Improving the Reproducibility of Genetic Association Results Using Genotype Resampling Methods

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)

Abstract

Replication may be an inadequate gold standard for substantiating the significance of results from genome-wide association studies (GWAS). Successful replication provides evidence supporting true results and against spurious findings, but various population attributes contribute to observed significance of a genetic effect. We hypothesize that failure to replicate an interaction observed to be significant in a GWAS of one population in a second population is sometimes attributable to differences in minor allele frequencies, and resampling the replication dataset by genotype to match the minor allele frequencies of the discovery data can improve estimates of the interaction significance. We show via simulation that resampling of the replication data produced results more concordant with the discovery findings. We recommend that failure to replicate GWAS results should not immediately be considered to refute previously-observed findings and conversely that replication does not guarantee significance, and suggest that datasets be compared more critically in biological context.

Keywords

GWAS SNPs Epistasis Complex diseases Reproducibility 

References

  1. 1.
    Peng, R.D.: Reproducible research in computational science. Science 334(6060), 1226–1227 (2011)CrossRefGoogle Scholar
  2. 2.
    Boettiger, C.: An introduction to Docker for reproducible research. ACM SIGOPS Oper. Syst. Rev. 49(1), 71–79 (2015)CrossRefGoogle Scholar
  3. 3.
    Patil, P., Peng, R.D., Leek, J.: A statistical definition for reproducibility and replicability. bioRxiv, 066803, 1 January 2016Google Scholar
  4. 4.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  5. 5.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Marchini, J., Donnelly, P., Cardon, L.R.: Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat. Genet. 37(4), 413–417 (2005)CrossRefGoogle Scholar
  7. 7.
    Moore, J.H.: The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum. Heredi. 56(1–3), 73–82 (2003)CrossRefGoogle Scholar
  8. 8.
    Urbanowicz, R.J., Kiralis, J., Sinnott-Armstrong, N.A., Heberling, T., Fisher, J.M., Moore, J.H.: GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures. BioData Min. 5(1), 1 (2012)CrossRefGoogle Scholar
  9. 9.
    Greene, C.S., Penrod, N.M., Williams, S.M., Moore, J.H.: Failure to replicate a genetic association may provide important clues about genetic architecture. PLoS One 4(6), e5639 (2009)CrossRefGoogle Scholar
  10. 10.
    Moore, J.H., Asselbergs, F.W., Williams, S.M.: Bioinformatics challenges for genome-wide association studies. Bioinformatics 26(4), 445–455 (2010)CrossRefGoogle Scholar
  11. 11.
    Yang, J., Ferreira, T., Morris, A.P., Medland, S.E., Madden, P.A., Heath, A.C., Martin, N.G., Montgomery, G.W., Weedon, M.N., Loos, R.J., Frayling, T.M.: Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44(4), 369–375 (2012)CrossRefGoogle Scholar
  12. 12.
    Buzdugan, L., Kalisch, M., Navarro, A., Schunk, D., Fehr, E., Bühlmann, P.: Assessing statistical significance in multivariable genome wide association analysis. Bioinformatics 32, 1990–2000 (2016)CrossRefGoogle Scholar
  13. 13.
    Panagiotou, O.A., Ioannidis, J.P.: What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations. Int. J. Epidemiol. 41(1), 273–286 (2012)CrossRefGoogle Scholar
  14. 14.
    Church, D.M., Schneider, V.A., Graves, T., Auger, K., Cunningham, F., Bouk, N., Chen, H.C., Agarwala, R., McLaren, W.M., Ritchie, G.R., Albracht, D.: Modernizing reference genome assemblies. PLoS Biol. 9(7), e1001091 (2011)CrossRefGoogle Scholar
  15. 15.
    Rosenberg, N.A., Huang, L., Jewett, E.M., Szpiech, Z.A., Jankovic, I., Boehnke, M.: Genome-wide association studies in diverse populations. Nat. Rev. Genet. 11(5), 356–366 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Graduate Group in Genomics and Computational Biology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Institute for Biomedical Informatics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations