Download

Full Text

Karamperis2024.pdf

Author postprint (5.77 MB)

Download

See more details

News (2)

X (4)

Mendeley (24)

6

CITATIONS

6 Total citations

6 Recent citations

n/a Field Citation Ratio

n/a Relative Citation Ratio

publications

0

supporting

0

mentioning

0

contrasting

0

Smart Citations

0

0

0

0

Citing PublicationsSupportingMentioningContrasting

View Citations

See how this article has been cited at scite.ai

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

• Le Louët, H., Pitts, P.J., Twenty-First Century Global ADR Management: A Need for Clarification, Redesign, and Coordinated Action. Ther. Innov. Regul. Sci. 57 (2023), 100–103, 10.1007/s43441-022-00443-8.
• Micaglio, E., Locati, E.T., Monasky, M.M., Romani, F., Heilbron, F., Pappone, C., Role of Pharmacogenetics in Adverse Drug Reactions: An Update towards Personalized Medicine. Front. Pharmacol., 12, 2021, 651720, 10.3389/FPHAR.2021.651720.
• Sultana, J., Cutroneo, P., Trifirò, G., Clinical and economic burden of adverse drug reactions. J. Pharmacol. Pharmacother. 4 (2013), S73–S77, 10.4103/0976-500X.120957.
• Božina, N., Vrkić Kirhmajer, M., Šimičević, L., Ganoci, L., Mirošević Skvrce, N., Klarica Domjanović, I., Merćep, I., Use of pharmacogenomics in elderly patients treated for cardiovascular diseases. Croat. Med. J. 61 (2020), 147–158, 10.3325/CMJ.2020.61.147.
• Thummel, K.E., Lin, Y.S., Sources of interindividual variability. Methods Mol. Biol. 1113 (2014), 363–415, 10.1007/978-1-62703-758-7_17.
• Alomar, M.J., Factors affecting the development of adverse drug reactions (Review article). Saudi Pharmaceut. J. 22 (2014), 83–94, 10.1016/J.JSPS.2013.02.003.
• Kaniwa, N., Saito, Y., Pharmacogenomics of severe cutaneous adverse reactions and drug-induced liver injury. J. Hum. Genet. 58 (2013), 317–326, 10.1038/jhg.2013.37.
• Skokou, M., Karamperis, K., Koufaki, M.I., Tsermpini, E.E., Pandi, M.T., Siamoglou, S., Ferentinos, P., Bartsakoulia, M., Katsila, T., Mitropoulou, C., et al. Clinical implementation of preemptive pharmacogenomics in psychiatry. EBioMedicine, 101, 2024, 105009, 10.1016/J.EBIOM.2024.105009.
• van der Wouden, C.H., Cambon-Thomsen, A., Cecchin, E., Cheung, K.C., Dávila-Fajardo, C.L., Deneer, V.H., Dolžan, V., Ingelman-Sundberg, M., Jönsson, S., Karlsson, M.O., et al. Implementing Pharmacogenomics in Europe: Design and Implementation Strategy of the Ubiquitous Pharmacogenomics Consortium. Clin. Pharmacol. Ther. 101 (2017), 341–358, 10.1002/CPT.602.
• Swen, J.J., van der Wouden, C.H., Manson, L.E., Abdullah-Koolmees, H., Blagec, K., Blagus, T., Böhringer, S., Cambon-Thomsen, A., Cecchin, E., Cheung, K.C., et al. A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study. Lancet 401 (2023), 347–356, 10.1016/S0140-6736(22)01841-4.
• Crews, K.R., Hicks, J.K., Pui, C.H., Relling, M.V., Evans, W.E., Pharmacogenomics and individualized medicine: translating science into practice. Clin. Pharmacol. Ther. 92 (2012), 467–475, 10.1038/CLPT.2012.120.
• Lavertu, A., McInnes, G., Daneshjou, R., Whirl-Carrillo, M., Klein, T.E., Altman, R.B., Pharmacogenomics and big genomic data: from lab to clinic and back again. Hum. Mol. Genet. 27 (2018), R72–R78, 10.1093/HMG/DDY116.
• Pirmohamed, M., Pharmacogenomics: current status and future perspectives. Nat. Rev. Genet. 24 (2023), 350–362, 10.1038/S41576-022-00572-8.
• Wei, C.Y., Lee, M.T.M., Chen, Y.T., Pharmacogenomics of adverse drug reactions: implementing personalized medicine. Hum. Mol. Genet. 21 (2012), R58–R65, 10.1093/HMG/DDS341.
