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Identification of genetic variants associated with tacrolimus metabolism in kidney transplant recipients by extreme phenotype sampling and next generation sequencing

The Pharmacogenomics Journal volume 19, pages 375–389 (2019)Cite this article

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Abstract

An extreme phenotype sampling (EPS) model with targeted next-generation sequencing (NGS) identified genetic variants associated with tacrolimus (Tac) metabolism in subjects from the Deterioration of Kidney Allograft Function (DeKAF) Genomics cohort which included 1,442 European Americans (EA) and 345 African Americans (AA). This study included 48 subjects separated into 4 groups of 12 (AA high, AA low, EA high, EA low). Groups were selected by the extreme phenotype of dose-normalized Tac trough concentrations after adjusting for common genetic variants and clinical factors. NGS spanned > 3 Mb of 28 genes and identified 18,661 genetic variants (3961 previously unknown). A group of 125 deleterious variants, by SIFT analysis, were associated with Tac troughs in EAs (burden test, p = 0.008), CYB5R2 was associated with Tac troughs in AAs (SKAT, p = 0.00079). In CYB5R2, rs61733057 (increased allele frequency in AAs) was predicted to disrupt protein function by SIFT and PolyPhen2 analysis. The variants merit further validation.

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Data availability

Raw sequence data, in fastq format, are available at the United States National Center for Biotechnology Information’s (NCBI) Sequence Read Archive (SRA) with SRA accession number: SRP156752. The associated phenotype and covariate data are available at NCBI’s Database for Genotypes and Phenotypes with dbGaP accession number: phs001670.v1.p1.

References

  1. Annual Data Report of the US Organ Procurement and Transplantation Network (OPTN) and the Scientific Registry of Transplant Recipients (SRTR). Preface. Am J Transpl. 2013;13(Suppl 1): 1–7.

  2. Endrenyi L, Tothfalusi L. Determination of bioequivalence for drugs with narrow therapeutic index: reduction of the regulatory burden. J Pharm Pharm Sci. 2013;16:676–82.

    Article  CAS  Google Scholar 

  3. Gaynor JJ, Ciancio G, Guerra G, Sageshima J, Roth D, Goldstein MJ, et al. Lower tacrolimus trough levels are associated with subsequently higher acute rejection risk during the first 12 months after kidney transplantation. Transpl Int. 2016;29:216–26.

    Article  CAS  Google Scholar 

  4. Laskow DA, Vincenti F, Neylan JF, Mendez R, Matas AJ. An open-label, concentration-ranging trial of FK506 in primary kidney transplantation: a report of the United States Multicenter FK506 Kidney Transplant Group. Transplantation. 1996;62:900–5.

    Article  CAS  Google Scholar 

  5. Lancia P, Jacqz-Aigrain E, Zhao W. Choosing the right dose of tacrolimus. Arch Dis Child. 2015;100:406–13.

    Article  Google Scholar 

  6. Egeland EJ, Robertsen I, Hermann M, Midtvedt K, Storset E, Gustavsen MT, et al. High tacrolimus clearance is a risk factor for acute rejection in the early phase after renal transplantation. Transplantation. 2017;101:e273–9.

    Article  CAS  Google Scholar 

  7. Oetting WS, Schladt DP, Guan W, Miller MB, Remmel RP, Dorr C, et al. Genomewide association study of tacrolimus concentrations in African American kidney transplant recipients identifies multiple CYP3A5 alleles. Am J Transplant. 2016;16:574–82.

    Article  CAS  Google Scholar 

  8. Sanghavi K, Brundage RC, Miller MB, Schladt DP, Israni AK, Guan W, et al. Genotype-guided tacrolimus dosing in African-American kidney transplant recipients. Pharm J. 2017;17:61–68.

    CAS  Google Scholar 

  9. Passey C, Birnbaum AK, Brundage RC, Oetting WS, Israni AK, Jacobson PA. Dosing equation for tacrolimus using genetic variants and clinical factors. Br J Clin Pharmacol. 2011;72:948–57.

