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Harris L, McDonagh EM, Zhang X, Fawcett K, Foreman A, Daneck P, Sergouniotis PI, Parkinson H, Mazzarotto F, Inouye M, Hollox EJ, Birney E, Fitzgerald T. Genome-wide association testing beyond SNPs. Nat Rev Genet 2024:10.1038/s41576-024-00778-y. [PMID: 39375560 DOI: 10.1038/s41576-024-00778-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/03/2024] [Indexed: 10/09/2024]
Abstract
Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.
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Affiliation(s)
- Laura Harris
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, Hinxton, UK
| | - Ellen M McDonagh
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, Hinxton, UK
| | - Xiaolei Zhang
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, Hinxton, UK
| | - Katherine Fawcett
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, Hinxton, UK
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Amy Foreman
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, Hinxton, UK
| | - Petr Daneck
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Panagiotis I Sergouniotis
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, Hinxton, UK
- Division of Evolution, Infection and Genomics, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, Hinxton, UK
| | - Francesco Mazzarotto
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Michael Inouye
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Edward J Hollox
- Department of Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Ewan Birney
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, Hinxton, UK
| | - Tomas Fitzgerald
- European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Wellcome Genome Campus, Hinxton, UK.
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De Brabander EY, van Amelsvoort T, van Westrhenen R. Unidentified CYP2D6 genotype does not affect pharmacological treatment for patients with first episode psychosis. J Psychopharmacol 2024:2698811241279022. [PMID: 39344086 DOI: 10.1177/02698811241279022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
BACKGROUND Research on the pharmacogenetic influence of hepatic CYP450 enzyme 2D6 (CYP2D6) on metabolism of drugs for psychosis and associated outcome has been inconclusive. Some results suggest increased risk of adverse reactions in poor and intermediate metabolizers, while others find no relationship. However, retrospective designs may fail to account for the long-term pharmacological treatment of patients. Previous studies found that clinicians adapted risperidone dose successfully without knowledge of patient CYP2D6 phenotype. AIM Here, we aimed to replicate the results of those studies in a Dutch cohort of patients with psychosis (N = 418) on pharmacological treatment. METHOD We compared chlorpromazine-equivalent dose between CYP2D6 metabolizer phenotypes and investigated which factors were associated with dosage. This was repeated in two smaller subsets; patients prescribed pharmacogenetics-actionable drugs according to published guidelines, and risperidone-only as done previously. RESULTS We found no relationship between chlorpromazine-equivalent dose and phenotype in any sample (complete sample: p = 0.3, actionable-subset: p = 0.82, risperidone-only: p = 0.34). Only clozapine dose was weakly associated with CYP2D6 phenotype (p = 0.03). CONCLUSION Clinicians were thus not intuitively adapting dose to CYP2D6 activity in this sample, nor was CYP2D6 activity associated with prescribed dose. Although the previous studies could not be replicated, this study may provide support for existing and future pharmacogenetic research.
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Affiliation(s)
- Emma Y De Brabander
- Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience Research Institute, Maastricht University Medical Centre, The Netherlands
| | - Thérèse van Amelsvoort
- Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience Research Institute, Maastricht University Medical Centre, The Netherlands
| | - Roos van Westrhenen
- Department of Psychiatry, Parnassia Groep BV, The Netherlands
- Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
- St. John's National Academy of Health Sciences, Bangalore, India
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Halman A, Lunke S, Sadedin S, Moore C, Conyers R. Benchmarking pharmacogenomics genotyping tools: Performance analysis on short-read sequencing samples and depth-dependent evaluation. Clin Transl Sci 2024; 17:e13911. [PMID: 39123290 PMCID: PMC11315677 DOI: 10.1111/cts.13911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 07/04/2024] [Accepted: 07/12/2024] [Indexed: 08/12/2024] Open
Abstract
Pharmacogenomics (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.
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Affiliation(s)
- Andreas Halman
- Cancer Therapies, Stem Cell MedicineMurdoch Children's Research InstituteParkvilleVictoriaAustralia
- Victorian Clinical Genetics ServicesMurdoch Children's Research InstituteMelbourneVictoriaAustralia
- School of Population and Global HealthThe University of MelbourneParkvilleVictoriaAustralia
| | - Sebastian Lunke
- Victorian Clinical Genetics ServicesMurdoch Children's Research InstituteMelbourneVictoriaAustralia
| | - Simon Sadedin
- Victorian Clinical Genetics ServicesMurdoch Children's Research InstituteMelbourneVictoriaAustralia
- Department of PaediatricsThe University of MelbourneParkvilleVictoriaAustralia
| | - Claire Moore
- Cancer Therapies, Stem Cell MedicineMurdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PaediatricsThe University of MelbourneParkvilleVictoriaAustralia
| | - Rachel Conyers
- Cancer Therapies, Stem Cell MedicineMurdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PaediatricsThe University of MelbourneParkvilleVictoriaAustralia
- The Novo Nordisk Foundation Centre for Stem Cell Medicine, ReNEW, Melbourne NodeParkvilleVictoriaAustralia
- Children's Cancer Centre, The Royal Children's HospitalParkvilleVictoriaAustralia
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John S, Klumsathian S, Own‐eium P, Eu‐ahsunthornwattana J, Sura T, Dejsuphong D, Sritara P, Vathesatogkit P, Thongchompoo N, Thabthimthong W, Teerakulkittipong N, Chantratita W, Sukasem C. A comprehensive Thai pharmacogenomics database (TPGxD-1): Phenotype prediction and variants identification in 942 whole-genome sequencing data. Clin Transl Sci 2024; 17:e13830. [PMID: 38853370 PMCID: PMC11163017 DOI: 10.1111/cts.13830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/21/2024] [Accepted: 04/27/2024] [Indexed: 06/11/2024] Open
Abstract
Computational methods analyze genomic data to identify genetic variants linked to drug responses, thereby guiding personalized medicine. This study analyzed 942 whole-genome sequences from the Electricity Generating Authority of Thailand (EGAT) cohort to establish a population-specific pharmacogenomic database (TPGxD-1) in the Thai population. Sentieon (version 201808.08) implemented the GATK best workflow practice for variant calling. We then annotated variant call format (VCF) files using Golden Helix VarSeq 2.5.0 and employed Stargazer v2.0.2 for star allele analysis. The analysis of 63 very important pharmacogenes (VIPGx) reveals 85,566 variants, including 13,532 novel discoveries. Notably, we identified 464 known PGx variants and 275 clinically relevant novel variants. The phenotypic prediction of 15 VIPGx demonstrated a varied metabolic profile for the Thai population. Genes like CYP2C9 (9%), CYP3A5 (45.2%), CYP2B6 (9.4%), NUDT15 (15%), CYP2D6 (47%) and CYP2C19 (43%) showed a high number of intermediate metabolizers; CYP3A5 (41%), and CYP2C19 (9.9%) showed more poor metabolizers. CYP1A2 (52.7%) and CYP2B6 (7.6%) were found to have a higher number of ultra-metabolizers. The functional prediction of the remaining 10 VIPGx genes reveals a high frequency of decreased functional alleles in SULT1A1 (12%), NAT2 (84%), and G6PD (12%). SLCO1B1 reports 20% poor functional alleles, while PTGIS (42%), SLCO1B1 (4%), and TPMT (5.96%) showed increased functional alleles. This study discovered new variants and alleles in the 63 VIPGx genes among the Thai population, offering insights into advancing clinical pharmacogenomics (PGx). However, further validation is needed using other computational and genotyping methods.
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Affiliation(s)
- Shobana John
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
- Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC)Ramathibodi HospitalBangkokThailand
| | - Sommon Klumsathian
- Center for Medical Genomics, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Paravee Own‐eium
- Center for Medical Genomics, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | | | - Thanyachai Sura
- Division of Medical Genetics and Molecular Medicine, Department of Internal Medicine, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Donniphat Dejsuphong
- Program in Translational Medicine, Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathobodi HospitalMahidol UniversityBang PhliSamutprakarnThailand
| | - Piyamitr Sritara
- Department of Medicine, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Prin Vathesatogkit
- Department of Medicine, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Nartthawee Thongchompoo
- Center for Medical Genomics, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Wiphaporn Thabthimthong
- Center for Medical Genomics, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Nuttinee Teerakulkittipong
- Department of Pharmacology and Biopharmaceutical Sciences, Faculty of Pharmaceutical SciencesBurapha UniversityChonburiThailand
| | - Wasun Chantratita
- Center for Medical Genomics, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
| | - Chonlaphat Sukasem
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
- Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC)Ramathibodi HospitalBangkokThailand
- Department of Pharmacology and Biopharmaceutical Sciences, Faculty of Pharmaceutical SciencesBurapha UniversityChonburiThailand
- Department of Pharmacology and Therapeutics, MRC Centre for Drug Safety Science, Institute of Systems, Molecular and Integrative BiologyUniversity of LiverpoolLiverpoolUK
- Pharmacogenomics and Precision MedicineThe Preventive Genomics & Family Check‐up Services Center, Bumrungrad International HospitalBangkokThailand
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Ike JI, Smith IT, Mosley D, Madden C, Grossarth S, Halle BR, Lewis A, Mentch F, Hakonarson H, Bastarache L, Wheless L. Voriconazole metabolism is associated with the number of skin cancers per patient. Arch Dermatol Res 2024; 316:303. [PMID: 38819581 PMCID: PMC11143062 DOI: 10.1007/s00403-024-03135-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 04/29/2024] [Accepted: 05/02/2024] [Indexed: 06/01/2024]
Abstract
Voriconazole exposure is associated with skin cancer, but it is unknown how the full spectrum of its metabolizer phenotypes impacts this association. We conducted a retrospective cohort study to determine how variation in metabolism of voriconazole as measured by metabolizer status of CYP2C19 is associated with the total number of skin cancers a patient develops and the rate of development of the first skin cancer after treatment. There were 1,739 organ transplant recipients with data on CYP2C19 phenotype. Of these, 134 were exposed to voriconazole. There was a significant difference in the number of skin cancers after transplant based on exposure to voriconazole, metabolizer phenotype, and the interaction of these two (p < 0.01 for all three). This increase was driven primarily by number of squamous cell carcinomas among rapid metabolizes with voriconazole exposure (p < 0.01 for both). Patients exposed to voriconazole developed skin cancers more rapidly than those without exposure (Fine-Grey hazard ratio 1.78, 95% confidence interval 1.19-2.66). This association was similarly driven by development of SCC (Fine-Grey hazard ratio 1.83, 95% confidence interval 1.14-2.94). Differences in voriconazoles metabolism are associated with an increase in the number of skin cancers developed after transplant, particularly SCC.
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Affiliation(s)
| | - Isabelle T Smith
- Vanderbilt University College of Arts and Sciences, Nashville, TN, USA
| | | | - Christopher Madden
- State University of New York Downstate College of Medicine, Brooklyn, NY, USA
| | - Sarah Grossarth
- Quillen College of Medicine, East Tennessee State University, Johnson City, TN, USA
| | - Briana R Halle
- Irvine Department of Dermatology, University of California, Davis, USA
| | - Adam Lewis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Frank Mentch
- Children's Hospital of Philadelphia Center for Applied Genomics, Philadelphia, USA
| | - Hakon Hakonarson
- Children's Hospital of Philadelphia Center for Applied Genomics, Philadelphia, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lee Wheless
- Tennessee Valley Healthcare System VA Medical Center, Nashville, TN, USA.
- Department of Medicine, Division of Epidemiology, Department of Dermatology, Vanderbilt University Medical Center, 719 Thompson Lane, Suite 26300, Nashville, TN, 37215, USA.
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6
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Kennedy AM, Griffiths AM, Muise AM, Walters TD, Ricciuto A, Huynh HQ, Wine E, Jacobson K, Lawrence S, Carman N, Mack DR, deBruyn JC, Otley AR, Deslandres C, El-Matary W, Zachos M, Benchimol EI, Critch J, Schneider R, Crowley E, Li M, Warner N, McGovern DPB, Li D, Haritunians T, Rudin S, Cohn I. Landscape of TPMT and NUDT15 Pharmacogenetic Variation in a Cohort of Canadian Pediatric Inflammatory Bowel Disease Patients. Inflamm Bowel Dis 2024:izae109. [PMID: 38788739 DOI: 10.1093/ibd/izae109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND Patients with inflammatory bowel disease (IBD) exhibit considerable interindividual variability in medication response, highlighting the need for precision medicine approaches to optimize and tailor treatment. Pharmacogenetics (PGx) offers the ability to individualize dosing by examining genetic factors underlying the metabolism of medications such as thiopurines. Pharmacogenetic testing can identify individuals who may be at risk for thiopurine dose-dependent adverse reactions including myelosuppression. We aimed to evaluate PGx variation in genes supported by clinical guidelines that inform dosing of thiopurines and characterize differences in the distribution of actionable PGx variation among diverse ancestral groups. METHODS Pharmacogenetic variation in TPMT and NUDT15 was captured by genome-wide genotyping of 1083 pediatric IBD patients from a diverse Canadian cohort. Genetic ancestry was inferred using principal component analysis. The proportion of PGx variation and associated metabolizer status phenotypes was compared across 5 genetic ancestral groups within the cohort (Admixed American, African, East Asian, European, and South Asian) and to prior global estimates from corresponding populations. RESULTS Collectively, 11% of the cohort was categorized as intermediate or poor metabolizers of thiopurines, which would warrant a significant dose reduction or selection of alternate therapy. Clinically actionable variation in TPMT was more prevalent in participants of European and Admixed American/Latino ancestry (8.7% and 7.5%, respectively), whereas variation in NUDT15 was more prevalent in participants of East Asian and Admixed American/Latino ancestry (16% and 15% respectively). CONCLUSIONS These findings demonstrate the considerable interpopulation variability in PGx variation underlying thiopurine metabolism, which should be factored into testing diverse patient populations.
