Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Pharmacogenomic survey of Qatari populations using whole-genome and exome sequences

The Pharmacogenomics Journal volume 18, pages 590–600 (2018)Cite this article

Subjects

Abstract

The Arabs represent one of the most genetically heterogeneous populations characterized by a high prevalence of Mendelian disorders due to consanguinity. Population-scale genomic datasets provide a unique opportunity to understand the epidemiology of variants associated with differential therapeutic response. We analyzed publicly available genomic data for 1005 Qatari individuals encompassing five subpopulations. The frequencies of known and novel pharmacogenetic variants were compared with global populations. Impact of genetic substructure on the pharmacogenetic landscape of the population was studied. We report an average of three clinically actionable pharmacogenetic variants with FDA-recommended genetic testing per Qatari individual regardless of their genetic ancestry. We observed extensive differences in the frequencies of clinically actionable pharmacogenetic variants among the Qatari subpopulations. Our analysis revealed 3579 deleterious pharmacogenetic variants potentially altering the function of 1163 genes associated with 1565 drugs. This study has thus compiled the first comprehensive landscape of pharmacogenetic variants for any Arab population.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science. 1999;286:487–91.

    Article  PubMed  CAS  Google Scholar 

  2. Table of Pharmacogenomic Biomarkers in Drug Labeling - FDA. https://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm. Accessed 1 May 2017.

  3. Relling MV, Klein TE. CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network. Clin Pharmacol Ther. 2011;89:464–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Ramos E, Doumatey A, Elkahloun AG, Shriner D, Huang H, Chen G, et al. Pharmacogenomics, ancestry and clinical decision making for global populations. Pharm J. 2014;14:217–22.

    CAS  Google Scholar 

  5. Wong L-P, Ong RT-H, Poh W-T, Liu X, Chen P, Li R, et al. Deep whole-genome sequencing of 100 Southeast Asian Malays. Am J Hum Genet. 2013;92:52–66.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Nagasaki M, Yasuda J, Katsuoka F, Nariai N, Kojima K, Kawai Y, et al. Rare variant discovery by deep whole-genome sequencing of 1,070 Japanese individuals. Nat Commun. 2015;6:8018.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Fakhro KA, Staudt MR, Ramstetter MD, Robay A, Malek JA, Badii R, et al. The Qatar genome: a population-specific tool for precision medicine in the Middle East. Hum Genome Var. 2016;3:16016.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Sivadas A, Salleh MZ, Teh LK, Scaria V. Genetic epidemiology of pharmacogenetic variants in South East Asian Malays using whole-genome sequences. Pharmacogenomics J. 2016. https://doi.org/10.1038/tpj.2016.39.

  9. Giri AK, Khan NM, Grover S, Kaur I, Basu A, Tandon N, et al. Genetic epidemiology of pharmacogenetic variations in CYP2C9, CYP4F2 and VKORC1 genes associated with warfarin dosage in the Indian population. Pharmacogenomics. 2014;15:1337–54.

    Article  PubMed  CAS  Google Scholar 

  10. Giri AK, Khan NM, Basu A, Tandon N, Scaria V, Bharadwaj D. Pharmacogenetic landscape of clopidogrel in north Indians suggest distinct interpopulation differences in allele frequencies. Pharmacogenomics. 2014;15:643–53.

    Article  PubMed  CAS  Google Scholar 

  11. Shan J, Mathew R, Al-Ali K, Chouchane L. Genetic disorders in Qatar. http://www.cags.org.ae/cb408c5.pdf. Accessed 22 Sep 2017.

  12. Hunter-Zinck H, Musharoff S, Salit J, Al-Ali KA, Chouchane L, Gohar A, et al. Population genetic structure of the people of Qatar. Am J Hum Genet. 2010;87:17–25.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Bu R, Gutiérrez MI, Al-Rasheed M, Belgaumi A, Bhatia K. Variable drug metabolism genes in Arab population. Pharm J. 2004;4:260–6.

    CAS  Google Scholar 

  14. Tanira MO, Al-Mukhaini MK, Al-Hinai AT, Al Balushi KA, Ahmed IS. Frequency of CYP2C9 genotypes among Omani patients receiving warfarin and its correlation with warfarin dose. Community Genet. 2007;10:32–7.

    Article  PubMed  Google Scholar 

  15. Djaffar-Jureidini I, Chamseddine N, Keleshian S, Naoufal R, Zahed L, Hakime N. Pharmacogenetics of coumarin dosing: prevalence of CYP2C9 and VKORC1 polymorphisms in the Lebanese population. Genet Test Mol Biomark. 2011;15:827–30.

    Article  CAS  Google Scholar 

  16. Qumsieh RY, Ali BR, Abdulrazzaq YM, Osman O, Akawi NA, Bastaki SMA. Identification of new alleles and the determination of alleles and genotypes frequencies at the CYP2D6 gene in Emiratis. PLoS ONE. 2011;6:e28943.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Alzahrani AM, Ragia G, Hanieh H, Manolopoulos VG. Genotyping of CYP2C9 and VKORC1 in the Arabic population of Al-Ahsa, Saudi Arabia. Biomed Res Int. 2013;2013:315980.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Sivadas A, Sharma P, Scaria V. Landscape of warfarin and clopidogrel pharmacogenetic variants in Qatari population from whole exome datasets. Pharmacogenomics. 2016;17:1891–1901.

