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This article is part of the supplement: Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci

Open Access Proceedings

Pattern-based mining strategy to detect multi-locus association and gene × environment interaction

Zhong Li14*, Tian Zheng2, Andrea Califano345* and Aris Floratos4

Author Affiliations

1 Department of Computational Genetics, High Throughput Biology Inc., 513 West Mount Pleasant Avenue, Livingston, New Jersey 07039, USA

2 Department of Statistics, Columbia University, Room 1005, MC4690, 1255 Amsterdam Avenue, New York, New York 10027, USA

3 Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic, 5th Floor, New York, New York 10032, USA

4 Center for Computational Biology and Bioinformatics, Columbia University, 1130 St. Nicholas Avenue, New York, New York 10032, USA

5 Co-senior author

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BMC Proceedings 2007, 1(Suppl 1):S16  doi:

Published: 18 December 2007


As genome-wide association studies grow in popularity for the identification of genetic factors for common and rare diseases, analytical methods to comb through large numbers of genetic variants efficiently to identify disease association are increasingly in demand. We have developed a pattern-based data-mining approach to discover unlinked multilocus genetic effects for complex disease and to detect genotype × phenotype/genotype × environment interactions. On a densely mapped chromosome 18 data set for rheumatoid arthritis that was made available by Genetic Analysis Workshop 15, this method detected two potential two-locus associations as well as a putative two-locus gene × gender interaction.