Stargazer: a software tool for calling star alleles from next-generation sequencing data using CYP2D6 as a model

Pharmacogenomic landscape of COVID-19 therapies from Indian population genomes

Aim: Numerous drugs are being widely prescribed for COVID-19 treatment without any direct evidence for the drug safety/efficacy in patients across diverse ethnic populations. Materials & methods: We analyzed whole genomes of 1029 Indian individuals (IndiGen) to understand the extent of drug–gene (pharmacogenetic), drug–drug and drug–drug–gene interactions associated with COVID-19 therapy in the Indian population. Results: We identified 30 clinically significant pharmacogenetic variants and 73 predicted deleterious pharmacogenetic variants. COVID-19-associated pharmacogenes were substantially overlapped with those of metabolic disorder therapeutics. CYP3A4, ABCB1 and ALB are the most shared pharmacogenes. Fifteen COVID-19 therapeutics were predicted as likely drug–drug interaction candidates when used with four CYP inhibitor drugs. Conclusion: Our findings provide actionable insights for future validation studies and improved clinical decisions for COVID-19 therapy in Indians.

Recommendations for Clinical CYP2D6 Genotyping Allele Selection: A Joint Consensus Recommendation of the Association for Molecular Pathology, College of American Pathologists, Dutch Pharmacogenetics Working Group of the Royal Dutch Pharmacists Association, and the European Society for Pharmacogenomics and Personalized Therapy

The goals of the Association for Molecular Pathology Clinical Practice Committee's Pharmacogenomics (PGx) Working Group are to define the key attributes of pharmacogenetic alleles recommended for clinical testing, and to determine a minimal set of variants that should be included in clinical PGx genotyping assays. This document series provides recommendations on a minimal panel of variant alleles (Tier 1) and an extended panel of variant alleles (Tier 2) that will aid clinical laboratories in designing assays for PGx testing. When developing these recommendations, the Association for Molecular Pathology PGx Working Group considered the functional impact of the variant alleles, allele frequencies in multiethnic populations, the availability of reference materials, as well as other technical considerations with regard to PGx testing. The ultimate goal of this Working Group is to promote standardization of PGx gene/allele testing across clinical laboratories. This document is focused on clinical CYP2D6 PGx testing that may be applied to all cytochrome P450 2D6-metabolized medications. These recommendations are not meant to be interpreted as prescriptive but to provide a reference guide for clinical laboratories that may be either implementing PGx testing or reviewing and updating their existing platform.

Biobank Scale Pharmacogenomics Informs the Genetic Underpinnings of Simvastatin Use

Studying drug-metabolizing enzymes, encoded by pharmacogenes, may inform biological mechanisms underlying the diseases for which a medication is prescribed. Until recently, pharmacogenes could not be studied at biobank scale. In 7,649 unrelated African-ancestry (AFR) and 326,214 unrelated European-ancestry (EUR) participants from the UK Biobank, we associated pharmacogene haplotypes from 50 genes with 265 (EUR) and 17 (AFR) medication use phenotypes using generalized linear models. In EUR, N-acetyltransferase 2 (NAT2) metabolizer phenotype and activity score were associated with simvastatin use. The dose of NAT2*1 was associated with simvastatin use when compared with NAT2*5 (the most common haplotype). This association was robust to effects of low-density lipoprotein cholesterol (LDL-C) concentration (NAT2*1 odds ratio (OR) = 1.07, 95% CI: 1.05-1.09, P = 1.14 × 10^-8 ) and polygenic risk for LDL-C concentration (NAT2*1 OR = 1.09, 95% CI: 1.04-1.14, P = 2.26 × 10^-4 ). Interactive effects between NAT2*1 and simvastatin use on LDL-C concentration (OR = 0.957, 95% CI: 0.916-0.998, P = 0.045) were replicated in the electronic Medical Records and Genomics Pharmacogenetic Sequencing Pilot (eMERGE-PGx) cohort (OR = 0.987, 95% CI: 0.976-0.998, P = 0.029). We used biobank-scale data to uncover and replicate an association between NAT2 locus variation and better response to statin therapy. Testing NAT2 alleles may be useful for making clinical decisions regarding the potential benefit (e.g., absolute risk reduction) in LDL-C concentration prior to statin treatment.