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Affiliation(s)
- April M Kennedy
- Division of Clinical Pharmacology and Toxicology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Anne M Griffiths
- SickKids IBD Centre, Division of Gastroenterology, Hepatology & Nutrition, The Hospital for Sick Children, Toronto, Ontario, Canada
- Child Health Evaluative Sciences, SickKids Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Aleixo M Muise
- SickKids IBD Centre, Division of Gastroenterology, Hepatology & Nutrition, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada
- Cell Biology Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Thomas D Walters
- SickKids IBD Centre, Division of Gastroenterology, Hepatology & Nutrition, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Amanda Ricciuto
- SickKids IBD Centre, Division of Gastroenterology, Hepatology & Nutrition, The Hospital for Sick Children, Toronto, Ontario, Canada
- Child Health Evaluative Sciences, SickKids Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Hien Q Huynh
- Edmonton Pediatric IBD Clinic, Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
| | - Eytan Wine
- Edmonton Pediatric IBD Clinic, Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
| | - Kevan Jacobson
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, BC Children's Hospital, University of British Columbia, Vancouver, BC, Canada
| | - Sally Lawrence
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, BC Children's Hospital, University of British Columbia, Vancouver, BC, Canada
| | - Nicholas Carman
- SickKids IBD Centre, Division of Gastroenterology, Hepatology & Nutrition, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - David R Mack
- CHEO IBD Centre, Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Children's Hospital of Eastern Ontario, CHEO Research Institute and Department of Pediatrics, University of Ottawa, Ottawa, Canada
| | - Jennifer C deBruyn
- Department of Pediatrics, Alberta Children's Hospital Research Institute (ACHRI), University of Calgary, Calgary, Alberta, Canada
| | - Anthony R Otley
- Division of Pediatric Gastroenterology & Nutrition, Department of Pediatrics, IWK Health Centre, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Colette Deslandres
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, CHU Sainte-Justine, Montréal, Quebec, Canada
| | - Wael El-Matary
- Section of Pediatric Gastroenterology, Winnipeg Children's Hospital, University of Manitoba, Winnipeg, MB, Canada
| | - Mary Zachos
- Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
| | - Eric I Benchimol
- SickKids IBD Centre, Division of Gastroenterology, Hepatology & Nutrition, The Hospital for Sick Children, Toronto, Ontario, Canada
- Child Health Evaluative Sciences, SickKids Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Jeffrey Critch
- Faculty of Medicine, Memorial University, St John's, Newfoundland & Labrador, Canada
| | - Rilla Schneider
- Division of Gastroenterology and Nutrition, Department of Pediatrics, Montreal Children's Hospital, Montreal, Quebec, Canada
| | - Eileen Crowley
- Department of Pediatrics, Division of Pediatric Gastroenterology & Hepatology, Children's Hospital Western Ontario, Western University, London, Ontario, Canada
| | - Michael Li
- The Centre for Computational Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Neil Warner
- Cell Biology Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
- SickKids IBD Centre, Division of Gastroenterology, Hepatology & Nutrition, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Dermot P B McGovern
- F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Dalin Li
- F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Talin Haritunians
- F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Sarah Rudin
- Division of Clinical Pharmacology and Toxicology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Iris Cohn
- Division of Clinical Pharmacology and Toxicology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
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Conyers R, Halman A, Moore C, Stenta T, Felmingham B, Collier L, Khatri D, Spelman T, Williams E, Dyas R, Kotecha RS, Jessop S, Mateos MK, Swen J, Elliott DA. Minimising Adverse Drug Reactions and Verifying Economic Legitimacy-Pharmacogenomics Implementation in Children (MARVEL- PIC): protocol for a national randomised controlled trial of pharmacogenomics implementation. BMJ Open 2024; 14:e085115. [PMID: 38760050 PMCID: PMC11103189 DOI: 10.1136/bmjopen-2024-085115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/18/2024] [Indexed: 05/19/2024] Open
Abstract
INTRODUCTION DNA-informed prescribing (termed pharmacogenomics, PGx) is the epitome of personalised medicine. Despite international guidelines existing, its implementation in paediatric oncology remains sparse. METHODS AND ANALYSIS Minimising Adverse Drug Reactions and Verifying Economic Legitimacy-Pharmacogenomics Implementation in Children is a national prospective, multicentre, randomised controlled trial assessing the impact of pre-emptive PGx testing for actionable PGx variants on adverse drug reaction (ADR) incidence in patients with a new cancer diagnosis or proceeding to haematopoetic stem cell transplant. All ADRs will be prospectively collected by surveys completed by parents/patients using the National Cancer Institute Pediatric Patient Reported [Ped-PRO]-Common Terminology Criteria for Adverse Events (CTCAE) (weeks 1, 6 and 12). Pharmacist will assess for causality and severity in semistructured interviews using the CTCAE and Liverpool Causality Assessment Tool. The primary outcome is a reduction in ADRs among patients with actionable PGx variants, where an ADR will be considered as any CTCAE grade 2 and above for non-haematological toxicities and any CTCAE grade 3 and above for haematological toxicities Cost-effectiveness of pre-emptive PGx (secondary outcome) will be compared with standard of care using hospital inpatient and outpatient data along with the validated Childhood Health Utility 9D Instrument. Power and statistics considerations: A sample size of 440 patients (220 per arm) will provide 80% power to detect a 24% relative risk reduction in the primary endpoint of ADRs (two-sided α=5%, 80% vs 61%), allowing for 10% drop-out. ETHICS AND DISSEMINATION The ethics approval of the trial has been obtained from the Royal Children's Hospital Ethics Committee (HREC/89083/RCHM-2022). The ethics committee of each participating centres nationally has undertaken an assessment of the protocol and governance submission. TRIAL REGISTRATION NUMBER NCT05667766.
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Affiliation(s)
- Rachel Conyers
- Cancer Therapies Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
- Children's Cancer Centre, The Royal Children's Hospital, Melbourne, Victoria, Australia
| | - Andreas Halman
- Cancer Therapies Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Claire Moore
- Cancer Therapies Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Tayla Stenta
- Cancer Therapies Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Ben Felmingham
- Cancer Therapies Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Lane Collier
- Cancer Therapies Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Dhrita Khatri
- Cancer Therapies Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Tim Spelman
- Department of Health Services Research and Implementation Science, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Burnet Institute, Melbourne, Victoria, Australia
| | - Elizabeth Williams
- Cancer Therapies Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Roxanne Dyas
- Cancer Therapies Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Rishi S Kotecha
- Perth Children's Hospital, Nedlands, Western Australia, Australia
- Curtin University Curtin Medical School, Bentley, Western Australia, Australia
| | - Sophie Jessop
- Michael Rice Centre for Haematology and Oncology, Women's and Children's Hospital Adelaide, North Adelaide, South Australia, Australia
| | - Marion K Mateos
- Sydney Children's Hospitals Network, Randwick, New South Wales, Australia
| | - Jesse Swen
- Leids Universitair Medisch Centrum, Leiden, Netherlands
| | - David A Elliott
- Cancer Therapies Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
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8
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Besterman AD. A genetics-guided approach to the clinical management of schizophrenia. Schizophr Res 2024; 267:462-469. [PMID: 37813777 DOI: 10.1016/j.schres.2023.09.042] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 09/27/2023] [Accepted: 09/29/2023] [Indexed: 10/11/2023]
Abstract
Schizophrenia is a highly heritable, severe mental illness characterized by hallucinations, delusions, social withdrawal, and cognitive dysfunction present in ∼1% of populations across cultures. There have been recent major advancements in our understanding of the genetic architecture of schizophrenia. Both rare, highly penetrant genetic variants as well as common, low-penetrant genetic variants can predispose individuals to schizophrenia and can impact the way people metabolize psychoactive medications used to treat schizophrenia. However, the impact of these findings on the clinical management of schizophrenia remains limited. This review highlights the few places where genetics currently informs schizophrenia management strategies, discusses major limitations, and reviews promising areas of genetics research that are most likely to impact future schizophrenia care. Specifically, I focuss on psychiatric genetic counseling, genetic testing strategies, pharmacogenetics, polygenic risk, and genetics-guided treatment. Lastly, I emphasize important ethical considerations in the clinical use of genetics for schizophrenia management, including the exacerbation of healthcare inequalities and unintended consequences of new genetic technologies.
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Affiliation(s)
- Aaron D Besterman
- University of California San Diego, Department of Psychiatry, San Diego, CA, USA; Rady Children's Hospital San Diego, Division of Behavioral Health Services, San Diego, CA, USA; Rady Children's Institute for Genomic Medicine, San Diego, CA, USA.
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9
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Ike JI, Smith IT, Mosley D, Madden C, Grossarth S, Halle BR, Lewis A, Mentch F, Hakonarson H, Bastarache L, Wheless L. Voriconazole Metabolism is Associated with the Number of Skin Cancers Per Patient. RESEARCH SQUARE 2024:rs.3.rs-4152279. [PMID: 38699337 PMCID: PMC11065087 DOI: 10.21203/rs.3.rs-4152279/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Voriconazole exposure is associated with skin cancer, but it is unknown how the full spectrum of its metabolizer phenotypes impacts this association. We conducted a retrospective cohort study to determine how variation in metabolism of voriconazole as measured by metabolizer status of CYP2C19 is associated with the total number of skin cancers a patient develops and the rate of development of the first skin cancer after treatment. There were 1,739 organ transplant recipients with data on CYP2C19 phenotype. Of these, 134 were exposed to voriconazole. There was a significant difference in the number of skin cancers after transplant based on exposure to voriconazole, metabolizer phenotype, and the interaction of these two (p < 0.01 for all three). This increase was driven primarily by number of squamous cell carcinomas among rapid metabolizes with voriconazole exposure (p < 0.01 for both). Patients exposed to voriconazole developed skin cancers more rapidly than those without exposure (Fine-Grey hazard ratio 1.78, 95% confidence interval 1.19-2.66). This association was similarly driven by development of SCC (Fine-Grey hazard ratio 1.83, 95% confidence interval 1.14-2.94). Differences in voriconazoles metabolism are associated with an increase in the number of skin cancers developed after transplant, particularly SCC.
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Affiliation(s)
| | | | | | | | - Sarah Grossarth
- Quillen College of Medicine, East Tennessee State University
| | - Briana R Halle
- University of California, Irvine, Department of Dermatology
| | - Adam Lewis
- Vanderbilt University Medical Center, Department of Biomedical Informatics
| | - Frank Mentch
- Children's Hospital of Philadelphia Center for Applied Genomics
| | | | - Lisa Bastarache
- Vanderbilt University Medical Center, Department of Biomedical Informatics
| | - Lee Wheless
- Tennessee Valley Healthcare System VA Medical Center
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10
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Calendo G, Kusic D, Madzo J, Gharani N, Scheinfeldt L. ursaPGx: a new R package to annotate pharmacogenetic star alleles using phased whole-genome sequencing data. FRONTIERS IN BIOINFORMATICS 2024; 4:1351620. [PMID: 38533129 PMCID: PMC10963438 DOI: 10.3389/fbinf.2024.1351620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/28/2024] [Indexed: 03/28/2024] Open
Abstract
Long-read sequencing technologies offer new opportunities to generate high-confidence phased whole-genome sequencing data for robust pharmacogenetic annotation. Here, we describe a new user-friendly R package, ursaPGx, designed to accept multi-sample phased whole-genome sequencing data VCF input files and output star allele annotations for pharmacogenes annotated in PharmVar.
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Affiliation(s)
- Gennaro Calendo
- Coriell Institute for Medical Research, Camden, NJ, United States
| | - Dara Kusic
- Coriell Institute for Medical Research, Camden, NJ, United States
| | - Jozef Madzo
- Coriell Institute for Medical Research, Camden, NJ, United States
- Cooper Medical School of Rowan University, Camden, NJ, United States
| | - Neda Gharani
- Coriell Institute for Medical Research, Camden, NJ, United States
- Gharani Consulting Limited, London, United Kingdom
| | - Laura Scheinfeldt
- Coriell Institute for Medical Research, Camden, NJ, United States
- Cooper Medical School of Rowan University, Camden, NJ, United States
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11
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Gustafson JA, Gibson SB, Damaraju N, Zalusky MPG, Hoekzema K, Twesigomwe D, Yang L, Snead AA, Richmond PA, De Coster W, Olson ND, Guarracino A, Li Q, Miller AL, Goffena J, Anderson Z, Storz SHR, Ward SA, Sinha M, Gonzaga-Jauregui C, Clarke WE, Basile AO, Corvelo A, Reeves C, Helland A, Musunuri RL, Revsine M, Patterson KE, Paschal CR, Zakarian C, Goodwin S, Jensen TD, Robb E, McCombie WR, Sedlazeck FJ, Zook JM, Montgomery SB, Garrison E, Kolmogorov M, Schatz MC, McLaughlin RN, Dashnow H, Zody MC, Loose M, Jain M, Eichler EE, Miller DE. Nanopore sequencing of 1000 Genomes Project samples to build a comprehensive catalog of human genetic variation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.05.24303792. [PMID: 38496498 PMCID: PMC10942501 DOI: 10.1101/2024.03.05.24303792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Less than half of individuals with a suspected Mendelian condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control datasets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project ONT Sequencing Consortium aims to generate LRS data from at least 800 of the 1000 Genomes Project samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37x and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs.
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Affiliation(s)
- Jonas A. Gustafson
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Sophia B. Gibson
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Nikhita Damaraju
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- Institute for Public Health Genetics, University of Washington, Seattle, WA, USA
| | - Miranda PG Zalusky
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - David Twesigomwe
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Lei Yang
- Pacific Northwest Research Institute, Seattle, WA, USA
| | | | | | - Wouter De Coster
- Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Nathan D. Olson
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Andrea Guarracino
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
- Human Technopole, Milan, Italy
| | - Qiuhui Li
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Angela L. Miller
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Joy Goffena
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Zachery Anderson
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Sophie HR Storz
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Sydney A. Ward
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Maisha Sinha
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Claudia Gonzaga-Jauregui
- International Laboratory for Human Genome Research, Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México
| | - Wayne E. Clarke
- New York Genome Center, New York, NY, USA
- Outlier Informatics Inc., Saskatoon, SK, Canada
| | | | | | | | | | | | - Mahler Revsine
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | | | - Cate R. Paschal
- Department of Laboratories, Seattle Children’s Hospital, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Christina Zakarian
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Sara Goodwin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Esther Robb
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | | | | | | | - Fritz J. Sedlazeck
- Human Genome Sequencing Center Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Justin M. Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | | | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Mikhail Kolmogorov
- Cancer Data Science Laboratory, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Michael C. Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Richard N. McLaughlin
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
- Pacific Northwest Research Institute, Seattle, WA, USA
| | - Harriet Dashnow
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Matt Loose
- Deep Seq, School of Life Sciences, University of Nottingham, Nottingham, England
| | - Miten Jain
- Department of Bioengineering, Department of Physics, Khoury College of Computer Sciences, Northeastern University, Boston, MA
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Danny E. Miller
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA
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12
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Zhou Q, Ghezelji M, Hari A, Ford MKB, Holley C, Mirabello L, Chanock S, Sahinalp SC, Numanagić I. Geny: A Genotyping Tool for Allelic Decomposition of Killer Cell Immunoglobulin-Like Receptor Genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.582413. [PMID: 38529502 PMCID: PMC10962708 DOI: 10.1101/2024.02.27.582413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Accurate genotyping of Killer cell Immunoglobulin-like Receptor (KIR) genes plays a pivotal role in enhancing our understanding of innate immune responses, disease correlations, and the advancement of personalized medicine. However, due to the high variability of the KIR region and high level of sequence similarity among different KIR genes, the currently available genotyping methods are unable to accurately infer copy numbers, genotypes and haplotypes of individual KIR genes from next-generation sequencing data. Here we introduce Geny, a new computational tool for precise genotyping of KIR genes. Geny utilizes available KIR haplotype databases and proposes a novel combination of expectation-maximization filtering schemes and integer linear programming-based combinatorial optimization models to resolve ambiguous reads, provide accurate copy number estimation and estimate the haplotype of each copy for the genes within the KIR region. We evaluated Geny on a large set of simulated short-read datasets covering the known validated KIR region assemblies and a set of Illumina short-read samples sequenced from 25 validated samples from the Human Pangenome Reference Consortium collection and showed that it outperforms the existing genotyping tools in terms of accuracy, precision and recall. We envision Geny becoming a valuable resource for understanding immune system response and consequently advancing the field of patient-centric medicine.
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13
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Zhou Y, Lauschke VM. Next-generation sequencing in pharmacogenomics - fit for clinical decision support? Expert Rev Clin Pharmacol 2024; 17:213-223. [PMID: 38247431 DOI: 10.1080/17512433.2024.2307418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
INTRODUCTION The technological advances of sequencing methods during the past 20 years have fuelled the generation of large amounts of sequencing data that comprise common variations, as well as millions of rare and personal variants that would not be identified by conventional genotyping. While comprehensive sequencing is technically feasible, its clinical utility for guiding personalized treatment decisions remains controversial. AREAS COVERED We discuss the opportunities and challenges of comprehensive sequencing compared to targeted genotyping for pharmacogenomic applications. Current pharmacogenomic sequencing panels are heterogeneous and clinical actionability of the included genes is not a major focus. We provide a current overview and critical discussion of how current studies utilize sequencing data either retrospectively from biobanks, databases or repurposed diagnostic sequencing, or prospectively using pharmacogenomic sequencing. EXPERT OPINION While sequencing-based pharmacogenomics has provided important insights into genetic variations underlying the safety and efficacy of a multitude pharmacological treatments, important hurdles for the clinical implementation of pharmacogenomic sequencing remain. We identify gaps in the interpretation of pharmacogenetic variants, technical challenges pertaining to complex loci and variant phasing, as well as unclear cost-effectiveness and incomplete reimbursement. It is critical to address these challenges in order to realize the promising prospects of pharmacogenomic sequencing.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
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14
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Abouelhoda M, Almuqati N, Abogosh A, Alfraih F, Maddirevula S, Alkuraya FS. Mining local exome and HLA data to characterize pharmacogenetic variants in Saudi Arabia. Hum Genet 2024; 143:125-136. [PMID: 38159139 DOI: 10.1007/s00439-023-02628-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024]
Abstract
Pharmacogenomics (PGx) is a promising field of precision medicine where efficacy of drugs is maximized while side effects are minimized for individual patients. Knowledge of the frequency of PGx-relevant variants (pharmacovariants) in the local population is a pre-requisite to informed policy making. Unfortunately, such knowledge is largely lacking from the Middle East. Here, we describe the use of a large clinical exome database (n = 13,473) and HLA haplotypes (n = 64,737) from Saudi Arabia, one of the largest countries in the Middle East, along with previously published data from the local population to ascertain allele frequencies of known pharmacovariants. In addition, we queried another exome database (n = 816) of well-phenotyped research subjects from Saudi Arabia to discover novel candidate variants in known PGx genes (pharmacogenes). Although our results show that only 26% (63/242) of class 1A/1B PharmGKB variants were identified, we estimate that 99.57% of the local population have at least one such variant. This translates to a minimum estimated impact of 9% of medications dispensed by our medical center annually. We also highlight the contribution of rare variants where 71% of the pharmacogenes devoid of common pharmacovariants had at least one potentially deleterious rare variant. Thus, we show that approaches that go beyond the use of commercial PGx kits that have been optimized for other populations should be implemented to ensure universal and equitable access of all members of the local population to personalized prescription practices.
