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Mouterde M, Daali Y, Rollason V, Čížková M, Mulugeta A, Al Balushi KA, Fakis G, Constantinidis TC, Al-Thihli K, Černá M, Makonnen E, Boukouvala S, Al-Yahyaee S, Yimer G, Černý V, Desmeules J, Poloni ES. Joint Analysis of Phenotypic and Genomic Diversity Sheds Light on the Evolution of Xenobiotic Metabolism in Humans. Genome Biol Evol 2022; 14:6852765. [PMID: 36445690 PMCID: PMC9750130 DOI: 10.1093/gbe/evac167] [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: 02/21/2022] [Revised: 11/03/2022] [Accepted: 11/22/2022] [Indexed: 11/30/2022] Open
Abstract
Variation in genes involved in the absorption, distribution, metabolism, and excretion of drugs (ADME) can influence individual response to a therapeutic treatment. The study of ADME genetic diversity in human populations has led to evolutionary hypotheses of adaptation to distinct chemical environments. Population differentiation in measured drug metabolism phenotypes is, however, scarcely documented, often indirectly estimated via genotype-predicted phenotypes. We administered seven probe compounds devised to target six cytochrome P450 enzymes and the P-glycoprotein (P-gp) activity to assess phenotypic variation in four populations along a latitudinal transect spanning over Africa, the Middle East, and Europe (349 healthy Ethiopian, Omani, Greek, and Czech volunteers). We demonstrate significant population differentiation for all phenotypes except the one measuring CYP2D6 activity. Genome-wide association studies (GWAS) evidenced that the variability of phenotypes measuring CYP2B6, CYP2C9, CYP2C19, and CYP2D6 activity was associated with genetic variants linked to the corresponding encoding genes, and additional genes for the latter three. Instead, GWAS did not indicate any association between genetic diversity and the phenotypes measuring CYP1A2, CYP3A4, and P-gp activity. Genome scans of selection highlighted multiple candidate regions, a few of which included ADME genes, but none overlapped with the GWAS candidates. Our results suggest that different mechanisms have been shaping the evolution of these phenotypes, including phenotypic plasticity, and possibly some form of balancing selection. We discuss how these contrasting results highlight the diverse evolutionary trajectories of ADME genes and proteins, consistent with the wide spectrum of both endogenous and exogenous molecules that are their substrates.
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Affiliation(s)
| | - Youssef Daali
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Victoria Rollason
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Martina Čížková
- Institute of Archaeology of the Academy of Sciences of the Czech Republic, Prague, Czech Republic
| | - Anwar Mulugeta
- Department of Pharmacology and Clinical Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Khalid A Al Balushi
- College of Pharmacy, National University of Science and Technology, Muscat, Sultanate of Oman
| | - Giannoulis Fakis
- Department of Molecular Biology and Genetics, Democritus University of Thrace, Alexandroupolis, Greece
| | | | - Khalid Al-Thihli
- Department of Genetics, Sultan Qaboos University Hospital, Muscat, Sultanate of Oman
| | - Marie Černá
- Department of Medical Genetics, Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Eyasu Makonnen
- Department of Pharmacology and Clinical Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia,Center for Innovative Drug Development and Therapeutic Trials for Africa, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Sotiria Boukouvala
- Department of Molecular Biology and Genetics, Democritus University of Thrace, Alexandroupolis, Greece
| | - Said Al-Yahyaee
- Department of Genetics, College of Medicine and Health Sciences, Sultan Qaboos University, Muscat, Sultanate of Oman
| | - Getnet Yimer
- Center for Global Genomics & Health Equity, Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Viktor Černý
- Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Prague, Czech Republic
| | - Jules Desmeules
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
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Relevance of CYP2D6 Gene Variants in Population Genetic Differentiation. Pharmaceutics 2022; 14:pharmaceutics14112481. [PMID: 36432672 PMCID: PMC9694252 DOI: 10.3390/pharmaceutics14112481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 11/18/2022] Open
Abstract
A significant portion of the variability in complex features, such as drug response, is likely caused by human genetic diversity. One of the highly polymorphic pharmacogenes is CYP2D6, encoding an enzyme involved in the metabolism of about 25% of commonly prescribed drugs. In a directed search of the 1000 Genomes Phase III variation data, 86 single nucleotide polymorphisms (SNPs) in the CYP2D6 gene were extracted from the genotypes of 2504 individuals from 26 populations, and then used to reconstruct haplotypes. Analyses were performed using Haploview, Phase, and Arlequin softwares. Haplotype and nucleotide diversity were high in all populations, but highest in populations of African ancestry. Pairwise FST showed significant results for eleven SNPs, six of which were characteristic of African populations, while four SNPs were most common in East Asian populations. A principal component analysis of CYP2D6 haplotypes showed that African populations form one cluster, Asian populations form another cluster with East and South Asian populations separated, while European populations form the third cluster. Linkage disequilibrium showed that all African populations have three or more haplotype blocks within the CYP2D6 gene, while other world populations have one, except for Chinese Dai and Punjabi in Pakistan populations, which have two.
