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Improving the Reproducibility of Genetic Association Results Using Genotype Resampling Methods

Part of the Lecture Notes in Computer Science book series (LNTCS,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

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Acknowledgements

This work was supported by National Institutes of Health grants LM009012, and AI116794.

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Correspondence to Elizabeth R. Piette .

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Piette, E.R., Moore, J.H. (2017). Improving the Reproducibility of Genetic Association Results Using Genotype Resampling Methods. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-55849-3_7

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