    Article  CAS  Google Scholar 

  10. Passey C, Birnbaum AK, Brundage RC, Schladt DP, Oetting WS, Leduc RE, et al. Validation of tacrolimus equation to predict troughs using genetic and clinical factors. Pharmacogenomics. 2012;13:1141–7.

    Article  CAS  Google Scholar 

  11. Andreu F, Colom H, Elens L, van Gelder T, van Schaik RH, Hesselink DA, et al. A new CYP3A5*3 and CYP3A4*22 cluster influencing tacrolimus target concentrations: a population approach. Clin Pharmacokinet. 2017;56:963–75.

    Article  CAS  Google Scholar 

  12. Jacobson PA, Oetting WS, Brearley AM, Leduc R, Guan W, Schladt D, et al. Novel polymorphisms associated with tacrolimus trough concentrations: results from a multicenter kidney transplant consortium. Transplantation. 2011;91:300–8.

    Article  CAS  Google Scholar 

  13. Oetting WS, Wu B, Schladt DP, Guan W, Remmel RP, Mannon RB, et al. Genome-wide association study identifies the common variants in CYP3A4 and CYP3A5 responsible for variation in tacrolimus trough concentration in Caucasian kidney transplant recipients. Pharmacogenomics J. 2017;18:501–5.

    Article  Google Scholar 

  14. Li D, Lewinger JP, Gauderman WJ, Murcray CE, Conti D. Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies. Genet Epidemiol. 2011;35:790–9.

    Article  Google Scholar 

  15. Li YR, van Setten J, Verma SS, Lu Y, Holmes MV, Gao H, et al. Concept and design of a genome-wide association genotyping array tailored for transplantation-specific studies. Genome Med. 2015;7:90.

    Article  Google Scholar 

  16. Jacobson PA, Schladt D, Oetting WS, Leduc R, Guan W, Matas AJ, et al. Lower calcineurin inhibitor doses in older compared to younger kidney transplant recipients yield similar troughs. Am J Transplant. 2012;12:3326–36.

    Article  CAS  Google Scholar 

  17. Andrew S. FastQC: a quality control tool for high throughput sequence data. 2010. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc. Accessed 6 November 2018.

  18. Li H, Durbin R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics. 2010;26:589–95.

    Article  Google Scholar 

  19. Chou LS, Liu CS, Boese B, Zhang X, Mao R. DNA sequence capture and enrichment by microarray followed by next-generation sequencing for targeted resequencing: neurofibromatosis type 1 gene as a model. Clin Chem. 2010;56:62–72.

    Article  CAS  Google Scholar 

  20. Lim BC, Lee S, Shin JY, Kim JI, Hwang H, Kim KJ, et al. Genetic diagnosis of Duchenne and Becker muscular dystrophy using next-generation sequencing technology: comprehensive mutational search in a single platform. J Med Genet. 2011;48:731–6.

    Article  CAS  Google Scholar 

  21. do Valle IF, Giampieri E, Simonetti G, Padella A, Manfrini M, Ferrari A, et al. Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole-exome sequencing data. BMC Bioinforma. 2016;17(Suppl 12):341.

    Article  Google Scholar 

  22. Zhu P, He L, Li Y, Huang W, Xi F, Lin L, et al. Correction: OTG-snpcaller: an optimized pipeline based on TMAP and GATK for SNP calling from ion torrent data. PLoS ONE. 2015;10:e0138824.

    Article  Google Scholar 

  23. McCormick RF, Truong SK, Mullet JE. RIG: Recalibration and interrelation of genomic sequence data with the GATK. G3. 2015;5:655–65.

    Article  Google Scholar 

  24. Zhu P, He L, Li Y, Huang W, Xi F, Lin L, et al. OTG-snpcaller: an optimized pipeline based on TMAP and GATK for SNP calling from ion torrent data. PLoS ONE. 2014;9:e97507.

    Article  Google Scholar 

  25. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics. 2013;43:11–33.

  26. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.

    Article  CAS  Google Scholar 

  27. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43:491–8.

    Article  CAS  Google Scholar 

  28. Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strainw1118; iso-2; iso-3. Fly. 2012;6:80–92.

    Article  CAS  Google Scholar 

  29. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, et al. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17:122.

    Article  Google Scholar 

  30. Vaser R, Adusumalli S, Leng SN, Sikic M, Ng PC. SIFT missense predictions for genomes. Nat Protoc. 2016;11:1–9.

    Article  CAS  Google Scholar 

  31. Sim NL, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 2012;40(Web Server issue):W452–457.

    Article  CAS  Google Scholar 

  32. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Ch 7. Curr Protoc Hum Genet. 2013. Available online at: https://doi.org/10.1002/0471142905.hg0720s76. Accessed 6 November 2018.

    Article  Google Scholar 

  33. Zou M, Baitei EY, Alzahrani AS, Parhar RS, Al-Mohanna FA, Meyer BF, et al. Mutation prediction by PolyPhen or functional assay, a detailed comparison of CYP27B1 missense mutations. Endocrine. 2011;40:14–20.

    Article  CAS  Google Scholar 

  34. Flanagan SE, Patch AM, Ellard S. Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations. Genet Test Mol Biomark. 2010;14:533–7.

    Article  CAS  Google Scholar 

  35. Sha Q, Zhang S. A rare variant association test based on combinations of single-variant tests. Genet Epidemiol. 2014;38:494–501.

    Article  Google Scholar 

  36. Ionita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin X. Sequence kernel association tests for the combined effect of rare and common variants. Am J Hum Genet. 2013;92:841–53.

    Article  CAS  Google Scholar 

  37. Lee S, Wu MC, Lin X. Optimal tests for rare variant effects in sequencing association studies. Biostatistics. 2012;13:762–75.

    Article  Google Scholar 

  38. Lee S, Emond MJ, Bamshad MJ, Barnes KC, Rieder MJ, Nickerson DA, et al. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet. 2012;91:224–37.

    Article  CAS  Google Scholar 

  39. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89:82–93.

    Article  CAS  Google Scholar 

  40. Liu Y, Kheradmand F, Davis CF, Scheurer ME, Wheeler D, Tsavachidis S, et al. Focused analysis of exome sequencing data for rare germline mutations in familial and sporadic lung cancer. J Thorac Oncol. 2016;11:52–61.

    Article  Google Scholar 

  41. Bruse S, Moreau M, Bromberg Y, Jang JH, Wang N, Ha H, et al. Whole exome sequencing identifies novel candidate genes that modify chronic obstructive pulmonary disease susceptibility. Hum Genom. 2016;10:1.

    Article  Google Scholar 

  42. Shtir C, Aldahmesh MA, Al-Dahmash S, Abboud E, Alkuraya H, Abouammoh MA, et al. Exome-based case-control association study using extreme phenotype design reveals novel candidates with protective effect in diabetic retinopathy. Hum Genet. 2016;135:193–200.

    Article  CAS  Google Scholar 

  43. Emond MJ, Louie T, Emerson J, Chong JX, Mathias RA, Knowles MR, et al. Exome sequencing of phenotypic extremes identifies CAV2 and TMC6 as interacting modifiers of chronic pseudomonas aeruginosa infection in cystic fibrosis. PLoS Genet. 2015;11:e1005273.

    Article  Google Scholar 

  44. McLaren CE, Emond MJ, Subramaniam VN, Phatak PD, Barton JC, Adams PC, et al. Exome sequencing in HFE C282Y homozygous men with extreme phenotypes identifies a GNPAT variant associated with severe iron overload. Hepatology. 2015;62:429–39.

    Article  CAS  Google Scholar 

  45. Lange LA, Hu Y, Zhang H, Xue C, Schmidt EM, Tang ZZ, et al. Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol. Am J Hum Genet. 2014;94:233–45.