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Affiliation(s)
- Mohamed Abouelhoda
- Department of Computational Sciences, Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Noura Almuqati
- Department of Translational Genomics, Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Ahmed Abogosh
- Department of Translational Genomics, Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Feras Alfraih
- Oncology Centre, Faisal Specialist Hospital and Research Centre, Riyadh, King, Saudi Arabia
| | - Sateesh Maddirevula
- Department of Translational Genomics, Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Fowzan S Alkuraya
- Department of Translational Genomics, Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
- Department of Anatomy and Cell Biology, College of Medicine, Alfaisal University, 11533, Riyadh, Saudi Arabia.
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15
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Ji Y, Shaaban S. Interrogating Pharmacogenetics Using Next-Generation Sequencing. J Appl Lab Med 2024; 9:50-60. [PMID: 38167765 DOI: 10.1093/jalm/jfad097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/18/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Pharmacogenetics or pharmacogenomics (PGx) is the study of the role of inherited or acquired sequence change in drug response. With the rapid evolution of molecular techniques, bioinformatic tools, and increased throughput of functional genomic studies, the discovery of PGx associations and clinical implementation of PGx test results have now moved beyond a handful variants in single pharmacogenes and multi-gene panels that interrogate a few pharmacogenes to whole-exome and whole-genome scales. Although some laboratories have adopted next-generation sequencing (NGS) as a testing platform for PGx and other molecular tests, most clinical laboratories that offer PGx tests still use targeted genotyping approaches. CONTENT This article discusses primarily the technical considerations for clinical laboratories to develop NGS-based PGx tests including whole-genome and whole-exome sequencing analyses and highlights the challenges and opportunities in test design, content selection, bioinformatic pipeline for PGx allele and diplotype assignment, rare variant classification, reporting, and briefly touches a few additional areas that are important for successful clinical implementation of PGx results. SUMMARY The accelerated speed of technology development associated with continuous cost reduction and enhanced ability to interrogate complex genome regions makes it inevitable for most, if not all, clinical laboratories to transition PGx testing to an NGS-based platform in the near future. It is important for laboratories and relevant professional societies to recognize both the potential and limitations of NGS-based PGx profiling, and to work together to develop a standard and consistent practice to maximize the variant or allele detection rate and utility of PGx testing.
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Affiliation(s)
- Yuan Ji
- Department of Pathology, University of Utah, Salt Lake City, UT, United States
- Molecular Genetics and Genomics, ARUP Laboratories, Salt Lake City, UT, United States
| | - Sherin Shaaban
- Department of Pathology, University of Utah, Salt Lake City, UT, United States
- Molecular Genetics and Genomics, ARUP Laboratories, Salt Lake City, UT, United States
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16
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Hsu JS, Wu DC, Shih SH, Liu JF, Tsai YC, Lee TL, Chen WA, Tseng YH, Lo YC, Lin HY, Chen YC, Chen JY, Chou TH, Chang DTH, Su MW, Guo WH, Mao HH, Chen CY, Chen PL. Complete genomic profiles of 1496 Taiwanese reveal curated medical insights. J Adv Res 2023:S2090-1232(23)00405-8. [PMID: 38159844 DOI: 10.1016/j.jare.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/03/2023] [Accepted: 12/27/2023] [Indexed: 01/03/2024] Open
Abstract
INTRODUCTION The population of Taiwan has a long history of ethno-cultural evolution. The Taiwanese population was isolated from other large populations such as the European, Han Chinese, and Japanese population. The Taiwan Biobank (TWB) project has built a nationwide database, particularly for personal whole-genome sequence (WGS) to facilitate basic and clinical collaboration nationally and internationally, making it one of the most valuable public datasets of the East Asian population. OBJECTIVES This study provides comprehensive medical genomic findings from TWB WGS data, for better characterization of disease susceptibility and the choice of ideal treatment regimens in Taiwanese population. METHODS We reanalyzed 1496 WGS using a PrecisionFDA Truth challenge winner method Sentieon DNAscope. Single nucleotide variants (SNV) and small insertions/deletions (INDEL) were benchmarked. We also analyzed pharmacogenomic (PGx) drug-associated alleles, and copy number variants (CNV). Multiple practicing clinicians reviewed and curated the clinically significant variants. Variant annotations can be browsed at TaiwanGenomes (https://genomes.tw). RESULTS We found that each participant had an average of 6,870.7 globally novel variants and 75.3% (831/1103) of the participants harbored at least one PharmGKB-selected high evidence level human leukocyte antigen (HLA) risk allele. 54 PharmGKB-reported high-level instances of evidence of Cytochrome P450 variant-drug pairs, with a population frequency of over 13.2%. We also identified 23 variants in the ACMG secondary finding V3 gene list from 25 participants, suggesting that 1.67% (25/1496) of the population is harboring at least one medical actionable variant. Our carrier status analyses suggest that one in 25 couples (3.94%) would risk having offspring with at least one pathogenic variant, which is in line with rates found in Japan and Singapore. For pathogenic CNV, we detected 6.88% and 2.02% carrier rates for alpha thalassemia and spinal muscular atrophy, respectively. CONCLUSION Our study highlights the overall medical insights of a complete Taiwanese genomic profile.
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Affiliation(s)
- Jacob Shujui Hsu
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei 100025, Taiwan; Institute of Molecular Medicine, National Taiwan University College of Medicine, Taipei 100233, Taiwan
| | - Dung-Chi Wu
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei 10617, Taiwan
| | - Shang-Hung Shih
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei 10617, Taiwan
| | - Jen-Feng Liu
- Institute of Molecular Medicine, National Taiwan University College of Medicine, Taipei 100233, Taiwan
| | - Ya-Chen Tsai
- Department of Biomechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Tung-Lin Lee
- Department of Medical Genetics, National Taiwan University Hospital, Taipei 100226, Taiwan
| | - Wei-An Chen
- Department of Medical Genetics, National Taiwan University Hospital, Taipei 100226, Taiwan
| | - Yi-Hsuan Tseng
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei 100025, Taiwan
| | - Yi-Chung Lo
- Department of Electrical Engineering, National Cheng-Kung University, Tainan 701401, Taiwan
| | - Hong-Ye Lin
- Department of Biomechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Yi-Chieh Chen
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei 100025, Taiwan
| | - Jing-Yi Chen
- Department of Electrical Engineering, National Cheng-Kung University, Tainan 701401, Taiwan
| | - Ting-Hsuan Chou
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei 100025, Taiwan
| | - Darby Tien-Hao Chang
- Department of Electrical Engineering, National Cheng-Kung University, Tainan 701401, Taiwan; Digital Technology Division, SinoPac Holdings, Taiwan
| | - Ming Wei Su
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115201, Taiwan
| | - Wei-Hong Guo
- Department of Biomechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Hsin-Hsiang Mao
- Department of Biomechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Chien-Yu Chen
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei 10617, Taiwan; Department of Biomechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan.
| | - Pei-Lung Chen
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei 100025, Taiwan; Institute of Molecular Medicine, National Taiwan University College of Medicine, Taipei 100233, Taiwan; Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei 10617, Taiwan; Department of Medical Genetics, National Taiwan University Hospital, Taipei 100226, Taiwan; Graduate Institute of Clinical Medicine, National Taiwan University College of Medicine, Taipei 100233, Taiwan.
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17
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Wroblewski TH, Witt KE, Lee SB, Malhi RS, Peede D, Huerta-Sánchez E, Villanea FA, Claw KG. Pharmacogenetic Variation in Neanderthals and Denisovans and Implications for Human Health and Response to Medications. Genome Biol Evol 2023; 15:evad222. [PMID: 38051947 PMCID: PMC10727477 DOI: 10.1093/gbe/evad222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 11/08/2023] [Accepted: 11/16/2023] [Indexed: 12/07/2023] Open
Abstract
Modern humans carry both Neanderthal and Denisovan (archaic) genome elements that are part of the human gene pool and affect the life and health of living individuals. The impact of archaic DNA may be particularly evident in pharmacogenes-genes responsible for the processing of exogenous substances such as food, pollutants, and medications-as these can relate to changing environmental effects, and beneficial variants may have been retained as modern humans encountered new environments. However, the health implications and contribution of archaic ancestry in pharmacogenes of modern humans remain understudied. Here, we explore 11 key cytochrome P450 genes (CYP450) involved in 75% of all drug metabolizing reactions in three Neanderthal and one Denisovan individuals and examine archaic introgression in modern human populations. We infer the metabolizing efficiency of these 11 CYP450 genes in archaic individuals and find important predicted phenotypic differences relative to modern human variants. We identify several single nucleotide variants shared between archaic and modern humans in each gene, including some potentially function-altering mutations in archaic CYP450 genes, which may result in altered metabolism in living people carrying these variants. We also identified several variants in the archaic CYP450 genes that are novel and unique to archaic humans as well as one gene, CYP2B6, that shows evidence for a gene duplication found only in Neanderthals and modern Africans. Finally, we highlight CYP2A6, CYP2C9, and CYP2J2, genes which show evidence for archaic introgression into modern humans and posit evolutionary hypotheses that explain their allele frequencies in modern populations.
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Affiliation(s)
- Tadeusz H Wroblewski
- Department of Biomedical Informatics, Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Kelsey E Witt
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, South Carolina, USA
| | - Seung-been Lee
- Precision Medicine Institute, Macrogen Inc., Seoul, Republic of Korea
| | - Ripan S Malhi
- Department of Anthropology and Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Illinois, USA
| | - David Peede
- Department of Ecology, Evolution, and Organismal Biology and Center for Computational and Molecular Biology, Brown University, Providence, Rhode Island, USA
- Institute at Brown for Environment and Society, Brown University, Providence, Rhode Island, USA
| | - Emilia Huerta-Sánchez
- Department of Ecology, Evolution, and Organismal Biology and Center for Computational and Molecular Biology, Brown University, Providence, Rhode Island, USA
| | | | - Katrina G Claw
- Department of Biomedical Informatics, Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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18
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Turner AJ, Nofziger C, Ramey BE, Ly RC, Bousman CA, Agúndez JAG, Sangkuhl K, Whirl-Carrillo M, Vanoni S, Dunnenberger HM, Ruano G, Kennedy MA, Phillips MS, Hachad H, Klein TE, Moyer AM, Gaedigk A. PharmVar Tutorial on CYP2D6 Structural Variation Testing and Recommendations on Reporting. Clin Pharmacol Ther 2023; 114:1220-1237. [PMID: 37669183 PMCID: PMC10840842 DOI: 10.1002/cpt.3044] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 08/23/2023] [Indexed: 09/07/2023]
Abstract
The Pharmacogene Variation Consortium (PharmVar) provides nomenclature for the highly polymorphic human CYP2D6 gene locus and a comprehensive summary of structural variation. CYP2D6 contributes to the metabolism of numerous drugs and, thus, genetic variation in its gene impacts drug efficacy and safety. To accurately predict a patient's CYP2D6 phenotype, testing must include structural variants including gene deletions, duplications, hybrid genes, and combinations thereof. This tutorial offers a comprehensive overview of CYP2D6 structural variation, terms, and definitions, a review of methods suitable for their detection and characterization, and practical examples to address the lack of standards to describe CYP2D6 structural variants or any other pharmacogene. This PharmVar tutorial offers practical guidance on how to detect the many, often complex, structural variants, as well as recommends terms and definitions for clinical and research reporting. Uniform reporting is not only essential for electronic health record-keeping but also for accurate translation of a patient's genotype into phenotype which is typically utilized to guide drug therapy.
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Affiliation(s)
- Amy J Turner
- Department of Pediatrics, Children’s Research Institute, The Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- RPRD Diagnostics LLC, Wauwatosa, Wisconsin, USA
| | | | | | - Reynold C Ly
- Department of Medical and Molecular Genetics, Division of Diagnostic Genomics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Chad A Bousman
- Department of Medical Genetics, University of Calgary, Calgary, Alberta, Canada
| | - José AG Agúndez
- University of Extremadura, Cáceres, Spain
- Institute of Molecular Pathology Biomarkers, Cáceres, Spain
| | - Katrin Sangkuhl
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | | | | | - Henry M Dunnenberger
- Mark R. Neaman Center for Personalized Medicine, NorthShore University Health System, Evanston, Illinois, USA
| | - Gualberto Ruano
- Institute of Living, Hartford Hospital (Hartford CT) and Department of Psychiatry, University of Connecticut School of Medicine (Farmington CT), USA
| | - Martin A Kennedy
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand
| | | | - Houda Hachad
- Houda Hachad, Department of Clinical Operations, AccessDx Laboratories, Houston, Texas, USA
| | - Teri E Klein
- Departments of Biomedical Data Science and Medicine (BMIR), Stanford University, Stanford, California, USA
| | - Ann M Moyer
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrea Gaedigk
- Children’s Mercy Research Institute (CMRI), Kansas City, Missouri, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, USA
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19
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Barthélémy D, Belmonte E, Pilla LD, Bardel C, Duport E, Gautier V, Payen L. Direct Comparative Analysis of a Pharmacogenomics Panel with PacBio Hifi ® Long-Read and Illumina Short-Read Sequencing. J Pers Med 2023; 13:1655. [PMID: 38138882 PMCID: PMC10744512 DOI: 10.3390/jpm13121655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/10/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND Pharmacogenetics (PGx) aims to determine genetic signatures that can be used in clinical settings to individualize treatment for each patient, including anti-cancer drugs, anti-psychotics, and painkillers. Taken together, a better understanding of the impacts of genetic variants on the corresponding protein function or expression permits the prediction of the pharmacological response: responders, non-responders, and those with adverse drug reactions (ADRs). OBJECTIVE This work provides a comparison between innovative long-read sequencing (LRS) and short-read sequencing (SRS) techniques. METHODS AND MATERIALS The gene panel captured using PacBio HiFi® sequencing was tested on thirteen clinical samples on GENTYANE's platform. SRS, using a comprehensive pharmacogenetics panel, was performed in routine settings at the Civil Hospitals of Lyon. We focused on complex regions analysis, including copy number variations (CNVs), structural variants, repeated regions, and phasing-haplotyping for three key pharmacogenes: CYP2D6, UGT1A1, and NAT2. RESULTS Variants and the corresponding expected star (*) alleles were reported. Although only 38.4% concordance was found for haplotype determination and 61.5% for diplotype, this did not affect the metabolism scoring. A better accuracy of LRS was obtained for the detection of the CYP2D6*5 haplotype in the presence of the duplicated wild-type CYP2D6*2 form. A total concordance was performed for UGT1A1 TA repeat detection. Direct phasing using the LRS approach allowed us to correct certain NAT2 profiles. CONCLUSIONS Combining an optimized variant-calling pipeline and with direct phasing analysis, LRS is a robust technique for PGx analysis that can minimize the risk of mis-haplotyping.
