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Bioinformatics Vol. 19 no. 14 2003
Pages 1781-1786
© 2003 Oxford University Press

Identification and quantification of disease-related gene clusters

Gábor Firneisz 1, Idit Zehavi 2,3,4, Csaba Vermes 1, Anita Hanyecz 1, Joshua A. Frieman 2,3,4 and Tibor T. Glant 1,*

1 Section of Biochemistry and Molecular Biology, Departments of Biochemistry, Orthopedic Surgery and Internal Medicine, Rush University at Rush-Presbyterian-St Luke's Medical Center Chicago, IL 60612, USA; 2 Department of Astronomy and Astrophysics, 3 Center for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA; 4 NASA/Fermilab Astrophysics Center, Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA

Received on July 16, 2002 ; revised on February 8, 2003 ; accepted on April 16, 2003

Motivation: DNA microarray technology and the completion of human and mouse genome sequencing programs are now offering new avenues for the investigation of complex genetic diseases. In particular, this makes possible the study of the spatial distribution of disease-related genes within the genome. We report on the first systematic search for clustering of genes associated with a polygenic autoimmune disease.

Results: Using a set of cDNA microarray chip experiments in two mouse models of rheumatoid arthritis, we have identified ~200 genes based on their expression in inflamed joints and mapped them into the genome. We compute the spatial autocorrelation function of the selected genes and find that they tend to cluster over scales of a few megabase pairs. We then identify significant gene clusters using a friends-of-friends algorithm. This approach should aid in discovering functionally related gene clusters in the mammalian genome.

Contact: tglant{at}rush.edu

* To whom correspondence should be addressed.


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