• Wang, L., McLeod, H.L., Weinshilboum, R.M., Genomics and Drug Response. N. Engl. J. Med. 364 (2011), 1144–1153, 10.1056/NEJMRA1010600.
• Goh, L.L., Lim, C.W., Sim, W.C., Toh, L.X., Leong, K.P., Analysis of Genetic Variation in CYP450 Genes for Clinical Implementation. PLoS One, 12, 2017, e0169233, 10.1371/JOURNAL.PONE.0169233.
• Zhao, M., Ma, J., Li, M., Zhang, Y., Jiang, B., Zhao, X., Huai, C., Shen, L., Zhang, N., He, L., Qin, S., Cytochrome p450 enzymes and drug metabolism in humans. Int. J. Mol. Sci., 22, 2021, 12808, 10.3390/ijms222312808.
• Tracy, T.S., Chaudhry, A.S., Prasad, B., Thummel, K.E., Schuetz, E.G., Zhong, X.B., Tien, Y.C., Jeong, H., Pan, X., Shireman, L.M., et al. Interindividual Variability in Cytochrome P450–Mediated Drug Metabolism. Drug Metab. Dispos. 44 (2016), 343–351, 10.1124/DMD.115.067900.
• Keogh, J.P., Membrane transporters in drug development. Adv. Pharmacol. 63 (2012), 1–42, 10.1016/B978-0-12-398339-8.00001-X.
• Arbitrio, M., Di Martino, M.T., Scionti, F., Barbieri, V., Pensabene, L., Tagliaferri, P., Pharmacogenomic Profiling of ADME Gene Variants: Current Challenges and Validation Perspectives. High. Throughput., 7, 2018, 40, 10.3390/HT7040040.
• Katara, P., Yadav, A., Pharmacogenes (PGx-genes): Current understanding and future directions. Gene, 718, 2019, 144050, 10.1016/J.GENE.2019.144050.
• Zanger, U.M., Schwab, M., Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol. Ther. 138 (2013), 103–141, 10.1016/J.PHARMTHERA.2012.12.007.
• Fischer, A., Smieško, M., A Conserved Allosteric Site on Drug-Metabolizing CYPs: A Systematic Computational Assessment. Int. J. Mol. Sci., 22, 2021, 13215, 10.3390/IJMS222413215.
• Zhou, S.F., Liu, J.P., Chowbay, B., Polymorphism of human cytochrome P450 enzymes and its clinical impact. Drug Metab. Rev. 41 (2009), 89–295, 10.1080/03602530902843483.
• Fujikura, K., Ingelman-Sundberg, M., Lauschke, V.M., Genetic variation in the human cytochrome P450 supergene family. Pharmacogenetics Genom. 25 (2015), 584–594, 10.1097/FPC.0000000000000172.
• Preissner, S.C., Hoffmann, M.F., Preissner, R., Dunkel, M., Gewiess, A., Preissner, S., Polymorphic Cytochrome P450 Enzymes (CYPs) and Their Role in Personalized Therapy. PLoS One, 8, 2013, 82562, 10.1371/JOURNAL.PONE.0082562.
• Li, J., Zhang, L., Zhou, H., Stoneking, M., Tang, K., Global patterns of genetic diversity and signals of natural selection for human ADME genes. Hum. Mol. Genet. 20 (2011), 528–540, 10.1093/HMG/DDQ498.
• Auwerx, C., Lepamets, M., Sadler, M.C., Patxot, M., Stojanov, M., Baud, D., Mägi, R., Estonian Biobank Research Team. Porcu, E., Reymond, A., Kutalik, Z., The individual and global impact of copy-number variants on complex human traits. Am. J. Hum. Genet. 109 (2022), 647–668, 10.1016/J.AJHG.2022.02.010.
• US Food and Drug Administration, FDA. https://www.fda.gov.
• European Medicine Agency, EMA. https://www.ema.europa.eu.
• Lee, M., Han, J.M., Lee, J., Oh, J.Y., Kim, J.S., Gwak, H.S., Choi, K.H., Comparison of pharmacogenomic information for drug approvals provided by the national regulatory agencies in Korea, Europe, Japan, and the United States. Front. Pharmacol., 14, 2023, 1205624, 10.3389/FPHAR.2023.1205624.
• Ehmann, F., Caneva, L., Prasad, K., Paulmichl, M., Maliepaard, M., Llerena, A., Ingelman-Sundberg, M., Papaluca-Amati, M., Pharmacogenomic information in drug labels: European Medicines Agency perspective. Pharmacogenomics J. 15 (2015), 201–210, 10.1038/TPJ.2014.86.