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Affiliation(s)
- David Barthélémy
- Institut of Pharmaceutical and Biological Sciences of Lyon, Claude Bernard Lyon I, 69373 Lyon, France; (D.B.); (C.B.)
- Department of Biochemistry and Molecular Biology, Lyon-Sud Hospital, Hospices Civils de Lyon, Réseau Francophone de Pharmacogénétique (RNPGx), 69495 Pierre-Bénite, France; (L.D.P.); (E.D.)
- Center for Innovation in Cancerology of Lyon (CICLY) EA 3738, Faculty of Medicine and Maieutic Lyon Sud, Claude Bernard University Lyon I, 69921 Oullins, France
| | - Elodie Belmonte
- Plateforme Génotypage et Séquençage en Auvergne (GENTYANE) UMR 1095 Génétique, Diversité Ecophysiologie des Céréales INRAE, Université Clermont Auvergne, 63100 Clermont Ferrand, France; (E.B.); (V.G.)
| | - Laurie Di Pilla
- Department of Biochemistry and Molecular Biology, Lyon-Sud Hospital, Hospices Civils de Lyon, Réseau Francophone de Pharmacogénétique (RNPGx), 69495 Pierre-Bénite, France; (L.D.P.); (E.D.)
| | - Claire Bardel
- Institut of Pharmaceutical and Biological Sciences of Lyon, Claude Bernard Lyon I, 69373 Lyon, France; (D.B.); (C.B.)
- Department of Bioinformatics, Hospices Civils de Lyon, 69008 Lyon, France
| | - Eve Duport
- Department of Biochemistry and Molecular Biology, Lyon-Sud Hospital, Hospices Civils de Lyon, Réseau Francophone de Pharmacogénétique (RNPGx), 69495 Pierre-Bénite, France; (L.D.P.); (E.D.)
| | - Veronique Gautier
- Plateforme Génotypage et Séquençage en Auvergne (GENTYANE) UMR 1095 Génétique, Diversité Ecophysiologie des Céréales INRAE, Université Clermont Auvergne, 63100 Clermont Ferrand, France; (E.B.); (V.G.)
| | - Léa Payen
- Institut of Pharmaceutical and Biological Sciences of Lyon, Claude Bernard Lyon I, 69373 Lyon, France; (D.B.); (C.B.)
- Department of Biochemistry and Molecular Biology, Lyon-Sud Hospital, Hospices Civils de Lyon, Réseau Francophone de Pharmacogénétique (RNPGx), 69495 Pierre-Bénite, France; (L.D.P.); (E.D.)
- Center for Innovation in Cancerology of Lyon (CICLY) EA 3738, Faculty of Medicine and Maieutic Lyon Sud, Claude Bernard University Lyon I, 69921 Oullins, France
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20
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Seligson ND, Kolesar JM, Alam B, Baker L, Lamba JK, Fridley BL, Salahudeen AA, Hertz DL, Hicks JK. Integrating pharmacogenomic testing into paired germline and somatic genomic testing in patients with cancer. Pharmacogenomics 2023; 24:731-738. [PMID: 37702060 DOI: 10.2217/pgs-2023-0125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023] Open
Abstract
Precision medicine has revolutionized clinical care for patients with cancer through the development of targeted therapy, identification of inherited cancer predisposition syndromes and the use of pharmacogenetics to optimize pharmacotherapy for anticancer drugs and supportive care medications. While germline (patient) and somatic (tumor) genomic testing have evolved separately, recent interest in paired germline/somatic testing has led to an increase in integrated genomic testing workflows. However, paired germline/somatic testing has generally lacked the incorporation of germline pharmacogenomics. Integrating pharmacogenomics into paired germline/somatic genomic testing would be an efficient method for increasing access to pharmacogenomic testing. In this perspective, the authors argue for the benefits of implementing a comprehensive approach integrating somatic and germline testing that is inclusive of pharmacogenomics in clinical practice.
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Affiliation(s)
- Nathan D Seligson
- Department of Pharmacotherapy & Translational Research, The University of Florida, Jacksonville, FL 32209, USA
- Center for Pharmacogenomics & Translational Research, Nemours Children's Health, Jacksonville, FL 32207, USA
| | - Jill M Kolesar
- Markey Cancer Center, University of Kentucky, Lexington, KY 40536, USA
- Department of Pharmacy Practice & Science, University of Kentucky, Lexington, KY 40536, USA
| | - Benish Alam
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI 48109, USA
| | - Laura Baker
- Nemours Center for Cancer & Blood Disorders, Nemours Children's Health, Wilmington, DE 19803, USA
| | - Jatinder K Lamba
- Department of Pharmacotherapy & Translational Research, The University of Florida, Gainesville, FL 32611, USA
| | - Brooke L Fridley
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Ameen A Salahudeen
- Department of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA
- Tempus Labs Inc., Chicago, IL 60654, USA
| | - Daniel L Hertz
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI 48109, USA
| | - J Kevin Hicks
- Department of Individualized Cancer Management, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
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21
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Tafazoli A, Mikros J, Khaghani F, Alimardani M, Rafigh M, Hemmati M, Siamoglou S, Golińska AK, Kamiński KA, Niemira M, Miltyk W, Patrinos GP. Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics. Hum Genomics 2023; 17:62. [PMID: 37452347 PMCID: PMC10347842 DOI: 10.1186/s40246-023-00508-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/03/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes. METHODS Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype-phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging. RESULTS Five deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99). CONCLUSION While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.
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Affiliation(s)
- Alireza Tafazoli
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy With the Division of Laboratory Medicine, Medical University of Bialystok, 15-089, Białystok, Poland
- Laboratory of Pharmacogenomics, Department of Molecular Neuropharmacology, Maj Institute of Pharmacology Polish Academy of Sciences, Kraków, Poland
| | - John Mikros
- Laboratory of Pharmacogenomics and Individualized Therapy, Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | - Faeze Khaghani
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Guilan University of Medical Sciences, Rasht, Iran
| | - Maliheh Alimardani
- Department of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahboobeh Rafigh
- Medical Genetics Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahboobeh Hemmati
- Department of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Stavroula Siamoglou
- Laboratory of Pharmacogenomics and Individualized Therapy, Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | | | - Karol A Kamiński
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
- Department of Cardiology, Medical University of Bialystok, Białystok, Poland
| | - Magdalena Niemira
- Clinical Research Centre, Medical University of Bialystok, Białystok, Poland
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy With the Division of Laboratory Medicine, Medical University of Bialystok, 15-089, Białystok, Poland.
| | - George P Patrinos
- Laboratory of Pharmacogenomics and Individualized Therapy, Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece.
- Zayed Center for Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.
- Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.
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22
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van Steenwijk HP, Winter E, Knaven E, Brouwers JF, van Baardwijk M, van Dalum JB, Luijendijk TJC, van Osch FHM, Troost FJ, Bast A, Semen KO, de Boer A. The beneficial effect of sulforaphane on platelet responsiveness during caloric load: a single-intake, double-blind, placebo-controlled, crossover trial in healthy participants. Front Nutr 2023; 10:1204561. [PMID: 37485383 PMCID: PMC10359317 DOI: 10.3389/fnut.2023.1204561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/15/2023] [Indexed: 07/25/2023] Open
Abstract
Background and aims As our understanding of platelet activation in response to infections and/or inflammatory conditions is growing, it is becoming clearer that safe, yet efficacious, platelet-targeted phytochemicals could improve public health beyond the field of cardiovascular diseases. The phytonutrient sulforaphane shows promise for clinical use due to its effect on inflammatory pathways, favorable pharmacokinetic profile, and high bioavailability. The potential of sulforaphane to improve platelet functionality in impaired metabolic processes has however hardly been studied in humans. This study investigated the effects of broccoli sprout consumption, as a source of sulforaphane, on urinary 11-dehydro-thromboxane B2 (TXB2), a stable thromboxane metabolite used to monitor eicosanoid biosynthesis and response to antithrombotic therapy, in healthy participants exposed to caloric overload. Methods In this double-blind, placebo-controlled, crossover trial 12 healthy participants were administered 16g of broccoli sprouts, or pea sprouts (placebo) followed by the standardized high-caloric drink PhenFlex given to challenge healthy homeostasis. Urine samples were collected during the study visits and analyzed for 11-dehydro-TXB2, sulforaphane and its metabolites. Genotyping was performed using Illumina GSA v3.0 DTCBooster. Results Administration of broccoli sprouts before the caloric load reduced urinary 11-dehydro-TXB2 levels by 50% (p = 0.018). The amount of sulforaphane excreted in the urine during the study visits correlated negatively with 11-dehydro-TXB2 (rs = -0.377, p = 0.025). Participants carrying the polymorphic variant NAD(P)H dehydrogenase quinone 1 (NQO1*2) showed decreased excretion of sulforaphane (p = 0.035). Conclusion Sulforaphane was shown to be effective in targeting platelet responsiveness after a single intake. Our results indicate an inverse causal relationship between sulforaphane and 11-dehydro-TXB2, which is unaffected by the concomitant intake of the metabolic challenge. 11-Dehydro-TXB2 shows promise as a non-invasive, sensitive, and suitable biomarker to investigate the effects of phytonutrients on platelet aggregation within hours. Clinical trial registration [https://clinicaltrials.gov/], identifier [NCT05146804].
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Affiliation(s)
- Hidde P. van Steenwijk
- Food Claims Centre Venlo, Faculty of Science and Engineering, Maastricht University, Maastricht, Netherlands
| | - Evi Winter
- Food Claims Centre Venlo, Faculty of Science and Engineering, Maastricht University, Maastricht, Netherlands
| | - Edward Knaven
- Research Group Analysis Techniques in the Life Sciences, Avans University of Applied Sciences, Breda, Netherlands
| | - Jos F. Brouwers
- Research Group Analysis Techniques in the Life Sciences, Avans University of Applied Sciences, Breda, Netherlands
| | - Myrthe van Baardwijk
- Omnigen B.V., Delft, Netherlands
- Department of Pathology and Clinical Bioinformatics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | | | | | - Frits H. M. van Osch
- Department of Clinical Epidemiology, VieCuri Medical Center, Venlo, Netherlands
- Department of Epidemiology, NUTRIM, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Freddy J. Troost
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Food Innovation and Health, Centre for Healthy Eating and Food Innovation, Maastricht University, Maastricht, Netherlands
| | - Aalt Bast
- University College Venlo, Faculty of Science and Engineering, Maastricht University, Maastricht, Netherlands
- Department of Pharmacology and Toxicology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Khrystyna O. Semen
- University College Venlo, Faculty of Science and Engineering, Maastricht University, Maastricht, Netherlands
| | - Alie de Boer
- Food Claims Centre Venlo, Faculty of Science and Engineering, Maastricht University, Maastricht, Netherlands
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23
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Shugg T, Ly RC, Osei W, Rowe EJ, Granfield CA, Lynnes TC, Medeiros EB, Hodge JC, Breman AM, Schneider BP, Sahinalp SC, Numanagić I, Salisbury BA, Bray SM, Ratcliff R, Skaar TC. Computational pharmacogenotype extraction from clinical next-generation sequencing. Front Oncol 2023; 13:1199741. [PMID: 37469403 PMCID: PMC10352904 DOI: 10.3389/fonc.2023.1199741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/22/2023] [Indexed: 07/21/2023] Open
Abstract
Background Next-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. Methods Germline 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. Results Mean 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. Conclusion Aldy 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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Affiliation(s)
- Tyler Shugg
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Reynold C. Ly
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Wilberforce Osei
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Elizabeth J. Rowe
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Caitlin A. Granfield
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Ty C. Lynnes
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Elizabeth B. Medeiros
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Jennelle C. Hodge
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Amy M. Breman
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Bryan P. Schneider
- Division of Hematology/Oncology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - S. Cenk Sahinalp
- Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD, United States
| | - Ibrahim Numanagić
- Department of Computer Science, University of Victoria, Victoria, BC, Canada
| | | | | | | | - Todd C. Skaar
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
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24
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Turner AJ, Derezinski AD, Gaedigk A, Berres ME, Gregornik DB, Brown K, Broeckel U, Scharer G. Characterization of complex structural variation in the CYP2D6-CYP2D7-CYP2D8 gene loci using single-molecule long-read sequencing. Front Pharmacol 2023; 14:1195778. [PMID: 37426826 PMCID: PMC10324673 DOI: 10.3389/fphar.2023.1195778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 05/30/2023] [Indexed: 07/11/2023] Open
Abstract
Complex regions in the human genome such as repeat motifs, pseudogenes and structural (SVs) and copy number variations (CNVs) present ongoing challenges to accurate genetic analysis, particularly for short-read Next-Generation-Sequencing (NGS) technologies. One such region is the highly polymorphic CYP2D loci, containing CYP2D6, a clinically relevant pharmacogene contributing to the metabolism of >20% of common drugs, and two highly similar pseudogenes, CYP2D7 and CYP2D8. Multiple complex SVs, including CYP2D6/CYP2D7-derived hybrid genes are known to occur in different configurations and frequencies across populations and are difficult to detect and characterize accurately. This can lead to incorrect enzyme activity assignment and impact drug dosing recommendations, often disproportionally affecting underrepresented populations. To improve CYP2D6 genotyping accuracy, we developed a PCR-free CRISPR-Cas9 based enrichment method for targeted long-read sequencing that fully characterizes the entire CYP2D6-CYP2D7-CYP2D8 loci. Clinically relevant sample types, including blood, saliva, and liver tissue were sequenced, generating high coverage sets of continuous single molecule reads spanning the entire targeted region of up to 52 kb, regardless of SV present (n = 9). This allowed for fully phased dissection of the entire loci structure, including breakpoints, to accurately resolve complex CYP2D6 diplotypes with a single assay. Additionally, we identified three novel CYP2D6 suballeles, and fully characterized 17 CYP2D7 and 18 CYP2D8 unique haplotypes. This method for CYP2D6 genotyping has the potential to significantly improve accurate clinical phenotyping to inform drug therapy and can be adapted to overcome testing limitations of other clinically challenging genomic regions.
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Affiliation(s)
| | | | - Andrea Gaedigk
- Children’s Mercy Research Institute, Kansas City, MO, United States
| | - Mark E. Berres
- Biotechnology Center, University of Wisconsin Madison, Madison, WI, United States
| | | | - Keith Brown
- Jumpcode Genomics, San Diego, CA, United States
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25
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Atiq MA, Peterson SE, Langman LJ, Baudhuin LM, Black JL, Moyer AM. Determination of the Duplicated CYP2D6 Allele Using Real-Time PCR Signal: An Alternative Approach. J Pers Med 2023; 13:883. [PMID: 37373874 DOI: 10.3390/jpm13060883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/17/2023] [Accepted: 05/20/2023] [Indexed: 06/29/2023] Open
Abstract
CYP2D6 duplication has important pharmacogenomic implications. Reflex testing with long-range PCR (LR-PCR) can resolve the genotype when a duplication and alleles with differing activity scores are detected. We evaluated whether visual inspection of plots from real-time-PCR-based targeted genotyping with copy number variation (CNV) detection could reliably determine the duplicated CYP2D6 allele. Six reviewers evaluated QuantStudio OpenArray CYP2D6 genotyping results and the TaqMan Genotyper plots for seventy-three well-characterized cases with three copies of CYP2D6 and two different alleles. Reviewers blinded to the final genotype visually assessed the plots to determine the duplicated allele or opt for reflex sequencing. Reviewers achieved 100% accuracy for cases with three CYP2D6 copies that they opted to report. Reviewers did not request reflex sequencing in 49-67 (67-92%) cases (and correctly identified the duplicated allele in each case); all remaining cases (6-24) were marked by at least one reviewer for reflex sequencing. In most cases with three copies of CYP2D6, the duplicated allele can be determined using a combination of targeted genotyping using real-time PCR with CNV detection without need for reflex sequencing. In ambiguous cases and those with >3 copies, LR-PCR and Sanger sequencing may still be necessary for determination of the duplicated allele.
