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Gene-Gene Interactions Detection Using a Two-Stage Model

  • Zhanyong Wang
  • Jae Hoon Sul
  • Sagi Snir
  • Jose A. Lozano
  • Eleazar Eskin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8394)

Abstract

Genome wide association studies (GWAS) have discovered numerous loci involved in genetic traits. Virtually all studies have reported associations between individual single nucleotide polymorphism (SNP) and traits. However, it is likely that complex traits are influenced by interaction of multiple SNPs. One approach to detect interactions of SNPs is the brute force approach which performs a pairwise association test between a trait and each pair of SNPs. The brute force approach is often computationally infeasible because of the large number of SNPs collected in current GWAS studies. We propose a two-stage model, Threshold-based Efficient Pairwise Association Approach (TEPAA), to reduce the number of tests needed while maintaining almost identical power to the brute force approach. In the first stage, our method performs the single marker test on all SNPs and selects a subset of SNPs that achieve a certain significance threshold. In the second stage, we perform a pairwise association test between traits and pairs of the SNPs selected from the first stage. The key insight of our approach is that we derive the joint distribution between the association statistics of a single SNP and the association statistics of pairs of SNPs. This joint distribution allows us to provide guarantees that the statistical power of our approach will closely approximate the brute force approach. We applied our approach to the Northern Finland Birth Cohort data and achieved 63 times speedup while maintaining 99% of the power of the brute force approach.

Keywords

Single Nucleotide Polymorphism Minor Allele Frequency Power Loss Sequence Kernel Association Test Brute Force Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zhanyong Wang
    • 1
  • Jae Hoon Sul
    • 1
  • Sagi Snir
    • 2
  • Jose A. Lozano
    • 3
  • Eleazar Eskin
    • 1
  1. 1.Computer Science DepartmentUniversity of California Los AngelesUnited States
  2. 2.Institute of Evolution, Department of Evolutionary and Environmental Biology, Faculty of Natural SciencesUniversity of HaifaIsrael
  3. 3.Intelligent Systems Group, Department of Computer Science and Artificial IntelligenceUniversity of the Basque CountryDonostiaSpain

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