Open Access
September 2012 Gene-centric gene–gene interaction: A model-based kernel machine method
Shaoyu Li, Yuehua Cui
Ann. Appl. Stat. 6(3): 1134-1161 (September 2012). DOI: 10.1214/12-AOAS545


Much of the natural variation for a complex trait can be explained by variation in DNA sequence levels. As part of sequence variation, gene–gene interaction has been ubiquitously observed in nature, where its role in shaping the development of an organism has been broadly recognized. The identification of interactions between genetic factors has been progressively pursued via statistical or machine learning approaches. A large body of currently adopted methods, either parametrically or nonparametrically, predominantly focus on pairwise single marker interaction analysis. As genes are the functional units in living organisms, analysis by focusing on a gene as a system could potentially yield more biologically meaningful results. In this work, we conceptually propose a gene-centric framework for genome-wide gene–gene interaction detection. We treat each gene as a testing unit and derive a model-based kernel machine method for two-dimensional genome-wide scanning of gene–gene interactions. In addition to the biological advantage, our method is statistically appealing because it reduces the number of hypotheses tested in a genome-wide scan. Extensive simulation studies are conducted to evaluate the performance of the method. The utility of the method is further demonstrated with applications to two real data sets. Our method provides a conceptual framework for the identification of gene–gene interactions which could shed novel light on the etiology of complex diseases.


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Shaoyu Li. Yuehua Cui. "Gene-centric gene–gene interaction: A model-based kernel machine method." Ann. Appl. Stat. 6 (3) 1134 - 1161, September 2012.


Published: September 2012
First available in Project Euclid: 31 August 2012

zbMATH: 06096525
MathSciNet: MR3012524
Digital Object Identifier: 10.1214/12-AOAS545

Keywords: Allele matching kernel , association study , gene-clustered SNPs , genomic similarity , quantitative traits , ‎reproducing kernel Hilbert ‎space

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 3 • September 2012
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