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Affiliation(s)
- Mazen A Atiq
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st Street Southwest, Rochester, MN 55905, USA
| | - Sandra E Peterson
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st Street Southwest, Rochester, MN 55905, USA
| | - Loralie J Langman
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st Street Southwest, Rochester, MN 55905, USA
| | - Linnea M Baudhuin
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st Street Southwest, Rochester, MN 55905, USA
| | - John L Black
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st Street Southwest, Rochester, MN 55905, USA
| | - Ann M Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st Street Southwest, Rochester, MN 55905, USA
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26
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Samarasinghe SR, Hoy W, Jadhao S, McMorran BJ, Guchelaar HJ, Nagaraj SH. The pharmacogenomic landscape of an Indigenous Australian population. Front Pharmacol 2023; 14:1180640. [PMID: 37284308 PMCID: PMC10241071 DOI: 10.3389/fphar.2023.1180640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 04/07/2023] [Indexed: 06/08/2023] Open
Abstract
Background: Population genomic studies of individuals of Indigenous ancestry have been extremely limited comprising <0.5% of participants in international genetic databases and genome-wide association studies, contributing to a "genomic gap" that limits their access to personalised medicine. While Indigenous Australians face a high burden of chronic disease and associated medication exposure, corresponding genomic and drug safety datasets are sorely lacking. Methods: To address this, we conducted a pharmacogenomic study of almost 500 individuals from a founder Indigenous Tiwi population. Whole genome sequencing was performed using short-read Illumina Novaseq6000 technology. We characterised the pharmacogenomics (PGx) landscape of this population by analysing sequencing results and associated pharmacological treatment data. Results: We observed that every individual in the cohort carry at least one actionable genotype and 77% of them carry at least three clinically actionable genotypes across 19 pharmacogenes. Overall, 41% of the Tiwi cohort were predicted to exhibit impaired CYP2D6 metabolism, with this frequency being much higher than that for other global populations. Over half of the population predicted an impaired CYP2C9, CYP2C19, and CYP2B6 metabolism with implications for the processing of commonly used analgesics, statins, anticoagulants, antiretrovirals, antidepressants, and antipsychotics. Moreover, we identified 31 potentially actionable novel variants within Very Important Pharmacogenes (VIPs), five of which were common among the Tiwi. We further detected important clinical implications for the drugs involved with cancer pharmacogenomics such as thiopurines and tamoxifen, immunosuppressants like tacrolimus and certain antivirals used in the hepatitis C treatment due to potential differences in their metabolic processing. Conclusion: The pharmacogenomic profiles generated in our study demonstrate the utility of pre-emptive PGx testing and have the potential to help guide the development and application of precision therapeutic strategies tailored to Tiwi Indigenous patients. Our research provides valuable insights on pre-emptive PGx testing and the feasibility of its use in ancestrally diverse populations, emphasizing the need for increased diversity and inclusivity in PGx investigations.
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Affiliation(s)
| | - Wendy Hoy
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Sudhir Jadhao
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Brendan J McMorran
- John Curtin School of Medical Research, College of Health and Medicine, Australian National University, Canberra, ACT, Australia
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
| | - Shivashankar H Nagaraj
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, Australia
- Translational Research Institute, Queensland University of Technology, Brisbane, QLD, Australia
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27
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Bertholim-Nasciben L, Scliar MO, Debortoli G, Thiruvahindrapuram B, Scherer SW, Duarte YAO, Zatz M, Suarez-Kurtz G, Parra EJ, Naslavsky MS. Characterization of pharmacogenomic variants in a Brazilian admixed cohort of elderly individuals based on whole-genome sequencing data. Front Pharmacol 2023; 14:1178715. [PMID: 37234706 PMCID: PMC10206227 DOI: 10.3389/fphar.2023.1178715] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 04/10/2023] [Indexed: 05/28/2023] Open
Abstract
Introduction: Research in the field of pharmacogenomics (PGx) aims to identify genetic variants that modulate response to drugs, through alterations in their pharmacokinetics (PK) or pharmacodynamics (PD). The distribution of PGx variants differs considerably among populations, and whole-genome sequencing (WGS) plays a major role as a comprehensive approach to detect both common and rare variants. This study evaluated the frequency of PGx markers in the context of the Brazilian population, using data from a population-based admixed cohort from Sao Paulo, Brazil, which includes variants from WGS of 1,171 unrelated, elderly individuals. Methods: The Stargazer tool was used to call star alleles and structural variants (SVs) from 38 pharmacogenes. Clinically relevant variants were investigated, and the predicted drug response phenotype was analyzed in combination with the medication record to assess individuals potentially at high-risk of gene-drug interaction. Results: In total, 352 unique star alleles or haplotypes were observed, of which 255 and 199 had a frequency < 0.05 and < 0.01, respectively. For star alleles with frequency > 5% (n = 97), decreased, loss-of-function and unknown function accounted for 13.4%, 8.2% and 27.8% of alleles or haplotypes, respectively. Structural variants (SVs) were identified in 35 genes for at least one individual, and occurred with frequencies >5% for CYP2D6, CYP2A6, GSTM1, and UGT2B17. Overall 98.0% of the individuals carried at least one high risk genotype-predicted phenotype in pharmacogenes with PharmGKB level of evidence 1A for drug interaction. The Electronic Health Record (EHR) Priority Result Notation and the cohort medication registry were combined to assess high-risk gene-drug interactions. In general, 42.0% of the cohort used at least one PharmGKB evidence level 1A drug, and 18.9% of individuals who used PharmGKB evidence level 1A drugs had a genotype-predicted phenotype of high-risk gene-drug interaction. Conclusion: This study described the applicability of next-generation sequencing (NGS) techniques for translating PGx variants into clinically relevant phenotypes on a large scale in the Brazilian population and explores the feasibility of systematic adoption of PGx testing in Brazil.
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Affiliation(s)
- Luciana Bertholim-Nasciben
- School of Public Health, University of São Paulo, São Paulo, Brazil
- Human Genome and Stem Cell Research Center, University of São Paulo, São Paulo, Brazil
| | - Marilia O. Scliar
- Human Genome and Stem Cell Research Center, University of São Paulo, São Paulo, Brazil
| | - Guilherme Debortoli
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, ON, Canada
| | | | - Stephen W. Scherer
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Molecular Genetics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Yeda A. O. Duarte
- Medical-Surgical Nursing Department, School of Nursing, University of São Paulo, São Paulo, Brazil
| | - Mayana Zatz
- Human Genome and Stem Cell Research Center, University of São Paulo, São Paulo, Brazil
- Department of Genetics and Evolutionary Biology, Biosciences Institute, University of São Paulo, São Paulo, Brazil
| | - Guilherme Suarez-Kurtz
- Divisão de Pesquisa Clínica e Desenvolvimento Tecnológico, Instituto Nacional de Câncer, Rio de Janeiro, Brazil
| | - Esteban J. Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, ON, Canada
| | - Michel S. Naslavsky
- Human Genome and Stem Cell Research Center, University of São Paulo, São Paulo, Brazil
- Department of Genetics and Evolutionary Biology, Biosciences Institute, University of São Paulo, São Paulo, Brazil
- Hospital Israelita Albert Einstein, São Paulo, Brazil
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28
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Nunez-Torres R, Pita G, Peña-Chilet M, López-López D, Zamora J, Roldán G, Herráez B, Álvarez N, Alonso MR, Dopazo J, Gonzalez-Neira A. A Comprehensive Analysis of 21 Actionable Pharmacogenes in the Spanish Population: From Genetic Characterisation to Clinical Impact. Pharmaceutics 2023; 15:pharmaceutics15041286. [PMID: 37111771 PMCID: PMC10140932 DOI: 10.3390/pharmaceutics15041286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/03/2023] [Accepted: 04/16/2023] [Indexed: 04/29/2023] Open
Abstract
The implementation of pharmacogenetics (PGx) is a main milestones of precision medicine nowadays in order to achieve safer and more effective therapies. Nevertheless, the implementation of PGx diagnostics is extremely slow and unequal worldwide, in part due to a lack of ethnic PGx information. We analysed genetic data from 3006 Spanish individuals obtained by different high-throughput (HT) techniques. Allele frequencies were determined in our population for the main 21 actionable PGx genes associated with therapeutical changes. We found that 98% of the Spanish population harbours at least one allele associated with a therapeutical change and, thus, there would be a need for a therapeutical change in a mean of 3.31 of the 64 associated drugs. We also identified 326 putative deleterious variants that were not previously related with PGx in 18 out of the 21 main PGx genes evaluated and a total of 7122 putative deleterious variants for the 1045 PGx genes described. Additionally, we performed a comparison of the main HT diagnostic techniques, revealing that after whole genome sequencing, genotyping with the PGx HT array is the most suitable solution for PGx diagnostics. Finally, all this information was integrated in the Collaborative Spanish Variant Server to be available to and updated by the scientific community.
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Affiliation(s)
- Rocio Nunez-Torres
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
| | - Guillermo Pita
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
| | - María Peña-Chilet
- Computational Medicine Platform, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, 41013 Sevilla, Spain
- Bioinformatics in Rare Diseases (BiER), Centre for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, 41013 Sevilla, Spain
- Computational Systems Medicine Group, Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Seville, Spain
| | - Daniel López-López
- Computational Medicine Platform, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, 41013 Sevilla, Spain
- Bioinformatics in Rare Diseases (BiER), Centre for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, 41013 Sevilla, Spain
- Computational Systems Medicine Group, Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Seville, Spain
| | - Jorge Zamora
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
| | - Gema Roldán
- Computational Medicine Platform, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, 41013 Sevilla, Spain
| | - Belén Herráez
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
| | - Nuria Álvarez
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
| | - María Rosario Alonso
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
| | - Joaquín Dopazo
- Computational Medicine Platform, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, 41013 Sevilla, Spain
- Bioinformatics in Rare Diseases (BiER), Centre for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, 41013 Sevilla, Spain
- Computational Systems Medicine Group, Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Seville, Spain
- Functional Genomics Node, FPS/ELIXIR-ES, Hospital Virgen del Rocío, 41013 Sevilla, Spain
| | - Anna Gonzalez-Neira
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER-U706), ISCIII, 28029 Madrid, Spain
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29
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Liu Y, Lin Z, Chen Q, Chen Q, Sang L, Wang Y, Shi L, Guo L, Yu Y. PAnno: A pharmacogenomics annotation tool for clinical genomic testing. Front Pharmacol 2023; 14:1008330. [PMID: 36778023 PMCID: PMC9909284 DOI: 10.3389/fphar.2023.1008330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 01/16/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction: Next-generation sequencing (NGS) technologies have been widely used in clinical genomic testing for drug response phenotypes. However, the inherent limitations of short reads make accurate inference of diplotypes still challenging, which may reduce the effectiveness of genotype-guided drug therapy. Methods: An automated Pharmacogenomics Annotation tool (PAnno) was implemented, which reports prescribing recommendations and phenotypes by parsing the germline variant call format (VCF) file from NGS and the population to which the individual belongs. Results: A ranking model dedicated to inferring diplotypes, developed based on the allele (haplotype) definition and population allele frequency, was introduced in PAnno. The predictive performance was validated in comparison with four similar tools using the consensus diplotype data of the Genetic Testing Reference Materials Coordination Program (GeT-RM) as ground truth. An annotation method was proposed to summarize prescribing recommendations and classify drugs into avoid use, use with caution, and routine use, following the recommendations of the Clinical Pharmacogenetics Implementation Consortium (CPIC), etc. It further predicts phenotypes of specific drugs in terms of toxicity, dosage, efficacy, and metabolism by integrating the high-confidence clinical annotations in the Pharmacogenomics Knowledgebase (PharmGKB). PAnno is available at https://github.com/PreMedKB/PAnno. Discussion: PAnno provides an end-to-end clinical pharmacogenomics decision support solution by resolving, annotating, and reporting germline variants.
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Affiliation(s)
- Yaqing Liu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zipeng Lin
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Leqing Sang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yunjin Wang
- Department of Breast Surgery, Precision Cancer Medicine Center, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Shanghai, China,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Li Guo
- State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China,School of Chemical Engineering, University of Chinese Academy of Sciences, Beijing, China,*Correspondence: Li Guo, ; Ying Yu,
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China,*Correspondence: Li Guo, ; Ying Yu,
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30
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Zhou Y, Lauschke VM. Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy. Handb Exp Pharmacol 2023; 280:237-260. [PMID: 35792943 DOI: 10.1007/164_2022_596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Over the last decade, next-generation sequencing (NGS) methods have become increasingly used in various areas of human genomics. In routine clinical care, their use is already implemented in oncology to profile the mutational landscape of a tumor, as well as in rare disease diagnostics. However, its utilization in pharmacogenomics is largely lacking behind. Recent population-scale genome data has revealed that human pharmacogenes carry a plethora of rare genetic variations that are not interrogated by conventional array-based profiling methods and it is estimated that these variants could explain around 30% of the genetically encoded functional pharmacogenetic variability.To interpret the impact of such variants on drug response a multitude of computational tools have been developed, but, while there have been major advancements, it remains to be shown whether their accuracy is sufficient to improve personalized pharmacogenetic recommendations in robust trials. In addition, conventional short-read sequencing methods face difficulties in the interrogation of complex pharmacogenes and high NGS test costs require stringent evaluations of cost-effectiveness to decide about reimbursement by national healthcare programs. Here, we illustrate current challenges and discuss future directions toward the clinical implementation of NGS to inform genotype-guided decision-making.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.
- University of Tuebingen, Tuebingen, Germany.
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31
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Genome screening, reporting, and genetic counseling for healthy populations. Hum Genet 2023; 142:181-192. [PMID: 36331656 PMCID: PMC9638226 DOI: 10.1007/s00439-022-02480-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/16/2022] [Indexed: 11/06/2022]
Abstract
Rapid advancements of genome sequencing (GS) technologies have enhanced our understanding of the relationship between genes and human disease. To incorporate genomic information into the practice of medicine, new processes for the analysis, reporting, and communication of GS data are needed. Blood samples were collected from adults with a PCR-confirmed SARS-CoV-2 (COVID-19) diagnosis (target N = 1500). GS was performed. Data were filtered and analyzed using custom pipelines and gene panels. We developed unique patient-facing materials, including an online intake survey, group counseling presentation, and consultation letters in addition to a comprehensive GS report. The final report includes results generated from GS data: (1) monogenic disease risks; (2) carrier status; (3) pharmacogenomic variants; (4) polygenic risk scores for common conditions; (5) HLA genotype; (6) genetic ancestry; (7) blood group; and, (8) COVID-19 viral lineage. Participants complete pre-test genetic counseling and confirm preferences for secondary findings before receiving results. Counseling and referrals are initiated for clinically significant findings. We developed a genetic counseling, reporting, and return of results framework that integrates GS information across multiple areas of human health, presenting possibilities for the clinical application of comprehensive GS data in healthy individuals.
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32
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Hari A, Zhou Q, Gonzaludo N, Harting J, Scott SA, Qin X, Scherer S, Sahinalp SC, Numanagić I. An efficient genotyper and star-allele caller for pharmacogenomics. Genome Res 2023; 33:61-70. [PMID: 36657977 PMCID: PMC9977157 DOI: 10.1101/gr.277075.122] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 12/12/2022] [Indexed: 01/20/2023]
Abstract
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 identification, 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 uses combinatorial optimization for accurate star-allele calling across different sequencing technologies. Aldy 4 adds support for long reads and uses 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 Pacific Biosciences (PacBio) HiFi. We show that Aldy 4 is the most accurate star-allele caller with near-perfect accuracy in all evaluated contexts, and hope that Aldy remains an invaluable tool in the clinical toolbox even with the advent of long-read sequencing technologies.
