Analytical Validation of a Computational Method for Pharmacogenetic Genotyping from Clinical Whole Exome Sequencing

Ly, Reynold C.; Shugg, Tyler; Ratcliff, Ryan; Osei, Wilberforce; Lynnes, Ty C.; Pratt, Victoria M.; Schneider, Bryan P.; Radovich, Milan; Bray, Steven M.; Salisbury, Benjamin A.; Parikh, Baiju; Sahinalp, S. Cenk; Numanagić, Ibrahim; Skaar, Todd C.

doi:10.1016/j.jmoldx.2022.03.008

Cited by 11 publications

(15 citation statements)

References 32 publications

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“…For such samples, calls that either used or ignored such ambiguous variants were deemed “correct”. Second, we have followed the common strategy employed in clinical studies (Ly et al ., 2022) by only comparing the major star-allele calls and ignoring the minor star-allele designations. In other words, only the phasing of functional variants was considered; non-functional and silent variants that do not alter the functionality of an allele were ignored (i.e., a *1A/*2B minor star-allele call was treated as a functionally equivalent *1/*2 major star-allele call 4 ).…”

Section: Resultsmentioning

confidence: 99%

Aldy 4: An efficient genotyper and star-allele caller for pharmacogenomics

Hari

Zhou

Gonzaludo

et al. 2022

Preprint

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High-throughput sequencing provides sufficient means for determining genotypes of clinically important pharmacogenes that can be used to tailor medical decisions to individual patients. However, pharmacogene genotyping, also known as star-allele calling, is a challenging problem that requires accurate copy number calling, structural variation discovery, variant calling and phasing within each pharmacogene copy present in the sample. Here we introduce Aldy 4, a fast and efficient tool for genotyping pharmacogenes that utilizes combinatorial optimization for accurate star-allele calling across different sequencing technologies. Aldy 4 adds support for long reads and ships with a novel phasing model and improved copy number and variant calling models. We compare Aldy 4 against the current state-of-the-art star-allele callers on a large and diverse set of samples and genes sequenced by various sequencing technologies, such as whole-genome and targeted Illumina sequencing, barcoded 10X Genomics and PacBio HiFi. We show that Aldy 4 is the most accurate star-allele caller with near-perfect accuracy in all evaluated contexts. We hope that Aldy remains an invaluable tool in the clinical toolbox even with the advent of long-read sequencing technologies. Aldy 4 is available at https://github.com/0xTCG/aldy.

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Section: Resultsmentioning

confidence: 99%

Aldy 4: An efficient genotyper and star-allele caller for pharmacogenomics

Hari

Zhou

Gonzaludo

et al. 2022

Preprint

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“…Similarly to laboratory reporting issues, a lack of evidence, especially for groups with the most impact, such as those taking medications metabolized by CYP2D6, was also discussed by participants. Although obtaining reliable and comprehensive CYP2D6 genotype results from standard exome sequencing currently may not be feasible due to noncoding variants and complex structural rearrangements; other methods including whole genome sequencing, increased sequencing depth and computational variant calling tools are evolving that show reliable accuracy and precision for other clinically significant pharmacogenomic genes [23,24].…”

Section: Discussionmentioning

confidence: 99%

Attitudes on pharmacogenomic results as secondary findings among medical geneticists

Bartos

Scott

Jabs

et al. 2022

Pharmacogenetics and Genomics

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Objectives As evidence mounts supporting the utility of pharmacogenomic-guided medication management, incorporating pharmacogenomic genes into secondary finding results from sequencing panels is increasingly under consideration. We studied medical geneticists' attitudes on receiving pharmacogenomic results as secondary finding.Methods Four focus groups with 16 medical geneticists total were conducted followed by thematic analysis. ResultsAll participants ordered genetic sequencing tests; however, the majority had rarely or never ordered pharmacogenomic tests (10/16) or prescribed medications with established response variability (11/16). In total 81.3% expressed low comfort interpreting pharmacogenomic results without appropriate clinical resources (13/16). The positives of receiving pharmacogenomic results as secondary finding included prevention of adverse drug reactions in adults, grateful information-seeking patients, the ability to rapidly prescribe more effective treatments and appreciation of the recent advances in both pharmacogenomic knowledge and available guidelines. Negatives included laboratory reporting issues, exclusivity of pharmacogenomic results to certain populations, lengthy reports concealing pharmacogenomic results in patient charts and laboratories marketing to individuals without prior pharmacogenomic knowledge or targeting inappropriate populations. The most desirable pharmacogenomic resources included a universal electronic health record clinical decision support tool to assist identifying and implementing pharmacogenomic results, a specialized pharmacist as part of the care team, additional pharmacogenomic training during medical/graduate school, and a succinct interpretation of pharmacogenomic results included on laboratory reports. ConclusionsThe majority of participants agreed that adding certain actionable pharmacogenomic genes to the American College of Medical Genetics and Genomics SF list is reasonable; however, this was qualified with a need for additional resources to support implementation.

