Skip to main content

Using SNPs to Characterize Genetic Effects in Clinical Trials

  • Conference paper
  • First Online:
Proceedings of the Fourth Seattle Symposium in Biostatistics: Clinical Trials

Part of the book series: Lecture Notes in Statistics ((LNSP,volume 1205))

  • 974 Accesses

Abstract

Characterizing the genetic basis of responses in clinical trials has been made substantially easier and more powerful through the use of single nucleotide polymorphism data. A million of more of these markers can now be scored cheaply with commercial SNP-chips and ten million or more additional SNPs can be inferred by imputation. These rich datasets offer a deep look at the human genome and they are likely to tag many of the response causal genes. It has already become common for SNP types to be included in drug box labels.The ease and low cost of obtaining SNP profiles in clinical trials comes with the price of noisy data: it is common to have to reject data at 10% of the assayed SNPs. However, those SNPs that pass rigorous data cleaning protocols not only offer the chance of identifying the genes that affect response variables but also may reveal information about the genetic architectures of the trial participants and the populations to which they belong, as well as the relationships among the participants. Among novel applications of SNP profiles is the ability to determine HLA type without expensive sequencing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Anderson JL, Horne BD, Stevens SM, Grove AS, Bartion S, Nicholas ZP, Kahn SFS, May HT, Samuelson KM, Muhlsetein JB, Carlquist JF (2007) Couma-Gen 1. Randomized trail of genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation. Circulation 116:2563–2570

    Google Scholar 

  2. Browning B, Browning S (2009) A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet 84:210–223

    Google Scholar 

  3. Bryll R, Gutierrez-Osuna R, Quek F (2003) Attribute bagging: improving accuracy of classifer ensembles by using random feature subsets. Pattern Recognit 36:1291–1302

    Google Scholar 

  4. Caldwell MD, Awad T, Johnson JA, Gage BF, Falkowski M, Gardina P, Hubbard J, Turpaz Y, Langaee TY, Eby C, King CR, Brower A, Schmelzew JR, Glurich I, Vidaillet HJ, Yale SH, Qi Zhang K, Berg RL, Burmester JK (2008) CYP4F2 genetic variant alters required warfarin dose. Blood 111:4106–4112

    Google Scholar 

  5. Caraco Y, Blotnik S, Muszkat M (2008) CYP2C9 genotype-guided warfarin prescribing enhanxcesthe efficacy and safety of anticoagulation: a prospective randomized controlled trial. Clin Pharmacol Ther 83:460–470

    Google Scholar 

  6. Chen P, Lin J-J, Lu C-S, Ong C-T, Hsieh PF, Yam C-C, Tai C-T, Wu S-L, Lu C-H et al (2011) Carbamazide-induced toxic effects and HLA-B*1502 screening in Taiwan. N Engl J Med 364:1126–1133

    Google Scholar 

  7. Cohen AL, Soldi R, Zhang H, Gustafson AM, Wilcox R, Weim BE, Chang JT, Johnson E, Spira A, Jeffrey SS, Bild AH (2011) A pharmacogenomic method for individualized prediction of drug sensitivity. Mol Syst Biol 7:513. doi:10.1038/msb.2011.47

    Google Scholar 

  8. Cooper GM et al (2008) A genome-wide scan for common genetic variants with a large influence on warfarin maintenance dose. Blood 112:1022–1027

    Google Scholar 

  9. Cornelis MC et al (2010) The Gene, environment association studies consortium (GENEVA): maximizing the knowledge obtained from GWAS by collaboration across studies of multiple conditions. Genet Epidemiol 34: 364–72

    Google Scholar 

  10. Daly AK (2011) Genome-wide association studies in pharmacogenomics. Nat Rev Genet 11:241–246

    Google Scholar 

  11. Dilthey AT, Moutsianas L, Leslie S, McVean G (2011) HLA*IMP: an integrated framework for imputing classical HLA alleles from SNP genotypes. Bioinformatics 27:968–972

    Google Scholar 

  12. Huff CD, Witherspoon DJ, Simonsen TS et al (2011) Maximum-likelihood estimation of recent shared ancestry (ERSA). Genome Res 21:768–774

    Google Scholar 

  13. Laurie CC, Doheny KF, Mirel DB, Pugh EW, Bierut LJ, Bhangale T, Boehm F, Caporaso NE, Cornelis MC, Edenberg HJ, Gabriel SB, Harris EL, Hu FB, Jacobs K, Kraft P, Landi MT, Lumley T, Manolio TA, McHugh C, Painter I, Paschall J, Rice JP, Rice KM, Zheng X, Weir BS for the GENEVA Investigators (2010) Quality control and quality assurance in genotypic data for genome-wide association studies. Genet Epidemiol 34:591–602