• Sahana, S., Bhoyar, R.C., Sivadas, A., Jain, A., Imran, M., Rophina, M., Senthivel, V., Kumar Diwakar, M., Sharma, D., Mishra, A., et al. Pharmacogenomic landscape of Indian population using whole genomes. Clin. Transl. Sci. 15 (2022), 866–877, 10.1111/CTS.13153.
• Nagar, S.D., Moreno, A.M., Norris, E.T., Rishishwar, L., Conley, A.B., O'Neal, K.L., Vélez-Gómez, S., Montes-Rodríguez, C., Jaraba-Álvarez, W.V., Torres, I., et al. Population Pharmacogenomics for Precision Public Health in Colombia. Front. Genet., 10, 2019, 241, 10.3389/FGENE.2019.00241.
• Bachtiar, M., Lee, C.G.L., Genetics of Population Differences in Drug Response. Curr. Genet. Med. Rep., 2013, 162–170, 10.1007/S40142-013-0017-3.
• Jordan, I.K., Sharma, S., Mariño-Ramírez, L., Population Pharmacogenomics for Health Equity. Genes, 14, 2023, 1840, 10.3390/GENES14101840.
• Ji, X., Ning, B., Liu, J., Roberts, R., Lesko, L., Tong, W., Liu, Z., Shi, T., Towards population-specific pharmacogenomics in the era of next-generation sequencing. Drug Discov. Today 26 (2021), 1776–1783, 10.1016/J.DRUDIS.2021.04.015.
• Lakiotaki, K., Kanterakis, A., Kartsaki, E., Katsila, T., Patrinos, G.P., Potamias, G., Exploring public genomics data for population pharmacogenomics. PLoS One, 12, 2017, e0182138, 10.1371/JOURNAL.PONE.0182138.
• Yang, H.C., Chen, C.W., Lin, Y.T., Chu, S.K., Genetic ancestry plays a central role in population pharmacogenomics. Commun. Biol., 4, 2021, 171, 10.1038/S42003-021-01681-6.
• Rosenberg, N.A., Pritchard, J.K., Weber, J.L., Cann, H.M., Kidd, K.K., Zhivotovsky, L.A., Feldman, M.W., Genetic structure of human populations. Science 298 (2002), 2381–2385, 10.1126/SCIENCE.1078311.
• Nebert, D.W., Menon, A.G., Pharmacogenomics, ethnicity, and susceptibility genes. Pharmacogenomics J. 1 (2001), 19–22, 10.1038/sj.tpj.6500002.
• Hernandez, W., Danahey, K., Pei, X., Yeo, K.T.J., Leung, E., Volchenboum, S.L., Ratain, M.J., Meltzer, D.O., Stranger, B.E., Perera, M.A., O'Donnell, P.H., Pharmacogenomic genotypes define genetic ancestry in patients and enable population-specific genomic implementation. Pharmacogenomics J. 20 (2020), 126–135, 10.1038/S41397-019-0095-Z.
• Mersha, T.B., Abebe, T., Self-reported race/ethnicity in the age of genomic research: Its potential impact on understanding health disparities. Hum. Genom., 9, 2015, 1, 10.1186/S40246-014-0023-x.
• Krainc, T., Fuentes, A., Genetic ancestry in precision medicine is reshaping the race debate. Proc. Natl. Acad. Sci. USA, 119, 2022, e2203033119, 10.1073/PNAS.2203033119.
• Zhou, Y., Lauschke, V.M., Population pharmacogenomics: an update on ethnogeographic differences and opportunities for precision public health. Hum. Genet. 141 (2022), 1113–1136, 10.1007/S00439-021-02385-X.
• Khoury, M.J., Gwinn, M.L., Glasgow, R.E., Kramer, B.S., A population approach to precision medicine. Am. J. Prev. Med. 42 (2012), 639–645, 10.1016/J.AMEPRE.2012.02.012.
• Ramamoorthy, A., Kim, H.H., Shah-Williams, E., Zhang, L., Racial and Ethnic Differences in Drug Disposition and Response: Review of New Molecular Entities Approved Between 2014 and 2019. J. Clin. Pharmacol. 62 (2022), 486–493, 10.1002/JCPH.1978.
• Runcharoen, C., Fukunaga, K., Sensorn, I., Iemwimangsa, N., Klumsathian, S., Tong, H., Vo, N.S., Le, L., Hlaing, T.M., Thant, M., et al. Prevalence of pharmacogenomic variants in 100 pharmacogenes among Southeast Asian populations under the collaboration of the Southeast Asian Pharmacogenomics Research Network (SEAPharm). Hum. Genome Var., 8, 2021, 7, 10.1038/S41439-021-00135-Z.