Comprehensive Allele Genotyping in Critical Pharmacogenes Reduces Residual Clinical Risk in Diverse Populations

Genomic‐guided pharmaceutical prescribing is increasingly recognized as an important clinical application of genetics. Accurate genotyping of pharmacogenomic (PGx) genes can be difficult, owing to their complex genetic architecture involving combinations of single‐nucleotide polymorphisms and structural variation. Here, we introduce the Helix PGx database, an open‐source star allele, genotype, and resulting metabolic phenotype frequency database for CYP2C9, CYP2C19, CYP2D6, and CYP4F2, based on short‐read sequencing of >86,000 unrelated individuals enrolled in the Helix DNA Discovery Project. The database is annotated using a pipeline that is clinically validated against a broad range of alleles and designed to call CYP2D6 structural variants with high (98%) accuracy. We find that CYP2D6 has greater allelic diversity than the other genes, manifest in both a long tail of low‐frequency star alleles, as well as a disproportionate fraction (36%) of all novel predicted loss‐of‐function variants identified. Across genes, we observe that many rare alleles (<0.1% frequency) in the overall cohort have 10 times higher frequency in one or more subgroups with non‐European genetic ancestry. Extending these PGx genotypes to predicted metabolic phenotypes, we demonstrate that >90% of the cohort harbors a high‐risk variant in one of the four pharmacogenes. Based on the recorded prescriptions for >30,000 individuals in the Healthy Nevada Project, combined with predicted PGx metabolic phenotypes, we anticipate that standard‐of‐care screening of these 4 pharmacogenes could impact nearly half of the general population.

PharmaKU: A Web-Based Tool Aimed at Improving Outreach and Clinical Utility of Pharmacogenomics

With the tremendous advancements in genome sequencing technology in the field of pharmacogenomics, data have to be made accessible to be more efficiently utilized by broader clinical disciplines. Physicians who require the drug–genome interactome information, have been challenged by the complicated pharmacogenomic star-based classification system. We present here an end-to-end web-based pharmacogenomics tool, PharmaKU, which has a comprehensive easy-to-use interface. PharmaKU can help to overcome several hurdles posed by previous pharmacogenomics tools, including input in hg38 format only, while hg19/GRCh37 is now the most popular reference genome assembly among clinicians and geneticists, as well as the lack of clinical recommendations and other pertinent dosage-related information. This tool extracts genetic variants from nine well-annotated pharmacogenes (for which diplotype to phenotype information is available) from whole genome variant files and uses Stargazer software to assign diplotypes and apply prescribing recommendations from pharmacogenomic resources. The tool is wrapped with a user-friendly web interface, which allows for choosing hg19 or hg38 as the reference genome version and reports results as a comprehensive PDF document. PharmaKU is anticipated to enable bench to bedside implementation of pharmacogenomics knowledge by bringing precision medicine closer to a clinical reality.

PharmVar GeneFocus: CYP2B6

The Pharmacogene Variation Consortium (PharmVar) catalogs star (*) allele nomenclature for the polymorphic human CYP2B6 gene. Genetic variation within the CYP2B6 gene locus impacts the metabolism or bioactivation of clinically important drugs. Of particular importance are efficacy and safety concerns regarding: efavirenz, which is used for the treatment of HIV type-1 infection; methadone, a mainstay in the treatment of opioid use disorder and as an analgesic; ketamine, used as an antidepressant and analgesic; and bupropion, which is prescribed to treat depression and for smoking cessation. This GeneFocus provides a comprehensive overview and summary of CYP2B6 and describes how haplotype information catalogued by PharmVar is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

StellarPGx: A Nextflow Pipeline for Calling Star Alleles in Cytochrome P450 Genes