    Article  CAS  Google Scholar 

  19. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31:3812–4.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7:248–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Schwarz JM, Cooper DN, Schuelke M, Seelow D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods. 2014;11:361–2.

    Article  PubMed  CAS  Google Scholar 

  24. Sangkuhl K, Berlin DS, Altman RB, Klein TE. PharmGKB: understanding the effects of individual genetic variants. Drug Metab Rev. 2008;40:539–51.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008;36:D901–6.

    Article  PubMed  CAS  Google Scholar 

  26. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27:2156–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. 1000 Genomes Project Consortium A, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.

    Article  CAS  Google Scholar 

  28. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285–91.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Scott EM, Halees A, Itan Y, Spencer EG, He Y, Azab MA, et al. Characterization of Greater Middle Eastern genetic variation for enhanced disease gene discovery. Nat Genet. 2016;48:1071–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Google Sankey. https://developers.google.com/chart/interactive/docs/gallery/sankey.

  31. R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2012.

    Google Scholar 

  32. Cadzow M, Boocock J, Nguyen HT, Wilcox P, Merriman TR, Black MA. A bioinformatics workflow for detecting signatures of selection in genomic data. Front Genet. 2014;5:293.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Al-Kaabi SK, Atherton A. Impact of noncommunicable diseases in the State of Qatar. Clin Outcomes Res. 2015;7:377–85.

    Article  Google Scholar 

  34. Voight BF, Kudaravalli S, Wen X, Pritchard JK. A map of recent positive selection in the human genome. PLoS Biol. 2006;4:e72.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Weir B, Cockerham C. Estimating F-statistics for the analysis of population structure. Evolution. 1984;38:1358–1370.

    PubMed  CAS  Google Scholar 

  36. Kozyra M, Ingelman-Sundberg M, Lauschke VM. Rare genetic variants in cellular transporters, metabolic enzymes, and nuclear receptors can be important determinants of interindividual differences in drug response. Genet Med. 2017;19:20–9.

    Article  PubMed  CAS  Google Scholar 

  37. Mohammed EMA. Multiple sclerosis is prominent in the Gulf states: review. Pathogenesis. 2016;3:19–38.

    Article  Google Scholar 

  38. Sadat-Ali M, Al-Turki H, Azam M, Al-Elq A. Genetic influence on circulating vitamin D among Saudi Arabians. Saudi Med J. 2016;37:996–1001.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Barroso E, Fernandez LP, Milne RL, Pita G, Sendagorta E, Floristan U, et al. Genetic analysis of the vitamin D receptor gene in two epithelial cancers: melanoma and breast cancer case-control studies. BMC Cancer. 2008;8:385.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Naeem Z. Vitamin d deficiency-an ignored epidemic. Int J Health Sci. 2010;4:V–VI.

    Google Scholar 

  41. Lurie G, Wilkens LR, Thompson PJ, Carney ME, Palmieri RT, Pharoah PDP, et al. Vitamin D receptor rs2228570 polymorphism and invasive ovarian carcinoma risk: pooled analysis in five studies within the Ovarian Cancer Association Consortium. Int J Cancer. 2011;128:936–43.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Alagarasu K, Honap T, Mulay AP, Bachal RV, Shah PS, Cecilia D. Association of vitamin D receptor gene polymorphisms with clinical outcomes of dengue virus infection. Hum Immunol. 2012;73:1194–9.

    Article  PubMed  CAS  Google Scholar 

  43. Chen X-E, Chen P, Chen S-S, Lu J, Ma T, Shi G, et al. A population association study of vitamin D receptor gene polymorphisms and haplotypes with the risk of systemic lupus erythematosus in a Chinese population. Immunol Res. 2017;65:750–56.

    Article  PubMed  CAS  Google Scholar 

  44. Handunnetthi L, Ramagopalan SV, Ebers GC. Multiple sclerosis, vitamin D, and HLA-DRB1*15. Neurology. 2010;74:1905–10.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Vos HL. Inherited defects of coagulation factor V: the thrombotic side. J Thromb Haemost. 2006;4:35–40.

    Article  PubMed  CAS  Google Scholar 

  46. Lindqvist PG, Dahlbäck B. Carriership of factor V Leiden and evolutionary selection advantage. Curr Med Chem. 2008;15:1541–4.

    Article  PubMed  CAS  Google Scholar 

  47. Jadaon MM. Epidemiology of activated protein C resistance and factor v leiden mutation in the mediterranean region. Mediterr J Hematol Infect Dis. 2011;3:e2011037.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We acknowledge help and support from the members of Vinod Scaria Lab. We acknowledge funding from the Council of Scientific and Industrial Research (CSIR), India through the grant BSC0212 (WGP).

Author information

Authors and Affiliations

  1. GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi, 110020, India

    Ambily Sivadas & Vinod Scaria

  2. Academy of Scientific and Innovative Research (AcSIR), New Delhi, 110025, India

    Ambily Sivadas & Vinod Scaria

Authors
  1. Ambily Sivadas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vinod Scaria
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vinod Scaria.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sivadas, A., Scaria, V. Pharmacogenomic survey of Qatari populations using whole-genome and exome sequences. Pharmacogenomics J 18, 590–600 (2018). https://doi.org/10.1038/s41397-018-0022-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41397-018-0022-8

This article is cited by

Search

Advanced search

Quick links