight that the location of the "enhancer" SNP must not be

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Characterization of Reference Materials for Genetic Testing of CYP2D6 Alleles

Abstract: NA18540 10/41 (36þ)10/41 NA18544 10/41 10/41 NA18563 1/(36) 1/36þ10 NA18564 2/[10 (36)] 2A/36þ10 NA18565 10/[10 (36)] 10/36Â2 NA18572 (36)/41 36þ10/41 NA18617 10/[10 (36)] 36þ10/36þ10 NA18959 2/[10 (36)] 2/36þ10 NA18973 1/2 (21) 1/21 NA18980 2/[10 (36)] 2/36þ*10 NA19109

Cited by 55 publications

References 31 publications

Added Value of Clinical Sequencing: WGS-Based Profiling of Pharmacogenes

Added Value of Clinical Sequencing: WGS-Based Profiling of Pharmacogenes

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

Long-Distance Phasing of a Tentative “Enhancer” Single-Nucleotide Polymorphism With CYP2D6 Star Allele Definitions

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