Bioinformatics pipelines for calling star alleles (haplotypes) in cytochrome P450 (CYP) genes are important for the implementation of precision medicine. Genotyping CYP genes using high throughput sequencing data is complicated, e.g., by being highly polymorphic, not to mention the structural variations especially in CYP2D6, CYP2A6, and CYP2B6. Genome graph-based variant detection approaches have been shown to be reliable for genotyping HLA alleles. However, their application to enhancing star allele calling in CYP genes has not been extensively explored. We present StellarPGx, a Nextflow pipeline for accurately genotyping CYP genes by combining genome graph-based variant detection, read coverage information from the original reference-based alignments, and combinatorial diplotype assignments. The implementation of StellarPGx using Nextflow facilitates its portability, reproducibility, and scalability on various user platforms. StellarPGx is currently able to genotype 12 important pharmacogenes belonging to the CYP1, 2, and 3 families. For purposes of validation, we use CYP2D6 as a model gene owing to its high degree of polymorphisms (over 130 star alleles defined to date, including complex structural variants) and clinical importance. We applied StellarPGx and three existing callers to 109 whole genome sequenced samples for which the Genetic Testing Reference Material Coordination Program (GeT-RM) has recently provided consensus truth CYP2D6 diplotypes. StellarPGx had the highest CYP2D6 diplotype concordance (99%) with GeT-RM compared with Cyrius (98%), Aldy (82%), and Stargazer (84%). This exemplifies the high accuracy of StellarPGx and highlights its importance for both research and clinical pharmacogenomics applications. The StellarPGx pipeline is open-source and available from https://github.com/SBIMB/StellarPGx.

PharmVar GeneFocus: CYP2C19

The Pharmacogene Variation Consortium (PharmVar) catalogues star (*) allele nomenclature for the polymorphic human CYP2C19 gene. CYP2C19 genetic variation impacts the metabolism of many drugs and has been associated with both efficacy and safety issues for several commonly prescribed medications. This GeneFocus provides a comprehensive overview and summary of CYP2C19 and describes how haplotype information catalogued by PharmVar is utilized by the Pharmacogenomics Knowledgebase and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Cyrius: accurate CYP2D6 genotyping using whole-genome sequencing data

Responsible for the metabolism of ~21% of clinically used drugs, CYP2D6 is a critical component of personalized medicine initiatives. Genotyping CYP2D6 is challenging due to sequence similarity with its pseudogene paralog CYP2D7 and a high number and variety of common structural variants (SVs). Here we describe a novel bioinformatics method, Cyrius, that accurately genotypes CYP2D6 using whole-genome sequencing (WGS) data. We show that Cyrius has superior performance (96.5% concordance with truth genotypes) compared to existing methods (84–86.8%). After implementing the improvements identified from the comparison against the truth data, Cyrius’s accuracy has since been improved to 99.3%. Using Cyrius, we built a haplotype frequency database from 2504 ethnically diverse samples and estimate that SV-containing star alleles are more frequent than previously reported. Cyrius will be an important tool to incorporate pharmacogenomics in WGS-based precision medicine initiatives.

Pharmacogenomics, How to Deal with Different Types of Variants in Next Generation Sequencing Data in the Personalized Medicine Area

Pharmacogenomics (PGx) is the knowledge of diverse drug responses and effects in people, based on their genomic profiles. Such information is considered as one of the main directions to reach personalized medicine in future clinical practices. Since the start of applying next generation sequencing (NGS) methods in drug related clinical investigations, many common medicines found their genetic data for the related metabolizing/shipping proteins in the human body. Yet, the employing of technology is accompanied by big obtained data, which most of them have no clear guidelines for consideration in routine treatment decisions for patients. This review article talks about different types of NGS derived PGx variants in clinical studies and try to display the current and newly developed approaches to deal with pharmacogenetic data with/without clear guidelines for considering in clinical settings.

Variation in 100 relevant pharmacogenes among emiratis with insights from understudied populations

Genetic variations have an established impact on the pharmacological response. Investigating this variation resulted in a compilation of variants in "pharmacogenes". The emergence of next-generation sequencing facilitated large-scale pharmacogenomic studies and exhibited the extensive variability of pharmacogenes. Some rare and population-specific variants proved to be actionable, suggesting the significance of population pharmacogenomic research. A profound gap exists in the knowledge of pharmacogenomic variants enriched in some populations, including the United Arab Emirates (UAE). The current study aims to explore the landscape of variations in relevant pharmacogenes among healthy Emiratis. Through the resequencing of 100 pharmacogenes for 100 healthy Emiratis, we identified 1243 variants, of which 63% are rare (minor allele frequency ≤ 0.01), and 30% were unique. Filtering the variants according to Pharmacogenomics Knowledge Base (PharmGKB) annotations identified 27 diplotypes and 26 variants with an evident clinical relevance. Comparison with global data illustrated a significant deviation of allele frequencies in the UAE population. Understudied populations display a distinct allelic architecture and various rare and unique variants. We underscored pharmacogenes with the highest variation frequencies and provided investigators with a list of candidate genes for future studies. Population pharmacogenomic studies are imperative during the pursuit of global pharmacogenomics implementation.