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Zhang T, Li Q, Dong B, Liang X, Jia M, Bai J, Yu J, Fu S. Genetic Polymorphism of Drug Metabolic Gene CYPs, VKORC1, NAT2, DPYD and CHST3 of Five Ethnic Minorities in Heilongjiang Province, Northeast China. Pharmgenomics Pers Med 2021; 14:1537-1547. [PMID: 34876832 PMCID: PMC8643223 DOI: 10.2147/pgpm.s339854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 11/05/2021] [Indexed: 11/23/2022] Open
Abstract
Introduction Genetic variability in genes encoding drug-metabolizing enzymes may contribute to the heterogeneity of drug responses in different populations. Extensive research in pharmacogenomics in major populations around the world provides us with a great deal of information about drug-related genetic polymorphisms. Objective The purpose of this study was to detect the genetic variation of drug-metabolism-related genes in the five ethnic minorities Daur, Hezhen, Ewenki, Mongolian and Manchu in China, and to analyze the distribution differences among ethnic groups. Methods We genotyped 32 SNPs of drug metabolism genes in 882 healthy Chinese volunteers from five ethnic groups. The genotype frequency and allele frequency of the five ethnic groups were calculated, and the different variants among the five ethnic groups were compared by chi-square test. Genetic parameters were analyzed using Popgene software. The genetic structure of five ethnic minorities was analyzed by principal component analysis, and compared with 26 populations. Results We found that SNPs of genes related to drug metabolism existed diversity in different populations. Among them, rs8192766 and rs9419082 in CYP2E1 showed statistical differences between Daur and Manchu, and NAT2 rs1801280 showed statistical differences between Hezhen and Mongolian. In addition, the five populations we studied had the smallest differences with EAS populations. There was haplotype diversity in CHST3, VKORC1, CYP1A2 and CYP2E1 genes in the five ethnic minorities, and these haplotype polymorphisms were related to the use of corresponding drug doses. Cluster analysis shows that the five ethnic minorities in Heilongjiang Province are clustered together with the EAS populations. Conclusion These results suggest that understanding the diversity of drug-related genetic markers is critical for individualized drug gene therapy programs in ethnic minorities in China as well as in populations highly mixed with these ethnic groups.
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Affiliation(s)
- Tingting Zhang
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, People's Republic of China.,Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, People's Republic of China
| | - Qiuyan Li
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, People's Republic of China.,Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, People's Republic of China.,Editorial Department of International Journal of Genetics, Harbin Medical University, Harbin, People's Republic of China
| | - Bonan Dong
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, People's Republic of China.,Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, People's Republic of China
| | - Xiao Liang
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, People's Republic of China.,Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, People's Republic of China
| | - Mansha Jia
- Scientific Research Centre, The Second Affiliated Hospital of Harbin Medical University, Harbin, People's Republic of China
| | - Jing Bai
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, People's Republic of China.,Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, People's Republic of China
| | - Jingcui Yu
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, People's Republic of China.,Scientific Research Centre, The Second Affiliated Hospital of Harbin Medical University, Harbin, People's Republic of China
| | - Songbin Fu
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, People's Republic of China.,Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, People's Republic of China
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Benton ML, Abraham A, LaBella AL, Abbot P, Rokas A, Capra JA. The influence of evolutionary history on human health and disease. Nat Rev Genet 2021; 22:269-283. [PMID: 33408383 PMCID: PMC7787134 DOI: 10.1038/s41576-020-00305-9] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2020] [Indexed: 01/29/2023]
Abstract
Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual's DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential.
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Affiliation(s)
- Mary Lauren Benton
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Computer Science, Baylor University, Waco, TX, USA
| | - Abin Abraham
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA
| | - Abigail L LaBella
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Patrick Abbot
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Antonis Rokas
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - John A Capra
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA.
- Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
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Yang HC, Chen CW, Lin YT, Chu SK. Genetic ancestry plays a central role in population pharmacogenomics. Commun Biol 2021; 4:171. [PMID: 33547344 PMCID: PMC7864978 DOI: 10.1038/s42003-021-01681-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Accepted: 01/06/2021] [Indexed: 12/12/2022] Open
Abstract
Recent studies have pointed out the essential role of genetic ancestry in population pharmacogenetics. In this study, we analyzed the whole-genome sequencing data from The 1000 Genomes Project (Phase 3) and the pharmacogenetic information from Drug Bank, PharmGKB, PharmaADME, and Biotransformation. Here we show that ancestry-informative markers are enriched in pharmacogenetic loci, suggesting that trans-ancestry differentiation must be carefully considered in population pharmacogenetics studies. Ancestry-informative pharmacogenetic loci are located in both protein-coding and non-protein-coding regions, illustrating that a whole-genome analysis is necessary for an unbiased examination over pharmacogenetic loci. Finally, those ancestry-informative pharmacogenetic loci that target multiple drugs are often a functional variant, which reflects their importance in biological functions and pathways. In summary, we develop an efficient algorithm for an ultrahigh-dimensional principal component analysis. We create genetic catalogs of ancestry-informative markers and genes. We explore pharmacogenetic patterns and establish a high-accuracy prediction panel of genetic ancestry. Moreover, we construct a genetic ancestry pharmacogenomic database Genetic Ancestry PhD (http://hcyang.stat.sinica.edu.tw/databases/genetic_ancestry_phd/). Hsin-Chou Yang et al. examine population structure in several genomic databases and identify that pharmacogenetic loci are enriched for markers of genetic ancestry. Their results suggest that genetic ancestry must be carefully considered in population pharmacogenetics studies.