    Article  CAS  Google Scholar 

  46. Xiao X, Zhao W, Tian F, Zhou X, Zhang J, Huang T, et al. Cytochrome b5 reductase 2 is a novel candidate tumor suppressor gene frequently inactivated by promoter hypermethylation in human nasopharyngeal carcinoma. Tumour Biol. 2014;35:3755–63.

    Article  CAS  Google Scholar 

  47. Ming H, Lan Y, He F, Xiao X, Zhou X, Zhang Z, et al. Cytochrome b5 reductase 2 suppresses tumor formation in nasopharyngeal carcinoma by attenuating angiogenesis. Chin J Cancer. 2015;34:459–67.

    Article  CAS  Google Scholar 

  48. Devaney JM, Wang S, Funda S, Long J, Taghipour DJ, Tbaishat R, et al. Identification of novel DNA-methylated genes that correlate with human prostate cancer and high-grade prostatic intraepithelial neoplasia. Prostate Cancer Prostatic Dis. 2013;16:292–300.

    Article  CAS  Google Scholar 

  49. Liu Q, Liu Y, Li W, Wang X, Sawaya R, Lang FF, et al. Genetic, epigenetic, and molecular landscapes of multifocal and multicentric glioblastoma. Acta Neuropathol. 2015;130:587–97.

    Article  CAS  Google Scholar 

  50. Dorr CR, Remmel RP, Muthusamy A, Fisher J, Moriarity B, Yasuda K, et al. CRISPR/Cas9 genetic modification of CYP3A5 *3 in HuH-7 human hepatocyte cell line leads to cell lines with increased midazolam and tacrolimus metabolism. Drug Metab. Dispos. 2017:957–65.

  51. Ge J, Wang J, Zhao H, Li K, Jing Y, Li G. Impact of FOXP3 polymorphisms on the blood level of tacrolimus in renal transplant recipients. Transplant Proc. 2016;48:1962–7.

    Article  CAS  Google Scholar 

  52. Min L, Nie M, Zhang A, Wen J, Noel SD, Lee V, et al. Computational analysis of missense variants of G protein-coupled receptors involved in the neuroendocrine regulation of reproduction. Neuroendocrinology. 2016;103:230–9.

    Article  CAS  Google Scholar 

  53. Hicks S, Wheeler DA, Plon SE, Kimmel M. Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed. Hum Mutat. 2011;32:661–8.

    Article  CAS  Google Scholar 

  54. Leong IU, Stuckey A, Lai D, Skinner JR, Love DR. Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations. BMC Med Genet. 2015;16:34.

    Article  Google Scholar 

  55. Crettol S, Venetz JP, Fontana M, Aubert JD, Pascual M, Eap CB. CYP3A7, CYP3A5, CYP3A4, and ABCB1 genetic polymorphisms, cyclosporine concentration, and dose requirement in transplant recipients. Ther Drug Monit. 2008;30:689–99.

    Article  CAS  Google Scholar 

  56. de Jonge H, de Loor H, Verbeke K, Vanrenterghem Y, Kuypers DR. In vivo CYP3A4 activity, CYP3A5 genotype, and hematocrit predict tacrolimus dose requirements and clearance in renal transplant patients. Clin Pharmacol Ther. 2012;92:366–75.

    Article  Google Scholar 

  57. Staatz CE, Tett SE. Clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplantation. Clin Pharmacokinet. 2004;43:623–53.

    Article  CAS  Google Scholar 

  58. Kamdem LK, Streit F, Zanger UM, Brockmoller J, Oellerich M, Armstrong VW, et al. Contribution of CYP3A5 to the in vitro hepatic clearance of tacrolimus. Clin Chem. 2005;51:1374–81.