Transfer learning enables prediction of CYP2D6 haplotype function

Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations in CYP2D6 are responsible for interindividual heterogeneity in drug response that can lead to drug toxicity and ineffective treatment, making CYP2D6 one of the most important pharmacogenes. Prediction of CYP2D6 phenotype relies on curation of literature-derived functional studies to assign a functional status to CYP2D6 haplotypes. As the number of large-scale sequencing efforts grows, new haplotypes continue to be discovered, and assignment of function is challenging to maintain. To address this challenge, we have trained a convolutional neural network to predict functional status of CYP2D6 haplotypes, called Hubble.2D6. Hubble.2D6 predicts haplotype function from sequence data and was trained using two pre-training steps with a combination of real and simulated data. We find that Hubble.2D6 predicts CYP2D6 haplotype functional status with 88% accuracy in a held-out test set and explains 47.5% of the variance in in vitro functional data among star alleles with unknown function. Hubble.2D6 may be a useful tool for assigning function to haplotypes with uncurated function, and used for screening individuals who are at risk of being poor metabolizers.

A Review of the Important Role of CYP2D6 in Pharmacogenomics

Cytochrome P450 2D6 (CYP2D6) is a critical pharmacogene involved in the metabolism of ~20% of commonly used drugs across a broad spectrum of medical disciplines including psychiatry, pain management, oncology and cardiology. Nevertheless, CYP2D6 is highly polymorphic with single-nucleotide polymorphisms, small insertions/deletions and larger structural variants including multiplications, deletions, tandem arrangements, and hybridisations with non-functional CYP2D7 pseudogenes. The frequency of these variants differs across populations, and they significantly influence the drug-metabolising enzymatic function of CYP2D6. Importantly, altered CYP2D6 function has been associated with both adverse drug reactions and reduced drug efficacy, and there is growing recognition of the clinical and economic burdens associated with suboptimal drug utilisation. To date, pharmacogenomic clinical guidelines for at least 48 CYP2D6-substrate drugs have been developed by prominent pharmacogenomics societies, which contain therapeutic recommendations based on CYP2D6-predicted categories of metaboliser phenotype. Novel algorithms to interpret CYP2D6 function from sequencing data that consider structural variants, and machine learning approaches to characterise the functional impact of novel variants, are being developed. However, CYP2D6 genotyping is yet to be implemented broadly into clinical practice, and so further effort and initiatives are required to overcome the implementation challenges and deliver the potential benefits to the bedside.

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

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.

Pharmacogenomics of Nicotine Metabolism: Novel CYP2A6 and CYP2B6 Genetic Variation Patterns in Alaska Native and American Indian Populations

INTRODUCTION

Alaska Native and American Indian (AN/AI) populations have higher tobacco use prevalence than other ethnic/racial groups. Pharmacogenetic testing to tailor tobacco cessation treatment may improve cessation rates. This study characterized polymorphic variations among AN/AI people in genes associated with metabolism of nicotine and drugs used for tobacco cessation.

METHODS

Recruitment of AN/AI individuals represented six subgroups, five geographic subgroups throughout Alaska and a subgroup comprised of AIs from the lower 48 states living in Alaska. We sequenced the CYP2A6 and CYP2B6 genes to identify known and novel gain, reduced, and loss-of-function alleles, including structural variation (eg, gene deletions, duplications, and hybridizations).

RESULTS

Variant allele frequencies differed substantially between AN/AI subgroups. The gene deletion CYP2A6*4 and reduced function CYP2A6*9 alleles were found at high frequency in Northern/Western subgroups and in Lower 48/Interior subgroups, respectively. The reduced function CYP2B6*6 allele was observed in all subgroups and a novel, predicted reduced function CYP2B6 variant was found at relatively high frequency in the Southeastern subgroup.