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“…Orthogonal confirmation was performed for any variant in the 14 assessed pharmacogenes that was detected by Aldy, not included in the genotyping platform, and had actionable recommendations in current Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. Detailed descriptions of these orthogonal confirmation methods have been previously published ( 17 ). To summarize, these variants were confirmed with PCR-based genotyping using commercial TaqMan reagents, when available, and a positive genomic control from a Coriell cell line with known genotypes from 1000 Genomes for TaqMan allelic discrimination.…”

Section: Methodsmentioning

confidence: 99%

“…We previously assessed the performance of the computational genotyping software Aldy (version 3.3) using clinical WES ( 17 ). We found that Aldy v3.3 genotype calls were concordant with those from our validated genotyping reference standard for 13 major pharmacogenes; however, we noted important limitations of Aldy v3.3 that included the inability to determine CYP2D6 copy number or to phase clinically relevant, multi-variant alleles in CYP2B6 , CYP2D6 , and TPMT .…”

Section: Introductionmentioning

confidence: 99%

Computational pharmacogenotype extraction from clinical next-generation sequencing

Shugg

Osei

et al. 2023

Front. Oncol.

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BackgroundNext-generation sequencing (NGS), including whole genome sequencing (WGS) and whole exome sequencing (WES), is increasingly being used for clinic care. While NGS data have the potential to be repurposed to support clinical pharmacogenomics (PGx), current computational approaches have not been widely validated using clinical data. In this study, we assessed the accuracy of the Aldy computational method to extract PGx genotypes from WGS and WES data for 14 and 13 major pharmacogenes, respectively.MethodsGermline DNA was isolated from whole blood samples collected for 264 patients seen at our institutional molecular solid tumor board. DNA was used for panel-based genotyping within our institutional Clinical Laboratory Improvement Amendments- (CLIA-) certified PGx laboratory. DNA was also sent to other CLIA-certified commercial laboratories for clinical WGS or WES. Aldy v3.3 and v4.4 were used to extract PGx genotypes from these NGS data, and results were compared to the panel-based genotyping reference standard that contained 45 star allele-defining variants within CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DPYD, G6PD, NUDT15, SLCO1B1, TPMT, and VKORC1.ResultsMean WGS read depth was >30x for all variant regions except for G6PD (average read depth was 29 reads), and mean WES read depth was >30x for all variant regions. For 94 patients with WGS, Aldy v3.3 diplotype calls were concordant with those from the genotyping reference standard in 99.5% of cases when excluding diplotypes with additional major star alleles not tested by targeted genotyping, ambiguous phasing, and CYP2D6 hybrid alleles. Aldy v3.3 identified 15 additional clinically actionable star alleles not covered by genotyping within CYP2B6, CYP2C19, DPYD, SLCO1B1, and NUDT15. Within the WGS cohort, Aldy v4.4 diplotype calls were concordant with those from genotyping in 99.7% of cases. When excluding patients with CYP2D6 copy number variation, all Aldy v4.4 diplotype calls except for one CYP3A4 diplotype call were concordant with genotyping for 161 patients in the WES cohort.ConclusionAldy v3.3 and v4.4 called diplotypes for major pharmacogenes from clinical WES and WGS data with >99% accuracy. These findings support the use of Aldy to repurpose clinical NGS data to inform clinical PGx.

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Analytical Validation of a Computational Method for Pharmacogenetic Genotyping from Clinical Whole Exome Sequencing

Cited by 11 publications

References 32 publications

Aldy 4: An efficient genotyper and star-allele caller for pharmacogenomics

Aldy 4: An efficient genotyper and star-allele caller for pharmacogenomics

Attitudes on pharmacogenomic results as secondary findings among medical geneticists

Computational pharmacogenotype extraction from clinical next-generation sequencing

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