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Affiliation(s)
- Hsin-Chou Yang
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. .,Institute of Statistics, National Cheng Kung University, Tainan, Taiwan. .,Institute of Public Health, National Yang-Ming University, Taipei, Taiwan.
| | - Chia-Wei Chen
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Yu-Ting Lin
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Shih-Kai Chu
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
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Kim V, Wal TVD, Nishi MY, Montenegro LR, Carrilho FJ, Hoshida Y, Ono SK. Brazilian cohort and genes encoding for drug-metabolizing enzymes and drug transporters. Pharmacogenomics 2020; 21:575-586. [PMID: 32486903 DOI: 10.2217/pgs-2020-0013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Background & aim: Genetic variability in drug absorption, distribution, metabolism and excretion (ADME) genes contributes to the high heterogeneity of drug responses. The present study investigated polymorphisms of ADME genes frequencies and compared the findings with populations from other continents, available in the 1000 Genome Project (1 KGP) and the Exome Aggregation Consortium (ExAC) databases. Methodology & results: We conducted a study of 100 patients in Brazil and a total of 2003 SNPs were evaluated by targeted next-generation sequencing in 148 genes, including Phase I enzymes (n = 50), Phase II enzymes (n = 38) and drug transporters (n = 60). Overall, the distribution of minor allele frequency (MAF) suggests that the distribution of 2003 SNPs is similar between Brazilian cohort, 1 KGP and ExAC; however, we found moderate SNP allele-frequency divergence between Brazilian cohort and both 1000 KGP and ExAC. These differences were observed in several relevant genes including CYP3A4, NAT2 and SLCO1B1. Conclusion: We concluded that the Brazilian population needs clinical assessment of drug treatment based on individual genotype rather than ethnicity.
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Affiliation(s)
- Vera Kim
- Division of Clinical Gastroenterology & Hepatology, Department of Gastroenterology, Faculdade de Medicina, Universidade de São Paulo, São Paulo, 05403-000, Brazil.,Division of Liver Diseases, Department of Medicine, Liver Cancer Program, Tisch Cancer Institute, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, NY 10029, USA
| | - Thijs van der Wal
- Division of Liver Diseases, Department of Medicine, Liver Cancer Program, Tisch Cancer Institute, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, NY 10029, USA
| | - Miriam Yumie Nishi
- Unidade de Endocrinologia do Desenvolvimento, Disciplina de Endocrinologia e Metabologia do Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, 05403-000, Brazil
| | - Luciana Ribeiro Montenegro
- Unidade de Endocrinologia do Desenvolvimento, Disciplina de Endocrinologia e Metabologia do Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, 05403-000, Brazil
| | - Flair Jose Carrilho
- Division of Clinical Gastroenterology & Hepatology, Department of Gastroenterology, Faculdade de Medicina, Universidade de São Paulo, São Paulo, 05403-000, Brazil
| | - Yujin Hoshida
- Division of Liver Diseases, Department of Medicine, Liver Cancer Program, Tisch Cancer Institute, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, NY 10029, USA.,Liver Tumor Transnational Research Program, Simmons Comprehensive Cancer Center, Division of Digestive & Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Suzane Kioko Ono
- Division of Clinical Gastroenterology & Hepatology, Department of Gastroenterology, Faculdade de Medicina, Universidade de São Paulo, São Paulo, 05403-000, Brazil
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Fuselli S. Beyond drugs: the evolution of genes involved in human response to medications. Proc Biol Sci 2019; 286:20191716. [PMID: 31640517 DOI: 10.1098/rspb.2019.1716] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
The genetic variation of our species reflects human demographic history and adaptation to diverse local environments. Part of this genetic variation affects individual responses to exogenous substances, such as food, pollutants and drugs, and plays an important role in drug efficacy and safety. This review provides a synthesis of the evolution of loci implicated in human pharmacological response and metabolism, interpreted within the theoretical framework of population genetics and molecular evolution. In particular, I review and discuss key evolutionary aspects of different pharmacogenes in humans and other species, such as the relationship between the type of substrates and rate of evolution; the selective pressure exerted by landscape variables or dietary habits; expected and observed patterns of rare genetic variation. Finally, I discuss how this knowledge can be translated directly or after the implementation of specific studies, into practical guidelines.