CONCLUSIONS

Diverse CYP2A6 and CYP2B6 variation among the subgroups highlight the need for comprehensive pharmacogenetic testing to guide tobacco cessation therapy for AN/AI populations.

IMPLICATIONS

Nicotine metabolism is largely determined by CYP2A6 genotype, and variation in CYP2A6 activity has altered the treatment success in other populations. These findings suggest pharmacogenetic-guided smoking cessation drug treatment could provide benefit to this unique population seeking tobacco cessation therapy.

CYP2D6 haplotypes with enhancer single-nucleotide polymorphism rs5758550 and rs16947 (*2 allele): implications for CYP2D6 genotyping panels

INTRODUCTION

CYP2D6 metabolizes ∼25% of all clinically used drugs, with numerous genetic polymorphisms affecting enzyme activity and drug response. Clinical utility of current CYP2D6 genotyping is partially compromised the unresolved complex haplotype structure of the CYP2D6 locus. We have identified a distal enhancer single-nucleotide polymorphism rs5758550 that robustly increases CYP2D6 expression, whereas rs16947 (CYP2D6*2), previously considered inert, reduces correct mRNA splicing and expression, thereby affecting presumed activity of other alleles on the *2 haplotype.

OBJECTIVE

This study aims to determine the structure and frequency of haplotypes containing either rs5758550 or rs16947, or both, together with other relevant CYP2D6 alleles, assigning predictive enzyme activity scores to each, and addressing ambiguities in estimating diplotypes in different populations.

METHODS

The structure and frequency of haplotypes containing rs5758550 and/or rs16947 in different populations were determined by using phased genotype data from 'The 1000 Genomes Project'. The assigned haplotype-phenotype relationship was tested by associating assigned CYP2D6 activity score with CYP2D6 enzyme activity in a cohort of 122 human liver microsomes.

RESULTS

Addition of enhancer single-nucleotide polymorphism rs5758550 and *2 to a CYP2D6 panel improves prediction of CYP2D6 activity. Moreover, the haplotype containing rs5758550 and rs16947 predict extensive CYP2D6 activity more accurately than CYP2D6*2A, a surrogate marker for extensive activity.

CONCLUSION

With further studies, the results support possible incorporation of rs5758550 and rs16947 into CYP2D6 biomarker panels for more accurate prediction of CYP2D6 metabolizer status.

Spinal muscular atrophy diagnosis and carrier screening from genome sequencing data

Purpose

Spinal muscular atrophy (SMA), caused by loss of the SMN1 gene, is a leading cause of early childhood death. Due to the near identical sequences of SMN1 and SMN2, analysis of this region is challenging. Population-wide SMA screening to quantify the SMN1 copy number (CN) is recommended by the American College of Medical Genetics and Genomics.

Methods

We developed a method that accurately identifies the CN of SMN1 and SMN2 using genome sequencing (GS) data by analyzing read depth and eight informative reference genome differences between SMN1/2.

Results

We characterized SMN1/2 in 12,747 genomes, identified 1568 samples with SMN1 gains or losses and 6615 samples with SMN2 gains or losses, and calculated a pan-ethnic carrier frequency of 2%, consistent with previous studies. Additionally, 99.8% of our SMN1 and 99.7% of SMN2 CN calls agreed with orthogonal methods, with a recall of 100% for SMA and 97.8% for carriers, and a precision of 100% for both SMA and carriers.

Conclusion

This SMN copy-number caller can be used to identify both carrier and affected status of SMA, enabling SMA testing to be offered as a comprehensive test in neonatal care and an accurate carrier screening tool in GS sequencing projects.

PharmVar GeneFocus: CYP2D6

The Pharmacogene Variation Consortium (PharmVar) provides nomenclature for the highly polymorphic human CYP2D6 gene locus. CYP2D6 genetic variation impacts the metabolism of numerous drugs and, thus, can impact drug efficacy and safety. This GeneFocus provides a comprehensive overview and summary of CYP2D6 genetic variation and describes how the information provided by PharmVar is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

Repurposing of Diagnostic Whole Exome Sequencing Data of 1,583 Individuals for Clinical Pharmacogenetics