    Article  CAS  Google Scholar 

  59. Smith HE, Jones JP 3rd, Kalhorn TF, Farin FM, Stapleton PL, Davis CL, et al. Role of cytochrome P450 2C8 and 2J2 genotypes in calcineurin inhibitor-induced chronic kidney disease. Pharm Genom. 2008;18:943–53.

    Article  CAS  Google Scholar 

  60. Gervasini G, Garcia M, Macias RM, Cubero JJ, Caravaca F, Benitez J. Impact of genetic polymorphisms on tacrolimus pharmacokinetics and the clinical outcome of renal transplantation. Transpl Int. 2012;25:471–80.

    Article  CAS  Google Scholar 

  61. Hubbard PA, Shen AL, Paschke R, Kasper CB, Kim JJ. NADPH-cytochrome P450 oxidoreductase. Structural basis for hydride and electron transfer. J Biol Chem. 2001;276:29163–70.

    Article  CAS  Google Scholar 

  62. Bruckmueller H, Werk AN, Renders L, Feldkamp T, Tepel M, Borst C, et al. Which Genetic determinants should be considered for tacrolimus dose optimization in kidney transplantation? A combined analysis of genes affecting the CYP3A locus. Ther Drug Monit. 2015;37:288–95.

    Article  CAS  Google Scholar 

  63. de Jonge H, Metalidis C, Naesens M, Lambrechts D, Kuypers DR. The P450 oxidoreductase *28 SNP is associated with low initial tacrolimus exposure and increased dose requirements in CYP3A5-expressing renal recipients. Pharmacogenomics. 2011;12:1281–91.

    Article  Google Scholar 

  64. Kurian JR, Bajad SU, Miller JL, Chin NA, Trepanier LA. NADH cytochrome b5 reductase and cytochrome b5 catalyze the microsomal reduction of xenobiotic hydroxylamines and amidoximes in humans. J Pharmacol Exp Ther. 2004;311:1171–8.

    Article  CAS  Google Scholar 

  65. Hebert MF. Contributions of hepatic and intestinal metabolism and P-glycoprotein to cyclosporine and tacrolimus oral drug delivery. Adv Drug Deliv Rev. 1997;27:201–14.

    Article  CAS  Google Scholar 

  66. Masuda S, Goto M, Okuda M, Ogura Y, Oike F, Kiuchi T, et al. Initial dosage adjustment for oral administration of tacrolimus using the intestinal MDR1 level in living-donor liver transplant recipients. Transplant Proc. 2005;37:1728–9.

    Article  CAS  Google Scholar 

  67. Sakurai A, Tamura A, Onishi Y, Ishikawa T. Genetic polymorphisms of ATP-binding cassette transporters ABCB1 and ABCG2: therapeutic implications. Expert Opin Pharmacother. 2005;6:2455–73.

    Article  CAS  Google Scholar 

  68. Shilbayeh S. The impact of genetic polymorphisms on time required to attain the target tacrolimus levels and subsequent pharmacodynamic outcomes in pediatric kidney transplant patients. Saudi J Kidney Dis Transplant. 2014;25:266–77.

    Article  Google Scholar 

  69. Pawarode A, Shukla S, Minderman H, Fricke SM, Pinder EM, O’Loughlin KL, et al. Differential effects of the immunosuppressive agents cyclosporin A, tacrolimus and sirolimus on drug transport by multidrug resistance proteins. Cancer Chemother Pharmacol. 2007;60:179–88.

    Article  CAS  Google Scholar 

  70. Ciftci HS, Ayna TK, Caliskan YK, Guney I, Bakkaloglu H, Nane I, et al. Effect of MDR1 polymorphisms on the blood concentrations of tacrolimus in Turkish renal transplant patients. Transplant Proc. 2013;45:895–900.

    Article  CAS  Google Scholar 

  71. Ogasawara K, Chitnis S, Gohh R, Christians U, Akhlaghi F. Multidrug resistance-associated protein 2 (MRP2/ABCC2) haplotypes significantly affect the pharmacokinetics of tacrolimus in kidney transplant recipients. Clin Pharmacokinet. 2013;52:751–62.