• Suarez-Kurtz, G., Pharmacogenomics in admixed populations. Trends Pharmacol. Sci. 26 (2005), 196–201, 10.1016/J.TIPS.2005.02.008.
• Corpas, M., Siddiqui, M.K., Soremekun, O., Mathur, R., Gill, D., Fatumo, S., Addressing Ancestry and Sex Bias in Pharmacogenomics. Annu. Rev. Pharmacol. Toxicol. 64 (2024), 53–64, 10.1146/ANNUREV-PHARMTOX-030823-111731.
• Westervelt, P., Cho, K., Bright, D.R., Kisor, D.F., Drug–Gene Interactions: Inherent Variability In Drug Maintenance Dose Requirements. P T 39 (2014), 630–637.
• Lo, C., Nguyen, S., Yang, C., Witt, L., Wen, A., Liao, T.V., Nguyen, J., Lin, B., Altman, R.B., Palaniappan, L., Pharmacogenomics in Asian Subpopulations and Impacts on Commonly Prescribed Medications. Clin. Transl. Sci. 13 (2020), 861–870, 10.1111/CTS.12771.
• Malki, M.A., Pearson, E.R., Drug–drug–gene interactions and adverse drug reactions. Pharmacogenomics J. 20 (2019), 355–366, 10.1038/s41397-019-0122-0.
• Ortega, V.E., Meyers, D.A., Pharmacogenetics: Implications of Race and Ethnicity on Defining Genetic Profiles for Personalized Medicine. J. Allergy Clin. Immunol. 133 (2014), 16–26, 10.1016/J.JACI.2013.10.040.
• Patrinos, G.P., Population pharmacogenomics: impact on public health and drug development. Pharmacogenomics 19 (2018), 3–6, 10.2217/PGS-2017-0166.
• Patrinos, G.P., Sketching the prevalence of pharmacogenomic biomarkers among populations for clinical pharmacogenomics. Eur. J. Hum. Genet. 28 (2020), 1–3, 10.1038/S41431-019-0499-X.
• Durinck, S., Spellman, P.T., Birney, E., Huber, W., Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4 (2009), 1184–1191, 10.1038/NPROT.2009.97.
• Durinck, S., Moreau, Y., Kasprzyk, A., Davis, S., De Moor, B., Brazma, A., Huber, W., BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21 (2005), 3439–3440, 10.1093/BIOINFORMATICS/BTI525.
• Adzhubei, I., Jordan, D.M., Sunyaev, S.R., Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet. 76 (2013), 7–20, 10.1002/0471142905.HG0720S76.
• Oksanen, J., Simpson, G., Blanchet, F., Kindt, R., Legendre, P., Minchin, P., O'Hara, R., Solymos, P., Stevens, M., Szoecs, E., et al. vegan: Community Ecology Package. 2022.
• Perera, M.A., Gamazon, E., Cavallari, L.H., Patel, S.R., Poindexter, S., Kittles, R.A., Nicolae, D., Cox, N.J., The Missing Association: Sequencing-Based Discovery of Novel SNPs in VKORC1 and CYP2C9 That Affect Warfarin Dose in African Americans. Clin. Pharmacol. Ther. 89 (2011), 408–415, 10.1038/CLPT.2010.322.
• Hatta, F.H.M., Lundblad, M., Ramsjo, M., Kang, J.H., Roh, H.K., Bertilsson, L., Eliasson, E., Aklillu, E., Differences in CYP2C9 Genotype and Enzyme Activity Between Swedes and Koreans of Relevance for Personalized Medicine: Role of Ethnicity, Genotype, Smoking, Age, and Sex. OMICS 19 (2015), 346–353, 10.1089/OMI.2015.0022.
• Shah, R.R., Gaedigk, A., Precision medicine: does ethnicity information complement genotype-based prescribing decisions?. Ther. Adv. Drug Saf. 9 (2018), 45–62, 10.1177/2042098617743393.
• Sistonen, J., Sajantila, A., Lao, O., Corander, J., Barbujani, G., Fuselli, S., CYP2D6 worldwide genetic variation shows high frequency of altered activity variants and no continental structure. Pharmacogenetics Genom. 17 (2007), 93–101, 10.1097/01.FPC.0000239974.69464.F2.