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Affiliation(s)
- Ananth Hari
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland 20742, USA;,Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Qinghui Zhou
- Department of Computer Science, University of Victoria, Victoria, British Columbia V8P 5C2, Canada
| | | | - John Harting
- Pacific Biosciences, Menlo Park, California 94025, USA
| | - Stuart A. Scott
- Department of Pathology, Stanford University, Palo Alto, California 94304, USA
| | - Xiang Qin
- Baylor College of Medicine Human Genome Sequencing Center, Houston, Texas 77030, USA
| | - Steve Scherer
- Baylor College of Medicine Human Genome Sequencing Center, Houston, Texas 77030, USA
| | - S. Cenk Sahinalp
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Ibrahim Numanagić
- Department of Computer Science, University of Victoria, Victoria, British Columbia V8P 5C2, Canada
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33
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Tafazoli A, van der Lee M, Swen JJ, Zeller A, Wawrusiewicz-Kurylonek N, Mei H, Vorderman RHP, Konopko K, Zankiewicz A, Miltyk W. Development of an extensive workflow for comprehensive clinical pharmacogenomic profiling: lessons from a pilot study on 100 whole exome sequencing data. THE PHARMACOGENOMICS JOURNAL 2022; 22:276-283. [PMID: 35963939 PMCID: PMC9674517 DOI: 10.1038/s41397-022-00286-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 07/20/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
This pilot study is aimed at implementing an approach for comprehensive clinical pharmacogenomics (PGx) profiling. Fifty patients with cardiovascular diseases and 50 healthy individuals underwent whole-exome sequencing. Data on 1800 PGx genes were extracted and analyzed through deep filtration separately. Theoretical drug induced phenoconversion was assessed for the patients, using sequence2script. In total, 4539 rare variants (including 115 damaging non-synonymous) were identified. Four publicly available PGx bioinformatics algorithms to assign PGx haplotypes were applied to nine selected very important pharmacogenes (VIP) and revealed a 45-70% concordance rate. To ensure availability of the results at point-of-care, actionable variants were stored in a web-hosted database and PGx-cards were developed for quick access and handed to the study subjects. While a comprehensive clinical PGx profile could be successfully extracted from WES data, available tools to interpret these data demonstrated inconsistencies that complicate clinical application.
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Affiliation(s)
- Alireza Tafazoli
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, 15-089, Bialystok, Poland
- Clinical Research Centre, University Hospital of Bialystok, 15-276, Bialystok, Poland
| | - Maaike van der Lee
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Jesse J Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Anna Zeller
- Clinical Research Centre, University Hospital of Bialystok, 15-276, Bialystok, Poland
| | | | - Hailiang Mei
- Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Ruben H P Vorderman
- Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Krzysztof Konopko
- Department of Photonics, Electronics, and Lighting Technology, Faculty of Electrical Engineering, Bialystok University of Technology, 15-351, Bialystok, Poland
| | - Andrzej Zankiewicz
- Department of Photonics, Electronics, and Lighting Technology, Faculty of Electrical Engineering, Bialystok University of Technology, 15-351, Bialystok, Poland
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, 15-089, Bialystok, Poland.
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34
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Wang WY, Twesigomwe D, Nofziger C, Turner AJ, Helmecke LS, Broeckel U, Derezinski AD, Hazelhurst S, Gaedigk A. Characterization of Novel CYP2D6 Alleles across Sub-Saharan African Populations. J Pers Med 2022; 12:1575. [PMID: 36294714 PMCID: PMC9605556 DOI: 10.3390/jpm12101575] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/11/2022] [Accepted: 09/19/2022] [Indexed: 07/16/2024] Open
Abstract
The CYP2D6 gene has been widely studied to characterize variants and/or star alleles, which account for a significant portion of variability in drug responses observed within and between populations. However, African populations remain under-represented in these studies. The increasing availability of high coverage genomes from African populations has provided the opportunity to fill this knowledge gap. In this study, we characterized computationally predicted novel CYP2D6 star alleles in 30 African subjects for whom DNA samples were available from the Coriell Institute. CYP2D6 genotyping and resequencing was performed using a variety of commercially available and laboratory-developed tests in a collaborative effort involving three laboratories. Fourteen novel CYP2D6 alleles and multiple novel suballeles were identified. This work adds to the growing catalogue of validated African ancestry CYP2D6 allelic variation in pharmacogenomic databases, thus laying the foundation for future functional studies and improving the accuracy of CYP2D6 genotyping, phenotype prediction, and the refinement of clinical pharmacogenomic implementation guidelines in African and global settings.
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Affiliation(s)
- Wendy Y. Wang
- Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children’s Mercy Research Institute, Kansas City, MO 64108, USA
| | - David Twesigomwe
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2001, South Africa
| | | | - Amy J. Turner
- Section of Genomic Pediatrics, Department of Pediatrics, Children’s Research Institute, The Medical College of Wisconsin, Milwaukee, WI 53226, USA
- RPRD Diagnostics LLC, Milwaukee, WI 53226, USA
| | - Lena-Sophie Helmecke
- PharmGenetix GmbH, A-5020 Niederalm, Austria
- Department of Biosciences, University of Salzburg, A-5020 Salzburg, Austria
| | - Ulrich Broeckel
- Section of Genomic Pediatrics, Department of Pediatrics, Children’s Research Institute, The Medical College of Wisconsin, Milwaukee, WI 53226, USA
- RPRD Diagnostics LLC, Milwaukee, WI 53226, USA
| | - Ashley D. Derezinski
- Section of Genomic Pediatrics, Department of Pediatrics, Children’s Research Institute, The Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa
- School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg 2001, South Africa
| | - Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children’s Mercy Research Institute, Kansas City, MO 64108, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA
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Booyse RP, Twesigomwe D, Hazelhurst S. Characterization of POR haplotype distribution in African populations and comparison with other global populations. Pharmacogenomics 2022; 23:771-782. [PMID: 36043428 PMCID: PMC9531186 DOI: 10.2217/pgs-2022-0082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/08/2022] [Indexed: 11/21/2022] Open
Abstract
Background & aim: POR is an enzyme that mediates electron transfer to enable the drug-metabolizing activity of CYP450 proteins. However, POR has been understudied in pharmacogenomics despite this vital role. This study aimed to characterize the genetic variation in POR across African populations and to compare the star allele (haplotype) distribution with that in other global populations. Materials & methods: POR star alleles were called from whole-genome sequencing data using the StellarPGx pipeline. Results: In addition to the common POR*1 and *28 (defined by rs1057868), five novel rare haplotypes were computationally inferred. No significant frequency differences were observed among the majority of African populations. However, POR*28 was observed at a higher frequency in individuals of non-African ancestry. Conclusion: This study highlights the distribution of POR alleles in Africa and across global populations with a view toward informing future precision medicine implementation.
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Affiliation(s)
- Ross P Booyse
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - David Twesigomwe
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
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36
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Verdez S, Albuisson J, Duffourd Y, Boidot R, Reda M, Thauvin-Robinet C, Fumet JD, Ladoire S, Nambot S, Callier P, Faivre L, Ghiringhelli F, Picard N. Detection of relevant pharmacogenetic information through exome sequencing in oncology. Pharmacogenomics 2022; 23:759-770. [PMID: 36043386 DOI: 10.2217/pgs-2022-0085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Background: Germline sequencing of individual genomes can detect alleles responsible for adverse drug reactions (ADRs) in relation to chemotherapy, targeted agents, antiemetics or pain treatment. Materials & methods: To evaluate the interest of such pharmacogenetic information, the authors retrospectively analyzed genes known to have an impact on cancer therapy in a cohort of 445 solid cancers patients. Results: Six patients treated with 5-fluorouracil carrying one DPYD variant classified as 1A showed decreased drug mean clearance (p = 0.01). Regarding CYP2D6, all patients (n = 5) with predicted CYP2D6 poor or ultra-rapid metabolizer status experienced adverse drug reactions related to opioid therapy. Conclusion: Genomic germline sequencing performed for theragnostic issues in patients with a solid tumor, can provide relevant information about common pharmacogenetic alleles.
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Affiliation(s)
- Simon Verdez
- UMR1231 GAD, Inserm - Université Bourgogne-Franche Comté, Dijon, France.,Unité Fonctionnelle Innovation en Diagnostic génomique des maladies rares, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, 21000, France
| | - Juliette Albuisson
- Platform of Transfer in Cancer Biology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, 21000, France.,Genomic & Immunotherapy Medical Institute, Dijon, 21000, France
| | - Yannis Duffourd
- UMR1231 GAD, Inserm - Université Bourgogne-Franche Comté, Dijon, France.,Unité Fonctionnelle Innovation en Diagnostic génomique des maladies rares, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, 21000, France
| | - Romain Boidot
- Platform of Transfer in Cancer Biology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, 21000, France.,Genomic & Immunotherapy Medical Institute, Dijon, 21000, France.,Department of Tumour Biology & Pathology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, 21000, France
| | - Manon Reda
- Platform of Transfer in Cancer Biology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, 21000, France.,Department of Tumour Biology & Pathology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, 21000, France.,Department of Medical Oncology, Georges François Leclerc Cancer Center - UNICANCER, 1 rue Professeur Marion, Dijon, 21000, France
| | - Christel Thauvin-Robinet
- Unité Fonctionnelle Innovation en Diagnostic génomique des maladies rares, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, 21000, France.,Genomic & Immunotherapy Medical Institute, Dijon, 21000, France.,Centre de Référence Maladies Rares "Anomalies du Développement et Syndromes Malformatifs", Centre de Génétique, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, 21000, France
| | - Jean-David Fumet
- Department of Medical Oncology, Georges François Leclerc Cancer Center - UNICANCER, 1 rue Professeur Marion, Dijon, 21000, France
| | - Sylvain Ladoire
- Department of Medical Oncology, Georges François Leclerc Cancer Center - UNICANCER, 1 rue Professeur Marion, Dijon, 21000, France
| | - Sophie Nambot
- Unité Fonctionnelle Innovation en Diagnostic génomique des maladies rares, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, 21000, France.,Centre de Référence Maladies Rares "Anomalies du Développement et Syndromes Malformatifs", Centre de Génétique, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, 21000, France
| | - Patrick Callier
- UMR1231 GAD, Inserm - Université Bourgogne-Franche Comté, Dijon, France.,Unité Fonctionnelle Innovation en Diagnostic génomique des maladies rares, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, 21000, France
| | - Laurence Faivre
- Unité Fonctionnelle Innovation en Diagnostic génomique des maladies rares, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, 21000, France.,Genomic & Immunotherapy Medical Institute, Dijon, 21000, France.,Centre de Référence Maladies Rares "Anomalies du Développement et Syndromes Malformatifs", Centre de Génétique, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, 21000, France
| | - François Ghiringhelli
- Platform of Transfer in Cancer Biology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, 21000, France.,Genomic & Immunotherapy Medical Institute, Dijon, 21000, France.,Department of Tumour Biology & Pathology, Georges François Leclerc Cancer Center - UNICANCER, Dijon, 21000, France.,Department of Medical Oncology, Georges François Leclerc Cancer Center - UNICANCER, 1 rue Professeur Marion, Dijon, 21000, France
| | - Nicolas Picard
- Inserm U1248, Service de Pharmacologie et Toxicologie, Université de Limoges, CHU de Limoges, Limoges, 87000, France
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Lu HF, Liu TY, Chou YP, Chang SS, Hsieh YW, Chang JG, Tsai FJ. Comprehensive characterization of pharmacogenes in a Taiwanese Han population. Front Genet 2022; 13:948616. [PMID: 36092904 PMCID: PMC9452738 DOI: 10.3389/fgene.2022.948616] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/06/2022] [Indexed: 11/15/2022] Open
Abstract
Pharmacogenetic (PGx) testing has not been well adopted in current clinical practice. The phenotypic distribution of clinically relevant pharmacogenes remains to be fully characterized in large population cohorts. In addition, no study has explored actionable PGx alleles in the East Asian population at a large scale. This study comprehensively analyzed 14 actionable pharmacogene diplotypes and phenotypes in 172,854 Taiwanese Han individuals by using their genotype data. Furthermore, we analyzed data from electronic medical records to investigate the effect of the actionable phenotypes on the individuals. The PGx phenotype frequencies were comparable between our cohort and the East Asian population. Overall, 99.9% of the individuals harbored at least one actionable PGx phenotype, and 29% of them have been prescribed a drug to which they may exhibit an atypical response. Our findings can facilitate the clinical application of PGx testing and the optimization of treatment and dosage individually.
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Affiliation(s)
- Hsing-Fang Lu
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Ting-Yuan Liu
- Center for Precision Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Yu-Pao Chou
- Center for Precision Medicine, China Medical University Hospital, Taichung, Taiwan
- Department of Laboratory Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Shih-Sheng Chang
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan
| | - Yow-Wen Hsieh
- Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan
- School of Pharmacy, College of Pharmacy, China Medical University, Taichung, Taiwan
| | - Jan-Gowth Chang
- Center for Precision Medicine, China Medical University Hospital, Taichung, Taiwan
- Epigenome Research Center, China Medical University Hospital, Taichung, Taiwan
- *Correspondence: Jan-Gowth Chang, ; Fuu-Jen Tsai,
| | - Fuu-Jen Tsai
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
- Division of Medical Genetics, Children’s Hospital of China Medical University, Taichung, Taiwan
- Department of Biotechnology and Bioinformatics, Asia University, Taichung, Taiwan
- *Correspondence: Jan-Gowth Chang, ; Fuu-Jen Tsai,
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38
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Tuteja S, Yu Z, Wilson O, Chen H, Wendt F, Chung CP, Shah SC, Hunt CM, Suzuki A, Chanfreau C, Gorman BR, Joseph J, Luoh S, Napolioni V, Robinson‐Cohen C, Tao R, Zhou J, Chang K, Hung AM. Pharmacogenetic variants and risk of remdesivir-associated liver enzyme elevations in Million Veteran Program participants hospitalized with COVID-19. Clin Transl Sci 2022; 15:1880-1886. [PMID: 35684976 PMCID: PMC9347806 DOI: 10.1111/cts.13313] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/04/2022] [Accepted: 05/06/2022] [Indexed: 01/17/2023] Open
Abstract
Remdesivir is the first US Food and Drug Administration (FDA)-approved drug for the treatment of coronavirus disease 2019 (COVID-19). We conducted a retrospective pharmacogenetic study to examine remdesivir-associated liver enzyme elevation among Million Veteran Program participants hospitalized with COVID-19 between March 15, 2020, and June 30, 2021. Pharmacogene phenotypes were assigned using Stargazer. Linear regression was performed on peak log-transformed enzyme values, stratified by population, adjusted for age, sex, baseline liver enzymes, comorbidities, and 10 population-specific principal components. Patients on remdesivir had higher peak alanine aminotransferase (ALT) values following treatment initiation compared with patients not receiving remdesivir. Remdesivir administration was associated with a 33% and 24% higher peak ALT in non-Hispanic White (NHW) and non-Hispanic Black (NHB) participants (p < 0.001), respectively. In a multivariable model, NHW CYP2C19 intermediate/poor metabolizers had a 9% increased peak ALT compared with NHW normal/rapid/ultrarapid metabolizers (p = 0.015); this association was not observed in NHB participants. In summary, remdesivir-associated ALT elevations appear to be multifactorial, and further studies are needed.
