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Bioinformatics Advance Access originally published online on September 7, 2009
Bioinformatics 2009 25(21):2735-2743; doi:10.1093/bioinformatics/btp531
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

Mitsunori Kayano 1,2, Ichigaku Takigawa 1,2, Motoki Shiga 1,2, Koji Tsuda 2,3 and Hiroshi Mamitsuka 1,2,*

1 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, 2 Institute for Bioinformatics Research and Development (BIRD), Japan Science and Technology Agency (JST) and 3 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan

* To whom correspondence should be addressed.


  Abstract

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 x 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 http://www.bic.kyoto-u.ac.jp/pathway/kayano/bioinfo_three-way.html

Contact: kayano{at}kuicr.kyoto-u.ac.jp

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Alfonso Valencia

Received on March 17, 2009; revised on August 6, 2009; accepted on August 25, 2009

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