• Zhou, Y., Dagli Hernandez, C., Lauschke, V.M., Population-scale predictions of DPD and TPMT phenotypes using a quantitative pharmacogene-specific ensemble classifier. Br. J. Cancer 123 (2020), 1782–1789, 10.1038/S41416-020-01084-0.
• Wright, G.E.B., Carleton, B., Hayden, M.R., Ross, C.J.D., The global spectrum of protein-coding pharmacogenomic diversity. Pharmacogenomics J. 18 (2018), 187–195, 10.1038/tpj.2016.77.
• Zhang, B., Lauschke, V.M., Genetic variability and population diversity of the human SLCO (OATP) transporter family. Pharmacol. Res. 139 (2019), 550–559, 10.1016/J.PHRS.2018.10.017.
• Gaedigk, A., Sangkuhl, K., Whirl-Carrillo, M., Klein, T., Leeder, J.S., Prediction of CYP2D6 phenotype from genotype across world populations. Genet. Med. 19 (2017), 69–76, 10.1038/GIM.2016.80.
• Mizzi, C., Dalabira, E., Kumuthini, J., Dzimiri, N., Balogh, I., Başak, N., Böhm, R., Borg, J., Borgiani, P., Bozina, N., et al. A European Spectrum of Pharmacogenomic Biomarkers: Implications for Clinical Pharmacogenomics. PLoS One, 11, 2016, e0162866, 10.1371/JOURNAL.PONE.0162866.
• Petrović, J., Pešić, V., Lauschke, V.M., Frequencies of clinically important CYP2C19 and CYP2D6 alleles are graded across Europe. Eur. J. Hum. Genet. 28 (2019), 88–94, 10.1038/s41431-019-0480-8.
• Kido, T., Sikora-Wohlfeld, W., Kawashima, M., Kikuchi, S., Kamatani, N., Patwardhan, A., Chen, R., Sirota, M., Kodama, K., Hadley, D., Butte, A.J., Are minor alleles more likely to be risk alleles?. BMC Med. Genom., 11, 2018, 3, 10.1186/S12920-018-0322-5.
• 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526 (2015), 68–74, 10.1038/NATURE15393.
• Bergström, A., McCarthy, S.A., Hui, R., Almarri, M.A., Ayub, Q., Danecek, P., Chen, Y., Felkel, S., Hallast, P., Kamm, J., et al. Insights into human genetic variation and population history from 929 diverse genomes. Science, 367, 2020, eaay5012, 10.1126/SCIENCE.AAY5012.
• Ang, H.X., Chan, S.L., Sani, L.L., Quah, C.B., Brunham, L.R., Tan, B.O.P., Winther, M.D., Pharmacogenomics in Asia: a systematic review on current trends and novel discoveries. Pharmacogenomics 18 (2017), 891–910, 10.2217/PGS-2017-0009.
• Reich, D., Thangaraj, K., Patterson, N., Price, A.L., Singh, L., Reconstructing Indian Population History. Nature 461 (2009), 489–494, 10.1038/NATURE08365.
• Moorjani, P., Thangaraj, K., Patterson, N., Lipson, M., Loh, P.R., Govindaraj, P., Berger, B., Reich, D., Singh, L., Genetic Evidence for Recent Population Mixture in India. Am. J. Hum. Genet. 93 (2013), 422–438, 10.1016/J.AJHG.2013.07.006.
• Amariuta, T., Ishigaki, K., Sugishita, H., Ohta, T., Koido, M., Dey, K.K., Matsuda, K., Murakami, Y., Price, A.L., Kawakami, E., et al. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat. Genet. 52 (2020), 1346–1354, 10.1038/s41588-020-00740-8.
• Hans, M., Gupta, S.K., Comparative evaluation of pharmacovigilance regulation of the United States, United Kingdom, Canada, India and the need for global harmonized practices. Perspect. Clin. Res. 9 (2018), 170–174, 10.4103/PICR.PICR_89_17.
• Khan, M.A.A., Hamid, S., Babar, Z.-U.-D., Pharmacovigilance in High-Income Countries: Current Developments and a Review of Literature. Pharmacy, 11, 2023, 10, 10.3390/PHARMACY11010010.
• Buffenstein, I., Kaneakua, B., Taylor, E., Matsunaga, M., Choi, S.Y., Carrazana, E., Viereck, J., Liow, K.K., Ghaffari-Rafi, A., Demographic recruitment bias of adults in United States randomized clinical trials by disease categories between 2008 to 2019: a systematic review and meta-analysis. Sci. Rep., 13, 2023, 42, 10.1038/S41598-022-23664-1.