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Affiliation(s)
- Silvia Fuselli
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
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8
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Škarić-Jurić T, Tomas Ž, Zajc Petranović M, Božina N, Smolej Narančić N, Janićijević B, Salihović MP. Characterization of ADME genes variation in Roma and 20 populations worldwide. PLoS One 2018; 13:e0207671. [PMID: 30452466 PMCID: PMC6242375 DOI: 10.1371/journal.pone.0207671] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 11/05/2018] [Indexed: 12/13/2022] Open
Abstract
The products of the polymorphic ADME genes are involved in Absorption, Distribution, Metabolism, and Excretion of drugs. The pharmacogenetic data have been studied extensively due to their clinical importance in the appropriate drug prescription, but such data from the isolated populations are rather scarce. We analyzed the distribution of 95 polymorphisms in 31 core ADME genes in 20 populations worldwide and in newly genotyped samples from the Roma (Gypsy) population living in Croatia. Global distribution of ADME core gene loci differentiated three major clusters; (1) African, (2) East Asian, and (3) joint European, South Asian and South American cluster. The SLCO1B3 (rs4149117) and CYP3A4 (rs2242480) genes differentiated at the highest level the African group of populations, while NAT2 gene loci (rs1208, rs1801280, and rs1799929) and VKORC1 (rs9923231) differentiated East Asian populations. The VKORC1 rs9923231 was among the investigated loci the one with the largest global minor allele frequency (MAF) range; its MAF ranged from 0.027 in Nigeria to 0.924 in Han Chinese. The distribution of the investigated gene loci positions Roma population within the joined European and South Asian clusters, suggesting that their ADME gene pool is a combination of ancestral (Indian) and more recent (European) surrounding, as it was already implied by other genetic markers. However, when compared to the populations worldwide, the Croatian Roma have extreme MAF values in 10 out of the 95 investigated ADME core gene loci. Among loci which have extraordinary MAFs in Roma population two have strong proof of clinical importance: rs1799853 (CYP2C9) for warfarin dosage, and rs12248560 (CYP2C19) for clopidogrel dosage, efficacy and toxicity. This finding confirms the importance of taking the Roma as well as the other isolated populations`genetic profiles into account in pharmaco-therapeutic practice.
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Affiliation(s)
| | - Željka Tomas
- Institute for Anthropological Research, Zagreb, Croatia
| | | | - Nada Božina
- Department for Pharmacogenomics and Therapy Individualization, University Hospital Center Zagreb, Department of Pharmacology, University of Zagreb School of Medicine, Zagreb, Croatia
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Adedokun SA, Seamans BN, Cox NT, Liou G, Akindele AA, Li Y, Ojurongbe O, Thomas BN. Interleukin-4 and STAT6 promoter polymorphisms but not interleukin-10 or 13 are essential for schistosomiasis and associated disease burden among Nigerian children. INFECTION GENETICS AND EVOLUTION 2018; 65:28-34. [PMID: 30010060 DOI: 10.1016/j.meegid.2018.07.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 07/09/2018] [Accepted: 07/11/2018] [Indexed: 01/21/2023]
Abstract
Schistosomiasis is endemic in many parts of rural Africa, with previous reports showing interleukin-13 polymorphisms as drivers of infectivity and disease severity in West Africa while IL-13/IL-4 polymorphisms contributes to patterns of reinfection in East Africa. We have shown that there is a genetic delineation in susceptibility to and severity of infectious diseases in Africa, in addition to sub-continental differences in disease pattern. Therefore, which immunoregulatory biomarkers are essential in driving S. haematobium infection or regulate disease burden among Nigerian school children? One hundred and thirty one age and sex-matched schistosome-infected children and 275 uninfected controls, of same ethnicity, recruited from southwestern Nigeria, were screened for variability of cytokine genes, IL-10 (rs1800872), IL-13 (rs7719175), IL-4 (rs2243250) and STAT6 (rs3024974), utilizing a polymerase chain reaction-restriction fragment length polymorphism assay. We found no difference in genotypic or allelic frequencies of IL-10 and IL-13 promoter polymorphisms alone or in association with disease. Contrariwise, we report significant differences in the frequencies of IL-4 and STAT6 variants between groups. For IL-4, the rs2243250 T/T variant was significantly different for genotypes (71.6% versus 51.2%; p < .0004) and alleles (82.6% versus 71.1%; p < .001) between disease and control groups respectively. For STAT6 (rs3024974), the frequencies of genotypes C/C and C/T are 75.4% and 24.6%, both showing an association with disease; none of the infected subjects had the T/T variant. Despite minor differences in disease covariates, we found no association between IL-4 and STAT6 variants with age, gender or anemia. However, mean egg count (indicative of disease burden), was regulated based on IL-4 variants, with highest burden in infected subjects with rs2243250 T/T variant (mean egg count: 207.5 eggs/10 ml of urine) versus rs2243250 C/T heterozygotes (mean egg count: 84.3 eggs/10 ml of urine) versus rs2243250 C/C (mean egg count: 127.9 eggs/10 ml of urine). Comparing rs2243250 C/T versus rs2243250 T/T (p < .008) or rs2243250 C/C + C/T versus rs2243250 T/T (p < .016) reveals an association with disease burden. We conclude that the IL-4 promoter gene is a susceptibility factor for schistosomiasis, and essential to regulate disease burden, with worse disease among carriers of the rs2243250 T/T variant. The absence of the STAT6, rs3024974T/T variant among infected subjects reveal the necessity of the STAT6 promoter gene in driving susceptibility to schistosomiasis in Nigeria.