    Google Scholar 

  14. Laurie CC, Laurie CA, Rice K, Doheny KF, Zelnick LR, McHugh CP, Ling H, Hetrick KN, Pugh EW, Amos C, Wei Q, Wang L, Lee JE, Barnes KC, Hansel NN, Mathias R, Daley D, Beaty TH, Scott AF, Ruczinski I, Scharpf RB, Bierut LJ, Hartz SM, Landi MT, Freedman ND, Goldin LR, Ginsburg D, Li J, Desch KC, Strom SS, Blot WJ, Signorello LB, Ingles SA, Chanock SJ, Berndt SI, Le Marchand L, Henderson BE, Monroe KR, Heit JA, de Andrade M, Armasu SM, Regnier C, Lowe WL, Hayes MG, Marazita ML, Feingold E, Murray JC, Melbye M, Feenstra B, Kang JH, Wiggs JL, Jarvik G, McDavid AN, Seshan VE, Mirel DB, Crenshaw A, Sharopova N, Wise A, Shen J, Crosslin DR, Levine DM, Zheng X, Udren JI, Bennett S, Nelson SC, Gogarten SM, Conomos MP, Heagerty P, Manolio T, Pasquale LR, Haiman CA, Caporaso N, Weir BS (2012) Detectable clonal mosaicism from birth to old age and its relationship to cancer. Somatic mosaicism for large chromosomal anomalies from birth to old age and its relationship to cancer. Nat Genet 44:642–650

    Google Scholar 

  15. Leslie S, Donnelly P, McVean G (2008) A statistical method for predicting classical HLA 323 alleles from SNP data. Am J Hum Genet 82:48–56

    Google Scholar 

  16. Mega JL, Hochholzer W, Frelnger AL, Kluk MJ, Angiolilo DJ, Kereiakes DJ, Isserman S, Rogers WJ, Huff CT, Contant C, Pencina MJ, Scirica BM, Longtine JA, Michelson AD, Sabatine MS (2011) Dosing clopidogrel based on CYP2C19 genotype and the effect of platelet reactivity in patients with stable cardiovascular disease. J Am Med Assoc 306:2221–2228

    Google Scholar 

  17. Novembre J, Johnson T, Bryc K, Kutalik Z, Boyko AR, Auton A, Indap A, King KS, Bergmann S, Nelson MR, Stephens S, Bustamente CD (2008) Genes mirror geography within Europe. Nature 456:98–101.

    Google Scholar 

  18. Purcell S, Neale B, Todd-Browne K, Thomas L, Ferreira MA et al (2007) PLINK: a tool set for whole-genome association and population-based analyses. Am J Hum Genet 81:559–575

    Google Scholar 

  19. Raychaudhuri S, Sandor C, Stahl EA, Freudenberg J, Lee HS, Jia X, Alfredsson L, Padyukov L, Klareskog L, Worthington J et al (2012) Five amino acids in three HLA proteins explain most of the association between mhc and seropositive rheumatoid arthritis. Nat Genet 44:291–296

    Google Scholar 

  20. Roden DM, Wilke RA, Kroemer KH, Stein CM (2011) Pharmacogenomics: the genetics of variable drug responses. Circulation 123:1661–1670.

    Google Scholar 

  21. Shuldiner AR, O’Connell JR, Bliden KP, Gandhi A, Ryan K, Horenstein RB, Damcott CM, Pakyz R, Tantry US, Gibson O, Pollin TI, Post W, Parsa A, Mitchell BD, Faraday N, Herzog W, Gorbel PA (2009) Association of Cytochrome P450 2C19 genotype with the antiplatelet effect and clinical efficacy of clopidogrel therapy. J Am Med Assoc 302:849–858

    Google Scholar 

  22. Takeuchi F, McGinnis R, Bourgeois S, Barnes C, Eriksson N, Soranzo N, Whittaker P, Raganath V, Kumanduri V, McLaren W, Holm L, Lindh J, Rane A, Wadelius M, Deloukas P (2009) A genome-wide association study confirms VKORC1, CYP2C9 and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet 5:e1000433

    Google Scholar 

  23. Tian C, Plenge RM, Ransom M, Lee A, Villoslada P, Selmi C, Klareskog L, Pulver AE, Qi LH, Gregersen PK, Seldin MF (2008) Analysis and application of European genetic substructure using 300K SNP information. PLoS Genet 4(1): article e4.

    Google Scholar 

  24. Weir BS (2010) Statistical genetic issues for genome-wide association studies. Genome 53:869–875

    Google Scholar 

  25. Zheng X, Shen J, Cox C, Wakefield J, Ehm M, Nelson M, Weir B (2012) HIBAG - HLA genotype imputation with attribute gagging. Annual Meeting of the American Society of Human Genetics, Platform Abstract 290.

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by NIH grants GM 075091, HG 004464 and HG 005157.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. S. Weir .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this paper

Cite this paper

Weir, B.S. (2013). Using SNPs to Characterize Genetic Effects in Clinical Trials. In: Fleming, T., Weir, B. (eds) Proceedings of the Fourth Seattle Symposium in Biostatistics: Clinical Trials. Lecture Notes in Statistics(), vol 1205. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5245-4_6

Download citation

Publish with us

Policies and ethics