• Ashraf, Q., Galor, O., The “Out of Africa” Hypothesis, Human Genetic Diversity, and Comparative Economic Development. Am. Econ. Rev. 103 (2013), 1–46, 10.1257/AER.103.1.1.
• Subramanian, S., Population size influences the type of nucleotide variations in humans. BMC Genet., 20, 2019, 93, 10.1186/S12863-019-0798-9.
• Fuselli, S., Beyond drugs: the evolution of genes involved in human response to medications. Proc. Biol. Sci., 286, 2019, 20191716, 10.1098/RSPB.2019.1716.
• Ahn, E., Park, T., Analysis of population-specific pharmacogenomic variants using next-generation sequencing data. Sci. Rep., 7, 2017, 8416, 10.1038/s41598-017-08468-y.
• Verma, S.S., Keat, K., Li, B., Hoffecker, G., Risman, M., Regeneron Genetics Center. Sangkuhl, K., Whirl-Carrillo, M., Dudek, S., Verma, A., et al. Evaluating the frequency and the impact of pharmacogenetic alleles in an ancestrally diverse Biobank population. J. Transl. Med., 20, 2022, 550, 10.1186/S12967-022-03745-5.
• Idda, M.L., Zoledziewska, M., Urru, S.A.M., McInnes, G., Bilotta, A., Nuvoli, V., Lodde, V., Orrù, S., Schlessinger, D., Cucca, F., Floris, M., Genetic Variation among Pharmacogenes in the Sardinian Population. Int. J. Mol. Sci., 23, 2022, 10058, 10.3390/IJMS231710058.
• Branco, C.C., Bento, M.S., Gomes, C.T., Cabral, R., Pacheco, P.R., Mota-Vieira, L., Azores Islands: genetic origin, gene flow and diversity pattern. Ann. Hum. Biol. 35 (2008), 65–74, 10.1080/03014460701793782.
• Russell, L.E., Zhou, Y., Almousa, A.A., Sodhi, J.K., Nwabufo, C.K., Lauschke, V.M., Pharmacogenomics in the era of next generation sequencing – from byte to bedside. Drug Metab. Rev. 53 (2021), 253–278, 10.1080/03602532.2021.1909613.
• Lauschke, V.M., Ingelman-Sundberg, M., Emerging strategies to bridge the gap between pharmacogenomic research and its clinical implementation. NPJ Genom. Med., 5, 2020, 9, 10.1038/s41525-020-0119-2.
• Olivier, C., Williams-Jones, B., Pharmacogenomic technologies: A necessary “luxury” for better global public health?. Glob. Health, 7, 2011, 30, 10.1186/1744-8603-7-30.
• Soko, N.D., Muyambo, S., Dandara, M.T.L., Kampira, E., Blom, D., Jones, E.S.W., Rayner, B., Shamley, D., Sinxadi, P., Dandara, C., Towards Evidence-Based Implementation of Pharmacogenomics in Southern Africa: Comorbidities and Polypharmacy Profiles across Diseases. J. Personalized Med., 13, 2023, 1185, 10.3390/JPM13081185.
• Koutsilieri, S., Tzioufa, F., Sismanoglou, D.C., Patrinos, G.P., Unveiling the guidance heterogeneity for genome-informed drug treatment interventions among regulatory bodies and research consortia. Pharmacol. Res., 153, 2020, 104590, 10.1016/J.PHRS.2019.104590.
• Karamperis, K., Katz, S., Melograna, F., Ganau, F.P., Van Steen, K., Patrinos, G.P., Lao, O., Genetic ancestry in Population Pharmacogenomics unravels distinct geographical patterns related to drug toxicity. Mendeley Data, V1, 2024, 10.17632/vtky42nggm.1.
• Mallick, S., Li, H., Lipson, M., Mathieson, I., Gymrek, M., Racimo, F., Zhao, M., Chennagiri, N., Nordenfelt, S., Tandon, A., et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature 538 (2016), 201–206, 10.1038/NATURE18964.
• Barbarino, J.M., Whirl-Carrillo, M., Altman, R.B., Klein, T.E., PharmGKB: A worldwide resource for pharmacogenomic information. Wiley Interdiscip. Rev. Syst. Biol. Med., 10, 2018, e1417, 10.1002/WSBM.1417.