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Affiliation(s)
- Samuel A Adedokun
- Department of Medical Microbiology and Parasitology, Ladoke Akintola University of Technology, Osogbo, Nigeria.
| | - Brooke N Seamans
- Department of Biomedical Sciences, College of Health Sciences and Technology, Rochester Institute of Technology, Rochester, NY, United States.
| | - Natalya T Cox
- Department of Biomedical Sciences, College of Health Sciences and Technology, Rochester Institute of Technology, Rochester, NY, United States.
| | - Gialeigh Liou
- Department of Biomedical Sciences, College of Health Sciences and Technology, Rochester Institute of Technology, Rochester, NY, United States.
| | - Akeem A Akindele
- Department of Medical Microbiology and Parasitology, Ladoke Akintola University of Technology, Osogbo, Nigeria; Department of Community Medicine, Ladoke Akintola University of Technology, Osogbo, Nigeria.
| | - Yi Li
- School of Statistics, Shanxi University of Finance & Economics, Shanxi, China.
| | - Olusola Ojurongbe
- Department of Medical Microbiology and Parasitology, Ladoke Akintola University of Technology, Osogbo, Nigeria.
| | - Bolaji N Thomas
- Department of Medical Microbiology and Parasitology, Ladoke Akintola University of Technology, Osogbo, Nigeria; Department of Biomedical Sciences, College of Health Sciences and Technology, Rochester Institute of Technology, Rochester, NY, United States.
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10
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Chu SK, Yang HC. Interethnic DNA methylation difference and its implications in pharmacoepigenetics. Epigenomics 2017; 9:1437-1454. [PMID: 28882057 DOI: 10.2217/epi-2017-0046] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
AIM This is the first systematic study to examine the population differentiation effect of DNA methylation on the treatment response and drug absorption, distribution, metabolism and excretion in multiple tissue types and cancer types. MATERIALS & METHODS We analyzed the whole methylome and transcriptome data of primary tumor tissues of four cancer types (breast, colon, head & neck and uterine corpus) and lymphoblastoid cell lines for African and European ancestry populations. RESULTS Ethnicity-associated CpG sites exhibited similar methylation patterns in the two studied populations, but the patterns differed between tumor tissues and lymphoblastoid cell lines. Ethnicity-associated CpG sites may have triggered gene expression, influenced drug absorption, distribution, metabolism and excretion, and showed tumor-specific patterns of methylation and gene regulation. CONCLUSION Ethnicity should be carefully accounted for in future pharmacoepigenetics research.
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Affiliation(s)
- Shih-Kai Chu
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.,Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan.,Institute of Biomedical Informatics, National Yang-Ming University, Taipei 112, Taiwan
| | - Hsin-Chou Yang
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.,Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan.,Department of Statistics, National Cheng Kung University, Tainan 701, Taiwan.,Institute of Statistics, National Tsing Hua University, Hsinchu 300, Taiwan.,Instutite of Public Health, National Yang-Ming University, Taipei 112, Taiwan.,School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
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Mizzi C, Dalabira E, Kumuthini J, Dzimiri N, Balogh I, Başak N, Böhm R, Borg J, Borgiani P, Bozina N, Bruckmueller H, Burzynska B, Carracedo A, Cascorbi I, Deltas C, Dolzan V, Fenech A, Grech G, Kasiulevicius V, Kádaši Ľ, Kučinskas V, Khusnutdinova E, Loukas YL, Macek M, Makukh H, Mathijssen R, Mitropoulos K, Mitropoulou C, Novelli G, Papantoni I, Pavlovic S, Saglio G, Setric J, Stojiljkovic M, Stubbs AP, Squassina A, Torres M, Turnovec M, van Schaik RH, Voskarides K, Wakil SM, Werk A, del Zompo M, Zukic B, Katsila T, Lee MTM, Motsinger-Rief A, Mc Leod HL, van der Spek PJ, Patrinos GP. A European Spectrum of Pharmacogenomic Biomarkers: Implications for Clinical Pharmacogenomics. PLoS One 2016; 11:e0162866. [PMID: 27636550 PMCID: PMC5026342 DOI: 10.1371/journal.pone.0162866] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 08/30/2016] [Indexed: 12/26/2022] Open
Abstract
Pharmacogenomics aims to correlate inter-individual differences of drug efficacy and/or toxicity with the underlying genetic composition, particularly in genes encoding for protein factors and enzymes involved in drug metabolism and transport. In several European populations, particularly in countries with lower income, information related to the prevalence of pharmacogenomic biomarkers is incomplete or lacking. Here, we have implemented the microattribution approach to assess the pharmacogenomic biomarkers allelic spectrum in 18 European populations, mostly from developing European countries, by analyzing 1,931 pharmacogenomics biomarkers in 231 genes. Our data show significant inter-population pharmacogenomic biomarker allele frequency differences, particularly in 7 clinically actionable pharmacogenomic biomarkers in 7 European populations, affecting drug efficacy and/or toxicity of 51 medication treatment modalities. These data also reflect on the differences observed in the prevalence of high-risk genotypes in these populations, as far as common markers in the CYP2C9, CYP2C19, CYP3A5, VKORC1, SLCO1B1 and TPMT pharmacogenes are concerned. Also, our data demonstrate notable differences in predicted genotype-based warfarin dosing among these populations. Our findings can be exploited not only to develop guidelines for medical prioritization, but most importantly to facilitate integration of pharmacogenomics and to support pre-emptive pharmacogenomic testing. This may subsequently contribute towards significant cost-savings in the overall healthcare expenditure in the participating countries, where pharmacogenomics implementation proves to be cost-effective.
