Journal Article

Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data

Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda and Hiroshi Mamitsuka

in Bioinformatics

Volume 25, issue 21, pages 2735-2743
Published in print November 2009 | ISSN: 1367-4803
Published online September 2009 | e-ISSN: 1460-2059 | DOI:

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Motivation: We address the issue of finding a three-way gene interaction, i.e. two interacting genes in expression under the genotypes of another gene, given a dataset in which expressions and genotypes are measured at once for each individual. This issue can be a general, switching mechanism in expression of two genes, being controlled by categories of another gene, and finding this type of interaction can be a key to elucidating complex biological systems. The most suitable method for this issue is likelihood ratio test using logistic regressions, which we call interaction test, but a serious problem of this test is computational intractability at a genome-wide level.

Results: We developed a fast method for this issue which improves the speed of interaction test by around 10 times for any size of datasets, keeping highly interacting genes with an accuracy of ∼85%. We applied our method to ∼3 × 108 three-way combinations generated from a dataset on human brain samples and detected three-way gene interactions with small P-values. To check the reliability of our results, we first conducted permutations by which we can show that the obtained P-values are significantly smaller than those obtained from permuted null examples. We then used GEO (Gene Expression Omnibus) to generate gene expression datasets with binary classes to confirm the detected three-way interactions by using these datasets and interaction tests. The result showed us some datasets with significantly small P-values, strongly supporting the reliability of the detected three-way interactions.

Availability: Software is available from


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6252 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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