    Article  CAS  Google Scholar 

  72. Pascussi JM, Gerbal-Chaloin S, Drocourt L, Maurel P, Vilarem MJ. The expression of CYP2B6, CYP2C9 and CYP3A4 genes: a tangle of networks of nuclear and steroid receptors. Biochim Biophys Acta. 2003;1619:243–53.

    Article  CAS  Google Scholar 

  73. Pascussi JM, Drocourt L, Gerbal-Chaloin S, Fabre JM, Maurel P, Vilarem MJ. Dual effect of dexamethasone on CYP3A4 gene expression in human hepatocytes. Sequential role of glucocorticoid receptor and pregnane X receptor. Eur J Biochem. 2001;268:6346–58.

    Article  CAS  Google Scholar 

  74. Rodriguez M, Felsenfeld AJ, Llach F. Aluminum administration in the rat separately affects the osteoblast and bone mineralization. J Bone Miner Res. 1990;5:59–67.

    Article  CAS  Google Scholar 

  75. Barraclough KA, Isbel NM, Lee KJ, Bergmann TK, Johnson DW, McWhinney BC, et al. NR1I2 polymorphisms are related to tacrolimus dose-adjusted exposure and BK viremia in adult kidney transplantation. Transplantation. 2012;94:1025–32.

    Article  CAS  Google Scholar 

  76. Chen D, Guo F, Shi J, Zhang C, Wang Z, Fan J, et al. Association of hemoglobin levels, CYP3A5, and NR1I3 gene polymorphisms with tacrolimus pharmacokinetics in liver transplant patients. Drug Metab Pharmacokinet. 2014;29:249–53.

    Article  CAS  Google Scholar 

  77. Tirona RG, Lee W, Leake BF, Lan LB, Cline CB, Lamba V, et al. The orphan nuclear receptor HNF4alpha determines PXR- and CAR-mediated xenobiotic induction of CYP3A4. Nat Med. 2003;9:220–4.

    Article  CAS  Google Scholar 

  78. Jover R, Bort R, Gomez-Lechon MJ, Castell JV. Down-regulation of human CYP3A4 by the inflammatory signal interleukin-6: molecular mechanism and transcription factors involved. FASEB J. 2002;16:1799–801.

    Article  CAS  Google Scholar 

  79. Rodriguez-Antona C, Bort R, Jover R, Tindberg N, Ingelman-Sundberg M, Gomez-Lechon MJ, et al. Transcriptional regulation of human CYP3A4 basal expression by CCAAT enhancer-binding protein alpha and hepatocyte nuclear factor-3 gamma. Mol Pharmacol. 2003;63:1180–9.

    Article  CAS  Google Scholar 

  80. Klein K, Thomas M, Winter S, Nussler AK, Niemi M, Schwab M, et al. PPARA: a novel genetic determinant of CYP3A4 in vitro and in vivo. Clin Pharmacol Ther. 2012;91:1044–52.

    Article  CAS  Google Scholar 

  81. Kurzawski M, Malinowski D, Dziewanowski K, Drozdzik M. Impact of PPARA and POR polymorphisms on tacrolimus pharmacokinetics and new-onset diabetes in kidney transplant recipients. Pharm Genom. 2014;24:397–400.

    CAS  Google Scholar 

  82. Lamba V, Panetta JC, Strom S, Schuetz EG. Genetic predictors of interindividual variability in hepatic CYP3A4 expression. J Pharmacol Exp Ther. 2010;332:1088–99.