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Affiliation(s)
- Clint Mizzi
- Erasmus University Medical Center, Faculty of Medicine, Department of Bioinformatics, Rotterdam, the Netherlands
- University of Malta, Faculty of Medicine and Surgery, Department of Physiology and Biochemistry, Msida, Malta
| | - Eleni Dalabira
- University of Patras School of Health Sciences, Department of Pharmacy, Patras, Greece
| | - Judit Kumuthini
- Center for Proteomic and Genomic Research, Observatory, Cape Town, South Africa
| | - Nduna Dzimiri
- King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | | | | | - Ruwen Böhm
- University of Kiel, Institute for Experimental and Clinical Pharmacology, Kiel, Germany
| | - Joseph Borg
- University of Malta, Department of Applied Biomedical Science, Faculty of Health Sciences, Msida, Malta
| | - Paola Borgiani
- University of Rome “Tor Vergata”, Department of Biomedicine and Prevention, Rome, Italy
| | | | - Henrike Bruckmueller
- University of Kiel, Institute for Experimental and Clinical Pharmacology, Kiel, Germany
| | - Beata Burzynska
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland
| | | | - Ingolf Cascorbi
- University of Kiel, Institute for Experimental and Clinical Pharmacology, Kiel, Germany
| | - Constantinos Deltas
- University of Cyprus, Molecular Medicine Research Center, Department of Biological Sciences, Nicosia, Cyprus
| | - Vita Dolzan
- University of Ljubljana Faculty of Medicine, Ljubljana, Slovenia
| | - Anthony Fenech
- University of Malta, Faculty of Medicine, Department of Surgery, Msida, Malta
| | - Godfrey Grech
- University of Malta, Faculty of Medicine, Department of Surgery, Msida, Malta
| | - Vytautas Kasiulevicius
- Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Ľudevít Kádaši
- Comenius University, Faculty of Natural Sciences, Bratislava, Slovakia
- Center for Molecular Medicine, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Vaidutis Kučinskas
- Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Elza Khusnutdinova
- Institute of Biochemistry and Genetics, Ufa Scientific Center, Russian Academy of Sciences, Ufa, Russia
- Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia
| | - Yiannis L. Loukas
- University of Athens, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Athens, Greece
| | - Milan Macek
- Charles University, 2nd Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Halyna Makukh
- Institute of Hereditary Pathology, Ukrainian National Academy of Medical Sciences, Lviv, Ukraine
| | - Ron Mathijssen
- Erasmus University Medical Center, Department of Clinical Chemistry, Rotterdam, the Netherlands
| | | | - Christina Mitropoulou
- Erasmus University Medical Center, Department of Clinical Chemistry, Rotterdam, the Netherlands
| | - Giuseppe Novelli
- University of Rome “Tor Vergata”, Department of Biomedicine and Prevention, Rome, Italy
| | - Ioanna Papantoni
- University of Patras School of Health Sciences, Department of Pharmacy, Patras, Greece
| | - Sonja Pavlovic
- Institute of Molecular Genetics and Genetic Engineering University of Belgrade, Laboratory of Molecular Biomedicine, Belgrade, Serbia
| | | | - Jadranka Setric
- University Hospital Centre, Zagreb, Croatia
- University of Zagreb School of Medicine, Zagreb, Croatia
| | - Maja Stojiljkovic
- Institute of Molecular Genetics and Genetic Engineering University of Belgrade, Laboratory of Molecular Biomedicine, Belgrade, Serbia
| | - Andrew P. Stubbs
- Erasmus University Medical Center, Faculty of Medicine, Department of Bioinformatics, Rotterdam, the Netherlands
| | - Alessio Squassina
- University of Cagliari, Department of Biomedical Sciences, Cagliari, Italy
| | - Maria Torres
- University of Santiago de Compostela, Santiago, Spain
| | - Marek Turnovec
- Charles University, 2nd Faculty of Medicine and University Hospital Motol, Prague, Czech Republic
| | - Ron H. van Schaik
- Erasmus University Medical Center, Department of Clinical Chemistry, Rotterdam, the Netherlands
| | - Konstantinos Voskarides
- University of Cyprus, Molecular Medicine Research Center, Department of Biological Sciences, Nicosia, Cyprus
| | - Salma M. Wakil
- King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Anneke Werk
- University of Kiel, Institute for Experimental and Clinical Pharmacology, Kiel, Germany
| | - Maria del Zompo
- University of Cagliari, Department of Biomedical Sciences, Cagliari, Italy
| | - Branka Zukic
- Institute of Molecular Genetics and Genetic Engineering University of Belgrade, Laboratory of Molecular Biomedicine, Belgrade, Serbia
| | - Theodora Katsila
- University of Patras School of Health Sciences, Department of Pharmacy, Patras, Greece
| | - Ming Ta Michael Lee
- RIKEN Institute, Center for Genomic Medicine, Laboratory for International Alliance, Yokohama, Japan
| | - Alison Motsinger-Rief
- North Carolina State University, Department of Statistics, Raleigh, NC, United States of America
| | | | - Peter J. van der Spek