• Gaedigk, A., Whirl-Carrillo, M., Pratt, V.M., Miller, N.A., Klein, T.E., PharmVar and the Landscape of Pharmacogenetic Resources. Clin. Pharmacol. Ther. 107 (2020), 43–46, 10.1002/CPT.1654.
• Gaedigk, A., Casey, S.T., Whirl-Carrillo, M., Miller, N.A., Klein, T.E., Pharmacogene Variation Consortium: A Global Resource and Repository for Pharmacogene Variation. Clin. Pharmacol. Ther. 110 (2021), 542–545, 10.1002/CPT.2321.
• Wishart, D.S., Feunang, Y.D., Guo, A.C., Lo, E.J., Marcu, A., Grant, J.R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46 (2018), D1074–D1082, 10.1093/NAR/GKX1037.
• Ensembl REST API. https://rest.ensembl.org.
• Huber, W., Carey, V.J., Gentleman, R., Anders, S., Carlson, M., Carvalho, B.S., Bravo, H.C., Davis, S., Gatto, L., Girke, T., et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12 (2015), 115–121, 10.1038/nmeth.3252.
• Gräler, B., Pebesma, E., Heuvelink, G., Spatio-Temporal Interpolation using gstat. Rom. Jahrb., 8, 2016, 204.
• Pebesma, E.J., Multivariable geostatistics in S: the gstat package. Comput. Geosci. 30 (2004), 683–691, 10.1016/J.CAGEO.2004.03.012.
• Pebesma E, B.R., Classes and methods for spatial data in R. R. News 5 (2005), 9–13.
• Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L.D.A., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Welcome to the Tidyverse. J. Open Source Softw., 4, 2019, 1686, 10.21105/JOSS.01686.
• Hijmans, R., raster: Geographic Data Analysis and Modeling. R package version 3 (2023), 6–20.
• Robert, J., van Etten, H., van Etten, J., raster: Geographic analysis and modeling with raster data. 2012.
• Kassambara. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. https://CRAN.R-project.org/package=rstatix.Rpackageversion0.7.2, 2023.
• South, A., rworldmap: A new R package for mapping global data. Rom. Jahrb., 3, 2011, 10.32614/RJ-2011-006.
• Scrucca, L., GA: A package for genetic algorithms in R. J. Stat. Software 53 (2013), 1–37, 10.18637/JSS.V053.I04.
• Kent, W.J., Sugnet, C.W., Furey, T.S., Roskin, K.M., Pringle, T.H., Zahler, A.M., Haussler, D., The human genome browser at UCSC. Genome Res. 12 (2002), 996–1006, 10.1101/GR.229102.
• Ensembl. https://www.ensembl.org.
• Relling, M.V., Klein, T.E., CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network. Clin. Pharmacol. Ther. 89 (2011), 464–467, 10.1038/CLPT.2010.279.
• Dutch Pharmacogenetics Working Group. DPWG. https://www.knmp.nl.
• Pharmacogenomics Knowledge Base. PharmGKB. https://www.pharmgkb.org.
• Clinical Pharmacogenetics Implementation Consortium. CPIC. https://cpicpgx.org.
• Thorn, C.F., Klein, T.E., Altman, R.B., PharmGKB: the Pharmacogenomics Knowledge Base. Methods Mol. Biol. 1015 (2013), 311–320, 10.1007/978-1-62703-435-7_20.
• Whirl-Carrillo, M., Huddart, R., Gong, L., Sangkuhl, K., Thorn, C.F., Whaley, R., Klein, T.E., An Evidence-Based Framework for Evaluating Pharmacogenomics Knowledge for Personalized Medicine. Clin. Pharmacol. Ther. 110 (2021), 563–572, 10.1002/CPT.2350.
• Kalman, L.V., Agúndez, J.A.G., Appell, M.L., Black, J.L., Bell, G.C., Boukouvala, S., Bruckner, C., Bruford, E., Caudle, K., Coulthard, S.A., et al. Pharmacogenetic allele nomenclature: International workgroup recommendations for test result reporting. Clin. Pharmacol. Ther. 99 (2016), 172–185, 10.1002/CPT.280.
• Robarge, J.D., Li, L., Desta, Z., Nguyen, A., Flockhart, D.A., The star-allele nomenclature: retooling for translational genomics. Clin. Pharmacol. Ther. 82 (2007), 244–248, 10.1038/SJ.CLPT.6100284.
