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Gene Size Matters

Author

Listed:
  • Alexandra Mirina
  • Gil Atzmon
  • Kenny Ye
  • Aviv Bergman

Abstract

In this work we show that in genome-wide association studies (GWAS) there is a strong bias favoring of genes covered by larger numbers of SNPs. Thus, we state here that there is a need for correction for such bias when performing downstream gene-level analysis, e.g. pathway analysis and gene-set analysis. We investigate several methods of obtaining gene level statistical significance in GWAS, and compare their effectiveness in correcting such bias. We also propose a simple algorithm based on first order statistic that corrects such bias.

Suggested Citation

  • Alexandra Mirina & Gil Atzmon & Kenny Ye & Aviv Bergman, 2012. "Gene Size Matters," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-6, November.
    • Handle: RePEc:plo:pone00:0049093
    DOI: 10.1371/journal.pone.0049093
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    1. Christian Timmann & Thorsten Thye & Maren Vens & Jennifer Evans & Jürgen May & Christa Ehmen & Jürgen Sievertsen & Birgit Muntau & Gerd Ruge & Wibke Loag & Daniel Ansong & Sampson Antwi & Emanuel Asaf, 2012. "Genome-wide association study indicates two novel resistance loci for severe malaria," Nature, Nature, vol. 489(7416), pages 443-446, September.
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    Cited by:

    1. Fan-Chi Yeh & Chung-Feng Kao & Po-Hsiu Kuo, 2015. "Explore the Features of Brain-Derived Neurotrophic Factor in Mood Disorders," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-20, June.

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