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A Novel Statistic for Genome-Wide Interaction Analysis

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Listed:
  • Xuesen Wu
  • Hua Dong
  • Li Luo
  • Yun Zhu
  • Gang Peng
  • John D Reveille
  • Momiao Xiong

Abstract

Although great progress in genome-wide association studies (GWAS) has been made, the significant SNP associations identified by GWAS account for only a few percent of the genetic variance, leading many to question where and how we can find the missing heritability. There is increasing interest in genome-wide interaction analysis as a possible source of finding heritability unexplained by current GWAS. However, the existing statistics for testing interaction have low power for genome-wide interaction analysis. To meet challenges raised by genome-wide interactional analysis, we have developed a novel statistic for testing interaction between two loci (either linked or unlinked). The null distribution and the type I error rates of the new statistic for testing interaction are validated using simulations. Extensive power studies show that the developed statistic has much higher power to detect interaction than classical logistic regression. The results identified 44 and 211 pairs of SNPs showing significant evidence of interactions with FDR

Suggested Citation

  • Xuesen Wu & Hua Dong & Li Luo & Yun Zhu & Gang Peng & John D Reveille & Momiao Xiong, 2010. "A Novel Statistic for Genome-Wide Interaction Analysis," PLOS Genetics, Public Library of Science, vol. 6(9), pages 1-15, September.
    • Handle: RePEc:plo:pgen00:1001131
    DOI: 10.1371/journal.pgen.1001131
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    Cited by:

    1. Qingrun Zhang & Quan Long & Jurg Ott, 2014. "AprioriGWAS, a New Pattern Mining Strategy for Detecting Genetic Variants Associated with Disease through Interaction Effects," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-14, June.
    2. Masao Ueki & Heather J Cordell, 2012. "Improved Statistics for Genome-Wide Interaction Analysis," PLOS Genetics, Public Library of Science, vol. 8(4), pages 1-19, April.

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