- Erasmus University Medical Center, Faculty of Medicine, Department of Bioinformatics, Rotterdam, the Netherlands
| | - George P. Patrinos
- Erasmus University Medical Center, Faculty of Medicine, Department of Bioinformatics, Rotterdam, the Netherlands
- University of Patras School of Health Sciences, Department of Pharmacy, Patras, Greece
- * E-mail:
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12
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Karaca S, Cesuroglu T, Karaca M, Erge S, Polimanti R. Genetic diversity of disease-associated loci in Turkish population. J Hum Genet 2015; 60:193-8. [PMID: 25716910 DOI: 10.1038/jhg.2015.8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Revised: 12/18/2014] [Accepted: 12/27/2014] [Indexed: 12/23/2022]
Abstract
Many consortia and international projects have investigated the human genetic variation of a large number of ethno-geographic groups. However, populations with peculiar genetic features, such as the Turkish population, are still absent in publically available datasets. To explore the genetic predisposition to health-related traits of the Turkish population, we analyzed 34 genes associated with different health-related traits (for example, lipid metabolism, cardio-vascular diseases, hormone metabolism, cellular detoxification, aging and energy metabolism). We observed relevant differences between the Turkish population and populations with non-European ancestries (that is, Africa and East Asia) in some of the investigated genes (that is, AGT, APOE, CYP1B1, GNB3, IL10, IL6, LIPC and PON1). As most complex traits are highly polygenic, we developed polygenic scores associated with different health-related traits to explore the genetic diversity of the Turkish population with respect to other human groups. This approach showed significant differences between the Turkish population and populations with non-European ancestries, as well as between Turkish and Northern European individuals. This last finding is in agreement with the genetic structure of European and Middle East populations, and may also agree with epidemiological evidences about the health disparities of Turkish communities in Northern European countries.
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Affiliation(s)
- Sefayet Karaca
- 1] School of Health Science, Aksaray University, Aksaray, Turkey [2] GENAR Institute for Public Health and Genomics Research, Ankara, Turkey
| | - Tomris Cesuroglu
- 1] GENAR Institute for Public Health and Genomics Research, Ankara, Turkey [2] Department of Social Medicine, Maastricht University, Maastricht, The Netherlands
| | - Mehmet Karaca
- Department of Biology, Faculty of Science and Arts, Aksaray University, Aksaray, Turkey
| | - Sema Erge
- 1] GENAR Institute for Public Health and Genomics Research, Ankara, Turkey [2] Department of Nutrition and Dietetics, Faculty of Health Science, Zirve University, Gaziantep, Turkey
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
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Li J, Lou H, Yang X, Lu D, Li S, Jin L, Pan X, Yang W, Song M, Mamatyusupu D, Xu S. Genetic architectures of ADME genes in five Eurasian admixed populations and implications for drug safety and efficacy. J Med Genet 2014; 51:614-22. [PMID: 25074363 DOI: 10.1136/jmedgenet-2014-102530] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Drug absorption, distribution, metabolism and excretion (ADME) contribute to the high heterogeneity of drug responses in humans. However, the same standard for drug dosage has been applied to all populations in China although genetic differences in ADME genes are expected to exist in different ethnic groups. In particular, the ethnic minorities in northwestern China with substantial ancestry contribution from Western Eurasian people might violate such a single unified standard. METHODS In this study, we used Affymetrix SNP Array 6.0 to investigate the genetic diversity of 282 ADME genes in five northwestern Chinese minority populations, namely, Tajik, Uyghur, Kazakh, Kirgiz and Hui, and attempted to identify the highly differential SNPs and haplotypes and further explore their clinical implications. RESULTS We found that genetic diversity of many ADME genes in the five minority groups was substantially different from those in the Han Chinese population. For instance, we identified 10 functional SNPs with substantial allele frequency differences, 14 functional SNPs with highly different heterozygous states and eight genes with significant haplotype differences between these admixed minority populations and the Han Chinese population. We further confirmed that these differences mainly resulted from the European gene flow, that is, this gene flow increased the genetic diversity in the admixed populations. CONCLUSIONS These results suggest that the ADME genes vary substantially among different Chinese ethnic groups. We suggest it could cause potential clinical risk if the same dosage of substances (eg, antitumour drugs) is used without considering population stratification.