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Affiliation(s)
- Sony Tuteja
- Corporal Michael J. Crescenz VA Medical Center and University of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Zhihong Yu
- Tennessee Valley Healthcare System Nashville VA and Vanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Otis Wilson
- Tennessee Valley Healthcare System Nashville VA and Vanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Hua‐Chang Chen
- Tennessee Valley Healthcare System Nashville VA and Vanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Frank Wendt
- VA CT Healthcare System and Yale School of Medicine Department of PsychiatryNew HavenConnecticutUSA
| | - Cecilia P. Chung
- Tennessee Valley Healthcare System Nashville VA and Vanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Shailja C. Shah
- VA San Diego Healthcare System and Division of GastroenterologyUniversity of California San DiegoSan DiegoCaliforniaUSA
| | - Christine M. Hunt
- Durham VA Healthcare System and Duke University School of MedicineDurhamNorth CarolinaUSA
| | - Ayako Suzuki
- Durham VA Healthcare System and Duke University School of MedicineDurhamNorth CarolinaUSA
| | - Catherine Chanfreau
- VA Informatics and Computing Infrastructure (VINCI)VA Salt Lake City Health Care SystemSalt Lake CityUtahUSA
| | | | - Jacob Joseph
- Cardiology Section, VA Boston Healthcare System and Cardiovascular DivisionBrigham & Women's HospitalBostonMassachusettsUSA
| | | | - Valerio Napolioni
- School of Biosciences and Veterinary MedicineUniversity of CamerinoCamerinoItaly,Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Cassianne Robinson‐Cohen
- Division of Nephrology and Hypertension, Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Ran Tao
- Department of BiostatisticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jin Zhou
- Department of Epidemiology and BiostatisticsUniversity of ArizonaTucsonArizonaUSA
| | - Kyong‐Mi Chang
- Corporal Michael J. Crescenz VA Medical Center and University of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Adriana M. Hung
- Tennessee Valley Healthcare System Nashville VA and Vanderbilt University Medical CenterNashvilleTennesseeUSA
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ClinPharmSeq: A targeted sequencing panel for clinical pharmacogenetics implementation. PLoS One 2022; 17:e0272129. [PMID: 35901010 PMCID: PMC9333201 DOI: 10.1371/journal.pone.0272129] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 07/12/2022] [Indexed: 12/02/2022] Open
Abstract
The accurate identification of genetic variants contributing to therapeutic drug response or adverse effects is the first step in implementation of precision drug therapy. Targeted sequencing has recently become a common methodology for large-scale studies of genetic variation thanks to its favorable balance between low cost, high throughput, and deep coverage. Here, we present ClinPharmSeq, a targeted sequencing panel of 59 genes with associations to pharmacogenetic (PGx) phenotypes, as a platform to explore the relationship between drug response and genetic variation, both common and rare. For validation, we sequenced DNA from 64 ethnically diverse Coriell samples with ClinPharmSeq to call star alleles (haplotype patterns) in 27 genes using the bioinformatics tool PyPGx. These reference samples were extensively characterized by multiple laboratories using PGx testing assays and, more recently, whole genome sequencing. We found that ClinPharmSeq can consistently generate deep-coverage data (mean = 274x) with high uniformity (30x or above = 94.8%). Our genotype analysis identified a total of 185 unique star alleles from sequencing data, and showed that diplotype calls from ClinPharmSeq are highly concordant with that from previous publications (97.6%) and whole genome sequencing (97.9%). Notably, all 19 star alleles with complex structural variation including gene deletions, duplications, and hybrids were recalled with 100% accuracy. Altogether, these results demonstrate that the ClinPharmSeq platform offers a feasible path for broad implementation of PGx testing and optimization of individual drug treatments.
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40
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Analytical Validation of a Computational Method for Pharmacogenetic Genotyping from Clinical Whole Exome Sequencing. J Mol Diagn 2022; 24:576-585. [PMID: 35452844 DOI: 10.1016/j.jmoldx.2022.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/31/2022] [Accepted: 03/04/2022] [Indexed: 12/24/2022] Open
Abstract
Germline whole exome sequencing from molecular tumor boards has the potential to be repurposed to support clinical pharmacogenomics. However, accurately calling pharmacogenomics-relevant genotypes from exome sequencing data remains challenging. Accordingly, this study assessed the analytical validity of the computational tool, Aldy, in calling pharmacogenomics-relevant genotypes from exome sequencing data for 13 major pharmacogenes. Germline DNA from whole blood was obtained for 164 subjects seen at an institutional molecular solid tumor board. All subjects had whole exome sequencing from Ashion Analytics and panel-based genotyping from an institutional pharmacogenomics laboratory. Aldy version 3.3 was operationalized on the LifeOmic Precision Health Cloud with copy number fixed to two copies per gene. Aldy results were compared with those from genotyping for 56 star allele-defining variants within CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DPYD, G6PD, NUDT15, SLCO1B1, and TPMT. Read depth was >100× for all variants except CYP3A4∗22. For 75 subjects in the validation cohort, all 3393 Aldy variant calls were concordant with genotyping. Aldy calls for 736 diplotypes containing alleles assessed by both platforms were also concordant. Aldy identified additional star alleles not covered by targeted genotyping for 139 diplotypes. Aldy accurately called variants and diplotypes for 13 major pharmacogenes, except for CYP2D6 variants involving copy number variations, thus allowing repurposing of whole exome sequencing to support clinical pharmacogenomics.
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41
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Hwang M, Medley S, Shakeel F, Vanderwerff B, Zawistowski M, Kidwell KM, Hertz DL. Lack of association of CYP2B6 pharmacogenetics with cyclophosphamide toxicity in patients with cancer. Support Care Cancer 2022; 30:7355-7363. [PMID: 35606478 DOI: 10.1007/s00520-022-07118-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/29/2022] [Indexed: 11/27/2022]
Abstract
PURPOSE Cyclophosphamide is a commonly used cancer agent that is metabolically activated by polymorphic enzymes. This study aims to investigate the association between predicted activity of candidate pharmacogenes with severe toxicity during cyclophosphamide treatment. METHODS Genome-wide genetic data was collected from an institutional genetic data repository for CYP2B6, CYP3A4, CYP2C9, CYP2C19, GSTA1, GSTP1, ALDH1A1, ALDH3A1, ABCC1, ABCB1, and ERCC1. Treatment and toxicity data were retrospectively collected from the patient's medical record. The a priori selected primary hypothesis was that patients who have CYP2B6 reduced metabolizer activity (poor or intermediate (PM/IM) vs. normal (NM) metabolizer) have lower risk of severe toxicity or cyclophosphamide treatment modification due to toxicity. RESULTS In the primary analysis of 510 cyclophosphamide-treated patients with available genetic data, there was no difference in the odds of severe toxicity or treatment modification due to toxicity in CYP2B6 PM/IM vs. NM (odds ratio = 0.97, 95% Confidence Interval: 0.62-1.50, p = 0.88). In an exploratory, statistically uncorrected secondary analysis, carriers of the ALDH1A1 rs8187996 variant had a lower risk of the primary toxicity endpoint compared with wild-type homozygous patients (odds ratio = 0.31, 95% Confidence Interval: 0.09-0.78, p = 0.028). None of the other tested phenotypes or genotypes was associated with the primary or secondary endpoints in unadjusted analysis (all p > 0.05). CONCLUSION The finding that patients who carry ALDH1A1 rs8187996 may have a lower risk of cyclophosphamide toxicity than wild-type patients contradicts a prior finding for this variant and should be viewed with skepticism. We found weak evidence that any of these candidate pharmacogenetic predictors of cyclophosphamide toxicity may be useful to personalize cyclophosphamide dosing to optimize therapeutic outcomes in patients with cancer.
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Affiliation(s)
- Mary Hwang
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Room 2560C, 428 Church St., Ann Arbor, MI, 48109-1065, USA
| | - Sarah Medley
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, 48109-2029, USA
| | - Faisal Shakeel
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Room 2560C, 428 Church St., Ann Arbor, MI, 48109-1065, USA
| | - Brett Vanderwerff
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Matthew Zawistowski
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Kelley M Kidwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, 48109-2029, USA
| | - Daniel L Hertz
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Room 2560C, 428 Church St., Ann Arbor, MI, 48109-1065, USA.
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42
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Gaedigk A, Boone EC, Scherer SE, Lee SB, Numanagić I, Sahinalp C, Smith JD, McGee S, Radhakrishnan A, Qin X, Wang WY, Farrow EG, Gonzaludo N, Halpern AL, Nickerson DA, Miller NA, Pratt VM, Kalman LV. CYP2C8, CYP2C9, and CYP2C19 Characterization Using Next-Generation Sequencing and Haplotype Analysis: A GeT-RM Collaborative Project. J Mol Diagn 2022; 24:337-350. [PMID: 35134542 PMCID: PMC9069873 DOI: 10.1016/j.jmoldx.2021.12.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/09/2021] [Accepted: 12/28/2021] [Indexed: 01/13/2023] Open
Abstract
Pharmacogenetic tests typically target selected sequence variants to identify haplotypes that are often defined by star (∗) allele nomenclature. Due to their design, these targeted genotyping assays are unable to detect novel variants that may change the function of the gene product and thereby affect phenotype prediction and patient care. In the current study, 137 DNA samples that were previously characterized by the Genetic Testing Reference Material (GeT-RM) program using a variety of targeted genotyping methods were recharacterized using targeted and whole genome sequencing analysis. Sequence data were analyzed using three genotype calling tools to identify star allele diplotypes for CYP2C8, CYP2C9, and CYP2C19. The genotype calls from next-generation sequencing (NGS) correlated well to those previously reported, except when novel alleles were present in a sample. Six novel alleles and 38 novel suballeles were identified in the three genes due to identification of variants not covered by targeted genotyping assays. In addition, several ambiguous genotype calls from a previous study were resolved using the NGS and/or long-read NGS data. Diplotype calls were mostly consistent between the calling algorithms, although several discrepancies were noted. This study highlights the utility of NGS for pharmacogenetic testing and demonstrates that there are many novel alleles that are yet to be discovered, even in highly characterized genes such as CYP2C9 and CYP2C19.
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Affiliation(s)
- Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri; University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | - Erin C Boone
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri
| | - Steven E Scherer
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Seung-Been Lee
- Precision Medicine Institute, Macrogen Inc., Seongnam, Republic of Korea
| | - Ibrahim Numanagić
- Department of Computer Science, University of Victoria, Victoria, British Columbia, Canada
| | - Cenk Sahinalp
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Joshua D Smith
- Department of Genome Sciences, University of Washington, Seattle, Washington
| | - Sean McGee
- Department of Genome Sciences, University of Washington, Seattle, Washington
| | | | - Xiang Qin
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Wendy Y Wang
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri
| | - Emily G Farrow
- University of Missouri-Kansas City School of Medicine, Kansas City, Missouri; Center for Genomic Medicine, Children's Mercy Kansas City, Kansas City, Missouri
| | - Nina Gonzaludo
- Medical Genomics Research, Illumina Inc., San Diego, California
| | - Aaron L Halpern
- Medical Genomics Research, Illumina Inc., San Diego, California
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, Washington
| | - Neil A Miller
- University of Missouri-Kansas City School of Medicine, Kansas City, Missouri; Center for Genomic Medicine, Children's Mercy Kansas City, Kansas City, Missouri
| | - Victoria M Pratt
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Lisa V Kalman
- Informatics and Data Science Branch, Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia.
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43
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Klanderman BJ, Koch C, Machini K, Parpattedar SS, Bandyadka S, Lin CF, Hynes E, Lebo MS, Amr SS. Automated Pharmacogenomic Reports for Clinical Genome Sequencing. J Mol Diagn 2022; 24:205-218. [PMID: 35041930 DOI: 10.1016/j.jmoldx.2021.12.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 11/09/2021] [Accepted: 12/07/2021] [Indexed: 11/26/2022] Open
Abstract
Clinical laboratories offering genome sequencing have the opportunity to return pharmacogenomic findings to patients, providing the added benefit of preemptive testing that could help inform medication selection or dosing throughout the lifespan. Implementation of pharmacogenomic reporting must address several challenges, including inherent limitations in short-read genome sequencing methods, gene and variant selection, standardization of genotype and phenotype nomenclature, and choice of guidelines and drugs to report. An automated pipeline, lmPGX, was developed as an end-to-end solution that produces two versions of a pharmacogenomic report, presenting either Clinical Pharmacogenetics Implementation Consortium or US Food and Drug Administration guidelines for 12 genes. The pipeline was validated for performance using reference samples and pharmacogenetic data from the Genetic Testing Reference Materials Coordination Program. To determine performance and limitations, lmPGX was compared with three additional publicly available pharmacogenomic pipelines. The lmPGX pipeline offers clinical laboratories an opportunity for seamless integration of pharmacogenomic results with genome reporting.
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Affiliation(s)
- Barbara J Klanderman
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts; Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Christopher Koch
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts
| | - Kalotina Machini
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts; Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts; Deparment of Pathology, Harvard Medical School, Boston, Massachusetts
| | - Shruti S Parpattedar
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts
| | - Shruthi Bandyadka
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts
| | - Chiao-Feng Lin
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts; Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Elizabeth Hynes
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts
| | - Matthew S Lebo
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts; Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts; Deparment of Pathology, Harvard Medical School, Boston, Massachusetts; Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
| | - Sami S Amr
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts; Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts; Deparment of Pathology, Harvard Medical School, Boston, Massachusetts.
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Brown KE, Staples JW, Woodahl EL. Keeping pace with CYP2D6 haplotype discovery: innovative methods to assign function. Pharmacogenomics 2022; 23:255-262. [PMID: 35083931 PMCID: PMC8890136 DOI: 10.2217/pgs-2021-0149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The discovery of haplotypes with unknown or uncertain function in the CYP2D6 pharmacogene is outpacing the capabilities of traditional in vitro and in vivo approaches to characterize their function. This challenge will undoubtedly grow as pharmacogenomic research becomes more inclusive of globally diverse populations. As accurate phenotypic assignment is paramount to the utility of pharmacogenomics, high-throughput technologies are needed for this complex pharmacogene. We describe the evolving landscape of innovative approaches to assign function to CYP2D6 haplotypes and possibilities for adopting these technologies into cohesive processes. Promising approaches include ADME-optimized prediction frameworks, machine learning algorithms, deep mutational scanning and phenoconversion predictions. Implementing these approaches will lead to improved personalization of treatment for patients.
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Affiliation(s)
- Karen E Brown
- Department of Biomedical & Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Montana, Missoula, MT 59812, USA,Skaggs Institute for Health Innovation, University of Montana, Missoula, MT 59812, USA
| | - Jack W Staples
- Department of Biomedical & Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Montana, Missoula, MT 59812, USA,Skaggs Institute for Health Innovation, University of Montana, Missoula, MT 59812, USA
| | - Erica L Woodahl
- Department of Biomedical & Pharmaceutical Sciences, Skaggs School of Pharmacy, University of Montana, Missoula, MT 59812, USA,Skaggs Institute for Health Innovation, University of Montana, Missoula, MT 59812, USA,Author for correspondence: Tel.: +1 406 243 4129;
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Lopes JL, Harris K, Karow MB, Peterson SE, Kluge ML, Kotzer KE, Lopes GS, Larson NB, Bielinski SJ, Scherer SE, Wang L, Weinshilboum RM, Black JL, Moyer AM. Targeted Genotyping in Clinical Pharmacogenomics: What Is Missing? J Mol Diagn 2022; 24:253-261. [PMID: 35041929 PMCID: PMC8961466 DOI: 10.1016/j.jmoldx.2021.11.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/09/2021] [Accepted: 11/29/2021] [Indexed: 01/01/2023] Open
Abstract
Clinical pharmacogenomic testing typically uses targeted genotyping, which only detects variants included in the test design and may vary among laboratories. To evaluate the potential patient impact of genotyping compared with sequencing, which can detect common and rare variants, an in silico targeted genotyping panel was developed based on the variants most commonly included in clinical tests and applied to a cohort of 10,030 participants who underwent sequencing for CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, DPYD, SLCO1B1, TPMT, UGT1A1, and VKORC1. The results of in silico targeted genotyping were compared with the clinically reported sequencing results. Of the 10,030 participants, 2780 (28%) had at least one potentially clinically relevant variant/allele identified by sequencing that would not have been detected in a standard targeted genotyping panel. The genes with the largest number of participants with variants only detected by sequencing were SLCO1B1, DPYD, and CYP2D6, which affected 13%, 6.3%, and 3.5% of participants, respectively. DPYD (112 variants) and CYP2D6 (103 variants) had the largest number of unique variants detected only by sequencing. Although targeted genotyping detects most clinically significant pharmacogenomic variants, sequencing-based approaches are necessary to detect rare variants that collectively affect many patients. However, efforts to establish pharmacogenomic variant classification systems and nomenclature to accommodate rare variants will be required to adopt sequencing-based pharmacogenomics.
