Leveraging Omics Biomarker Data in Drug Development: With a GWAS Case Study

  • Weidong ZhangEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 218)


Biomarkers have proven powerful for target identification, understanding disease progression, drug safety and treatment responses in drug development. Recent development of omics technology has offered great opportunities for identifications of omics biomarkers at low cost. Although biomarkers have brought many promises to drug development, steep challenges arise due to high dimensionality of data, complexity of technology and lack of full understanding of biology. In this article, the application of omics data in drug development will be reviewed. A genome wide association study (GWAS) will be presented.


Biomarker Omics Simulation GWAS 


  1. 1.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. 57(1), 289–300 (1995)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Biomarkers Definition Working Group Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin Pharmacol Therapeutics. 69, 89–95 (2001)CrossRefGoogle Scholar
  3. 3.
    Collins, F.S., Varmus, H.: A new initiative on precision medicine. N. Engl. J. Med. 372(9), 793–795 (2015)CrossRefGoogle Scholar
  4. 4.
    Conover, W.J.: Practical Nonparametric Statistics. John Wiley Chichester, New York (1999)Google Scholar
  5. 5.
    Fadista, J., Manning, A., Florez, J., Groop, L.: The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants. Eur. J. Hum. Genet. 24, 1202–1205 (2016)CrossRefGoogle Scholar
  6. 6.
    Fleming, T.R., DeMets, D.L.: Surrogate end points in clinical trials: are we being misled? Ann. Intern. Med. 125(7), 605–613 (1996)CrossRefGoogle Scholar
  7. 7.
    Gosho, M., Nagashima, K., Sato, Y.: Study designs and statistical analyses for biomarker research. Sensors 12, 8966–8986 (2012)CrossRefGoogle Scholar
  8. 8.
    Johnstone, I., Titterington, D.: Statistical challenges of high-dimensional data. Phil. Trans. R. Soc. A 367, 4237–4253 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Kang, H.M., Zaitlen, N.A., Wade, C.M., Kirby, A., Heckerman, D., et al.: Efficient control of population structure in model organism association mapping. Genetics 178, 1709–1723 (2008)CrossRefGoogle Scholar
  10. 10.
    Katz, R.: Biomarkers and Surrogate Markers: an FDA Perspective. NeuroRx 1(2), 189–195 (2004)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Knijnenburg, T.A., Wessels, L.F., Reinders, M.J., Shmulevich, I.: Fewer permutations, more accurate P-values. Bioinformatics 25, i161–i168 (2009)CrossRefGoogle Scholar
  12. 12.
    Meienberg, J., Bruggmann, R., Oexle, K., Matyas, G.: Clinical sequencing: is WGS the better WES? Hum. Genet. 135, 359–362 (2016)CrossRefGoogle Scholar
  13. 13.
    Morgan, P., Van Der Graaf, P.H., Arrowsmith, J., Feltner, D.E., Drummond, K.S., Wegner, C.D., Street, S.D.: Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug. Discov. Today 17, 419–424 (2012)CrossRefGoogle Scholar
  14. 14.
    Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., et al.: A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351(27), 2817–2826 (2004)CrossRefGoogle Scholar
  15. 15.
    Panagiotou, O.A., Ioannidis, J.P.: Genome-wide significance project. What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations. Int. J. Epidemiol. 41(1), 273–86 (2012)CrossRefGoogle Scholar
  16. 16.
    Pe’er, I., Yelensky, R., Altshuler, D., Daly, M.: Estimation of the multiple testing burden for Genomewide association studies of nearly all common variants. Genet. Epidemiol. 32, 381–385 (2008)CrossRefGoogle Scholar
  17. 17.
    Shyr, D., Liu, Q.: Next generation sequencing in cancer research and clinical application. Biol. Proced. Online 15, 4 (2013)CrossRefGoogle Scholar
  18. 18.
    Storey, J.D.: A direct approach to false discovery rates. J. Roy. Stat. Soc. 64, 479–498 (2002)MathSciNetCrossRefGoogle Scholar
  19. 19.
    TESARO’s Niraparib Significantly Improved Progression-Free Survival for Patients With Ovarian Cancer in Both Cohorts of the Phase 3 NOVA Trial (2016).
  20. 20.
    Thomas, D.W., Burns, J., Audette, J., Carroll, A., Dow-Hygelund, C., Hay, M.: Clinical Development Success Rates 2006–2015. June 2016.,%20Biomedtracker,%20Amplion%202016.pdf
  21. 21.
    Valdar, W., Holmes, C.C., Mott, R., Flint, J.: Mapping in structured populations by resample model averaging. Genetics 182, 1263–1277 (2009)CrossRefGoogle Scholar
  22. 22.
    Wetterstrand, K.A.: DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) (2016). Accessed 23 Dec 2016
  23. 23.
    Zhang, W., Korstanje, R., Thaisz, J., Staedtler, F., Harttman, N., Xu, L., Feng, M., Yanas, L., Yang, H., Valdar, W., Churchill, G.A., DiPetrillo, K.: Genome-wide association mapping of quantitative traits in outbred mice. G3: Genes Genomes Genet. 2(2), 167–174 (2012)CrossRefGoogle Scholar

Copyright information

© Pfizer, Inc. 2019

Authors and Affiliations

  1. 1.Pfizer Inc.CambridgeUSA

Personalised recommendations