For ~ 80 drugs, widely recognized pharmacogenetics dosing guidelines are available. However, the use of these guidelines in clinical practice remains limited as only a fraction of patients is subjected to pharmacogenetic screening. We investigated the feasibility of repurposing whole exome sequencing (WES) data for a panel of 42 variants in 11 pharmacogenes to provide a pharmacogenomic profile. Existing diagnostic WES‐data from child‐parent trios totaling 1,583 individuals were used. Results were successfully extracted for 39 variants. No information could be extracted for three variants, located in CYP2C19, UGT1A1, and CYP3A5, and for CYP2D6 copy number. At least one actionable phenotype was present in 86% of the individuals. Haplotype phasing proved relevant for CYP2B6 assignments as 1.5% of the phenotypes were corrected after phasing. In conclusion, repurposing WES‐data can yield meaningful pharmacogenetic profiles for 7 of 11 important pharmacogenes, which can be used to guide drug treatment.

Concepts Driving Pharmacogenomics Implementation Into Everyday Healthcare

Pharmacogenomics (PGx) is often promoted as the domain of precision medicine with the greatest potential to readily impact everyday healthcare. Rapid advances in PGx knowledge derived from extensive basic and clinical research along with decreasing costs of laboratory testing have led to an increased interest in PGx and expectations of imminent clinical translation with substantial clinical impact. However, the implementation of PGx into clinical workflows is neither simple nor straightforward, and comprehensive processes and multidisciplinary collaboration are required. Several national and international institutions have pioneered models for implementing clinical PGx, and these initial models have led to a better understanding of unresolved challenges. In this review, we have categorized and explored the most relevant of these challenges to highlight potential gaps and present possible solutions. We describe the ongoing need for basic and clinical research to drive further developments in evidence-based medicine. Integration into daily clinical workflows introduces new challenges requiring innovative solutions; specifically those related to the electronic health record and embedded clinical decision support. We describe advances in PGx testing and result reporting and describe the critical need for increased standardization in these areas across laboratories. We also explore the complexity of the PGx knowledge required for clinical practice and the need for educational strategies to ensure adequate understanding among members of current and future healthcare teams. Finally, we evaluate knowledge obtained from previous implementation efforts and discuss how to best apply these learnings to future projects. Despite these challenges, the future of precision medicine appears promising due to the rapidity of recent advances in the field and current multidisciplinary efforts to effectively translate PGx to everyday clinical practice.

Interrogation of CYP2D6 Structural Variant Alleles Improves the Correlation Between CYP2D6 Genotype and CYP2D6-Mediated Metabolic Activity

The cytochrome P450 2D6 (CYP2D6) gene locus is challenging to accurately genotype due to numerous single nucleotide variants and complex structural variation. Our goal was to determine whether the CYP2D6 genotype‐phenotype correlation is improved when diplotype assignments incorporate structural variation, identified by the bioinformatics tool Stargazer, with next‐generation sequencing data. Using CYP2D6 activity measured with substrates dextromethorphan and metoprolol, activity score explained 40% and 34% of variability in metabolite formation rates, respectively, when diplotype calls incorporated structural variation, increasing from 36% and 31%, respectively, when diplotypes did not incorporate structural variation. We also investigated whether the revised Clinical Pharmacogenetics Implementation Consortium (CPIC) recommendations for translating genotype to phenotype improve CYP2D6 activity predictions over the current system. Although the revised recommendations do not improve the correlation between activity score and CYP2D6 activity, perhaps because of low frequency of the CYP2D6*10 allele, the correlation with metabolizer phenotype group was significantly improved for both substrates. We also measured the function of seven rare coding variants: one (A449D) exhibited decreased (44%) and another (R474Q) increased (127%) activity compared with reference CYP2D6.1 protein. Allele‐specific analysis found that A449D is part of a novel CYP2D6*4 suballele, CYP2D6*4.028. The novel haplotype containing R474Q was designated CYP2D6*138 by PharmVar; another novel haplotype containing R365H was designated CYP2D6*139. Accuracy of CYP2D6 phenotype prediction is improved when the CYP2D6 gene locus is interrogated using next‐generation sequencing coupled with structural variation analysis. Additionally, revised CPIC genotype to phenotype translation recommendations provides an improvement in assigning CYP2D6 activity.