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Affiliation(s)
- Jing Li
- Max Planck Independent Research Group on Population Genomics, Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Haiyi Lou
- Max Planck Independent Research Group on Population Genomics, Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xiong Yang
- Max Planck Independent Research Group on Population Genomics, Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Dongsheng Lu
- Max Planck Independent Research Group on Population Genomics, Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Shilin Li
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Xinwei Pan
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Wenjun Yang
- Key Laboratory of Reproduction and Heredity of Ningxia Region, Ningxia Medical University, Yinchuan, Ningxia, China
| | - Manshu Song
- School of Public Health, Capital Medical University and Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Dolikun Mamatyusupu
- College of the Life Sciences and Technology, Xinjiang University, Urumqi, China
| | - Shuhua Xu
- Max Planck Independent Research Group on Population Genomics, Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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14
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Yang HC, Lin CW, Chen CW, Chen JJ. Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics. BMC Genomics 2014; 15:319. [PMID: 24779372 PMCID: PMC4236814 DOI: 10.1186/1471-2164-15-319] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2013] [Accepted: 04/15/2014] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Gene-based analysis has become popular in genomic research because of its appealing biological and statistical properties compared with those of a single-locus analysis. However, only a few, if any, studies have discussed a mapping of expression quantitative trait loci (eQTL) in a gene-based framework. Neither study has discussed ancestry-informative eQTL nor investigated their roles in pharmacogenetics by integrating single nucleotide polymorphism (SNP)-based eQTL (s-eQTL) and gene-based eQTL (g-eQTL). RESULTS In this g-eQTL mapping study, the transcript expression levels of genes (transcript-level genes; T-genes) were correlated with the SNPs of genes (sequence-level genes; S-genes) by using a method of gene-based partial least squares (PLS). Ancestry-informative transcripts were identified using a rank-score-based multivariate association test, and ancestry-informative eQTL were identified using Fisher's exact test. Furthermore, key ancestry-predictive eQTL were selected in a flexible discriminant analysis. We analyzed SNPs and gene expression of 210 independent people of African-, Asian- and European-descent. We identified numerous cis- and trans-acting g-eQTL and s-eQTL for each population by using PLS. We observed ancestry information enriched in eQTL. Furthermore, we identified 2 ancestry-informative eQTL associated with adverse drug reactions and/or drug response. Rs1045642, located on MDR1, is an ancestry-informative eQTL (P = 2.13E-13, using Fisher's exact test) associated with adverse drug reactions to amitriptyline and nortriptyline and drug responses to morphine. Rs20455, located in KIF6, is an ancestry-informative eQTL (P = 2.76E-23, using Fisher's exact test) associated with the response to statin drugs (e.g., pravastatin and atorvastatin). The ancestry-informative eQTL of drug biotransformation genes were also observed; cross-population cis-acting expression regulators included SPG7, TAP2, SLC7A7, and CYP4F2. Finally, we also identified key ancestry-predictive eQTL and established classification models with promising training and testing accuracies in separating samples from close populations. CONCLUSIONS In summary, we developed a gene-based PLS procedure and a SAS macro for identifying g-eQTL and s-eQTL. We established data archives of eQTL for global populations. The program and data archives are accessible at http://www.stat.sinica.edu.tw/hsinchou/genetics/eQTL/HapMapII.htm. Finally, the results from our investigations regarding the interrelationship between eQTL, ancestry information, and pharmacodynamics provide rich resources for future eQTL studies and practical applications in population genetics and medical genetics.
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Affiliation(s)
- Hsin-Chou Yang
- Institute of Statistical Science, Academia Sinica, No 128, Academia Road, Section 2, Nankang, Taipei, Taiwan
- School of Public Health, National Defense Medical Center, Taipei, Taiwan
| | - Chien-Wei Lin
- Institute of Statistical Science, Academia Sinica, No 128, Academia Road, Section 2, Nankang, Taipei, Taiwan
| | - Chia-Wei Chen
- Institute of Statistical Science, Academia Sinica, No 128, Academia Road, Section 2, Nankang, Taipei, Taiwan
| | - James J Chen
- National Center for Toxicological Research, Food and Drug Administration, Little Rock, Arkansas, USA
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