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J Comput Biol. 2015 Jun;22(6):563-76. doi: 10.1089/cmb.2014.0163. Epub 2015 Apr 14.

Gene-Gene Interactions Detection Using a Two-stage Model.

Author information

1Computer Science Department, University of California Los Angeles, Los Angeles, California.
2Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
3Institute of Evolution, Department of Evolutionary and Environmental Biology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel.
4Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia Spain.


Genome-wide association studies (GWAS) have discovered numerous loci involved in genetic traits. Virtually all studies have reported associations between individual single nucleotide polymorphisms (SNPs) 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.


GWAS; epistasis; gene–gene interaction

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