Pharmacogenomics Clinical Annotation Tool (PharmCAT)

Pharmacogenomics (PGx) decision support and return of results is an active area of precision medicine. One challenge of implementing PGx is extracting genomic variants and assigning haplotypes in order to apply prescribing recommendations and information from the Clinical Pharmacogenetics Implementation Consortium (CPIC), the US Food and Drug Administration (FDA), the Pharmacogenomics Knowledgebase (PharmGKB), etc. Pharmacogenomics Clinical Annotation Tool (PharmCAT) (i) extracts variants specified in guidelines from a genetic data set derived from sequencing or genotyping technologies, (ii) infers haplotypes and diplotypes, and (iii) generates a report containing genotype/diplotype-based annotations and guideline recommendations. We describe PharmCAT and a pilot validation project comparing results for 1000 Genomes Project sequences of Coriell samples with corresponding Genetic Testing Reference Materials Coordination Program (GeT-RM) sample characterization. PharmCAT was highly concordant with the GeT-RM data. PharmCAT is available in GitHub to evaluate, test, and report results back to the community. As precision medicine becomes more prevalent, our ability to consistently, accurately, and clearly define and report PGx annotations and prescribing recommendations is critical.

Characterizing the pharmacogenome using molecular inversion probes for targeted next-generation sequencing

Aim: This study assesses the technical performance and cost of a targeted next-generation sequencing (NGS) multigene pharmacogenetic (PGx) test. Materials & methods: A genetic test was developed for 21 PGx genes using molecular inversion probes to generate library fragments for NGS. Performance of this test was assessed using 53 unique reference control cell lines from the Genetic Testing Reference Materials Coordination Program (GeT-RM). Results: 93.7% of variants were successfully called and the repeatability rate was 99.9%. Reference calls were available for 78.4% of diplotype calls resulting from PGx testing, and concordance for the test was 85.7%. Cost per sample was $32–$56. Conclusion: A targeted NGS assay using molecular inversion probe technology is able to characterize the pharmacogenome efficiently.

Calling Star Alleles With Stargazer in 28 Pharmacogenes With Whole Genome Sequences

Variation in the enzymatic activity of pharmacogenes is defined by star alleles (haplotypes) comprised of single-nucleotide variants, small insertion-deletions, and large structural variants. We recently developed Stargazer, a next-generation sequencing-based tool to call star alleles for the clinically important CYP2D6 gene. Here, we present the utility of extending Stargazer to call star alleles for 28 pharmacogenes using whole genome sequencing (WGS) data. We applied Stargazer to WGS data from 70 ethnically diverse samples from the Genetic Testing Reference Materials Coordination Program (GeT-RM). These reference samples were extensively characterized by GeT-RM using multiple pharmacogenetic testing assays. In all 28 genes, Stargazer recalled 100% of star alleles (N = 92) present in GeT-RM's consensus genotypes (N = 1,559). Stargazer also detected star alleles not previously reported by GeT-RM, including complex structural variants. Our results demonstrate that combining WGS data and Stargazer enables automated, accurate, and comprehensive genotyping of pharmacogenes in the human genome.

Multi-site investigation of strategies for the clinical implementation of CYP2D6 genotyping to guide drug prescribing

Purpose:

A number of institutions have clinically implemented CYP2D6 genotyping to guide drug prescribing. We compared implementation strategies of early adopters of CYP2D6 testing, barriers faced by both early adopters and institutions in the process of implementing CYP2D6 testing, and approaches taken to overcome these barriers.

Methods:

We surveyed eight early adopters of CYP2D6 genotyping and eight institutions in the process of adoption. Data were collected on testing approaches, return of results procedures, applications of genotype results, challenges faced, and lessons learned.

Results:

Among early adopters, CYP2D6 testing was most commonly ordered to assist with opioid and antidepressant prescribing. Key differences among programs included test ordering and genotyping approaches, result reporting, and clinical decision support. However, all sites tested for copy number variation and 9 common variants, and reported results in the medical record. Most sites provided automatic consultation and had designated personnel to assist with genotype-informed therapy recommendations. Primary challenges were related to stakeholder support, CYP2D6 gene complexity, phenotype assignment, and sustainability.

Conclusion:

There are specific challenges unique to CYP2D6 testing given the complexity of the gene and its relevance to multiple medications. Consensus lessons learned may guide those interested in pursuing similar clinical pharmacogenetic programs.