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Affiliation(s)
- Jaime L. Lopes
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Kimberley Harris
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Mary Beth Karow
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Sandra E. Peterson
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Michelle L. Kluge
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Katrina E. Kotzer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Guilherme S. Lopes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Nicholas B. Larson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | | | - Steven E. Scherer
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
| | - Richard M. Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
| | - John L. Black
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Ann M. Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota,Address correspondence to Ann M. Moyer, M.D., Ph.D., Mayo Clinic, 200 First St SW, Rochester, MN 55905.
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Wankaew N, Chariyavilaskul P, Chamnanphon M, Assawapitaksakul A, Chetruengchai W, Pongpanich M, Shotelersuk V. Genotypic and phenotypic landscapes of 51 pharmacogenes derived from whole-genome sequencing in a Thai population. PLoS One 2022; 17:e0263621. [PMID: 35176049 PMCID: PMC8853512 DOI: 10.1371/journal.pone.0263621] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 01/22/2022] [Indexed: 12/30/2022] Open
Abstract
Differences in drug responses in individuals are partly due to genetic variations in pharmacogenes, which differ among populations. Here, genome sequencing of 171 unrelated Thai individuals from all regions of Thailand was used to call star alleles of 51 pharmacogenes by Stargazer, determine allele and genotype frequencies, predict phenotype and compare high-impact variant frequencies between Thai and other populations. Three control genes, EGFR, VDR, and RYR1, were used, giving consistent results. Every individual had at least three genes with variant or altered phenotype. Forty of the 51 pharmacogenes had at least one individual with variant or altered phenotype. Moreover, thirteen genes had at least 25% of individuals with variant or altered phenotype including SLCO1B3 (97.08%), CYP3A5 (88.3%), CYP2C19 (60.82%), CYP2A6 (60.2%), SULT1A1 (56.14%), G6PD (54.39%), CYP4B1 (50.00%), CYP2D6 (48.65%), CYP2F1 (46.41%), NAT2 (40.35%), SLCO2B1 (28.95%), UGT1A1 (28.07%), and SLCO1B1 (26.79%). Allele frequencies of high impact variants from our samples were most similar to East Asian. Remarkably, we identified twenty predicted high impact variants which have not previously been reported. Our results provide information that contributes to the implementation of pharmacogenetic testing in Thailand and other Southeast Asian countries, bringing a step closer to personalized medicine.
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Affiliation(s)
- Natnicha Wankaew
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
| | - Pajaree Chariyavilaskul
- Clinical Pharmacokinetics and Pharmacogenomics Research Unit, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of Pharmacology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Monpat Chamnanphon
- Clinical Pharmacokinetics and Pharmacogenomics Research Unit, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of Pathology, Faculty of Medicine, Srinakharinwirot University, Nakornnayok, Thailand
| | - Adjima Assawapitaksakul
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
| | - Wanna Chetruengchai
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
| | - Monnat Pongpanich
- Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Age-related Inflammation and Degeneration Research Unit, Chulalongkorn University, Bangkok, Thailand
- * E-mail:
| | - Vorasuk Shotelersuk
- Center of Excellence for Medical Genomics, Medical Genomics Cluster, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Excellence Center for Genomics and Precision Medicine, King Chulalongkorn Memorial Hospital, the Thai Red Cross Society, Bangkok, Thailand
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Carvalho Henriques B, Buchner A, Hu X, Wang Y, Yavorskyy V, Wallace K, Dong R, Martens K, Carr MS, Behroozi Asl B, Hague J, Sivapalan S, Maier W, Dernovsek MZ, Henigsberg N, Hauser J, Souery D, Cattaneo A, Mors O, Rietschel M, Pfeffer G, Hume S, Aitchison KJ. Methodology for clinical genotyping of CYP2D6 and CYP2C19. Transl Psychiatry 2021; 11:596. [PMID: 34811360 PMCID: PMC8608805 DOI: 10.1038/s41398-021-01717-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 10/28/2021] [Indexed: 01/10/2023] Open
Abstract
Many antidepressants, atomoxetine, and several antipsychotics are metabolized by the cytochrome P450 enzymes CYP2D6 and CYP2C19, and guidelines for prescribers based on genetic variants exist. Although some laboratories offer such testing, there is no consensus regarding validated methodology for clinical genotyping of CYP2D6 and CYP2C19. The aim of this paper was to cross-validate multiple technologies for genotyping CYP2D6 and CYP2C19 against each other, and to contribute to feasibility for clinical implementation by providing an enhanced range of assay options, customizable automated translation of data into haplotypes, and a workflow algorithm. AmpliChip CYP450 and some TaqMan single nucleotide variant (SNV) and copy number variant (CNV) data in the Genome-based therapeutic drugs for depression (GENDEP) study were used to select 95 samples (out of 853) to represent as broad a range of CYP2D6 and CYP2C19 genotypes as possible. These 95 included a larger range of CYP2D6 hybrid configurations than have previously been reported using inter-technology data. Genotyping techniques employed were: further TaqMan CNV and SNV assays, xTAGv3 Luminex CYP2D6 and CYP2C19, PharmacoScan, the Ion AmpliSeq Pharmacogenomics Panel, and, for samples with CYP2D6 hybrid configurations, long-range polymerase chain reactions (L-PCRs) with Sanger sequencing and Luminex. Agena MassARRAY was also used for CYP2C19. This study has led to the development of a broader range of TaqMan SNV assays, haplotype phasing methodology with TaqMan adaptable for other technologies, a multiplex genotyping method for efficient identification of some hybrid haplotypes, a customizable automated translation of SNV and CNV data into haplotypes, and a clinical workflow algorithm.
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Affiliation(s)
| | - Avery Buchner
- grid.17089.370000 0001 2190 316XDepartment of Psychiatry, University of Alberta, Edmonton, Canada ,grid.17089.370000 0001 2190 316XNeuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Xiuying Hu
- grid.17089.370000 0001 2190 316XDepartment of Psychiatry, University of Alberta, Edmonton, Canada
| | - Yabing Wang
- grid.17089.370000 0001 2190 316XDepartment of Psychiatry, University of Alberta, Edmonton, Canada
| | - Vasyl Yavorskyy
- grid.17089.370000 0001 2190 316XDepartment of Psychiatry, University of Alberta, Edmonton, Canada ,grid.17089.370000 0001 2190 316XDepartment of Biological Sciences, University of Alberta, Edmonton, Canada
| | - Keanna Wallace
- grid.17089.370000 0001 2190 316XDepartment of Psychiatry, University of Alberta, Edmonton, Canada
| | - Rachael Dong
- grid.17089.370000 0001 2190 316XNeuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Kristina Martens
- grid.22072.350000 0004 1936 7697Department of Clinical Neurosciences, Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Michael S. Carr
- grid.17089.370000 0001 2190 316XDepartment of Psychiatry, University of Alberta, Edmonton, Canada ,grid.17089.370000 0001 2190 316XDepartment of Pharmacology, University of Alberta, Edmonton, Canada
| | - Bahareh Behroozi Asl
- grid.17089.370000 0001 2190 316XDepartment of Psychiatry, University of Alberta, Edmonton, Canada ,grid.17089.370000 0001 2190 316XNeuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Joshua Hague
- grid.17089.370000 0001 2190 316XDepartment of Psychiatry, University of Alberta, Edmonton, Canada ,grid.17089.370000 0001 2190 316XDepartment of Medical Genetics, University of Alberta, Edmonton, Canada
| | - Sudhakar Sivapalan
- grid.17089.370000 0001 2190 316XDepartment of Psychiatry, University of Alberta, Edmonton, Canada
| | - Wolfgang Maier
- grid.10388.320000 0001 2240 3300Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | | | - Neven Henigsberg
- grid.4808.40000 0001 0657 4636Croatian Institute for Brain Research, Centre for Excellence for Basic, Clinical and Translational Research, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Joanna Hauser
- grid.22254.330000 0001 2205 0971Departnent of Psychiatry, Poznan University of Medical Sciences, Poznań, Poland
| | - Daniel Souery
- grid.4989.c0000 0001 2348 0746Laboratoire de Psychologie Médicale, Université Libre de Bruxelles and Psy Pluriel, Centre Européen de Psychologie Médicale, Brussels, Belgium
| | - Annamaria Cattaneo
- grid.419422.8Biological Psychiatry Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy ,grid.4708.b0000 0004 1757 2822Department of Pharmacological and Biomolecular Sciences, University of Milan, via Balzaretti 9, 20133 Milan, Italy
| | - Ole Mors
- grid.154185.c0000 0004 0512 597XPsychosis Research Unit, Aarhus University Hospital, Risskov, Denmark
| | - Marcella Rietschel
- grid.7700.00000 0001 2190 4373Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty of Mannheim, Heidelberg University, Mannheim, Germany
| | - Gerald Pfeffer
- grid.22072.350000 0004 1936 7697Department of Clinical Neurosciences, Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada ,grid.22072.350000 0004 1936 7697Alberta Child Health Research Institute & Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Stacey Hume
- grid.17089.370000 0001 2190 316XDepartment of Medical Genetics, University of Alberta, Edmonton, Canada ,Alberta Precision Laboratories, Edmonton, Canada
| | - Katherine J. Aitchison
- grid.17089.370000 0001 2190 316XDepartment of Psychiatry, University of Alberta, Edmonton, Canada ,grid.17089.370000 0001 2190 316XNeuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada ,grid.17089.370000 0001 2190 316XDepartment of Medical Genetics, University of Alberta, Edmonton, Canada ,grid.413574.00000 0001 0693 8815Alberta Health Services, Edmonton, Canada ,grid.13097.3c0000 0001 2322 6764King’s College London, London, UK
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48
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Piriyapongsa J, Sukritha C, Kaewprommal P, Intarat C, Triparn K, Phornsiricharoenphant K, Chaosrikul C, Shaw PJ, Chantratita W, Mahasirimongkol S, Tongsima S. PharmVIP: A Web-Based Tool for Pharmacogenomic Variant Analysis and Interpretation. J Pers Med 2021; 11:1230. [PMID: 34834582 PMCID: PMC8618518 DOI: 10.3390/jpm11111230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 10/17/2021] [Accepted: 11/16/2021] [Indexed: 11/29/2022] Open
Abstract
The increasing availability of next generation sequencing (NGS) for personal genomics could promote pharmacogenomics (PGx) discovery and application. However, current tools for analysis and interpretation of pharmacogenomic variants from NGS data are inadequate, as none offer comprehensive analytic functions in a simple, web-based platform. In addition, no tools exist to analyze human leukocyte antigen (HLA) genes for determining potential risks of immune-mediated adverse drug reaction (IM-ADR). We describe PharmVIP, a web-based PGx tool, for one-stop comprehensive analysis and interpretation of genome-wide variants obtained from NGS platforms. PharmVIP comprises three main interpretation modules covering analyses of pharmacogenes involved in pharmacokinetics, pharmacodynamics and IM-ADR. The Guideline module provides Clinical Pharmacogenetics Implementation Consortium (CPIC) drug guideline recommendations based on the translation of genotypic data in genes having guidelines. The HLA module reports HLA genotypes, potential adverse drug reactions, and the relevant drug guidelines. The Pharmacogenes module is employed for prioritizing variants according to variant effect on gene function. Detailed, customizable reports are provided as exportable files and as an interactive web version. PharmVIP is a new integrated NGS workflow for the PGx community to facilitate discovery and clinical application.
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Affiliation(s)
- Jittima Piriyapongsa
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Chanathip Sukritha
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Pavita Kaewprommal
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Chalermpong Intarat
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Kwankom Triparn
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Krittin Phornsiricharoenphant
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Chadapohn Chaosrikul
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
| | - Philip J. Shaw
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand;
| | - Wasun Chantratita
- Center for Medical Genomics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Phayathai, Bangkok 10400, Thailand;
| | - Surakameth Mahasirimongkol
- Division of Genomic Medicine and Innovation Support, Department of Medical Sciences, Ministry of Public Health, Nonthaburi 11000, Thailand;
| | - Sissades Tongsima
- National Biobank of Thailand, National Science and Technology Development Agency, Klong Luang, Pathum Thani 12120, Thailand; (C.S.); (P.K.); (C.I.); (K.T.); (K.P.); (C.C.); (S.T.)
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49
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Tafazoli A, Guchelaar HJ, Miltyk W, Kretowski AJ, Swen JJ. Applying Next-Generation Sequencing Platforms for Pharmacogenomic Testing in Clinical Practice. Front Pharmacol 2021; 12:693453. [PMID: 34512329 PMCID: PMC8424415 DOI: 10.3389/fphar.2021.693453] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Pharmacogenomics (PGx) studies the use of genetic data to optimize drug therapy. Numerous clinical centers have commenced implementing pharmacogenetic tests in clinical routines. Next-generation sequencing (NGS) technologies are emerging as a more comprehensive and time- and cost-effective approach in PGx. This review presents the main considerations for applying NGS in guiding drug treatment in clinical practice. It discusses both the advantages and the challenges of implementing NGS-based tests in PGx. Moreover, the limitations of each NGS platform are revealed, and the solutions for setting up and management of these technologies in clinical practice are addressed.
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Affiliation(s)
- Alireza Tafazoli
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, Bialystok, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
- Leiden Network of Personalized Therapeutics, Leiden, Netherlands
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Adam J. Kretowski
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Jesse J. Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands
- Leiden Network of Personalized Therapeutics, Leiden, Netherlands
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50
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Hertz DL, Arwood MJ, Stocco G, Singh S, Karnes JH, Ramsey LB. Planning and Conducting a Pharmacogenetics Association Study. Clin Pharmacol Ther 2021; 110:688-701. [PMID: 33880756 DOI: 10.1002/cpt.2270] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/04/2021] [Indexed: 12/13/2022]
Abstract
Pharmacogenetics (PGx) association studies are used to discover, replicate, and validate the association between an inherited genotype and a treatment outcome. The objective of this tutorial is to provide trainees and novice PGx researchers with an overview of the major decisions that need to be made when designing and conducting a PGx association study. The first critical decision is to determine whether the objective of the study is discovery, replication, or validation. Next, the researcher must identify a patient cohort that has all of the data necessary to conduct the intended analysis. Then, the investigator must select and define the treatment outcome, or phenotype, that will be analyzed. Next, the investigator must determine what genotyping approach and genetic data will be included in the analysis. Finally, the association between the genotype and phenotype is tested using some statistical analysis methodology. This tutorial is divided into five sections; each section describes commonly used approaches and provides suggestions and resources for designing and conducting a PGx association study. Successful PGx association studies are necessary to discover and validate associations between inherited genetic variation and treatment outcomes, which enable clinical translation to improve efficacy and reduce toxicity of treatment.
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Affiliation(s)
- Daniel L Hertz
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, Michigan, USA
| | - Meghan J Arwood
- Tabula Rasa HealthCare, Precision Pharmacotherapy Research and Development Institute, Orlando, Florida, USA
| | - Gabriele Stocco
- Department of Life Sciences, University of Trieste, Trieste, Italy
| | - Sonal Singh
- Takeda California, San Diego, California, USA
| | - Jason H Karnes
- Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, Arizona, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Laura B Ramsey
- Divisions of Clinical Pharmacology & Research in Patient Services, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
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