    Article  CAS  Google Scholar 

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Acknowledgements

The authors wish to thank the research subjects for their participation in this study. We acknowledge the dedication and hard work of our coordinators at each of the DeKAF Genomics clinical sites: University of Alberta, Nicoleta Bobocea, Tina Wong, Adrian Geambasu and Alyssa Sader; University of Manitoba, Myrna Ross and Kathy Peters; University of Minnesota, Mandi DeGrote, Monica Meyers, Danielle Berglund and Ashley Roman; Hennepin County Medical Center, Lisa Berndt; Mayo Clinic, Tom DeLeeuw; University of Iowa, Wendy Wallace and Tammy Lowe; University of Alabama, Jacquelin Vaughn, Valencia Stephens and Tena Hilario. We also acknowledge the dedicated work of our research scientist Marcia Brott. This study was supported in part by NIH/NIAID grants 5U19-AI070119 and 5U01-AI058013 and by NIH/NIAID grant K01AI130409 to Casey Dorr.

DeKAF Genomics Investigators

Arthur J. Matas9, J. Michael Cecka10, John E. Connett11, Fernando G. Cosio12, Robert S. Gaston13, Rosalyn B. Mannon13, Sita Gourishankar14, Joseph P. Grande15, Lawrence G. Hunsicker16, Bertram L. Kasiske17, David N. Rush18

Author information

Authors and Affiliations

  1. Hennepin Healthcare Research Institute, Minneapolis, MN, USA

    Casey R. Dorr, Amutha Muthusamy, David P. Schladt & Ajay K. Israni

  2. Department of Medicine, Hennepin Healthcare, University of Minnesota, Minneapolis, MN, USA

    Casey R. Dorr & Ajay K. Israni

  3. Department of Biostatistics, University of Minnesota, Minneapolis, MN, USA

    Baolin Wu & Weihua Guan

  4. Department of Medicinal Chemistry, University of Minnesota, Minneapolis, MN, USA

    Rory P. Remmel

  5. Informatics Institute, University of Minnesota, Minneapolis, MN, USA

    Juan E. Abrahante

  6. Departments of Medicine and Surgery, University of Alabama, Birmingham, AL, USA

    Roslyn B. Mannon

  7. Department of Surgery, University of Minnesota, Minneapolis, MN, USA

    Arthur J. Matas

  8. Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA

    William S. Oetting & Pamala A. Jacobson

  9. Department of Surgery, University of Minnesota, Minneapolis, MN, USA

    Arthur J. Matas

  10. UCLA Immunogenetics Center, Los Angeles, CA, USA

    J. Michael Cecka

  11. Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA

    John E. Connett

  12. Division of Nephrology, Mayo Clinic, Rochester, MN, USA

    Fernando G. Cosio

  13. Division of Nephrology, University of Alabama, Birmingham, AL, USA

    Robert S. Gaston & Rosalyn B. Mannon

  14. Division of Nephrology and Immunology, University of Alberta, Edmonton, AB, Canada

    Sita Gourishankar

  15. Mayo Clinic College of Medicine, Rochester, MN, USA

    Joseph P. Grande

  16. Nephrology Division, Iowa City, IA, USA

    Lawrence G. Hunsicker

  17. Division of Nephrology, Hennepin County Medical Center, Minneapolis, MN, USA

    Bertram L. Kasiske

  18. Health Sciences Center, Winnipeg, MB, Canada

    David N. Rush

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  1. Casey R. Dorr
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Consortia

for DeKAF Genomics

  • Arthur J. Matas
  • , J. Michael Cecka
  • , John E. Connett
  • , Fernando G. Cosio
  • , Robert S. Gaston
  • , Rosalyn B. Mannon
  • , Sita Gourishankar
  • , Joseph P. Grande
  • , Lawrence G. Hunsicker
  • , Bertram L. Kasiske
  •  & David N. Rush

Corresponding author

Correspondence to Casey R. Dorr.

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Dorr, C.R., Wu, B., Remmel, R.P. et al. Identification of genetic variants associated with tacrolimus metabolism in kidney transplant recipients by extreme phenotype sampling and next generation sequencing. Pharmacogenomics J 19, 375–389 (2019). https://doi.org/10.1038/s41397-018-0063-z

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  • DOI: https://doi.org/10.1038/s41397-018-0063-z

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