• Martin, F.J., Amode, M.R., Aneja, A., Austine-Orimoloye, O., Azov, A.G., Barnes, I., Becker, A., Bennett, R., Berry, A., Bhai, J., et al. Ensembl 2023. Nucleic Acids Res. 51 (2023), D933–D941, 10.1093/NAR/GKAC958.
• Pharmacogene Variation Consortium. Pharmvar. https://www.pharmvar.org.
• Koromina, M., Pandi, M.T., van der Spek, P.J., Patrinos, G.P., Lauschke, V.M., The ethnogeographic variability of genetic factors underlying G6PD deficiency. Pharmacol. Res., 173, 2021, 105904, 10.1016/J.PHRS.2021.105904.
• Appell, M.L., Berg, J., Duley, J., Evans, W.E., Kennedy, M.A., Lennard, L., Marinaki, T., McLeod, H.L., Relling, M.V., Schaeffeler, E., et al. Nomenclature for alleles of the thiopurine methyltransferase gene. Pharmacogenetics Genom. 23 (2013), 242–248, 10.1097/FPC.0B013E32835F1CC0.
• Hein, D.W., Doll, M.A., Accuracy of various human NAT2 SNP genotyping panels to infer rapid, intermediate and slow acetylator phenotypes. Pharmacogenomics 13 (2012), 31–41, 10.2217/PGS.11.122.
• Huddart, R., Fohner, A.E., Whirl-Carrillo, M., Wojcik, G.L., Gignoux, C.R., Popejoy, A.B., Bustamante, C.D., Altman, R.B., Klein, T.E., Standardized Biogeographic Grouping System for Annotating Populations in Pharmacogenetic Research. Clin. Pharmacol. Ther. 105 (2019), 1256–1262, 10.1002/CPT.1322.
• Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., Fitzhugh, W., et al. Initial sequencing and analysis of the human genome. Nature 409 (2001), 860–921, 10.1038/35057062.
• McDonald, J., Lambert, D.G., Drug–receptor interactions in anaesthesia. BJA Educ. 22 (2022), 20–25, 10.1016/J.BJAE.2021.07.009.
• Marc, J., 7. Pharmacogenetics of Drug Receptors. EJIFCC 19 (2008), 48–53.
• Nigam, S.K., What do drug transporters really do?. Nat. Rev. Drug Discov. 14 (2015), 29–44, 10.1038/NRD4461.
• Santos, R., Ursu, O., Gaulton, A., Bento, A.P., Donadi, R.S., Bologa, C.G., Karlsson, A., Al-Lazikani, B., Hersey, A., Oprea, T.I., Overington, J.P., A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16 (2017), 19–34, 10.1038/NRD.2016.230.
• Crettol, S., Petrovic, N., Murray, M., Pharmacogenetics of phase I and phase II drug metabolism. Curr. Pharmaceut. Des. 16 (2010), 204–219, 10.2174/138161210790112674.
• DrugBank. Database for Drug and Drug Target Info. https://go.drugbank.com.
• Gower, J.C., Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis. Biometrika, 53, 1966, 325, 10.2307/2333639.
• Mardia, K.V., Some properties of clasical multi-dimesional scaling. Commun. Stat. Theor. Methods 7 (1978), 1233–1241, 10.1080/03610927808827707.
• Conway, J.R., Lex, A., Gehlenborg, N., UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33 (2017), 2938–2940, 10.1093/BIOINFORMATICS/BTX364.
• MacQueen, J.B., Some Methods for Classification and Analysis of Multivariate Observations, 1, 1967, 281–297.
• Park, J.H., Gail, M.H., Weinberg, C.R., Carroll, R.J., Chung, C.C., Wang, Z., Chanock, S.J., Fraumeni, J.F., Chatterjee, N., Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants. Proc. Natl. Acad. Sci. USA 108 (2011), 18026–18031, 10.1073/PNAS.1114759108.
• Chen, S., Francioli, L.C., Goodrich, J.K., Collins, R.L., Kanai, M., Wang, Q., Alföldi, J., Watts, N.A., Vittal, C., Gauthier, L.D., et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625 (2024), 92–100, 10.1038/S41586-023-06045-0.
• R Core Team. R A Language and Environment for Statistical Computing. 2022, R Foundation for Statistical Computing https://www.scirp.org/reference/referencespapers?referenceid=3456808.